| Woerl, Ann-Christin, et al. “Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.” European Urology (2020). |
bladder |
molecular subtype |
| Xu, Hongming, et al. “Using transfer learning on whole slide images to predict tumor mutational burden in bladder cancer patients.” bioRxiv (2019). |
bladder |
TMB |
| Xu, Hongming, et al. “Deep transfer learning approach to predict tumor mutation burden (TMB) and delineate spatial heterogeneity of TMB within tumors from whole slide images.” bioRxiv (2020). |
bladder, lung |
TMB |
| Couture, Heather, et al. “Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype.” NPJ Breast Cancer (2018). |
breast |
hormone receptor status:
ER genomic subtype: PAM50 |
| Couture, Heather, et al. “Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology.” International Conference on Medical Image Computing and Computer-Assisted Intervention (2018). |
breast |
hormone receptor status: ER genomic subtype: PAM50 |
| Jaber, Mustafa I., et al. “A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.” Breast Cancer Research (2020). |
breast |
genomic subtype: PAM50 |
| Bychkov, Dmitrii, et al. “Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy.” Scientific reports (2021). |
breast |
hormone receptor status: HER2 |
| La Barbera, David, Kevin Roitero, and Vincenzo Della Mea. “Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning.” Journal of Imaging (2020). |
breast |
hormone receptor status: HER2 |
| Naik, Nikhil, et al. “Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.” Nature communications (2020). |
breast |
hormone receptor status: ER |
| Oliveira, Sara P., et al. “Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides.” Applied Sciences (2020). |
breast |
hormone receptor status: HER2 |
| Rawat, Rishi R., et al. “Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images.” Scientific Reports (2020). |
breast |
hormone receptor status: ER, PR, HER2 |
| Shamai, Gil, et al. “Artificial intelligence algorithms to assess hormonal status from tissue microarrays in patients with breast cancer.” JAMA (2019). |
breast |
hormone receptor status: ER, PR, HER2 |
| Chauhan, Ruchi, et al. “Exploring Genetic-histologic Relationships in Breast Cancer.” ISBI (2021). |
breast |
mutations: TP53, PIK3CA hormone receptor status: ER, PR, HER2 intrinsic subtypes |
| Xu, Zhuoran, et al. “Deep learning predicts chromosomal instability from histopathology images.” iScience (2021). |
breast |
chromosomal instability |
| Valieris, Renan, et al. “Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer.” Cancers (2020). |
breast and gastric |
DNA repair deficiency |
| Echle, Amelie, et al. “Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning.” Gastroenterology (2020). |
colorectal |
MSI |
| Krause, Jeremias, et al. “Deep learning detects genetic alterations in cancer histology generated by adversarial networks .” The Journal of Pathology (2021). |
colorectal |
MSI |
| Jang, Hyun-Jong, et al. “Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.” World Journal of Gastroenterology (2020). |
colorectal |
mutations: APC, KRAS, PIK3CA, SMAD4, TP53 |
| Hildebrand, Lindsey A., et al. “Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer.” Cancers (2021). |
colorectal |
MSI |
| Kather, Jakob Nikolas, et al. “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.” Nature Medicine (2019). |
colorectal |
MSI |
| Yamashita, Rikiya, et al. “Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.” The Lancet Oncology (2021). |
colorectal |
MSI |
| Popovici, Vlad, et al. “Image-based surrogate biomarkers for molecular subtypes of colorectal cancer.” Bioinformatics (2017). |
colorectal |
genomic subtypes |
| Sirinukunwattana, Korsuk, et al. “Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.” Gut (2020). |
colorectal |
genomic subtypes |
| Zhang, Hongrun, et al. “Piloting a deep learning model for predicting nuclear BAP1 immunohistochemical expression of uveal melanoma from hematoxylin-and-eosin sections.” Translational Vision Science & Technology (2020). |
eye |
protein: BAP1 |
| Kather, Jakob Nikolas, et al. “Deep learning detects virus presence in cancer histology.” bioRxiv (2019). |
gastric head and neck |
virus: EBV
virus: HPV |
| Klein, Sebastian, et al. “Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&E Stains.” Clinical Cancer Research (2021). |
oropharyngeal |
virus: HPV |
| Chen, Mingyu, et al. “Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning.” npj Precision Oncology(2020). |
liver |
mutations: CTNNB1, FMN2, TP53, ZFX4 |
| Coudray, Nicolas, et al. “Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.” Nature Medicine (2018) |
lung |
mutations: STK11, EGFR, FAT1, SETBP1, KRAS, TP53 |
| Yu, Kun-Hsing, et al. “Classifying non-small cell lung cancer histopathology types and transcriptomic subtypes using convolutional neural networks.” bioRxiv (2019). |
lung |
genomic subtypes |
| Jain, Mika S., and Tarik F. Massoud. “Predicting tumour mutational burden from histopathological images using multiscale deep learning.” Nature Machine Intelligence (2020). |
lung |
TMB |
| Schaumberg, Andrew J., Mark A. Rubin, and Thomas J. Fuchs. “H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer.” BioRxiv (2018). |
prostate |
mutation: SPOP |
| Shao, Yanan, et al. “Improving Prostate Cancer Classification in H&E Tissue Micro Arrays Using Ki67 and P63 Histopathology.” Computers in Biology and Medicine (2020). |
prostate |
protein: Ki-67, p63 |
| Kim, Randie H., et al. “A deep learning approach for rapid mutational screening in melanoma.” bioRxiv (2019). |
skin |
mutation: BRAF |
| Dammak, Salma, et al. “Prediction of tumour mutational burden of squamous cell carcinoma using histopathology images of surgical specimens.” Medical Imaging 2020: Digital Pathology Photonics. |
skin |
TMB |
| Kather, Jakob Nikolas, et al. “Pan-cancer image-based detection of clinically actionable genetic alterations.” Nature Cancer (2020). |
multiple |
mutations
genomic subtypes
hormone receptor status |
| Fu, Yu, et al. “Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.” Nature Cancer (2020). |
multiple |
mutations
gene expression |
| Schmauch, Benoît, et al. “A deep learning model to predict RNA-Seq expression of tumours from whole slide images.” Nature communications (2020). |
multiple |
gene expression |
| Liu, Yiqing, et al. “Predict Ki-67 positive cells in H&E-stained images using deep learning independently from IHC-stained images.” Frontiers in Molecular Biosciences (2020). |
multiple |
protein: Ki-67 |