ReviewDiscover computing2026
Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.
Review in Discover computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells.Biosensors · 2026Article
- Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].Journal of thoracic disease · 2026Article
- Beyond DNA damage: 3D tumor models and the integrin mechanobiology of radioresistance.Journal of experimental & clinical cancer research : CR · 2026Review
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is transforming cancer imaging by enhancing diagnosis, automating image analysis, and improving treatment planning. This review explores key ML applications, including tumor detection, radiomics, multi-modal imaging, and therapy monitoring. We discuss fundamental ML techniques, deep learning architectures, and data preprocessing strategies essential for medical imaging. ML-driven approaches have improved tumor segmentation, feature extraction, and computer-aided diagnosis across various cancer types. In cancer therapy, artificial intelligence (AI) aids radiotherapy planning, treatment response prediction, and real-time image-guided interventions. However, challenges such as data scarcity, model bias, and regulatory hurdles limit clinical adoption. Emerging solutions include explainable AI, federated learning for data privacy, and quantum computing for advanced imaging. Addressing these challenges through interdisciplinary collaboration will accelerate AI integration into clinical practice, enhancing cancer diagnosis and treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.