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      About Cytogenetic and Genome Research: 1.7 Impact Factor I 3.1 CiteScore I 0.385 Scimago Journal & Country Rank (SJR)

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      Malignes Pleuramesotheliom: Künstliche Intelligenz in der Bildanalyse

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      Karger Kompass Onkologie
      S. Karger AG
      Imaging/CT MRI, Mesothelioma, Pleural disease

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          Abstract

          Background: Background In 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. Methods: In 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. Results: Human 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<sup>3</sup> (p=0.182), 95% limits 345 to +407 cm<sup>3</sup>). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival. Conclusion: We 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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          Most cited references5

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

          Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria1. The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities2. Here we have developed a new approach-based on deep convolutional neural networks-called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.
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            Radiomics and deep learning in lung cancer

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              Artificial intelligence applications for thoracic imaging

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                Author and article information

                Journal
                KKO
                10.1159/issn.2296-5416
                Karger Kompass Onkologie
                S. Karger AG
                2296-5416
                2296-5386
                2022
                September 2022
                06 September 2022
                : 9
                : 3
                : 127-128
                Affiliations
                Universitätsklinikum Düsseldorf, Klinik für Kardiologie, Pneumologie und Angiologie, Düsseldorf, Deutschland
                Article
                526740 Kompass Onkol 2022;9:127–128
                10.1159/000526740
                ff5c4226-2238-4132-9bb4-fbb4da539487
                © 2022 S. Karger GmbH, Freiburg

                Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.

                History
                Page count
                Pages: 2
                Categories
                Wissenstransfer

                Oncology & Radiotherapy,Pathology,Surgery,Obstetrics & Gynecology,Pharmacology & Pharmaceutical medicine,Hematology
                Pleural disease,Imaging/CT MRI,Mesothelioma

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