دورية أكاديمية
Deep learning facilitates distinguishing histologic subtypes of pulmonary neuroendocrine tumors on digital whole-slide images
| العنوان: | Deep learning facilitates distinguishing histologic subtypes of pulmonary neuroendocrine tumors on digital whole-slide images |
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| المؤلفون: | Ilie, Marius, Benzaquen, Jonathan, Tourniaire, Paul, Heeke, Simon, Ayache, Nicholas, Delingette, Hervé, Long-Mira, Elodie, Lassalle, Sandra, Hamila, Marame, Fayada, Julien, Otto, Josiane, Cohen, Charlotte, Gomez Caro, Abel, Berthet, Jean Philippe, Marquette, Charles Hugo, Hofman, Véronique, Bontoux, Christophe, Hofman, Paul |
| المساهمون: | FHU OncoAge - Pathologies liées à l’âge CHU Nice (OncoAge), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), Institut de Recherche sur le Cancer et le Vieillissement (IRCAN), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), Université Côte d'Azur (UniCA), Laboratoire de Pathologie Clinique et Expérimentale. Hôpital Pasteur Nice, Hôpital Pasteur Nice (CHU), Département Oncologie Médicale Nice, Centre de Lutte contre le Cancer Antoine Lacassagne Nice (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UniCA)-UNICANCER-Université Côte d'Azur (UniCA), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), The University of Texas M.D. Anderson Cancer Center Houston, UNICANCER-Université Côte d'Azur (UniCA), Centre Hospitalier Universitaire de Nice (CHU Nice), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015) |
| المصدر: | ISSN: 2072-6694. |
| بيانات النشر: | HAL CCSD MDPI |
| سنة النشر: | 2022 |
| المجموعة: | Université de Rennes 1: Publications scientifiques (HAL) |
| مصطلحات موضوعية: | lung, neuroendocrine carcinoma, deep learning, CNN, HALO-AI, [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| الوصف: | International audience ; The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | hal-03621585; https://inria.hal.science/hal-03621585Test; https://inria.hal.science/hal-03621585/documentTest; https://inria.hal.science/hal-03621585/file/cancers-14-01740-1.pdfTest; PUBMEDCENTRAL: PMC8996915 |
| DOI: | 10.3390/cancers14071740 |
| الإتاحة: | https://doi.org/10.3390/cancers14071740Test https://inria.hal.science/hal-03621585Test https://inria.hal.science/hal-03621585/documentTest https://inria.hal.science/hal-03621585/file/cancers-14-01740-1.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.EF1625A9 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/cancers14071740 |
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