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المؤلفEhteshami Bejnordi, Babak
المؤلفVeta, Mitko
المؤلفJohannes van Diest, Paul
المؤلفvan Ginneken, Bram
المؤلفKarssemeijer, Nico
المؤلفLitjens, Geert
المؤلفvan der Laak, Jeroen A W M
المؤلفthe CAMELYON16 Consortium
المؤلفHermsen, Meyke
المؤلفManson, Quirine F
المؤلفBalkenhol, Maschenka
المؤلفGeessink, Oscar
المؤلفStathonikos, Nikolaos
المؤلفvan Dijk, Marcory Crf
المؤلفBult, Peter
المؤلفBeca, Francisco
المؤلفBeck, Andrew H
المؤلفWang, Dayong
المؤلفKhosla, Aditya
المؤلفGargeya, Rishab
المؤلفIrshad, Humayun
المؤلفZhong, Aoxiao
المؤلفDou, Qi
المؤلفLi, Quanzheng
المؤلفChen, Hao
المؤلفLin, Huang-Jing
المؤلفHeng, Pheng-Ann
المؤلفHaß, Christian
المؤلفBruni, Elia
المؤلفWong, Quincy
المؤلفHalici, Ugur
المؤلفÖner, Mustafa Ümit
المؤلفCetin-Atalay, Rengul
المؤلفBerseth, Matt
المؤلفKhvatkov, Vitali
المؤلفVylegzhanin, Alexei
المؤلفKraus, Oren
المؤلفShaban, Muhammad
المؤلفRajpoot, Nasir
المؤلفAwan, Ruqayya
المؤلفSirinukunwattana, Korsuk
المؤلفQaiser, Talha
المؤلفTsang, Yee-Wah
المؤلفTellez, David
المؤلفAnnuscheit, Jonas
المؤلفHufnagl, Peter
المؤلفValkonen, Mira
المؤلفKartasalo, Kimmo
المؤلفLatonen, Leena
المؤلفRuusuvuori, Pekka
المؤلفLiimatainen, Kaisa
المؤلفAlbarqouni, Shadi
المؤلفMungal, Bharti
المؤلفGeorge, Ami
المؤلفDemirci, Stefanie
المؤلفNavab, Nassir
المؤلفWatanabe, Seiryo
المؤلفSeno, Shigeto
المؤلفTakenaka, Yoichi
المؤلفMatsuda, Hideo
المؤلفAhmady Phoulady, Hady
المؤلفKovalev, Vassili
المؤلفKalinovsky, Alexander
المؤلفLiauchuk, Vitali
المؤلفBueno, Gloria
المؤلفFernandez-Carrobles, M Milagro
المؤلفSerrano, Ismael
المؤلفDeniz, Oscar
المؤلفRacoceanu, Daniel
المؤلفVenâncio, Rui
تاريخ الإتاحة2020-08-20T08:06:22Z
تاريخ النشر2017
اسم المنشورJAMA - Journal of the American Medical Association
المصدرScopus
الرقم المعياري الدولي للكتاب987484
معرّف المصادر الموحدhttp://dx.doi.org/10.1001/jama.2017.14585
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15710
الملخصIMPORTANCE: Application of deep learning algorithms to whole-slide pathology imagescan potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. DESIGN, SETTING, AND PARTICIPANTS: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). EXPOSURES: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. MAIN OUTCOMES AND MEASURES: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. RESULTS: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P <.001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). CONCLUSIONS AND RELEVANCE: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. 2017 American Medical Association. All rights reserved.
راعي المشروعData collection and annotation were funded by Stichting IT Projecten and by the Fonds Economische Structuurversterking (tEPIS/TRAIT project; LSH-FES Program 2009; DFES1029161 and FES1103JJT8U). Fonds Economische Structuurversterking also supported (in kind) web-access to whole-slide images. This work was supported by grant 601040 from the Seventh Framework Programme for Research-funded VPH-PRISM project of the European Union (Mr Ehteshami Bejnordi).
اللغةen
الناشرAmerican Medical Association
الموضوعHematoxylin
Cancer Classification
Histopathology
العنوانDiagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
النوعArticle
الصفحات2199-2210
رقم العدد22
رقم المجلد318
dc.accessType Open Access


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