Machine learning applications for the diagnosis, treatment and prognosis of cancer Cancer MachineLearning Stanford CellCellPress
By Pooja Toshniwal PahariaMar 19 2023Reviewed by Benedette Cuffari, M.Sc. Machine learning models have been increasingly used in clinical oncology for cancer diagnosis, outcome predictions, and informing oncological therapy planning. The early identification and prompt treatment of cancer, revolutionized by rapid and precise analysis of radiological and pathological images of tissues using ML algorithms, can improve the likelihood of survival and quality of care provided to cancer patients.
Common ML models in oncology ML models are based on supervised learning, with each data point having an associated label. Commonly used ML models include random forest models, support vector machines , regression models, neural networks, recurrent neural network models, convolutional neural network models, transformers, and graph neural network models.
CNN models apply neural patches or ‘filters’ that scan images and identify patterns. The initial layers detect low-level characteristics such as edges, whereas subsequent layers detect high-level characteristics like the morphology of tumor cells. Transformers analyze sequential information by repeated application of the attention operation for comparing the sequential to other components and updating internal sequence representations.
ML for and cancer diagnosis, prognosis, and treatment For every patient, images are captured using pathological, radiological, and other imaging modalities. The high-resolution image is broken down into image tiles that span the entire image or only the region of interest for processing by ML models. CNN models process the image tiles and generate pixel- or tile-level predictions, with heatmaps predicting sites where tumors are likely to arise.
Common molecular datasets, which can be obtained by single-cell transcriptomics and spatial proteomics, bulk ribonucleic acid sequencing of tumor biopsies, and whole-genome sequencing, include circulating cell-free deoxyribonucleic acid , fragmentomics, epigenetic modifications, and the status of DNA methylation. These datasets are incorporated into SVMs, elastic net models, random forest classifiers, and Bayesian models for selecting the type of and predicting response to cancer therapies.
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