ArticleCancer discovery2021
Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.
Article in Cancer discovery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis 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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Consolidated Evidence and New Frontiers of Liquid Biopsy in Lung Cancer: A Narrative Review.Cells · 2026Review
- NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.BMJ open ophthalmology · 2026Observational
- Artificial intelligence for immunotherapy response assessment in lung cancer using PET/CT reports.Japanese journal of radiology · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Empirical evaluation of artificial intelligence distillation techniques for ascertaining cancer outcomes from electronic health records.NPJ digital medicine · 2025Article
- Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2025Article
- Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation.AIMS public health · 2025Review
- Article
- Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer.NPJ precision oncology · 2024Article
- Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.Nature communications · 2024Article
- Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.Scientific reports · 2024Article
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
- Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.JCO clinical cancer informatics · 2024Article
- Review
- Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow.European journal of cancer (Oxford, England : 1990) · 2024Review
- Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.Frontiers in immunology · 2024Review
- Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects.Oncology reviews · 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
16 authors.
Funding
Abstract
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine-learning techniques to integrate and interrogate large and otherwise underutilized datasets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep-learning model could be trained to use radiology text reports to estimate gold-standard RECIST-defined outcomes. Using text reports from patients with non-small cell lung cancer treated with PD-1 blockade in a training cohort and two test cohorts, we developed a deep-learning model to accurately estimate best overall response and progression-free survival. Our model may be a tool to determine outcomes at scale, enabling analyses of large clinical databases. SIGNIFICANCE: We developed and validated a deep-learning model trained on radiology text reports to estimate gold-standard objective response categories used in clinical trial assessments. This tool may facilitate analysis of large real-world oncology datasets using objective outcome metrics determined more reliably and at greater scale than currently possible.
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.