MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer.
Cancers
AIM:To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with + - invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm. MATERIALS AND METHODS:Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS. RESULTS:248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable ( = 771), not variant ( = 82), and highly intercorrelated ( = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%. CONCLUSIONS:Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.
10.3390/cancers15061840
Oncotype DX results increase concordance in adjuvant chemotherapy recommendations for early-stage breast cancer.
NPJ breast cancer
Adjuvant chemotherapy recommendations for ER+/HER2- early-stage breast cancers (eBC) involve integrating prognostic and predictive information which rely on physician judgment; this can lead to discordant recommendations. In this study we aim to evaluate whether Oncotype DX improves confidence and agreement among oncologists in adjuvant chemotherapy recommendations. We randomly select 30 patients with ER+/HER2- eBC and recurrence score (RS) available from an institutional database. We ask 16 breast oncologists with varying years of clinical practice in Italy and the US to provide recommendation for the addition of chemotherapy to endocrine therapy and their degree of confidence in the recommendation twice; first, based on clinicopathologic features only (pre-RS), and then with RS result (post-RS). Pre-RS, the average rate of chemotherapy recommendation is 50.8% and is higher among junior (62% vs 44%; p < 0.001), but similar by country. Oncologists are uncertain in 39% of cases and recommendations are discordant in 27% of cases (interobserver agreement K 0.47). Post-RS, 30% of physicians change recommendation, uncertainty in recommendation decreases to 5.6%, and discordance decreases to 7% (interobserver agreement K 0.85). Interpretation of clinicopathologic features alone to recommend adjuvant chemotherapy results in 1 out of 4 discordant recommendations and relatively high physician uncertainty. Oncotype DX results decrease discordancy to 1 out of 15, and reduce physician uncertainty. Genomic assay results reduce subjectivity in adjuvant chemotherapy recommendations for ER +/HER2- eBC.
10.1038/s41523-023-00559-6
Oncotype DX Breast Recurrence Score Distribution and Chemotherapy Benefit Among Women of Different Age Groups With HR-Positive, HER2-Negative, Node-Negative Breast Cancer in the SEER Database.
Cheng Ran,Kong Xiangyi,Wang Xiangyu,Fang Yi,Wang Jing
Frontiers in oncology
To explore the distribution of Oncotype DX Breast Recurrence Score (RS), the proportion of receiving chemotherapy, and the relationship between RS and chemotherapy benefit according to detailed age groups in women with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-negative (HR+/HER2-/N0) breast cancer. This was an extensive, comprehensive, population-based retrospective study. Data on individuals with breast cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program. The cohort was divided into five groups by age (≤ 35, 36-50, 51-65, 66-80, >80 years). RS distribution and chemotherapy proportion among different age groups were analyzed, and the overall survivals between patients receiving chemotherapy and those not/unknown were compared in each age group. The study cohort comprised 49,539 patients and the largest age group was 51-65 years. The percentage of patients with low-risk RS (0-10) increased with age, whereas those with intermediate-risk RS (11-25) decreased with age (except for the group of 36-50 years, which had the highest rate of intermediate-risk RS). The age group ≤35 years has the greatest rate of high-risk RS (26-100). The proportion of receiving chemotherapy decreased with age in all RS risk categories. Overall survival was benefited by chemotherapy only in the age group of 66-80 years with intermediate- and high-risk RS, and chemotherapy seemed to do more harm than good for patients older than 80 years. In the present study, we identified the distribution of RS, the proportion of receiving chemotherapy, and the relationship between RS and chemotherapy benefit according to a detailed age grouping for women with HR+/HER2-/N0 breast cancer, which may help in making individualized clinical decisions.
10.3389/fonc.2020.01583
Impact of Oncotype DX testing on ER+ breast cancer treatment and survival in the first decade of use.
Breast cancer research : BCR
BACKGROUND:The Oncotype DX breast recurrence score has been introduced more than a decade ago to aid physicians in determining the need for systemic adjuvant chemotherapy in patients with early-stage, estrogen receptor (ER)+, lymph node-negative breast cancer. METHODS:In this study, we utilized data from The Surveillance, Epidemiology, and End Results (SEER) Program to investigate temporal trends in Oncotype DX usage among US breast cancer patients in the first decade after the introduction of the Oncotype DX assay. RESULTS:We found that the use of Oncotype DX has steadily increased in the first decade of use and that this increase is associated with a decreased usage of chemotherapy. Patients who utilized the Oncotype DX test tended to have improved survival compared to patients who did not use the assay even after adjusting for clinical variables associated with prognosis. In addition, chemotherapy usage in patients with high-risk scores is associated with significantly longer overall and breast cancer-specific survival compared to high-risk patients who did not receive chemotherapy. On the contrary, patients with low-risk scores who were treated with chemotherapy tended to have shorter overall survival compared to low-risk patients who forwent chemotherapy. CONCLUSION:We have provided a comprehensive temporal overview of the use of Oncotype DX in breast cancer patients in the first decade after Oncotype DX was introduced. Our results suggest that the use of Oncotype DX is increasing in ER+ breast cancer and that the Oncotype DX test results provide valuable information for patient treatment and prognosis.
10.1186/s13058-021-01453-4
Oncotype DX Recurrence Score in premenopausal women.
Zhang Shiliang,Fitzsimmons Kasey C,Hurvitz Sara A
Therapeutic advances in medical oncology
In the past 20 years, clinicians have shifted away from relying solely on clinicopathologic indicators toward increasing use of multigene expression assays in guiding treatment decisions regarding adjuvant chemotherapy for early-stage hormone receptor (HR)-positive, HER2-negative breast cancer. Oncotype DX Recurrence Score (RS) is one of the most widely used multigene assays when considering indications for adjuvant chemotherapy, and guidelines have recently incorporated its use in women with early HR-positive HER2-negative breast cancer and up to three positive lymph nodes. While multiple retrospective and prospective clinical studies have demonstrated that most women with a low- to mid-range RS (0-25) can safely forgo chemotherapy, premenopausal women remain an important subgroup for which recommendations based on RS are ill-defined. The majority of patients included in clinical trials and retrospective analyses validating the use of RS have been postmenopausal women. In the subgroup of premenopausal women with HR-positive HER2-negative breast cancer, studies indicate that traditional clinicopathologic methods for assessing risk continue to be powerful tools when combined with RS to predict benefit from chemotherapy. This suggests that there is an element of uncaptured risk inherent to the premenopausal state that evades characterization by RS alone. This review describes the evidence that has supported the recommendation of RS in clinical guidelines, specifically focusing on data for its current use in premenopausal women. We review available data regarding the impact of the menstrual cycle on hormonally regulated gene expression, which may drive variations in the RS. Further research on the reliability and interpretation of the RS in the premenopausal subgroup is necessary and represents a gap in knowledge of how the RS should be applied in premenopausal women.
10.1177/17588359221081077
Oncotype DX Breast Recurrence Score: A Review of its Use in Early-Stage Breast Cancer.
Syed Yahiya Y
Molecular diagnosis & therapy
Oncotype DX Breast Recurrence Score is a 21-gene prognostic and predictive assay indicated for use in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, lymph node (LN)-negative or up to three LN-positive, early-stage breast cancer. The assay is used worldwide and is conducted at a CLIA-certified central laboratory in the USA. The 21-gene assay generates a Recurrence Score for each tumour sample, based on expression levels of 16 breast cancer-related genes, normalized to five reference genes. The Recurrence Score is a continuous variable that provides an individualized estimate of 9-year distant recurrence risk and the likelihood of adjuvant chemotherapy benefit. The 21-gene Recurrence Score assay is extensively validated in clinical studies, which are well supported by real-world registry studies. The assay informs treatment decisions and reduces adjuvant chemotherapy use in routine clinical practices, and is estimated to be cost-effective. The 21-gene Recurrence Score assay is included in all major international treatment guidelines. Currently, it is the only multigene assay validated for prediction of chemotherapy benefit, as well as for prognosis.
10.1007/s40291-020-00482-7
Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE:To update recommendations on appropriate use of breast cancer biomarker assay results to guide adjuvant endocrine and chemotherapy decisions in early-stage breast cancer. METHODS:An updated literature search identified randomized clinical trials and prospective-retrospective studies published from January 2016 to October 2021. Outcomes of interest included overall survival and disease-free or recurrence-free survival. Expert Panel members used informal consensus to develop evidence-based recommendations. RESULTS:The search identified 24 studies informing the evidence base. RECOMMENDATIONS:Clinicians may use Onco DX, MammaPrint, Breast Cancer Index (BCI), and EndoPredict to guide adjuvant endocrine and chemotherapy in patients who are postmenopausal or age > 50 years with early-stage estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+ and HER2-) breast cancer that is node-negative or with 1-3 positive nodes. Prosigna and BCI may be used in postmenopausal patients with node-negative ER+ and HER2- breast cancer. In premenopausal patients, clinicians may use Onco in patients with node-negative ER+ and HER2- breast cancer. Current data suggest that premenopausal patients with 1-3 positive nodes benefit from chemotherapy regardless of genomic assay result. There are no data on use of genomic tests to guide adjuvant chemotherapy in patients with ≥ 4 positive nodes. Ki67 combined with other parameters or immunohistochemistry 4 score may be used in postmenopausal patients without access to genomic tests to guide adjuvant therapy decisions. BCI may be offered to patients with 0-3 positive nodes who received 5 years of endocrine therapy without evidence of recurrence to guide decisions about extended endocrine therapy. None of the assays are recommended for treatment guidance in individuals with HER2-positive or triple-negative breast cancer. Treatment decisions should also consider disease stage, comorbidities, and patient preferences.Additional information is available at www.asco.org/breast-cancer-guidelines.
10.1200/JCO.22.00069
Prognostic and predictive biomarkers in breast cancer: Past, present and future.
Nicolini Andrea,Ferrari Paola,Duffy Michael J
Seminars in cancer biology
Following a diagnosis of breast cancer, the most immediate challenges in patient management are the determination of prognosis and identification of the most appropriate adjuvant systemic therapy. Determining prognosis can best be addressed with a combination of traditional clinicopathological prognostic factors, biomarkers such as HER2/neu and specific multigene genes tests. Amongst the best validated prognostic multigene tests are uPA/PAI1, Oncotype DX and MammaPrint. Oncotype DX and MammaPrint, may be used for predicting outcome and aiding adjunct therapy decision making in patients with ER-positive, HER2-negative breast cancers that are either lymph node-negative or node positive (1-3 metastatic nodes), while uPA/PAI-1 may be similarly used in ER-positive, lymph node-negative patients. For selecting likely response to endocrine therapy, both estrogen receptors (ER) and progesterone receptors (PR) should be measured. On the other hand, for identifying likely response to anti-HER2 therapy, determination of HER2 gene amplification or overexpression is necessary. To identify new prognostic and predictive biomarkers for breast cancer, current research is focusing on tumor and circulating DNA (ctDNA) and RNA (e.g., micro RNAs) and circulating tumor cells. A promising ctDNA biomarker is the mutational status of ER (ESR1) for predicting the emergence of resistance to aromatase inhibitors. Challenges for future research include the identification of biomarkers for predicting response to radiotherapy and specific forms of chemotherapy.
10.1016/j.semcancer.2017.08.010
Molecular Drivers of Onco DX, Prosigna, EndoPredict, and the Breast Cancer Index: A TransATAC Study.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE:The Onco DX Recurrence Score (RS), Prosigna Prediction Analysis of Microarray 50 (PAM50) Risk of Recurrence (ROR), EndoPredict (EP), and Breast Cancer Index (BCI) are used clinically for estimating risk of distant recurrence for patients receiving endocrine therapy. Discordances in estimates occur between them. We aimed to identify the molecular features that drive the tests and lead to these differences. PATIENTS AND METHODS:Analyses for RS, ROR, EP, and BCI were conducted by the manufacturers in the TransATAC sample collection that consisted of the tamoxifen or anastrozole arms of the ATAC trial. Estrogen receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative cases without chemotherapy treatment were included in which all four tests were available (n = 785). Clinicopathologic features included in some tests were excluded from the comparisons. Estrogen, proliferation, invasion, and HER2 module scores from RS were used to characterize the respective molecular features. Spearman correlation and analysis of variance tests were applied. RESULTS:There were moderate to strong correlations among the four molecular scores (ρ = 0.63-0.74) except for RS versus ROR (ρ = 0.32) and RS versus BCI (ρ = 0.35). RS had strong negative correlation with its estrogen module (ρ = -0.79) and moderate positive correlation with its proliferation module (ρ = 0.36). RS's proliferation module explained 72.5% of ROR's variance, while the estrogen module explained only 0.6%. Most of EP's and BCI's variation was accounted for by the proliferation module (50.0% and 54.3%, respectively) and much less by the estrogen module (20.2% and 2.7%, respectively). CONCLUSION:In contrast to common understanding, RSs are determined more strongly by estrogen-related features and only weakly by proliferation markers. However, the EP, BCI, and particularly ROR scores are determined largely by proliferative features. These relationships help to explain the differences in the prognostic performance of the tests.
10.1200/JCO.20.00853
Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers.
Amornsiripanitch Nita,Nguyen Vicky T,Rahbar Habib,Hippe Daniel S,Gadi Vijayakrishna K,Rendi Mara H,Partridge Savannah C
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Hormone receptor-positive breast cancer is the most common subtype; better tools to identify which patients in this group would derive clear benefit from chemotherapy are needed. PURPOSE:To evaluate the prognostic potential of diffusion-weighted MRI (DWI) by investigating associations with pathologic biomarkers and a genomic assay for 10-year recurrence risk. STUDY TYPE:Retrospective. SUBJECTS:In all, 107 consecutive patients (from 2/2010 to 1/2013) with estrogen receptor (ER)-positive/HER2neu-negative invasive breast cancer who had the 21-gene recurrence score (RS) test (Oncotype DX, Genomic Health). FIELD STRENGTH/SEQUENCE:Each subject underwent presurgical 3T breast MRI, which included DWI (b = 0, 800 s/mm ). ASSESSMENT:Apparent diffusion coefficient (ADC) and contrast-to-noise ratio (CNR) were measured for each lesion by a fifth year radiology resident. Pathological markers (Nottingham histologic grade, Ki-67, RS) were determined from pathology reports. Medical records were reviewed to assess recurrence-free survival. STATISTICAL TESTS:RS was stratified into low (<18), moderate (18-30), and high (>30)-risk groups. Associations of DWI characteristics with pathologic biomarkers were evaluated by binary or ordinal logistic regression, as appropriate, with adjustment for multiple comparisons. Post-hoc comparisons between specific groups were also performed. RESULTS:ADCmean (odds ratio [OR] = 0.61 per 1 standard deviation [SD] increase, adj. P = 0.044) and CNR (OR = 1.76 per 1-SD increase, adj. P = 0.026) were significantly associated with increasing tumor grade. DWI CNR was also significantly associated with a high (Ki-67 ≥14%) proliferation rate (OR = 2.55 per 1-SD increase, adj. P = 0.026). While there were no statistically significant linear associations in ADC (adj. P = 0.80-0.85) and CNR (adj. P = 0.56) across all three RS groups by ordinal logistic regression, post-hoc analyses suggested that high RS lesions exhibited lower ADCmean (P = 0.037) and ADCmax (P = 0.004) values and higher CNR (P = 0.008) compared to lesions with a low or moderate RS. DATA CONCLUSION:DWI characteristics correlated with tumor grade, proliferation index, and RS, and may potentially help to identify those with highest recurrence risk and most potential benefit from chemotherapy. LEVEL OF EVIDENCE:3 Technical Efficacy Stage 3 J. Magn. Reson. Imaging 2017.
10.1002/jmri.25909
The role of diffusion weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence?
Roknsharifi Shima,Fishman Michael D C,Agarwal Monica D,Brook Alexander,Kharbanda Vritti,Dialani Vandana
Academic radiology
RATIONALE AND OBJECTIVES:To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence. METHOD AND MATERIALS:In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant. RESULTS:Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10 vs 1481 ± 276 × 10 mm/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status. CONCLUSION:ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.
10.1016/j.acra.2018.09.003
Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.
Ha Richard,Chang Peter,Mutasa Simukayi,Karcich Jenika,Goodman Sarah,Blum Elyse,Kalinsky Kevin,Liu Michael Z,Jambawalikar Sachin
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / human epidermal growth factor receptor-2 negative (ER+/HER2-)/node negative invasive breast carcinoma. Although effective, the test is invasive and expensive, which has motivated this investigation to determine the potential role of radiomics. HYPOTHESIS:We hypothesized that convolutional neural network (CNN) can be used to predict Oncotype Dx RS using an MRI dataset. STUDY TYPE:Institutional Review Board (IRB)-approved retrospective study from January 2010 to June 2016. POPULATION:In all, 134 patients with ER+/HER2- invasive ductal carcinoma who underwent both breast MRI and Oncotype Dx RS evaluation. Patients were classified into three groups: low risk (group 1, RS <18), intermediate risk (group 2, RS 18-30), and high risk (group 3, RS >30). FIELD STRENGTH/SEQUENCE:1.5T and 3.0T. Breast MRI, T postcontrast. ASSESSMENT:Each breast tumor underwent 3D segmentation. In all, 1649 volumetric slices in 134 tumors (mean 12.3 slices/tumor) were evaluated. A CNN consisted of four convolutional layers and max-pooling layers. Dropout at 50% was applied to the second to last fully connected layer to prevent overfitting. Three-class prediction (group 1 vs. group 2 vs. group 3) and two-class prediction (group 1 vs. group 2/3) models were performed. STATISTICAL TESTS:A 5-fold crossvalidation test was performed using 80% training and 20% testing. Diagnostic accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were evaluated. RESULTS:The CNN achieved an overall accuracy of 81% (95% confidence interval [CI] ± 4%) in three-class prediction with specificity 90% (95% CI ± 5%), sensitivity 60% (95% CI ± 6%), and the area under the ROC curve was 0.92 (SD, 0.01). The CNN achieved an overall accuracy of 84% (95% CI ± 5%) in two-class prediction with specificity 81% (95% CI ± 4%), sensitivity 87% (95% CI ± 5%), and the area under the ROC curve was 0.92 (SD, 0.01). DATA CONCLUSION:It is feasible for current deep CNN architecture to be trained to predict Oncotype DX RS. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:518-524.
10.1002/jmri.26244
Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores.
Dialani Vandana,Gaur Shantanu,Mehta Tejas S,Venkataraman Shambhavi,Fein-Zachary Valerie,Phillips Jordana,Brook Alexander,Slanetz Priscilla J
Radiology
Purpose To review mammographic, ultrasonographic (US), and magnetic resonance (MR) imaging features and pathologic characteristics of estrogen receptor (ER)-positive, lymph node-negative invasive breast cancer and to determine the relationship of these characteristics to Oncotype DX (Genomic Health, Redwood City, Calif) test recurrence scores (ODRS) for breast cancer recurrence. Materials and Methods This institutional review board-approved retrospective study was performed in a single large academic medical center. The study population included patients with ER-positive, lymph node-negative invasive breast cancer who underwent genomic testing from January 1, 2009, to December 31, 2013. Imaging features of the tumor were classified according to the Breast Imaging Reporting and Data System lexicon by breast imagers who were blinded to the ODRS. Mammography was performed in 86% of patients, US was performed in 84%, and MR imaging was performed in 33%, including morphologic and kinetic evaluation. Images from each imaging modality were evaluated. Each imaging finding, progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status, and tumor grade were then individually correlated with ODRS. Analysis of variance was used to determine differences for each imaging feature. Regression analysis was used to calculate prediction of recurrence on the basis of imaging features combined with histopathologic features. Results The 319 patients had a mean age ± standard deviation of 55 years ± 8.7 (range, 31-82 years). Imaging features with a positive correlation with ODRS included a well-circumscribed oval mass (P = .024) at mammography, vascularity (P = .047) and posterior enhancement (P = .004) at US, and lobulated mass (P = .002) at MR imaging. Recurrence scores were predicted by using these features in combination with PR and HER2 status and tumor grade by using the threshold of more than 30 as a high recurrence score. With a regression tree, there was correlation (r = 0.79) with 89% sensitivity and 83% specificity. Conclusion On the basis of preliminary data, information obtained routinely for breast cancer diagnosis can reliably be used to predict the ODRS with high sensitivity and specificity. (©) RSNA, 2016.
10.1148/radiol.2016151149
A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.
Journal of cancer research and clinical oncology
PURPOSE:To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores. METHODS:A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set. RESULTS:High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56-0.98, p < 0.003). Low against intermediate and high ODX score was predicted with AUC 0.51 (95% CI 0.41-0.61, p = 0.75). CONCLUSION:A moderate association between imaging and ODX score was identified. The evaluated models currently do not warrant replacement of ODX with imaging alone.
10.1007/s00432-018-2595-7
Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: A potential predictor for an oncotype Dx test recurrence score.
Thakur Sunitha B,Durando Manuela,Milans Soledad,Cho Gene Y,Gennaro Lucas,Sutton Elizabeth J,Giri Dilip,Morris Elizabeth A
Journal of magnetic resonance imaging : JMRI
PURPOSE:To measure the apparent diffusion coefficient (ADC) values in estrogen receptor-positive (ER+) and axillary lymph node-negative (LN-) invasive breast cancer and investigate the correlation of ADC with Oncotype Dx test recurrence scores (ODxRS). MATERIALS AND METHODS:This was a Health Insurance Portability and Accountability Act (HIPAA)-compliant single-site retrospective study. Patients underwent preoperative 3.0T MRI scans with additional diffusion-weighted imaging sequential scans (b = 0, 600 and b = 0, 1000 s/mm ) from January 2011 to 2013. The study population included 31 ER+/LN- invasive breast cancers, which underwent ODxRS genomic testing. ADC and ADC parametric maps were generated, and ADC values were calculated from a user-drawn region of interest. ODxRS predicts 10-year recurrence risk in individual patients: low (RS <18), intermediate (RS: 18-30), or high (RS >30). All breast lesions, including subgroups of invasive ductal carcinoma (IDC) lesions and mass-only lesions were dichotomized by RS scores, low-risk versus intermediate/high-risk, and statistical analysis was performed using Mann-Whitney's test (statistical significance at P < 0.05) and receiver operating characteristic (ROC) curves. Multivariate analysis was also performed. RESULTS:Invasive breast cancers, when scored as low-risk by ODxRS, had significantly higher ADC values compared with intermediate/high-risk lesions for both ADC (P = 0.007) and ADC (P = 0.008) mean values. This was true both when analyzing only mass-lesions (P = 0.03 and 0.01) or only IDCs (P = 0.001 and 0.009). CONCLUSION:Preliminary findings suggest that lesion ADC values correlate with recurrence risk likelihood stratified using ODxRS. Hence, ADC is a potential surrogate biomarker for tumor aggressiveness. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:401-409.
10.1002/jmri.25796
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
Li Hui,Zhu Yitan,Burnside Elizabeth S,Drukker Karen,Hoadley Katherine A,Fan Cheng,Conzen Suzanne D,Whitman Gary J,Sutton Elizabeth J,Net Jose M,Ganott Marie,Huang Erich,Morris Elizabeth A,Perou Charles M,Ji Yuan,Giger Maryellen L
Radiology
Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. RSNA, 2016 Online supplemental material is available for this article.
10.1148/radiol.2016152110
Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features.
Woodard Genevieve A,Ray Kimberly M,Joe Bonnie N,Price Elissa R
Radiology
Purpose To evaluate the association between Breast Imaging Reporting and Data System (BI-RADS) mammographic and magnetic resonance (MR) imaging features and breast cancer recurrence risk in patients with estrogen receptor-positive breast cancer who underwent the Oncotype DX assay. Materials and Methods In this institutional review board-approved and HIPAA-compliant protocol, 408 patients diagnosed with invasive breast cancer between 2004 and 2013 who underwent the Oncotype DX assay were identified. Mammographic and MR imaging features were retrospectively collected according to the BI-RADS lexicon. Linear regression assessed the association between imaging features and Oncotype DX test recurrence score (ODxRS), and post hoc pairwise comparisons assessed ODxRS means by using imaging features. Results Mammographic breast density was inversely associated with ODxRS (P ≤ .05). Average ODxRS for density category A was 24.4 and that for density category D was 16.5 (P < .02). Both indistinct mass margins and fine linear branching calcifications at mammography were significantly associated with higher ODxRS (P < .01 and P < .03, respectively). Masses with indistinct margins had an average ODxRS of 31.3, which significantly differed from the ODxRS of 18.5 for all other mass margins (P < .01). The average ODxRS for fine linear branching calcifications was 29.6, whereas the ODxRS for all other suspicious calcification morphologies was 19.4 (P < .03). Average ODxRS was significantly higher for irregular mass margins at MR imaging compared with spiculated mass margins (24.0 vs 17.6; P < .02). The presence of nonmass enhancement at MR imaging was associated with lower ODxRS than was its absence (16.4 vs 19.9; P < .05). Conclusion The BI-RADS features of mammographic breast density, calcification morphology, mass margins at mammography and MR imaging, and nonmass enhancement at MR imaging have the potential to serve as imaging biomarkers of breast cancer recurrence risk. Further prospective studies involving larger patient cohorts are needed to validate these preliminary findings. RSNA, 2017 Online supplemental material is available for this article.
10.1148/radiol.2017162333
Background Parenchymal Enhancement on Breast MRI as a Prognostic Surrogate: Correlation With Breast Cancer Oncotype Dx Score.
Zhang Michelle,Sadinski Meredith,Haddad Dana,Bae Min Sun,Martinez Danny,Morris Elizabeth A,Gibbs Peter,Sutton Elizabeth J
Frontiers in oncology
Purpose:Breast MRI background parenchymal enhancement (BPE) can potentially serve as a prognostic marker, by possible correlation with molecular subtype. Oncotype Dx, a gene assay, is a prognostic and predictive surrogate for tumor aggressiveness and treatment response. The purpose of this study was to investigate the association between contralateral non-tumor breast magnetic resonance imaging (MRI) background parenchymal enhancement and tumor oncotype score. Methods:In this retrospective study, patients with ER+ and HER2- early stage invasive ductal carcinoma who underwent preoperative breast MRI, oncotype risk scoring, and breast conservation surgery from 2008-2010 were identified. After registration, BPE from the pre and three post-contrast phases was automatically extracted using a k-means clustering algorithm. Four metrics were calculated: initial enhancement (IE) relative to the pre-contrast signal, late enhancement, overall enhancement (OE), and area under the enhancement curve (AUC). Histogram analysis was performed to determine first order metrics which were compared to oncotype risk score groups using Mann-Whitney tests and Spearman rank correlation analysis. Results:This study included 80 women (mean age = 51.1 ± 10.3 years); 46 women were categorized as low risk (≤17) and 34 women were categorized as intermediate/high risk (≥18) according to Oncotype Dx. For the mean of the top 10% pixels, significant differences were noted for IE (p = 0.032), OE (p = 0.049), and AUC (p = 0.044). Using the risk score as a continuous variable, correlation analysis revealed a weak but significant correlation with the mean of the top 10% pixels for IE (r = 0.26, p = 0.02), OE (r = 0.25, p = 0.02), and AUC (r = 0.27, p = 0.02). Conclusion:BPE metrics of enhancement in the non-tumor breast are associated with tumor Oncotype Dx recurrence score, suggesting that the breast microenvironment may relate to likelihood of recurrence and magnitude of chemotherapy benefit.
10.3389/fonc.2020.595820
Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor-Positive Breast Cancer: A Multicohort Study.
Fan Ming,Cui Yajing,You Chao,Liu Li,Gu Yajia,Peng Weijun,Bai Qianming,Gao Xin,Li Lihua
Radiology
Background Radiogenomics explores the association between imaging features and genomic assays to uncover relevant prognostic features; however, the prognostic implications of the derived signatures remain unclear. Purpose To identify preoperative radiogenomic signatures of estrogen receptor-positive breast cancer associated with the Oncotype DX recurrence score (RS) and to evaluate whether they are biomarkers for survival and responses to neoadjuvant chemotherapy (NACT). Materials and Methods In this retrospective multicohort study, three data sets were analyzed. The radiogenomic development data set, with preoperative dynamic contrast-enhanced MRI and RS data obtained between January 2016 and October 2019 was used to identify radiogenomic signatures. Prognostic implications of the imaging signatures were assessed by measuring overall survival and recurrence-free survival in the prognostic assessment data set using a multivariable Cox proportional hazards model. The therapeutic implication of the radiogenomic signatures was evaluated by determining their ability to predict the response to NACT using the treatment assessment data set obtained between August 2015 and March 2019. Prediction performance was estimated by using the area under the receiver operating characteristic curve (AUC). Results The final cohorts included a radiogenomic development data set with 130 women (mean age, 52 years ± 10 [standard deviation]), a prognostic assessment data set with 116 women (mean age, 48 years ± 9), and a treatment assessment data set with 135 women (mean age, 50 years ± 11). Radiogenomic signatures ( = 11) of texture and morphologic and statistical features were identified to generate the predicted RS ( = 0.33, < .001). A predicted RS greater than 29.9 was associated with poor overall and recurrence-free survival ( = .001 and = .007, respectively); predicted RS was greater in women with a good NACT response (30.51 ± 6.92 vs 27.35 ± 4.04 [responders vs nonresponders], = .001). By combining the predicted RS and complementary features, the model achieved improved performance in prediction of the NACT response (AUC, 0.85; < .001). Conclusion Radiogenomic signatures associated with genomic assays provide markers of prognosis and treatment in estrogen receptor-positive breast cancer. © RSNA, 2021
10.1148/radiol.2021210738
Radiological predictive factors on preoperative multimodality imaging are related to Oncotype DX recurrence score in estrogen-positive/human epidermal growth factor receptor 2-negative invasive breast cancer: a cross-sectional study.
Annals of nuclear medicine
OBJECTIVE:The Oncotype DX (ODX) estimates the 10-year risk of metastasis or recurrence of breast cancer and indicates whether chemotherapy is likely to be effective; however, the high cost of this test may limit its use for patients. The aim of this study was to evaluate the potential of preoperative imaging using mammography (MMG), ultrasonography (US), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and positron emission tomography/computed tomography (PET/CT) metabolic parameters in predicting the ODX recurrence score (ODXRS), which prognosticates estrogen receptor-positive (ER +)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer. METHODS:This retrospective study was conducted on 51 patients with ER+/ HER2- early-stage breast cancer with preoperative images available. Surgical specimens were sent for ODX assay and the ODXRS was categorized as low (<18) or intermediate/high (≥18). MMG/US findings were classified according to BI-RADS categories. For MRI analysis, tumor growth orientation was evaluated in addition to morphological assessment in BI-RADS. For PET/CT analysis, standardized uptake value (SUV) of the tumor were measured. Patient, tumor, and image characteristics were compared between the two groups, and predictors of the low ODXRS group were determined by logistic regression analysis. Two-sided P values less than 0.05 were considered statistically significant. RESULTS:Thirty-two (63%) and 19 (37%) patients were categorized as low and intermediate/high ODXRS, respectively. On univariate analysis, nuclear grade, tumor margin, and tumor growth orientation on MRI, and SUV on PET/CT were significantly associated with a low ODXRS. Multivariate analysis revealed that tumor growth orientation perpendicular to the Cooper's ligament on MRI (P = 0.031) and a low SUV on PET/CT (P = 0.016) were independent prognostic factors for a low ODXRS. As a predictor of low ODXRS, the receiver operating characteristic (ROC) analysis of the SUV showed that using 3.0 as the optimal cut-off value has a sensitivity and specificity of 94.4% and 73.0%, respectively, with an area under the curve (AUC) of 0.923. CONCLUSIONS:The combination of perpendicular tumor growth orientation to Cooper's ligaments on MRI and a low SUV on PET/CT may predict a low ODXRS.
10.1007/s12149-022-01767-z
Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores.
Medicine
To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers.
10.1097/MD.0000000000015871
Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer.
Kim Hee Jeong,Choi Woo Jung,Kim Hak Hee,Cha Joo Hee,Shin Hee Jung,Chae Eun Young
Clinical imaging
BACKGROUND:Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited. PURPOSE:To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer. MATERIAL AND METHODS:We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS. RESULTS:Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS. CONCLUSION:MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.
10.1016/j.clinimag.2021.01.021
Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.
Sutton Elizabeth J,Oh Jung Hun,Dashevsky Brittany Z,Veeraraghavan Harini,Apte Aditya P,Thakur Sunitha B,Deasy Joseph O,Morris Elizabeth A
Journal of magnetic resonance imaging : JMRI
PURPOSE:To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI). MATERIALS AND METHODS:This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant. RESULTS:Ninety-five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0-45). Using stepwise multiple linear regression modeling, two MR-derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R-squared value of 0.23 (adjusted R-squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001). CONCLUSION:A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image-based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit.
10.1002/jmri.24890
Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: association between intratumoral heterogeneity and recurrence risk.
Kim Jin You,Kim Jin Joo,Hwangbo Lee,Lee Ji Won,Lee Nam Kyung,Nam Kyung Jin,Choo Ki Seok,Kang Taewoo,Park Heeseung,Son Yohan,Grimm Robert
European radiology
OBJECTIVES:To investigate possible associations between quantitative apparent diffusion coefficient (ADC) metrics derived from whole-lesion histogram analysis and breast cancer recurrence risk in women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-negative breast cancer who underwent the Oncotype DX assay. METHODS:This retrospective study was conducted on 105 women (median age, 48 years) with ER-positive, HER2-negative, node-negative breast cancer who underwent the Oncotype DX test and preoperative diffusion-weighted imaging (DWI). Histogram analysis of pixel-based ADC data of whole tumors was performed, and various ADC histogram parameters (mean, 5th, 25th, 50th, 75th, and 95th percentiles of ADCs) were extracted. The ADC difference value (defined as the difference between the 5th and 95th percentiles of ADCs) was calculated to assess intratumoral heterogeneity. Associations between quantitative ADC metrics and the recurrence risk, stratified using the Oncotype DX recurrence score (RS), were evaluated. RESULTS:Whole-lesion histogram analysis showed that the ADC difference value was different between the low-risk recurrence (RS < 18) and the non-low-risk recurrence (RS ≥ 18; intermediate to high risk of recurrence) groups (0.600 × 10 mm/s vs. 0.746 × 10 mm/s, p < 0.001). Multivariate regression analysis demonstrated that a lower ADC difference value (< 0.559 × 10 mm/s; odds ratio [OR] = 5.998; p = 0.007) and a small tumor size (≤ 2 cm; OR = 3.866; p = 0.012) were associated with a low risk of recurrence after adjusting for clinicopathological factors. CONCLUSIONS:The ADC difference value derived from whole-lesion histogram analysis might serve as a quantitative DWI biomarker of the recurrence risk in women with ER-positive, HER2-negative, node-negative invasive breast cancer. KEY POINTS:• A lower ADC difference value and a small tumor size were associated with a low risk of recurrence of breast cancer. • The ADC difference value could be a quantitative marker for intratumoral heterogeneity. • Whole-lesion histogram analysis of the ADC could be helpful for discriminating the low-risk from non-low-risk recurrence groups.
10.1007/s00330-019-06383-6
Breast cancer tumor heterogeneity has only little impact on the estimation of the Oncotype DX® recurrence score using Magee Equations and Magee Decision Algorithm™.
Remoué Annabelle,Conan-Charlet Virginie,Deiana Laura,Tyulyandina Alexandra,Marcorelles Pascale,Schick Ulrike,Uguen Arnaud
Human pathology
Oncotype DX® assay is used to guide therapeutic decisions in early-stage invasive breast carcinoma but remains expensive. Magee Equations (MEs) and Magee Decision Algorithm (MDA) predict the Oncotype DX® recurrence score (RS) on the basis of histopathological parameters. The influence of intratumor heterogeneity on MEs and MDA remains uncertain. We compared Ki-67, estrogen and progesterone receptors, and human erb-b2 receptor tyrosine kinase 2 (HER2) status on tissue microarray cores with the corresponding findings on the whole slides to calculate MEs scores and to decide if Oncotype DX® testing was required as per MDA in two sets of 175 and 59 tumors, without and with Oncotype DX® results, respectively. Agreements in the interpretation of Ki-67, estrogen and progesterone receptors, and HER2 status were very good between limited areas and whole-slide analyses. This resulted also in very good agreements about the results of MEs and MDA. For 7 of 175 (4%) and 3 of 59 (5.1%) cases, MEs and MDA results in different tumor areas would have changed the indication to perform or not perform Oncotype DX® assays. Oncotype DX® RSs were significantly correlated with MEs and MDA results, but among cases initially predicted to have an RS ≤25 using MDA, 3 of 34 cases (8.8%) had in fact an RS >25. Tumor heterogeneity appears to have little impact on the estimation of the Oncotype DX® RS using MEs and MDA and would have permitted to avoid half of Oncotype DX® assays in our series. Caution is nevertheless required in discarding Oncotype DX® assay in cases with ME scores >18 associated with low mitotic activity.
10.1016/j.humpath.2020.11.006