Division of Hematology Oncology, Department of Medicine; Department of Pathology
Artificial Intelligence and Robotics | Diagnosis | Hemic and Lymphatic Diseases | Neoplasms
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional neural networks, to classify pathologic images by using smaller datasets. We analyze human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL pathologic images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100 percent (100% for hospital A, 99.71% for hospital B and 100% for hospital C). The technical variability introduced by slide preparation and image collection reduces AI model performance in cross-hospital tests, but the 100% diagnostic accuracy is maintained after its elimination. It is now clinically practical to utilize deep learning models for diagnosis of DLBCL and ultimately other human hematopoietic malignancies.
Lymphoma, Machine learning, Cancer imaging
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DOI of Published Version
Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, Li S. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020 Nov 26;11(1):6004. doi: 10.1038/s41467-020-19817-3. PMID: 33244018; PMCID: PMC7691991. Link to article on publisher's site
Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, Li S. (2020). A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Open Access Publications by UMMS Authors. https://doi.org/10.1038/s41467-020-19817-3. Retrieved from https://escholarship.umassmed.edu/oapubs/4474
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.