Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.
Context.--Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a ma...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 2; pp. 182 - 194 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154881464&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154881464 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Feb2022 vid: 146 iid: 2 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 154881464 154881464 154881464 10.5858/arpa.2020-0510-OA 154881464 ppf: 182 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies. aug: au: Irshaid, Lina Bleiberg, Jonathan Weinberger, Ethan Garritano, James Shallis, Rory M. Patsenker, Jonathan Lindenbaum, Ofir Kluger, Yuval Katz, Samuel G. Xu, Mina L. affil: Department of Pathology, Yale New Haven Hospital, Yale School of Medicine, New Haven, Connecticut sug: subj: Lymphoma Diagnosis Bone Marrow Biopsy Histocytochemistry Machine Learning Deep Learning Neoplasm Grading Health Care Costs Morbidity Consensus Human Retrospective Design Reproducibility of Results Neural Networks (Computer) Disease Progression ab: Context.--Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established. Objective.--To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis. Design.--We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation. Results.--Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data. Conclusions.--These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist--augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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