ArticleJournal of applied clinical medical physics2023
3D deep convolution neural network for radiation pneumonitis prediction following stereotactic body radiotherapy.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Pooled it
- Explainable AI for Image-Based Auxiliary Assessment of Radiation Pneumonitis on Post-Treatment CT Images Using GAN Grad-CAM and Ensemble Learning Techniques.Life (Basel, Switzerland) · 2026Article
- Radiation Dose-Dependent and -Independent Pulmonary Infiltrates in Patients with High-Grade Pneumonitis After Radiochemotherapy and Durvalumab Consolidation for Stage III NSCLC.Diagnostics (Basel, Switzerland) · 2026Article
- CT-based radiomics combined with deep learning for predicting radiation pneumonitis in patients with esophageal cancer: a two-center study.Frontiers in oncology · 2026Article
- A lightweight DeepME model based on improved YOLOv11 architecture for macular edema detection and treatment monitoring.Frontiers in endocrinology · 2026Article
- CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck.Acta oncologica (Stockholm, Sweden) · 2025Article
- Review
- Radiation Pneumonitis Prediction Using Dual-Modal Data Fusion Based on Med3D Transfer Network.Journal of imaging informatics in medicine · 2025Article
- Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Dose prediction for cervical cancer in radiotherapy based on the beam channel generative adversarial network.Heliyon · 2024Article
- Infrastructure tools to support an effective Radiation Oncology Learning Health System.Journal of applied clinical medical physics · 2023Article
- 3D deep convolution neural network for radiation pneumonitis prediction following stereotactic body radiotherapy.Journal of applied clinical medical physics · 2023Article
- Attention-based Vision Transformer Enables Early Detection of Radiotherapy-Induced Toxicity in Magnetic Resonance Images of a Preclinical Model.Technology in cancer research & treatmentArticle
- Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.Technology in cancer research & treatmentArticle
- Predicting Stereotactic Body Radiation Therapy Response Using an AI-Based Tumor Vessel Biomarker.Technology in cancer research & treatmentArticle
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
In this study, we investigated 3D convolutional neural networks (CNNs) with input from radiographic and dosimetric datasets of primary lung tumors and surrounding lung volumes to predict the likelihood of radiation pneumonitis (RP). Pre-treatment, 3- and 6-month follow-up computed tomography (CT) and 3D dose datasets from one hundred and ninety-three NSCLC patients treated with stereotactic body radiotherapy (SBRT) were retrospectively collected and analyzed for this study. DenseNet-121 and ResNet-50 models were selected for this study as they are deep neural networks and have been proven to have high accuracy for complex image classification tasks. Both were modified with 3D convolution and max pooling layers to accept 3D datasets. We used a minority class oversampling approach and data augmentation to address the challenges of data imbalance and data scarcity. We built two sets of models for classification of three (No RP, Grade 1 RP, Grade 2 RP) and two (No RP, Yes RP) classes as outputs. The 3D DenseNet-121 models performed better (F1 score [0.81], AUC [0.91] [three class]; F1 score [0.77], AUC [0.84] [two class]) than the 3D ResNet-50 models (F1 score [0.54], AUC [0.72] [three-class]; F1 score [0.68], AUC [0.71] [two-class]) (p = 0.017 for three class predictions). We also attempted to identify salient regions within the input 3D image dataset via integrated gradient (IG) techniques to assess the relevance of the tumor surrounding volume for RP stratification. These techniques appeared to indicate the significance of the tumor and surrounding regions in the prediction of RP. Overall, 3D CNNs performed well to predict clinical RP in our cohort based on the provided image sets and radiotherapy dose information.
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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.