AIMC Topic: Neoplasms

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Cancer in a drop: Advances in liquid biopsy in 2024.

Critical reviews in oncology/hematology
Over the past decade, liquid biopsy (LB) has emerged as a key tool in oncology. Its utility in non-invasive sampling and real-time monitoring has made it a cornerstone in precision medicine. Since 2020, publications on LB in solid tumors have doubled...

Role of artificial intelligence in cancer drug discovery and development.

Cancer letters
The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to mark...

Crosstalk of glutamine metabolism between cancer-associated fibroblasts and cancer cells.

Cellular signalling
Glutamine (Gln), a critical metabolic substrate, fuels the uncontrolled proliferation of cancer cells. Cancer-associated fibroblasts (CAFs), essential components of the tumor microenvironment, facilitate tumor progression by supplying Gln to cancer c...

Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Artificial intelligence models like GPT-4 (OpenAI) have the potential to support clinical decision-making in oncology. This study aimed to assess the consistency between multidisciplinary tumor board (MTB) decisions and GPT-4 model predictio...

Impact of health policy reforms on telemedicine and AI integration for early cancer detection among low-income populations in South Asia: A comparative policy analysis.

African journal of reproductive health
Early cancer detection remains a major challenge in low- and middle-income countries (LMICs), where late diagnoses and unequal healthcare access are widespread. While digital innovations such as telemedicine and artificial intelligence (AI) offer sig...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

ACS chemical neuroscience
Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive...

Prediction of trihalomethane occurrence and cancer risk using interpretable machine learning and virtual data augmentation.

Journal of hazardous materials
Trihalomethanes (THMs) in drinking water are regulated for carcinogenic health risks. However, frequent water quality monitoring imposes significant resource burdens. This study proposes a framework integrating interpretable machine learning (ML) wit...

The emerging role of artificial intelligence applied to exosome analysis: from cancer biology to other biomedical fields.

Life sciences
In recent years, exosomes versatility has prompted their study in the biomedical field for diagnostic, prognostic, and therapeutic applications. Exosomes are bi-lipid small extracellular vesicles (30-150 nm) secreted by various cell types, containing...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Journal of chemical information and modeling
Recent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like MEK1, MEK2, PI3Kα, PTEN, SHAP2, and RAS are linked various can...

Efficient Training Corpus Retrieval for Large Language Model Fine Tuning: A Case Study in Cancer.

Studies in health technology and informatics
The objective is to create an automated knowledge extraction tool for cancer research that builds high-quality academic corpora for LLM fine-tuning while investigating its effectiveness in interleukin-6 and bladder cancer domains. To address the curr...