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Article
Open AccessPredicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning
Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning ...
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Article
Open AccessDeep learning models for histologic grading of breast cancer and association with disease prognosis
Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form th...
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Article
Open AccessArtificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions h...
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Article
Open AccessComparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehen...
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Article
Open AccessDetermining breast cancer biomarker status and associated morphological features using deep learning
Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and...
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Article
Open AccessPredicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading
Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools hav...
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Article
Open AccessInterpretable survival prediction for colorectal cancer using deep learning
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specifi...
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Article
Open AccessPublisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Open AccessReply: ‘The importance of study design in the application of artificial intelligence methods in medicine’
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Article
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions o...
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Article
Open AccessSimilar image search for histopathology: SMILY
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically...
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Article
Open AccessDevelopment and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective micr...
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Article
Open AccessRB loss in resistant EGFR mutant lung adenocarcinomas that transform to small-cell lung cancer
Tyrosine kinase inhibitors are effective treatments for non-small-cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, relapse typically occurs after an average of 1 year of...
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Article
Discovery and saturation analysis of cancer genes across 21 tumour types
Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive cata...
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Article
Open AccessPan-cancer patterns of somatic copy number alteration
Rameen Beroukhim and colleagues analyzed somatic structural alterations in 12 tumor types. Whole-genome doubling was found in over a third of all cancers, associated with TP53 mutation. Fifteen new significantly ...
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Article
Mutational heterogeneity in cancer and the search for new cancer-associated genes
As the sample size in cancer genome studies increases, the list of genes identified as significantly mutated is likely to include more false positives; here, this problem is identified as stemming largely from...
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Article
Open AccessGISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and...
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Article
The histone methyltransferase SETDB1 is recurrently amplified in melanoma and accelerates its onset
Transgenic zebrafish carrying the human oncogene BRAF(V600E), the most common mutation in melanoma patients, provide a convenient model for melanoma. Two papers from Leonard Zon and colleagues demonstrate the pot...
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Article
Subtype-specific genomic alterations define new targets for soft-tissue sarcoma therapy
Samuel Singer and colleagues report an integrative genomic analysis of soft-tissue sarcomas. They survey sequence, copy number and mRNA expression in 207 individuals diagnosed with one of seven major high-grad...
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The landscape of somatic copy-number alteration across human cancers
A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-...
