Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment
Year: 2024
Type: article
Abstract: This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
Source: npj Precision Oncology
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,790
Funders: U.S. Department of Defense, U.S. Department of Veterans Affairs, Eli Lilly and Company, AstraZeneca, Emory University, National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, DOD Prostate Cancer Research Program
Awards: G100871905, RadxTools for assessing tumor treatment response on imaging, SYSTEMS BIOLOGY APPROACH TO PREDICTING AND ASSESSING RESPONSE TO CHEMORADIATION FOR BRAIN TUMORS, G1119724336, G1266336578, G1357738703, G1478154455, G1505200503, G1596146291, G1817069843, G2228475719, G2291306530, G233950145, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G2689512122, G2800195642, G2931214990, G3068466812, G3137332008, G338730684, G3564444776, G406353596, G4119534169, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, G431899019, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, FUSED BIOMARKER-BASED PREDICTION OF AGGRESSIVE FROM INDOLENT PROSTATE CANCER, G5758079931, G5850682332, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, G6123616905, G7124118669, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, G761790451, G8166743225, G854738250, G86714149, COMPUTATIONAL HISTOMORPHOMETRIC PREDICTOR OF PATHOLOGIC RESPONSE AND DISEASE RECURRENCE FOR TRIPLE-NEGATIVE BREAST CANCER, G9000000780, G9000016842, G9000021175, G9000023716, G9000064711, G9000153031, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G942918344
OpenAlex ID: W4393316741