ArticleThorax2022
Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria.
Article in Thorax, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Outcome prediction based on [18F]FDG PET/CT in patients with pleural mesothelioma treated with ipilimumab and nivolumab +/- UV1 telomerase vaccine.European journal of nuclear medicine and molecular imaging · 2025Trial
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Review
- ESR Essentials: a step-by-step guide of segmentation for radiologists-practice recommendations by the European Society of Medical Imaging Informatics.European radiology · 2025Review
- CT radiomic stratification signature to optimize clinical decisions for ovarian cancer patients receiving neoadjuvant chemotherapy and the underlying biological basis: a multicenter retrospective study.Journal of translational medicine · 2025Article
- Diagnosis of Mesothelioma Using Image Segmentation and Class-Based Deep Feature Transformations.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence for response prediction and personalisation in radiation oncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Prognostic Value of Chest CT Volumetric Analysis in Patients with Malignant Pleural Mesothelioma.Journal of clinical medicine · 2025Article
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Feasibility of an artificial intelligence system for tumor response evaluation.BMC medical imaging · 2024Article
- Contemporary management of mesothelioma.Breathe (Sheffield, England) · 2024Review
- Imaging of pleural disease.Breathe (Sheffield, England) · 2024Review
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects.Oncology reviews · 2024Review
- Article
- Overview of approaches to estimate real-world disease progression in lung cancer.JNCI cancer spectrum · 2023Review
- A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.Translational cancer research · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundIn malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.
methodsIn a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen's kappa. Survival was assessed using Kaplan-Meier methodology.
resultsHuman and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm
conclusionWe have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.
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