| Author |
Bejnordi, Babak Ehteshami Veta, Mitko Diest, Paul Johannes van Ginneken, Bram van Karssemeijer, Nico Litjens, Geert Laak, Jeroen A. W. M. van der Hermsen, Meyke Manson, Quirine F Balkenhol, Maschenka Geessink, Oscar Stathonikos, Nikolaos Dijk, Marcory CRF van Bult, Peter Beca, Francisco Beck, Andrew H Wang, Dayong Khosla, Aditya Gargeya, Rishab Irshad, Humayun Zhong, Aoxiao Dou, Qi Li, Quanzheng Chen, Hao Lin, Huang-Jing Heng, Pheng-Ann Haß, Christian Bruni, Elia Wong, Quincy Halici, Ugur Öner, Mustafa Ümit Cetin-Atalay, Rengul Berseth, Matt Khvatkov, Vitali Vylegzhanin, Alexei Kraus, Oren Shaban, Muhammad Rajpoot, Nasir Awan, Ruqayya inukunwattana, Korsuk Qaiser, Talha Tsang, Yee-Wah Tellez, David Annuscheit, Jonas Hufnagl, Peter Valkonen, Mira Kartasalo, Kimmo Latonen, Leena Ruusuvuori, Pekka Liimatainen, Kaisa Albarqouni, Shadi Mungal, Bharti George, Ami Demirci, Stefanie Navab, Nassir Watanabe, Seiryo Seno, Shigeto Takenaka, Yoichi Matsuda, Hideo Phoulady, Hady Ahmady Kovalev, Vassili Kalinovsky, Alexander Liauchuk, Vitali Bueno, Gloria Fernandez-Carrobles, M. Milagro Serrano, Ismael Deniz, Oscar Racoceanu, Daniel Venâncio, Rui |
| Abstract |
Importance: Application of deep learning algorithms to whole-slide pathology images can 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. |
