Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.Comment: Accepted for MICCAI DEMI Workshop 202

A new diagnostic algorithm for Burkitt and diffuse large B-cell lymphomas based on the expression of CSE1L and STAT3 and on MYC rearrangement predicts outcome

Background Aggressive mature B-cell non-Hodgkin's lymphomas (BCL) sharing features of Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) (intermediate BL/DLBCL) but deviating with respect to one or more characteristics are increasingly recognized. The limited knowledge about these biologically heterogeneous lymphomas hampers their assignment to a known entity, raising incertitude about optimal treatment approaches. We therefore searched for discriminative, prognostic, and predictive factors for their better characterization. Patients and methods We analyzed 242 cytogenetically defined aggressive mature BCL for differential protein expression. Marker selection was based on recent gene-expression profile studies. Predictive models for diagnosis were established and validated by a different set of lymphomas. Results CSE1L- and inhibitor of DNA binding-3 (ID3)-overexpression was associated with the diagnosis of BL and signal transduction and transcription-3 (STAT3) with DLBCL (P<0.001 for all markers). All three markers were associated with patient outcome in DLBCL. A new algorithm discriminating BL from DLBCL emerged, including the expression of CSE1L, STAT3, and MYC translocation. This ‘new classifier' enabled the identification of patients with intermediate BL/DLBCL who benefited from intensive chemotherapy regimens. Conclusion The proposed algorithm, which is based on markers with reliable staining properties for routine diagnostics, represents a novel valid tool in separating BL from DLBCL. Most interestingly, it allows segregating intermediate BL/DLBCL into groups with different treatment requirement

Epstein-Barr virus infection and altered control of apoptotic pathways in posttransplant lymphoproliferative disorders.

Posttransplant lymphoproliferative disorders (PTLD) represent a spectrum of lymphoid diseases complicating the clinical course of transplant recipients. Most PTLD are Epstein-Barr virus (EBV) associated with viral latency type III. Several in vitro studies have revealed an interaction between EBV latency proteins and molecules of the apoptosis pathway. Data on human PTLD regarding an association between Bcl-2 family proteins and EBV are scarce. We analyzed 60 primary PTLD for expression of 8 anti- (Bcl-2, Bcl-XL, and Mcl-1) and proapoptotic proteins (Bak and Bax), the so-called BH3-only proteins (Bad, Bid, Bim, and Puma), as well as the apoptosis effector cleaved PARP by immunohistochemistry. Bim and cleaved PARP were both significantly (p = 0.001 and p = 5.251e-6) downregulated in EBV-positive compared to EBV-negative PTLD [Bim: 6/40 (15%), cleaved PARP: 10/43 (23%), vs. Bim: 13/16 (81%), cleaved PARP: 12/17 (71%)]. Additionally, we observed a tendency toward increased Bcl-2 protein expression (p = 0.24) in EBV-positive PTLD. Hence, we provide evidence of a distinct regulation of Bcl-2 family proteins in EBV-positive versus negative PTLD. The low-expression pattern of the proapoptotic proteins Bim and cleaved PARP together with the high-expression pattern of the antiapoptotic protein Bcl-2 by trend in EBV-positive tumor cells suggests disruption of the apoptotic pathway by EBV in PTLD, promoting survival signals in the host cell