Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas.
Park Yae Won,Ahn Sung Soo,Park Chae Jung,Han Kyunghwa,Kim Eui Hyun,Kang Seok-Gu,Chang Jong Hee,Kim Se Hoon,Lee Seung-Koo
OBJECTIVES:Epidermal growth factor receptor (EGFR) amplification and telomerase reverse transcriptase promoter (TERTp) mutation status of isocitrate dehydrogenase-wildtype (IDHwt) lower-grade gliomas (LGGs; grade II/III) are crucial for identifying IDHwt LGG with an aggressive clinical course. The purpose of this study was to assess whether parameters from diffusion tensor imaging, dynamic susceptibility contrast (DSC), and diffusion tensor imaging, dynamic contrast-enhanced imaging can predict the EGFR amplification and TERTp mutation status of IDHwt LGGs. METHODS:A total of 49 patients with IDHwt LGGs with either known EGFR amplification (39 non-amplified, 10 amplified) or TERTp mutation (19 wildtype, 21 mutant) statuses underwent MRI. The mean ADC, fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow (nCBF), volume transfer constant (K), rate transfer coefficient (K), extravascular extracellular volume fraction (V), and plasma volume fraction (V) values were assessed. Univariate and multivariate logistic regression models were constructed. RESULTS:EGFR-amplified tumors showed lower mean ADC values than EGFR-non-amplified tumors (p = 0.019). Mean ADC was an independent predictor of EGFR amplification, with an AUC of 0.75. TERTp mutant tumors showed higher mean nCBV (p = 0.020), higher mean nCBF (p = 0.017), and higher mean V (p = 0.002) than TERTp wildtype tumors. With multivariate logistic regression, mean V was the independent predictor of TERTp mutation status, with an AUC of 0.85. CONCLUSION:This exploratory pilot study shows that lower ADC values may be useful for prediction of EGFR amplification, whereas higher V values may be useful for prediction of the TERTp mutation status of IDHwt LGGs. KEY POINTS:• EGFR amplification and TERTp mutation are key molecular markers that predict an aggressive clinical course of IDHwt LGGs. • EGFR-amplified tumors showed lower ADC values than EGFR-non-amplified tumors, suggesting higher cellularity. • TERTp mutant tumors showed a higher plasma volume fraction than TERTp wildtype tumors, suggesting higher vascular proliferation and tumor angiogenesis.
Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.
Li Zhi-Cheng,Bai Hongmin,Sun Qiuchang,Li Qihua,Liu Lei,Zou Yan,Chen Yinsheng,Liang Chaofeng,Zheng Hairong
OBJECTIVES:To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM). METHODS:In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors. RESULTS:The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis. CONCLUSIONS:Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. KEY POINTS:• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis.
Chen Richard J,Lu Ming Y,Wang Jingwen,Williamson Drew F K,Rodig Scott J,Lindeman Neal I,Mahmood Faisal
IEEE transactions on medical imaging
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we propose Pathomic Fusion, an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNASeq) features for survival outcome prediction. Our approach models pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations, and controls the expressiveness of each representation via a gatingbased attention mechanism. Following supervised learning, we are able to interpret and saliently localize features across each modality, and understand how feature importance shifts when conditioning on multimodal input. We validate our approach using glioma and clear cell renal cell carcinoma datasets from the Cancer Genome Atlas (TCGA), which contains paired wholeslide image, genotype, and transcriptome data with ground truth survival and histologic grade labels. In a 15-fold cross-validation, our results demonstrate that the proposed multimodal fusion paradigm improves prognostic determinations from ground truth grading and molecular subtyping, as well as unimodal deep networks trained on histology and genomic data alone. The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine heterogeneous data streams for understanding diseases and predicting response and resistance to treatment. Code and trained models are made available at: https://github.com/mahmoodlab/PathomicFusion.
Brain Imaging Genomics: Integrated Analysis and Machine Learning.
Shen Li,Thompson Paul M
Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.