ArticleJournal for immunotherapy of cancer2020
Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade.
Article in Journal for immunotherapy of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06854107 (Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma), which is not on this map. Cited by 76 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.
Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma
Who cites it
76 citing papers in PubMed, 2 syntheses or guidelines pooled it, 116 citations in OpenAlex.
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled 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
- Applications of circulating tumor DNA in unresectable and advanced non-small cell lung cancer immunotherapy: from stratification to therapeutic guidance.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- 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
- A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.NPJ Parkinson's disease · 2026Article
- CT radiomics approach to predict response to bronchial arterial chemoembolization in advanced lung cancer by multicenter study.Scientific reports · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- QVT score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy.Journal for immunotherapy of cancer · 2026Article
- Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non-Small Cell Lung Cancer.JAMA network open · 2026Article
- Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer: a literature review.Frontiers in immunology · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Medical imaging in immunotherapy response evaluation: from RECIST to AI-driven longitudinal models, and the distinction between risk prediction and kinetic diagnosis.Frontiers in medicine · 2026Review
- Foundation model-enhanced multimodal radiomics for predicting response to chemo-immunotherapy in advanced lung squamous cell carcinoma.Frontiers in oncology · 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
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- Development of a prognostic multiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first line immunotherapy.Scientific reports · 2025Article
- 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
- Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases.Radiation oncology (London, England) · 2025Article
16 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
9 authors at 5 institutions in 1 country.
Funding
Abstract
purposeHyperprogression is an atypical response pattern to immune checkpoint inhibition that has been described within non-small cell lung cancer (NSCLC). The paradoxical acceleration of tumor growth after immunotherapy has been associated with significantly shortened survival, and currently, there are no clinically validated biomarkers to identify patients at risk of hyperprogression. EXPERIMENTAL
designA total of 109 patients with advanced NSCLC who underwent monotherapy with Programmed cell death protein-1 (PD1)/Programmed death-ligand-1 (PD-L1) inhibitors were included in the study. Using RECIST measurements, we divided the patients into responders (n=50) (complete/partial response or stable disease) and non-responders (n=59) (progressive disease). Tumor growth kinetics were used to further identify hyperprogressors (HPs, n=19) among non-responders. Patients were randomized into a training set (D
resultsThe random forest classifier using the top features associated with hyperprogression was able to distinguish between HP and other radiographical response patterns with an area under receiver operating curve of 0.85±0.06 in the training set (D
conclusionsOur study suggests that image-based radiomics markers extracted from baseline CTs of advanced NSCLC treated with PD-1/PD-L1 inhibitors may help identify patients at risk of hyperprogressions.
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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.