ArticleLung cancer (Amsterdam, Netherlands)2019
Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report.
Article in Lung cancer (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 5 of them syntheses that pooled it.
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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
93 citing papers in PubMed, 5 syntheses or guidelines pooled it, 137 citations in OpenAlex.
- Hyperprogressive disease in patients suffering from solid malignancies treated by immune checkpoint inhibitors: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2021Pooled it
- Investigation on potential biomarkers of hyperprogressive disease (HPD) triggered by immune checkpoint inhibitors (ICIs).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021Pooled it
- Definition, Incidence, and Challenges for Assessment of Hyperprogressive Disease During Cancer Treatment With Immune Checkpoint Inhibitors: A Systematic Review and Meta-analysis.JAMA network open · 2021Pooled it
- Clinical characteristics of hyperprogressive disease in NSCLC after treatment with immune checkpoint inhibitor: a systematic review and meta-analysis.BMC cancer · 2020Pooled it
- Low Risk of Hyperprogression with First-Line Chemoimmunotherapy for Advanced Non-Small Cell Lung Cancer: Pooled Analysis of 7 Clinical Trials.The oncologist · 2023Trial
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Multimodal multitask deep learning for grading management system in non-small cell lung cancer.Nature communications · 2026Article
- A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.NPJ digital medicine · 2026Article
- Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.Scientific reports · 2026Article
- Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.Frontiers in immunology · 2026Review
- Article
- The Black Hole of Immune Checkpoint Blocking Therapy for Gastric Cancer: Hyperprogressive Disease.Cancer science · 2025Review
- Imaging biomarkers related to tumor-associated macrophage in immunotherapy treatment planning for non-small cell lung cancer.Translational lung cancer research · 2025Article
- [Research Progress on the Potential Mechanisms of Hyper-progressive Disease in Immune Checkpoint Blockade Therapy of Solid Tumors].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Long-Term Outcomes in Patients with Locally Advanced and Metastatic Non-Small Cell Lung Cancer with High PD-L1 Expression.Current oncology (Toronto, Ont.) · 2025Article
- Article
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Hyperprogressive disease in patients with advanced cancer treated with immune checkpoint inhibitors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesImmune-checkpoint blockades have exhibited durable responses and improved long-term survival in a subset of advanced non-small cell lung cancer (NSCLC) patients. However, highly predictive markers of positive and negative responses to immunotherapy are a significant unmet clinical need. The objective of this study was to identify clinical and computational image-based predictors of rapid disease progression phenotypes in NSCLC patients treated with immune-checkpoint blockades. MATERIALS AND
methodsUsing time-to-progression (TTP) and/or tumor growth rates, rapid disease progression phenotypes were developed including hyperprogressive disease. The pre-treatment baseline predictors that were used to identify these phenotypes included patient demographics, clinical data, driver mutations, hematology data, and computational image-based features (radiomics) that were extracted from pre-treatment computed tomography scans. Synthetic Minority Oversampling Technique (SMOTE) was used to subsample minority groups to eliminate classification bias. Patient-level probabilities were calculated from the final clinical-radiomic models to subgroup patients by progression-free survival (PFS).
resultsAmong 228 NSCLC patients treated with single agent or double agent immunotherapy, we identified parsimonious clinical-radiomic models with modest to high ability to predict rapid disease progression phenotypes with area under the receiver-operator characteristics ranging from 0.804 to 0.865. Patients who had TTP < 2 months or hyperprogressive disease were classified with 73.41% and 82.28% accuracy after SMOTE subsampling, respectively. When the patient subgroups based on patient-level probabilities were analyzed for survival outcomes, patients with higher probability scores had significantly worse PFS.
conclusionsThe models found in this study have potential important translational implications to identify highly vulnerable NSCLC patients treated with immunotherapy that experience rapid disease progression and poor survival outcomes.
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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.