AIMC Topic: Neoplasms

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Extent of use of artificial intelligence & machine learning protocols in cancer diagnosis: A scoping review.

The Indian journal of medical research
BACKGROUND & OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) have shown promising results in cancer diagnosis in validation tests involving retrospective patient databases. This study was aimed to explore the extent of actual use o...

Cancer Detection Based on Medical Image Analysis with the Help of Machine Learning and Deep Learning Techniques: A Systematic Literature Review.

Current medical imaging
BACKGROUND: Cancer is a deadly disease. It is crucial to diagnose cancer in its early stages. This can be done with medical imaging. Medical imaging helps us scan and view internal organs. The analysis of these images is a very important task in the ...

A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.

Methods in molecular biology (Clifton, N.J.)
Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline...

Prediction of Cancer Treatment Using Advancements in Machine Learning.

Recent patents on anti-cancer drug discovery
Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Resistance may develop at any stage of therapy, even at the beginning. Several factors influence curre...

GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable v...

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Inter...

Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer.

JCO clinical cancer informatics
PURPOSE: Predicting short-term mortality in patients with advanced cancer remains challenging. Whether digitalized clinical text can be used to build models to enhance survival prediction in this population is unclear.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

Journal for immunotherapy of cancer
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for d...