ArticleJournal for immunotherapy of cancer2024
Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.
Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics-Based AI for Predicting Neoadjuvant Immunochemotherapy Pathological Response in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- CT-based radiomics in predicting the efficacy of preoperative neoadjuvant chemoimmunotherapy for non-small cell lung cancer: a systematic review and meta-analysis.Frontiers in immunology · 2026Pooled it
- Diagnostic accuracy of combined TCF-21 protein expression and computed tomography morphological features for predicting microvascular invasion in early-stage non-small cell lung cancer.Journal of thoracic disease · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Habitat model based on CEUS for noninvasive prediction of EGFR mutation status in peripheral NSCLC.BMC cancer · 2026Article
- Fusion of Clinical and Deep Learning Features for Predicting Pembrolizumab Monotherapy Response in Advanced Non-Small Cell Lung Cancer.Journal of clinical medicine · 2026Article
- A review of deep learning-based multimodal data integration of lung cancer.Clinical and experimental medicine · 2026Review
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Opportunities and challenges of immunotherapy for patients with resectable and potentially resectable stage III non-small cell lung cancer.Cancer biology & medicine · 2026Review
- Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.BMC medicine · 2026Article
- Serum exosomal miR-192-5p as a novel predictive biomarker for immunochemotherapy response in advanced non-small cell lung cancer.BMC cancer · 2026Article
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Temporal radiomics for non-invasive preoperative prediction of pathologic complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.Cancer biology & medicine · 2026Article
- Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma.NPJ digital medicine · 2026Article
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- Deep learning models based on CT predict prognosis in patients with locally advanced esophageal squamous cell carcinoma who received neoadjuvant immunotherapy and chemotherapy.Frontiers in immunology · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- Predicting response to immune checkpoint inhibitor plus chemotherapy in EGFR-mutant lung adenocarcinoma following first-generation TKI resistance: a multicenter deep learning study.Frontiers in immunology · 2026Article
- Radiomics and deep learning for immune checkpoint inhibitor outcomes: imaging-derived immune phenotyping and clinical translation across solid tumors.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAlthough neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.
methodsThis study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.
resultsThe training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.
conclusionsBy extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.
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