Browsing by Author "Amgad, Mohamed"

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    NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
    (Oxford University Press, 2022) Amgad, Mohamed; Atteya, Lamees A.; Hussein, Hagar; Mohammed, Kareem Hosny; Hafiz, Ehab; Elsebaie, Maha A.T.; Alhusseiny, Ahmed M.; AlMoslemany, Mohamed Atef; Elmatboly, Abdelmagid M.; Pappalardo, Philip A.; Sakr, Rokia Adel; Mobadersany, Pooya; Rachid, Ahmad; Saad, Anas M.; Alkashash, Ahmad M.; Ruhban, Inas A.; Alrefai, Anas; Elgazar, Nada M.; Abdulkarim, Ali; Farag, Abo-Alela; Etman, Amira; Elsaeed, Ahmed G.; Alagha, Yahya; Amer, Yomna A.; Raslan, Ahmed M.; Nadim, Menatalla K.; Elsebaie, Mai A.T.; Ayad, Ahmed; Hanna, Liza E.; Gadallah, Ahmed; Elkady, Mohamed; Drumheller, Bradley; Jaye, David; Manthey, David; Gutman, David A.; Elfandy, Habiba; Cooper, Lee A.D.; Pathology and Laboratory Medicine, School of Medicine
    Background: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. Results: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. Conclusions: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.
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    Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
    (Nature Research, 2020-05-12) Amgad, Mohamed; Stovgaard, Elisabeth Specht; Balslev, Eva; Thagaard, Jeppe; Chen, Weijie; Dudgeon, Sarah; Sharma, Ashish; Kerner, Jennifer K.; Denkert, Carsten; Yuan, Yinyin; AbdulJabbar, Khalid; Wienert, Stephan; Savas, Peter; Voorwerk, Leonie; Beck, Andrew H.; Madabhushi, Anant; Hartman, Johan; Sebastian, Manu M.; Horlings, Hugo M.; Hudeček, Jan; Ciompi, Francesco; Moore, David A.; Singh, Rajendra; Roblin, Elvire; Balancin, Marcelo Luiz; Mathieu, Marie-Christine; Lennerz, Jochen K.; Kirtani, Pawan; Chen, I-Chun; Braybrooke, Jeremy P.; Pruneri, Giancarlo; Demaria, Sandra; Adams, Sylvia; Schnitt, Stuart J.; Lakhani, Sunil R.; Rojo, Federico; Comerma, Laura; Badve, Sunil S.; Khojasteh, Mehrnoush; Symmans, W. Fraser; Sotiriou, Christos; Gonzalez-Ericsson, Paula; Pogue-Geile, Katherine L.; Kim, Rim S.; Rimm, David L.; Viale, Giuseppe; Hewitt, Stephen M.; Bartlett, John M. S.; Penault-Llorca, Frédérique; Goel, Shom; Lien, Huang-Chun; Loibl, Sibylle; Kos, Zuzana; Loi, Sherene; Hanna, Matthew G.; Michiels, Stefan; Kok, Marleen; Nielsen, Torsten O.; Lazar, Alexander J.; Bago-Horvath, Zsuzsanna; Kooreman, Loes F. S.; Van der Laak, Jeroen A.W. M.; Saltz, Joel; Gallas, Brandon D.; Kurkure, Uday; Barnes, Michael; Salgado, Roberto; Cooper, Lee A. D.; International Immuno-Oncology Biomarker Working Group; Pathology and Laboratory Medicine, School of Medicine
    Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.