ArticleFrontiers in immunology2022
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- An exploratory study on whole-lung radiomics features from computed tomography for prognostic prediction in non-small cell lung cancer with concurrent chemoradiotherapy.Translational cancer research · 2026Article
- 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
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Computed tomography-based quantification of intra-tumoral heterogeneity for predicting treatment response to concurrent chemoradiotherapy in non-small cell lung cancer: a multicenter retrospective study.Translational cancer research · 2025Article
- Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance.Translational oncology · 2025Article
- Utilizing ctDNA to discover mechanisms of resistance to targeted therapies in patients with metastatic NSCLC: towards more informative trials.Nature reviews. Clinical oncology · 2025Review
- Development and validation of an interpretable machine learning model for predicting progression-free survival after immunotherapy in patients with non-small cell lung cancer: a multicenter study.Frontiers in immunology · 2025Article
- Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma.Journal of translational medicine · 2024Article
- Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma.Aging and disease · 2024Article
- Review
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma.Journal of translational medicine · 2024Article
- Determining the prognosis of Lung cancer from mutated genes using a deep learning survival model: a large multi-center study.Cancer cell international · 2023Article
- Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer.Frontiers in oncology · 2023Review
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949-0.978,
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