ArticleFrontiers in oncology2021
Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis.Frontiers in artificial intelligence · 2026Pooled it
- Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.Thoracic cancer · 2025Pooled it
- Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.BMC medical research methodology · 2026Review
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- The capability of deep-radiomics to predict pathological response to neoadjuvant immunochemotherapy in non-small cell lung cancer: a retrospective multicenter study.Frontiers in immunology · 2026Article
- Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation.Frontiers in immunology · 2026Review
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Molecular markers for the efficacy of neoadjuvant immunotherapy for head and neck squamous cell carcinoma.Frontiers in oncology · 2025Review
- Ethical and legal concerns in artificial intelligence applications for the diagnosis and treatment of lung cancer: a scoping review.Frontiers in public health · 2025Article
- Integrated noninvasive diagnostics for prediction of survival in immunotherapy.Immuno-oncology technology · 2024Article
- "I'll Be Back": This is Just the Beginning of the AI Revolution.Annals of surgical oncology · 2024Article
- [Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?Journal of cancer research and clinical oncology · 2024Article
- Magnetic Resonance Spectroscopy for Cervical Cancer: Review and Potential Prognostic Applications.Cancers · 2024Review
- A guide to artificial intelligence for cancer researchers.Nature reviews. Cancer · 2024Review
- Review
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- Review
- Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer.Frontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.
methodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.
resultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (
conclusionsOur results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.
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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.