ReviewGenomics, proteomics & bioinformatics2022
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.
Review in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 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
111 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Cost-effectiveness of lung cancer screening: insights from risk stratification, guidelines, and emerging technologies-a systematic review.NPJ primary care respiratory medicine · 2026Pooled it
- Pooled it
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Comparative predictive performance of three machine learning algorithms for acute radiation enteritis risk among patients with cervical cancer undergoing radiotherapy: A prospective cohort study.Asia-Pacific journal of oncology nursing · 2026Article
- Circular RNAs in NSCLC: Their Role in Tumorigenesis, Tumor Microenvironment Reshaping, and Response to Therapies.Cancers · 2026Review
- Characterization of Patients with Non-Small Cell Lung Cancer Using Machine Learning Tools: Relationship with Prognostic Biomarkers and Overall Survival-A Pilot Study.Journal of clinical medicine · 2026Article
- LungCNET: A High-Performance Deep CNN Model for Lung Cancer Detection Evaluated Against Widely Used CNN Benchmarks.Bioengineering (Basel, Switzerland) · 2026Article
- Development of an Intelligent Clinical Decision Support System for Predicting One-Year CPAP Adherence in Patients with Obstructive Sleep Apnea: A Pilot Study.Journal of clinical medicine · 2026Article
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- 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
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Hybrid Modelling of Pulmonary Cancer Risk Prediction Using Classical Algorithms to Modern Machine Learning Techniques.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- A machine learning-based basement membrane gene signature model for predicting ovarian cancer survival.Translational cancer research · 2026Article
- Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- OSBPL3 promotes LUAD metastasis and glycolysis through transcriptional upregulation of PLAU via NFE2L2.Translational oncology · 2026Article
- Screening and validation of potential molecular markers for colorectal cancer: based on bioinformatics analysis and machine learning.Clinical and experimental medicine · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- A clinically practical machine learning nomogram for preoperative CLNM prediction in PTMC: tumor-capsule distance and D2-40 evidence.Frontiers in oncology · 2026Article
- EV-Mediated Oncogenic Regulation in Lung Cancer and Clinical Translation: From Liquid Biopsy to Targeted Delivery Systems.International journal of nanomedicine · 2026Review
51 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
5 authors.
Funding
Abstract
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.
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.