Predicting Tumor Mutational Burden from Liver Cancer Pathological Images Using Convolutional Neural Network

Zhang, Hong; Zhang, Fa; Ren, Fei; Wang, Zhong-Lie; Rao, Xiaosong; Li, Li; Hao, Junbo; Yan, Rui; Luo, Jiancheng; Du, Ming

doi:10.1109/bibm47256.2019.8983139

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…9 In the case of MSI, the genotype-phenotype correlation is consistent enough to robustly infer the genotype just by observing morphological features in a histological image, as we have previously shown. 10 Other previous studies have identified genotypephenotype links for selected genetic features in lung cancer 11,12 , prostate cancer 13 , head and neck 14 and liver 15 cancer, among others. Building on these previous studies, we systematically investigated the presence of genotype-phenotype links for a wide range of clinically relevant molecular features across all major solid tumor types.…”

Section: Introductionmentioning

confidence: 99%

Pan-cancer image-based detection of clinically actionable genetic alterations

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Pan-cancer image-based detection of clinically actionable genetic alterations

et al. 2020

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“…In 2018, a seminal study showed that these images are not only a valuable resource for tumor diagnosis, but that genetic alterations in clinically relevant driver genes an be detected by Deep Learning in lung cancer (Coudray et al, 2018). In 2018 to 2021, a number of studies extended these findings to other tumor types and a wide range of genetic alterations (Couture et al, 2018;Sha et al, 2019;Sun et al, 2019;Zhang et al, 2019;Echle et al, 2020a). In particular in 2020, multiple studies have applied supervised Deep Learning for pan-cancer detection of genetic alterations from snap-frozen samples (Fu et al, 2020;Schmauch et al, 2020) of the TCGA database.…”

Section: Discussionmentioning

confidence: 99%

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

et al. 2022

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In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.

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“…Deep learning tools are becoming increasingly popular for histologic analyses and have been used to predict tumor biomarker status, clinical variables, tumor subtypes, and mutation status in a variety of cancers [24][25][26][27]. In thyroid cancer specifically, several groups have demonstrated that histologic features associated with BRAF V600E and RAS mutations are detectable using deep learning [28,29].…”

Section: Introductionmentioning

confidence: 99%

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features

et al. 2021

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Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are follicular-patterned thyroid neoplasms defined by nuclear atypia and indolent behavior. They harbor RAS mutations, rather than BRAFV600E mutations as is observed in papillary thyroid carcinomas with extensive follicular growth. Reliably identifying NIFTPs aids in safe therapy de-escalation, but has proven to be challenging due to interobserver variability and morphologic heterogeneity. The genomic scoring system BRS (BRAF-RAS score) was developed to quantify the extent to which a tumor’s expression profile resembles a BRAFV600E or RAS-mutant neoplasm. We proposed that deep learning prediction of BRS could differentiate NIFTP from other follicular-patterned neoplasms. A deep learning model was trained on slides from a dataset of 115 thyroid neoplasms to predict tumor subtype (NIFTP, PTC-EFG, or classic PTC), and was used to generate predictions for 497 thyroid neoplasms within The Cancer Genome Atlas (TCGA). Within follicular-patterned neoplasms, tumors with positive BRS (RAS-like) were 8.5 times as likely to carry an NIFTP prediction than tumors with negative BRS (89.7% vs 10.5%, P < 0.0001). To test the hypothesis that BRS may serve as a surrogate for biological processes that determine tumor subtype, a separate model was trained on TCGA slides to predict BRS as a linear outcome. This model performed well in cross-validation on the training set (R2 = 0.67, dichotomized AUC = 0.94). In our internal cohort, NIFTPs were near universally predicted to have RAS-like BRS; as a sole discriminator of NIFTP status, predicted BRS performed with an AUC of 0.99 globally and 0.97 when restricted to follicular-patterned neoplasms. BRAFV600E-mutant PTC-EFG had BRAFV600E-like predicted BRS (mean −0.49), nonmutant PTC-EFG had more intermediate predicted BRS (mean −0.17), and NIFTP had RAS-like BRS (mean 0.35; P < 0.0001). In summary, histologic features associated with the BRAF-RAS gene expression spectrum are detectable by deep learning and can aid in distinguishing indolent NIFTP from PTCs.

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Predicting Tumor Mutational Burden from Liver Cancer Pathological Images Using Convolutional Neural Network

Cited by 15 publications

References 12 publications

Pan-cancer image-based detection of clinically actionable genetic alterations

Pan-cancer image-based detection of clinically actionable genetic alterations

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features

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