Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …
emerging that have enabled researchers to access multi-layer information from the genome …
Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness
Clear cell renal cell carcinomas (ccRCCs) represent∼ 75% of RCC cases and account for
most RCC-associated deaths. Inter-and intratumoral heterogeneity (ITH) results in varying …
most RCC-associated deaths. Inter-and intratumoral heterogeneity (ITH) results in varying …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Artificial intelligence to identify genetic alterations in conventional histopathology
Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma
Purpose: Several biomarkers of response to immune checkpoint inhibitors (ICI) show
potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep …
potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep …
Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined …
Background Endometrial cancer can be molecularly classified into POLE mut, mismatch
repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) …
repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) …
AI-based histopathology image analysis reveals a distinct subset of endometrial cancers
Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and
therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) …
therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) …
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology
Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …
of cancer. In recent years, development of deep learning-based methods in computational …
[HTML][HTML] Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology
J Shao, J Ma, Q Zhang, W Li, C Wang - Seminars in cancer biology, 2023 - Elsevier
Personalized treatment strategies for cancer frequently rely on the detection of genetic
alterations which are determined by molecular biology assays. Historically, these processes …
alterations which are determined by molecular biology assays. Historically, these processes …