AIMC Topic: Biomarkers, Tumor

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Strategies for Testing Intervention Matching Schemes in Cancer.

Clinical pharmacology and therapeutics
Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogene...

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

Gut
OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), hi...

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Nature communications
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address...

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The journal of pathology. Clinical research
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study.

Molecular oncology
Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contain...

Integrating spatial gene expression and breast tumour morphology via deep learning.

Nature biomedical engineering
Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the develop...

Radiomics in neuro-oncology: Basics, workflow, and applications.

Methods (San Diego, Calif.)
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...