Measurement of Perfusion Heterogeneity within Tumor Habitats on Magnetic Resonance Imaging and Its Association with Prognosis in Breast Cancer Patients.
Cancers
The purpose of this study was to identify perfusional subregions sharing similar kinetic characteristics from dynamic contrast-enhanced magnetic resonance imaging (MRI) using data-driven clustering, and to evaluate the effect of perfusional heterogeneity based on those subregions on patients' survival outcomes in various risk models. From two hospitals, 308 and 147 women with invasive breast cancer who underwent preoperative MRI between October 2011 and July 2012 were retrospectively enrolled as development and validation cohorts, respectively. Using the Cox-least absolute shrinkage and selection operator model, a habitat risk score (HRS) was constructed from the radiomics features from the derived habitat map. An HRS-only, clinical, combined habitat, and two conventional radiomics risk models to predict patients' disease-free survival (DFS) were built. Patients were classified into low-risk or high-risk groups using the median cutoff values of each risk score. Five habitats with distinct perfusion patterns were identified. An HRS was an independent risk factor for predicting worse DFS outcomes in the HRS-only risk model (hazard ratio = 3.274 [95% CI = 1.378-7.782]; = 0.014) and combined habitat risk model (hazard ratio = 4.128 [95% CI = 1.744-9.769]; = 0.003) in the validation cohort. In the validation cohort, the combined habitat risk model (hazard ratio = 4.128, = 0.003, C-index = 0.760) showed the best performance among five different risk models. The quantification of perfusion heterogeneity is a potential approach for predicting prognosis and may facilitate personalized, tailored treatment strategies for breast cancer.
10.3390/cancers14081858
Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI.
Tudorica Alina,Oh Karen Y,Chui Stephen Y-C,Roy Nicole,Troxell Megan L,Naik Arpana,Kemmer Kathleen A,Chen Yiyi,Holtorf Megan L,Afzal Aneela,Springer Charles S,Li Xin,Huang Wei
Translational oncology
The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters K(trans) (contrast agent plasma/interstitium transfer rate constant), ve (extravascular and extracellular volume fraction), kep (intravasation rate constant), and SSM-unique τi (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT K(trans), τi, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.
10.1016/j.tranon.2015.11.016
Dynamic breast magnetic resonance imaging: pretreatment prediction of tumor response to neoadjuvant chemotherapy.
Dongfeng He,Daqing Ma,Erhu Jin
Clinical breast cancer
BACKGROUND:Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential of predicting response to neoadjuvant chemotherapy for patients with breast cancer. However, most of these studies focused on evaluating hot-spot characteristics. To thoroughly reflect tumor status, the cold spot and heterogeneity characteristics should also be evaluated. PATIENTS AND METHODS:DCE-MRIs from 60 patients newly diagnosed with primary invasive breast cancer were reviewed. Kinetic parameters (including cold spot, hot spot, and heterogeneity parameters) derived from DCE-MRI data were used to describe cold spot, hot spot, and heterogeneity features. Patients with a pathologic complete response (pCR) or a ductal carcinoma in situ with microinvasion after chemotherapy were categorized into the pCR group. Pretreatment kinetic parameters in the pCR and non-pCR groups were compared by using univariate tests. Binary logistic regression analysis was used to identify the independent predictors for pCR. The best cutoff value of the independent predictor at pretreatment, with which to differentiate between patients who had a pCR and a non-pCR, was calculated by using receiver operating characteristic curve analysis. RESULTS:After chemotherapy, 10 (16.7%) patients were categorized into the pCR group and 50 (83.3%) into non-pCR group. Multivariate analysis showed that pretreatment washout slope at a cold spot (washout(C)) was the only significant and independent predictor of pCR (β = 26.128; P = .005). The best pretreatment washout(C) cutoff value with which to differentiate between patients who had pCR and those with non-pCR was 0.0277, which yielded a sensitivity of 80.0% (95% CI, 44.4%-97.5%) and a specificity of 74.0% (95% CI, 59.7%-85.4%). CONCLUSION:Washout(C) may be used as a predictor for pCR in patients with breast cancer who undergo neoadjuvant chemotherapy.
10.1016/j.clbc.2011.11.002
Role of MR Imaging in Neoadjuvant Therapy Monitoring.
Le-Petross Huong T,Lim Bora
Magnetic resonance imaging clinics of North America
Neoadjuvant chemotherapy (NAC) has become an important treatment approach for stage II/III breast cancers to downsize tumor and enable breast-conserving surgery for patients that may otherwise undergo mastectomy. MR imaging has the potential to identify early response or disease progression, enabling potential modification to NAC regimens. Detection of size and morphologic changes is better appreciated with MR imaging than other modalities and is different between molecular subtypes of breast cancer. The combination of DCE-MR imaging and DWI provides the highest sensitivity and specificity. Other new modalities such as FDG PET/MR imaging and molecular breast imaging are still undergoing research.
10.1016/j.mric.2017.12.011
The Diagnostic Performance of DCE-MRI in Evaluating the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis.
Cheng Qingqing,Huang Jiaxi,Liang Jianye,Ma Mengjie,Ye Kunlin,Shi Changzheng,Luo Liangping
Frontiers in oncology
Neoadjuvant chemotherapy (NAC) is commonly utilized in preoperative treatment for local breast cancer, and it gives high clinical response rates and can result in pathologic complete response (pCR) in 6-25% of patients. In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used to assess the pathological response of breast cancer to NAC. In present analysis, we assess the diagnostic performance of DCE-MRI in evaluating the pathological response of breast cancer to NAC. A systematic search in PubMed, the Cochrane Library, and Web of Science for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data to reconstruct 2 × 2 tables were obtained. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis and sensitivity analysis were performed using Stata version 12.0 (StataCorp LP, College Station, TX). Eighteen studies (969 patients with breast cancer) were included in the present meta-analysis. The pooled sensitivity and specificity of DCE-MRI were 0.80 (95% confidence interval [CI]: 0.70, 0.88) and 0.84 (95% [CI]: 0.79, 0.88), respectively. Meta-regression analysis found no significant factors affecting heterogeneity. Sensitivity analysis showed that studies that set pathological complete response (pCR) ( = 14) as a responder showed a tendency for higher sensitivity compared with those that set pCR and near pCR together ( = 5) as a responder (0.83 vs. 0.72), and studies ( = 14) that used DCE-MRI to early predict the pathological response of breast cancer had a higher sensitivity (0.83 vs. 0.71) and equivalent specificity (0.80 vs. 0.86) compared to studies ( = 5) that assessed the response after NAC completion. Our results indicated that DCE-MRI could be considered an important auxiliary method for evaluating the pathological response of breast cancer to NAC and used as an effective method for dynamically monitoring the efficacy during NAC. DCE-MRI also performed well in predicting the pCR of breast cancer to NAC. However, due to the heterogeneity of the included studies, caution should be exercised in applying our results.
10.3389/fonc.2020.00093
Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging.
Cho Nariya,Im Seock-Ah,Park In-Ae,Lee Kyung-Hun,Li Mulan,Han Wonshik,Noh Dong-Young,Moon Woo Kyung
Radiology
PURPOSE:To prospectively compare the performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging using parametric response map (PRM) analysis with that using pharmacokinetic parameters (transfer constant [K(trans)], rate constant [kep ], and relative extravascular extracellular space [ve]) in the early prediction of pathologic responses to neoadjuvant chemotherapy (NAC) in breast cancer patients. MATERIALS AND METHODS:The institutional review board approved this study; informed consent was obtained. Between August 2010 and December 2012, 48 women (mean age, 46.4 years; range, 29-65 years) with breast cancer were enrolled and treated with an anthracycline-taxane regimen. DCE MR imaging was performed before and after the first cycle of chemotherapy, and the pathologic response was assessed after surgery. Tumor size and volume, PRM characteristics, and pharmacokinetic parameters (K(trans), kep, and ve) on MR images were assessed and compared according to the pathologic responses by using the Fisher exact test or the independent-sample t test. RESULTS:Six of 48 (12%) patients showed pathologic complete response (CR) (pCR) and 42 (88%) showed nonpathologic CR (npCR). Thirty-eight (79%) patients showed a good response (Miller-Payne score of 3, 4, or 5), and 10 (21%) showed a minor response (Miller-Payne score of 1 or 2). The mean proportion of voxels with increased signal intensity (PRMSI+) in the pCR or good response group was significantly lower than that in the npCR or minor response group (14.0% ± 6.5 vs 40.7% ± 27.2, P < .001; 34.3% ± 26.4 vs 52.8% ± 24.9, P = .041). Area under the receiver operating characteristic curve for PRMSI+ in the pCR group was 0.770 (95% confidence interval: 0.626, 0.879), and that for the good response group was 0.716 (95% confidence interval: 0.567, 0.837). No difference in tumor size, tumor volume, or pharmacokinetic parameters was found between groups. CONCLUSION:PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K(trans), kep, and ve) do not.
10.1148/radiol.14131332
Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI.
Georgiou Leonidas,Sharma Nisha,Broadbent David A,Wilson Daniel J,Dall Barbara J,Gangi Anmol,Buckley David L
Magnetic resonance in medicine
PURPOSE:To evaluate an interleaved MRI sampling strategy that acquires both high temporal resolution (HTR) dynamic contrast-enhanced (DCE) data for quantifying breast tumor blood flow (TBF) and high spatial resolution (HSR) DCE data for clinical reporting, following a single standard injection of contrast agent. METHODS:A simulation study was used to evaluate the performance of the interleaved technique under different conditions. In a prospective clinical study, 18 patients with primary breast cancer, who were due to undergo neoadjuvant chemotherapy (NACT), were examined using interleaved HTR and HSR DCE-MRI at 1.5 Tesla. Tumor regions of interest were analyzed with a two-compartment tracer kinetic model. Paired parameters (n = 10) from the data acquired before and post-cycle 2 of NACT were compared using the nonparametric Wilcoxon signed-rank test. RESULTS:Simulations demonstrated that TBF was reliably estimated using the proposed strategy. The region of interest analysis revealed significant changes in TBF (0.81-0.43 mL/min/mL; P = 0.002) following two cycles of NACT. The HSR data were reported in the normal way and enabled the assessment of tumor volume, which decreased by 53% following NACT (P = 0.065). CONCLUSIONS:TBF can be measured reliably using the proposed strategy without compromising a standard clinical protocol. Furthermore, in our feasibility study, TBF decreased significantly following NACT, whereas capillary permeability surface-area product did not. Magn Reson Med 79:317-326, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
10.1002/mrm.26684
Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI.
Drisis Stylianos,El Adoui Mohammed,Flamen Patrick,Benjelloun Mohammed,Dewind Roland,Paesmans Mariane,Ignatiadis Michail,Bali Maria,Lemort Marc
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Early prediction of nonresponse is essential in order to avoid inefficient treatments. PURPOSE:To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response. STUDY TYPE:This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study. POPULATION:Sixty patients were initially recruited, with 39 women participating in the final cohort. FIELD STRENGTH/SEQUENCE:A 1.5T scanner was used for MRI examinations. ASSESSMENT:Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0. STATISTICAL TESTS:T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis. RESULTS:PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference. DATA CONCLUSION:PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling. LEVEL OF EVIDENCE:1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.
10.1002/jmri.26996
Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy.
Drisis Stylianos,Metens Thierry,Ignatiadis Michael,Stathopoulos Konstantinos,Chao Shih-Li,Lemort Marc
European radiology
OBJECTIVES:To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. METHODS:Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. RESULTS:At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR CONCLUSIONS: Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. KEY POINTS:• DCE-MRI-derived pharmacokinetic parameters can predict response status of neoadjuvant chemotherapy treatment. • Ktrans can better predict pCR for the triple negative group. • No pharmacokinetic parameter could predict response for the ER+/HER2- group.
10.1007/s00330-015-3948-0
Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.
Wu Jia,Gong Guanghua,Cui Yi,Li Ruijiang
Journal of magnetic resonance imaging : JMRI
PURPOSE:To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS:In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. RESULTS:Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). CONCLUSION:The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.
10.1002/jmri.25279
Predictive value of DCE-MRI for early evaluation of pathological complete response to neoadjuvant chemotherapy in resectable primary breast cancer: A single-center prospective study.
Sun Ying-Shi,He Ying-Jian,Li Jie,Li Yan-Ling,Li Xiao-Ting,Lu Ai-Ping,Fan Zhao-Qing,Cao Kun,Ouyang Tao
Breast (Edinburgh, Scotland)
OBJECTIVE:This study proposed to establish a predictive model using dynamic enhanced MRI multi-parameters for early predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS:In this prospective cohort study, 170 breast cancer patients treated with NAC were enrolled and were randomly grouped into training sample (136 patients) and validation sample (34 patients). DCE-MRI parameters achieved at the end of the first cycle of NAC were screened to establish the predictive model by using multivariate logistic regression model according to pCR status. Receiver operating characteristic curves were conducted to assess the predictive capability. The association between MRI-predicted pCR and actual pCR in survival outcomes was estimated by using the Kaplan-Meier method with log-rank test. RESULTS:Multivariate analysis showed ΔAreamax and ΔSlopemax were independent predictors for pCR, odds ratio were 0.939 (95%CI, 0.915 to 0.964), and 0.966 (95%CI, 0.947 to 0.986), respectively. A predictive model was established using training sample as "Y = -0.063*ΔAreamax - 0.034*ΔSlopemax", a cut-off point of 3.0 was determined. The AUC for training and validation sample were 0.931 (95%CI, 0.890-0.971) and 0.971 (95%CI, 0.923-1.000), respectively. MRI-predicted pCR patients showed similar RFS (p = 0.347), DDFS (p = 0.25) and OS (p = 0.423) with pCR patients. CONCLUSION:The multi-parameter MRI model can be potentially used for early prediction of pCR status at the end of the first NAC cycle, which might allow timely regimen refinement before definitive surgical treatment.
10.1016/j.breast.2016.08.017
Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.
Jahani Nariman,Cohen Eric,Hsieh Meng-Kang,Weinstein Susan P,Pantalone Lauren,Hylton Nola,Newitt David,Davatzikos Christos,Kontos Despina
Scientific reports
We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
10.1038/s41598-019-48465-x
Ultrafast dynamic contrast-enhanced breast MRI: association with pathologic complete response in neoadjuvant treatment of breast cancer.
European radiology
OBJECTIVES:The purpose of this study was to investigate whether pretreatment kinetic features from ultrafast DCE-MRI are associated with pathological complete response (pCR) in patients with invasive breast cancer and according to immunohistochemistry (IHC) subtype. METHODS:Between August 2018 and June 2019, 256 consecutive breast cancer patients (mean age, 50.2 years; range, 25-86 years) who underwent both ultrafast and conventional DCE-MRI and surgery following neoadjuvant chemotherapy were included. DCE-MRI kinetic features were obtained from pretreatment MRI data. Time-to-enhancement, maximal slope (MS), and volumes at U1 and U2 (U1, time point at which the lesion starts to enhance; U2, subsequent time point after U1) were derived from ultrafast MRI. Logistic regression analysis was performed to identify factors associated with pCR. RESULTS:Overall, 41.4% of all patients achieved pCR. None of the kinetic features was associated with pCR when including all cancers. Among ultrafast DCE-MRI kinetic features, a lower MS (OR, 0.982; p = 0.040) was associated with pCR at univariable analysis in hormone receptor (HR)-positive cancers. In triple-negative cancers, a higher volume ratio U1/U2 was associated with pCR at univariable (OR, 11.787; p = 0.006) and multivariable analysis (OR, 14.811; p = 0.005). Among conventional DCE-MRI kinetic features, a lower peak enhancement (OR, 0.993; p = 0.031) and a lower percentage of washout (OR, 0.904; p = 0.039) was associated with pCR only in HR-positive cancers at univariable analysis. CONCLUSIONS:A higher volume ratio of U1/U2 derived from ultrafast DCE-MRI was independently associated with pCR in triple-negative invasive breast cancer. KEY POINTS:• The ratio of tumor volumes obtained at the first (U1) and second time points (U2) of enhancement was independently associated with pCR in triple-negative invasive breast cancers. • Ultrafast MRI has the potential to improve accuracy in predicting treatment response and personalizing therapy.
10.1007/s00330-021-08530-4
The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis.
Li Zhifan,Li Jinkui,Lu Xingru,Qu Mengmeng,Tian Jinhui,Lei Junqiang
European journal of radiology
PURPOSE:To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC). METHODS:We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis. RESULTS:A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity. CONCLUSIONS:DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.
10.1016/j.ejrad.2021.109931
Potential of combination of DCE-MRI and DWI with serum CA125 and CA199 in evaluating effectiveness of neoadjuvant chemotherapy in breast cancer.
Zhang Jun,Huang Yongbo,Chen Jianghui,Wang Xia,Ma Hongyu
World journal of surgical oncology
BACKGROUND:To determine the potential of the combination of DCE-MRI imaging method with DWI and serum CA125 and CA199 levels in the evaluation of the efficacy of neoadjuvant chemotherapy in breast cancer patients. METHODS:Sixty-five breast cancer patients who received neoadjuvant chemotherapy in our hospital from April 2016 to April 2017 were selected as research subjects. The patients received 4 courses of neoadjuvant chemotherapy. Lesions were monitored using DCE-MRI and DWI, while ELISA was used to measure the serum expression levels of the tumour markers CA125 and CA199. The patients were divided into the remission group and ineffective group based on pathological diagnosis. RESULTS:There were significant differences in K, K, ADC, ADC, tumour volume, and serum levels of CA125 and CA199 in patients in the remission group, before and after neoadjuvant chemotherapy, and there were significant differences in post-chemotherapy values of these indexes between the remission group and the ineffective group (p < 0.01). CONCLUSION:Combination of DCE-MRI diagnostic imaging with DWI can directly reflect the lesions in breast cancer patients after neoadjuvant chemotherapy. Serum levels of CA125 and CA199 levels are useful for evaluation of the impact of neoadjuvant chemotherapy on breast cancer patients, including risk of cancer cell metastasis and changes in some small lesions.
10.1186/s12957-021-02398-w
Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models.
Suo Shiteng,Yin Yan,Geng Xiaochuan,Zhang Dandan,Hua Jia,Cheng Fang,Chen Jie,Zhuang Zhiguo,Cao Mengqiu,Xu Jianrong
Journal of translational medicine
BACKGROUND:To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. METHODS:In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed. RESULTS:Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001). CONCLUSION:Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.
10.1186/s12967-021-02886-3
Accuracy of Contrast-Enhanced Ultrasound Compared With Magnetic Resonance Imaging in Assessing the Tumor Response After Neoadjuvant Chemotherapy for Breast Cancer.
Lee Sandy C,Grant Edward,Sheth Pulin,Garcia Agustin A,Desai Bhushan,Ji Lingyun,Groshen Susan,Hwang Darryl,Yamashita Mary,Hovanessian-Larsen Linda
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES:This pilot study compared contrast enhanced ultrasound (US) with contrast-enhanced magnetic resonance imaging (MRI) in assessing the treatment response in patients with breast cancer receiving preoperative neoadjuvant chemotherapy (NAC). METHODS:This prospective Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study included 30 patients, from January 2014 to October 2015, with invasive breast cancer detected by mammography, conventional US imaging, or both and scheduled for NAC. Informed consent was obtained. Contrast-enhanced US (perflutren lipid microspheres, 10 μL/kg) and MRI (gadopentetate dimeglumine, 0.1 mmol/kg) scans were performed at baseline before starting NAC and after completing NAC before surgery. Results of the imaging techniques were compared with each other and with histopathologic findings obtained at surgery using the Spearman correlation. Tumor size and enhancement parameters were compared for 15 patients with contrast-enhanced US, MRI, and surgical pathologic findings. RESULTS:The median tumor size at baseline was 3.1 cm on both contrast-enhanced US and MRI scans. The Spearman correlation showed strong agreement in tumor size at baseline between contrast-enhanced US and MRI (r = 0.88; P < .001) but less agreement in tumor size after NAC (r = 0.66; P = .004). Trends suggested that contrast-enhanced US (r = 0.75; P < .001) had a better correlation than MRI (r = 0.42; P = .095) with tumor size at surgery. Contrast-enhanced US was as effective as MRI in predicting a complete pathologic response (4 patients; 75.0% accuracy for both) and a non-complete pathologic response (11 patients; 72.7% accuracy for both). CONCLUSIONS:Contrast enhanced US is a valuable imaging modality for assessing the treatment response in patients receiving NAC and had a comparable correlation as MRI with breast cancer size at surgery.
10.7863/ultra.16.05060
Evaluation of the Tumor Response After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation Between Dynamic Contrast-enhanced Magnetic Resonance Imaging and Pathologic Tumor Cellularity.
Choi Woo Jung,Kim Won Kyung,Shin Hee Jung,Cha Joo Hee,Chae Eun Young,Kim Hak Hee
Clinical breast cancer
BACKGROUND:We evaluated the tumor response after neoadjuvant chemotherapy (NAC) in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging parameters assessed using a commercially available computer-aided system. We also analyzed their correlation with pathologic tumor cellularity. MATERIALS AND METHODS:We retrospectively reviewed the data from 130 patients with breast cancer who had undergone NAC followed by surgery from January to October 2013. Maximum diameter, volume, peak enhancement, and persistent, plateau, and washout-enhancing components were measured using a computer-aided system on DCE MR images and correlated with the Miller-Payne grading system. Patients with a Miller-Payne grade of 5 were classified into the pathologic complete response (pCR) group. Patients with grades 1, 2, 3, and 4 were included in the non-pCR group. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. RESULTS:Twenty patients were included in the pCR group and 110 patients in the non-pCR group. Of the 6 parameters, the rate of tumor volume reduction (r = 0.729, P < .001) showed the strongest correlation with the Miller-Payne grading system, followed by the maximum diameter (r = 0.706, P < .001) and washout component (r = 0.606, P < .001). The area under the receiver operating characteristic curve (Az value) was the largest for the rate of volume reduction (Az = 0.895), followed by the maximum diameter (Az = 0.891). CONCLUSION:The tumor volume changes in breast cancers before and after NAC, measured automatically using a commercially available computer-aided system and a clinical DCE MR imaging protocol might be the most accurate tool for evaluation of the pathologic response after NAC.
10.1016/j.clbc.2017.08.003
Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI.
Magnetic resonance imaging
PURPOSE:To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS:Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal - Signal )/Signal . Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. RESULTS:The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. CONCLUSIONS:Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.
10.1016/j.mri.2022.05.018
Breast MRI and tumour biology predict axillary lymph node response to neoadjuvant chemotherapy for breast cancer.
Al-Hattali Samia,Vinnicombe Sarah J,Gowdh Nazleen Muhammad,Evans Andrew,Armstrong Sharon,Adamson Douglas,Purdie Colin A,Macaskill E Jane
Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND:In patients who have had axillary nodal metastasis diagnosed prior to neoadjuvant chemotherapy for breast cancer, there is little consensus on how to manage the axilla subsequently. The aim of this study was to explore whether a combination of breast magnetic resonance imaging (MRI) assessed response and primary tumour pathology factors could identify a subset of patients that might be spared axillary node clearance. METHODS:A retrospective data analysis was performed of patients with core biopsy-proven axillary nodal metastasis prior to commencement of neoadjuvant chemotherapy (NAC) who had subsequent axillary node clearance (ANC) at definitive breast surgery. Breast tumour and axillary response at MRI before, during and on completion of NAC, core biopsy tumour grade, tumour type and immunophenotype were correlated with pathological response in the breast and the number of metastatic nodes in the ANC specimens. RESULTS:Of 87 consecutive patients with MRI at baseline, interim and after neoadjuvant chemotherapy who underwent ANC at time of breast surgery, 33 (38%) had no residual macrometastatic axillary disease, 28 (32%) had 1-2 metastatic nodes and 26 (30%) had more than 2 metastatic nodes. Factors that predicted axillary nodal complete response were MRI complete response in the breast (p < 0.0001), HER2 positivity (p = 0.02) and non-lobular tumour type (p = 0.015). CONCLUSION:MRI assessment of breast tumour response to NAC and core biopsy factors are predictive of response in axillary nodes, and can be used to guide decision making regarding appropriate axillary surgery.
10.1186/s40644-019-0279-4
Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI.
Lu Nannan,Dong Jie,Fang Xin,Wang Lufang,Jia Wei,Zhou Qiong,Wang Lingyu,Wei Jie,Pan Yueyin,Han Xinghua
BMC medical imaging
BACKGROUND:This study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. METHODS:Fifty patients (mean age, 48.7 years) with stage II-III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed. RESULTS:Clinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021-0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024-95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05). CONCLUSIONS:Our findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage.
10.1186/s12880-021-00688-z
Role of Magnetic Resonance Imaging in the Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy.
Pasquero Giorgia,Surace Alessandra,Ponti Antonio,Bortolini Massimiliano,Tota Donatella,Mano Maria Piera,Arisio Riccardo,Benedetto Chiara,Baù Maria Grazia
In vivo (Athens, Greece)
BACKGROUND/AIM:The aim of the study was to evaluate whether residual tumor assessment by magnetic resonance imaging (MRI) after neoadjuvant chemotherapy (NACT) is fundamental for a successive surgical strategy. PATIENTS AND METHODS:We collected 55 MRIs performed after NACT. RESULTS:Pathological response rate was 20%. MRI's sensitivity, specificity, PPV and NPV were 50%, 88%, 54% and 86%, respectively. We observed a high variability between the different subgroups, with high number of false positives in luminal A/B tumors. Triple negative and HER2+ tumors had almost the same specificity and sensitivity (81% and 50%). Nevertheless, in the HER2+ group, PPV was greater than that in the triple negative group (71% and 33% respectively) and the NPV of the triple negative group was greater than that of the HER2+ one (90% and 64%, respectively). Statistical analysis showed a weak but significant correlation between MRI and pathological assessment of residual tumor dimension. CONCLUSION:The present study, confirms literature data about MRI accuracy in diagnosing HER2+ and triple negative tumors, but suggests caution in case of luminal tumors' evaluation.
10.21873/invivo.11857
Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy.
Eun Na Lae,Kang Daesung,Son Eun Ju,Youk Ji Hyun,Kim Jeong-Ah,Gweon Hye Mi
European radiology
OBJECTIVES:To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC). METHODS:This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test. RESULTS:Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.83). The texture analysis with the RF method showed the highest diagnostic performances for pretreatment T2-weighted images and midtreatment DWI and ADC maps showed better diagnostic performance than that of an analysis of common features (AUC, 0.94 vs. 0.83, p < 0.05). The RF model based on all sequences showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers. CONCLUSIONS:Texture analysis using an RF model for pretreatment and midtreatment MRI may provide valuable prognostic information for predicting recurrence in patients with breast cancer treated with NAC and surgery. KEY POINTS:• RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p < 0.05) for predicting recurrence in breast cancer after NAC. • Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps. • RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.
10.1007/s00330-021-07816-x
Magnetic Resonance Imaging (MRI) Assessment of Residual Breast Cancer After Neoadjuvant Chemotherapy: Relevance to Tumor Subtypes and MRI Interpretation Threshold.
Kim Yunju,Sim Sung Hoon,Park Boram,Lee Keun Seok,Chae In Hye,Park In Hae,Kwon Youngmi,Jung So-Youn,Lee Seeyoun,Ko Kyounglan,Kang Han-Sung,Lee Chan Wha,Lee Eun Sook
Clinical breast cancer
PURPOSE:To investigate the diagnostic performance of magnetic resonance imaging (MRI) for predicting pathologic complete response after neoadjuvant chemotherapy (NAC) depending on subtypes of breast cancer using different interpretation thresholds of MRI negativity. PATIENTS AND METHODS:A total of 353 women with breast cancer who had undergone NAC were included. Pathologic examination after complete surgical excision was the reference standard. Tumors were divided into 4 subtypes on the basis of expression of hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2). Tumor enhancement was assessed on early and late phases of MRI. MRI negativity was divided into radiologic complete response (rCR, complete absence of enhancement on both early and late phases) and near-rCR (no discernible early enhancement but observed late enhancement). RESULTS:Ninety (25.5%) of 353 patients experienced pathologic complete response. When analyzing the data of all patients, sensitivity of MRI was higher for rCR versus near-rCR (97.72% vs. 90.49%, P < .0001), whereas specificity was lower for rCR versus near-rCR (44.44% vs. 72.22%, P < .0001). Accuracy was equivalent (84.14% vs. 85.84%). In HR-HER2 tumors, 100% sensitivity and negative predictive value were achieved by assessing early enhancement only. In HRHER2- tumors, sensitivity of MRI was higher for rCR versus near-rCR (96.12% vs. 86.82%, P = .0005). CONCLUSION:Diagnostic performance of MRI after NAC differs in accordance with the subtypes and threshold of MRI negativity. MRI assessment with consideration of tumor subtypes is required, along with standardization of MRI interpretation criteria in the NAC setting.
10.1016/j.clbc.2018.05.009
Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.
European radiology
OBJECTIVES:To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS:A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. RESULTS:Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively. CONCLUSIONS:Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. KEY POINTS:• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.
10.1007/s00330-021-08291-0
Comparison of Magnetic Resonance Imaging With Positron Emission Tomography/Computed Tomography in the Evaluation of Response to Neoadjuvant Therapy of Breast Cancer.
The Journal of surgical research
INTRODUCTION:The present study aims to determine the diagnostic accuracy of magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) in predicting a pathological response of molecular subtypes of breast cancer to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS:We retrospectively analyzed patients with breast cancer who were operated after NAC between January 2018 and May 2020. Radiological responses were evaluated as per the Response Evaluation Criteria in Solid Tumors (RECIST) and changes in contrast enhancement patterns on MRI and the classification of PET Response Criteria in Solid Tumors (PERCIST) on PET-CT. The presence of a pathological response was evaluated based on the Sataloff classification. The agreement between the radiological response determined through imaging modalities before and after the NAC and the postoperative pathological complete response (pCR) was evaluated and compared statistically. Among the patients diagnosed with breast cancer between the ages of 18 and 80 y, those with N (+) at the time of diagnosis, those with T2 and advanced tumors, and those who were planned for breast conserving surgery were included in our study. Male patients, patients with distant metastases at the time of diagnosis, and patients with other system malignancies were excluded. RESULTS:The study included 88 patients who had undergone surgery for breast cancer after NAC between January 2018 and May 2020. The study was conducted retrospectively in a single center. The tumor diameters and standard uptake values significantly decreased after NAC (P < 0.001). Estrogen receptor (ER) and progesterone receptor (PR) positivity were negatively associated with pCR (P = 0.03 and P = 0.03, respectively), whereas there was a significant positive association between HER-2 positivity and pCR (P = 0.004). There was a moderate agreement between the RECIST criteria used with MRI and pCR (k: 0.46). Moreover, a good agreement between PET-CT-PERCIST and pCR was detected (k: 0.61). In predicting pCR after NAC, MRI showed a selectivity of 80.7%, a sensitivity of 65.2%, a positive predictive value (PPV) of 75%, and a negative predictive value (NPV) of 72.4%. The corresponding rates for PET-CT were 75.7%, 100%, 57.9%, and 100%. CONCLUSIONS:When evaluating pCR after NAC, MRI was found to be more sensitive in patients with ER-positive cancer cell nuclei with weak to medium staining intensity and a loss of E-cadherin expression, whereas PET-CT was found to be more sensitive in patients with HER-2 overexpression, Luminal B, or Ki-67 proliferation >14% (P = 0.01).
10.1016/j.jss.2022.04.063
Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.
European radiology
OBJECTIVES:To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. METHODS:From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. RESULTS:In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. CONCLUSIONS:Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS:• Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.
10.1007/s00330-022-08667-w