Development and validation of a deep learning-based microsatellite instability predictor from prostate cancer whole-slide images.

Microsatellite instability-high (MSI-H) is a tumor-agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing to evaluate their eligibility for immunotherapy and need for Lynch syndrome testing. Prostate biopsies and surgical resections from prostate cancer patients referred to our institution were analyzed. MSI status was determined by next-generation sequencing. Patients sequenced before a cutoff date formed an algorithm development set (n = 4015, MSI-H 1.8%) and a paired validation set (n = 173, MSI-H 19.7%) that consisted of two serial sections from each sample, one stained and scanned internally and the other at an external site. Patients sequenced after the cutoff date formed a temporally independent validation set (n = 1350, MSI-H 2.3%). Attention-based multiple instance learning models were trained to predict MSI-H from H&E WSIs. The predictor achieved area under the receiver operating characteristic curve values of 0.78 (95% CI [0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the internally prepared, externally prepared, and temporal validation sets, respectively, showing effective predictability and generalization to both external staining/scanning processes and temporally independent samples. While MSI-H status is significantly correlated with Gleason score, the model remained predictive within each Gleason score subgroup.

Tissue microarrays - brief history, techniques and clinical future.

INTRODUCTION AND AIM: There is a growing need for better, cheaper and faster histopathological diagnostic. The authors reviewed the main steps of the efforts towards the improvement of the pre-analytical phase of tissue processing for histological examination. RESULTS: Since their introduction decades ago tissue microarrays (TMAs) proved their value by increasing efficiency, standardization and accuracy of many histological techniques, such as histochemistry, histoenzymology, immunohistochemistry, in situ hybridization, etc. By allowing the simultaneous analysis and comparison of multiple different tissues on a single histology slide (up to 1000 individual samples), TMAs are also having a significant economic advantage (consumables and labor). From its first description until recent years, the TMA techniques have evolved steadily but slowly despite many attempts to adapt it for clinical diagnostics. In this paper, we are reviewing the main techniques of obtaining TMA blocks from the beginning to the present day, as well as recent developments that are expanding their scope into high accuracy/efficiency clinical diagnostics. CONCLUSIONS: Considering recent developments, we believe that the prospect of high-throughput histology might be achievable in the not-so-distant future.

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients.

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

TdT+ T-lymphoblastic populations are increased in Castleman disease, in Castleman disease in association with follicular dendritic cell tumors, and in angioimmunoblastic T-cell lymphoma.

T-lymphoblastic lymphoma is an aggressive neoplasm requiring prompt clinical treatment. Conversely, indolent T-lymphoblastic proliferation mimics T-lymphoblastic lymphoma but consists of a proliferation of non-neoplastic TdT+ T cells, requiring no treatment. Recently, we identified several cases of indolent T-lymphoblastic proliferations in extrathymic lymphoid tissues: 1 in a patient suffering from Castleman disease (CD) associated with a follicular dendritic cell sarcoma/tumor, 1 in a patient with a history of angioimmunoblastic T-cell lymphoma (AITL), and 1 in association with acinic cell carcinoma. Interestingly, in the case of the patient with a history of AITL, these TdT+ T cells were seen in multiple anatomic sites over the span of 5 years. Here we review these 3 cases and extend our findings by demonstrating that TdT+ T-lymphoblastic populations are increased in lymph nodes of patients with CD (P=0.011), CD in association with follicular dendritic cell tumors, and AITL (P<0.01) compared with other T-cell or B-cell lymphomas or reactive lymph nodes. Finally, analysis of 352 nonhematolymphoid tumors including carcinomas, melanomas, and sarcomas demonstrates that TdT+ T cells are not increased in these tumors. Our studies not only present several detailed cases of indolent T-lymphoblastic proliferations, but also correlate these populations with specific hematologic diseases.

Diffuse large B-cell lymphoma with aberrant expression of the T-cell antigens CD2 and CD7.

Diffuse large B-cell lymphoma is the most common type of non-Hodgkin lymphoma. Although, aberrant expression of a single T-cell-associated antigen (exclusive of CD5) on diffuse large B-cell lymphoma has occasionally been described in the literature, cases that show coexpression of ≥2 T-cell antigens on a well-documented case of diffuse large B-cell lymphoma are extremely rare. Here, we describe a well-characterized case of diffuse large B-cell lymphoma that showed aberrant coexpression of 2 T-cell-associated antigens, CD2 and CD7. Recognition of these types of cases is important to help ensure accurate diagnoses are made.