Deep learning-based classification of mesothelioma improves prediction of patient outcome

By P. Courtiol, C. Maussion, Matahi Moarii, E. Pronier, Samuel Pilcer, M. Sefta, P. Manceron, Sylvain Toldo, Mikhail Zaslavskiy, N. Le Stang and 7 more. 2019

488 citations recorded by the source corpus.

Subfields: Immunology, Oncology, Pulmonary and Respiratory Medicine, Medical Imaging, Medical & Health AI

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “pharmaceuticals discovery [21], electronic health records mining [22], medical data segmentation [23], [24], and smart manufacturing [25].”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…the gold standard is to evaluate models on an independent test set collected from a different hospital (e.g.,Beck et al. (2011); Liu et al. (2017); Courtiol et al. (2019); McKinney et al. (2020)) or at least with a different scanner within the same hospital (e.g., Campanella et al. (2019)).”
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (2019, background): “Moreover, subjective interpretation of histology slides does not make use of the rich phenotypic information that has shown to have prognostic relevance [6].”
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021, methodology): “…learning of WSIs, in which: 1) instance-level feature representations are extracted from randomly sampled image patches in the WSI, and then 2) global aggregation schemes are applied to the bag of instances to obtain a WSI-level representation for subsequent supervision [23, 59, 11, 38, 69].”
Deep learning in cancer pathology: a new generation of clinical biomarkers (2020, methodology): “predicted OS in a large cohort of patients with malignant mesothelioma and visualised histological features associated with long or short survival identified by the DL network.(46) Concurrently, disease-specific survival was estimated by DL-based prediction of the development of distant metastatic r”
Throughput-Optimal Topology Design for Cross-Silo Federated Learning (2020, background): “While the first FL papers [72, 51] emphasized the cross-device setting, the cross-silo scenario has become popular for distributed training among banks [107], hospitals [19, 93, 69], pharmaceutical labs [67], and manufacturers [74].”
Deep neural network models for computational histopathology: A survey (2019, background): “Examples include survival prediction in mesothelioma (Courtiol et al., 2019) and colorectal cancer (Kather et al.”
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022, unknown): “- subtype, grade, stage, survival, origin) [12, 19, 37, 38, 52, 53, 68, 70, 85].”
AI in health and medicine (2022, background): “In other cases, humans may identify a smaller region of interest, such as part of a slide image that contains a tumor, and crop the image before feeding it into an AI system, though this intervention adds a manual step into what might otherwise be a fully automated workflow 32,93 .”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “Among them, MesoNet [65] stands out for incorporating a feature contribution explainability algorithm called CHOWDER [126] on H&E tissue sections of mesothelioma to identify that the features contributing the most to survival predictions were primarily stromal cells associated with inflammation, cel”
Artificial intelligence for digital and computational pathology (2023, background): “Most of these studies used slide-level labels, a limitation of common bulk sequencing assays that can only provide a WSI-level description.”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, methodology): “[229] present an approach employing DL for predicting patient outcome in the case of mesothelioma.”
Harnessing multimodal data integration to advance precision oncology (2021, background): “Several recent seminal studies do not provide the source code, claiming that internal code dependencies prevent code sharing and that textual descriptions are sufficient to reproduce the results 97,129,130 .”
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “provide an example of this type of model and workflow for prognosis in mesothelioma.(52) First, they trained a U-Net CNN on several hundred manually annotated histology images to perform tissue segmentation.”