AIMC Topic: Neoplasms

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Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

Cancer cell
The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how the...

Human and machine: Better at pathology together?

Cancer cell
Meaningful integration of artificial intelligence (AI) will transform the application of "big data" for patient care, diagnosis, and research. In this issue of Cancer Cell, Chen et al. describe a transparent system to integrate histopathology and mol...

An Active Learning Framework Improves Tumor Variant Interpretation.

Cancer research
A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.

Methodology in Conventional Head and Neck Reconstruction Following Robotic Cancer Surgery: A Bridgehead Robotic Head and Neck Reconstruction.

Yonsei medical journal
PURPOSE: Robotic head and neck surgery is widespread nowadays. However, in the reconstruction field, the use of robotic operations is not. This article aimed to examine methodologies for conventional head and neck reconstruction after robotic tumor s...

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Nucleic acids research
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal...

Hospital-Agnostic Image Representation Learning in Digital Pathology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent diagnosis challeng...

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer...

Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer.

Studies in health technology and informatics
The accelerating impact of genomic data in clinical decision-making has generated a paradigm shift from treatment based on the anatomic origin of the tumor to the incorporation of key genomic features to guide therapy. Assessing the clinical validity...

Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

The oncologist
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current meth...

Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity.

Cancer discovery
Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance quality care and precision medicine efforts, but also the potential to worsen existing health dispa...