ReviewComputational and structural biotechnology journal2021
Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 3 of them syntheses that pooled it.
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
64 citing papers in PubMed, 3 syntheses or guidelines pooled it, 144 citations in OpenAlex.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.Mediators of inflammation · 2022Pooled it
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- Cancer staging data collection using rules-based natural language processing for entity extraction from pathology notifications: the WA cancer staging project.BMC medical informatics and decision making · 2026Article
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- Is source data verification a valid tool to improve data quality of tumour documentation data? A critical assessment.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Computational theranostics strategy for pancreatic ductal adenocarcinoma.Molecular diversity · 2026Article
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Machine-Learning-Based Prediction of Biochemical Recurrence in Prostate Cancer Integrating Fatty-Acid Metabolism and Stemness.International journal of molecular sciences · 2026Article
- A Neuroendocrine Differentiation-related Molecular Model for Prognosis Prediction in Prostate Cancer Patients.Current medicinal chemistry · 2026Article
- lncAPNet enables the deciphering of lncRNA-mRNA connections in patient transcriptomic data.Bioinformatics advances · 2026Article
- Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Deep learning-based predictive models for assessing the impact of clinical factors and second primary malignancy on survival in patients with colorectal cancer.European journal of medical research · 2025Article
- Personalized Mortality Risk Stratification in ALD- and MASLD-Related Hepatocellular Carcinoma Using a Machine Learning Approach.Metabolites · 2025Article
- Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.JMIR formative research · 2025Article
- Personalized health monitoring using explainable AI: bridging trust in predictive healthcare.Scientific reports · 2025Article
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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.