Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models.

AIMS: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. METHODS: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. RESULTS: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. CONCLUSIONS: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

PAX5-Negative Classical Hodgkin Lymphoma: A Case Report of a Rare Entity and Review of the Literature.

Classical Hodgkin lymphoma (CHL) is recognized as a B-cell neoplasm arising from germinal center or postgerminal center B-cells. The hallmark of CHL is the presence of CD30 (+) Hodgkin and Reed-Sternberg (HRS) cells with dim expression of PAX5. Nearly all of the HRS cells are positive for PAX5. However, a small minority of HRS cells may lack PAX5 expression, which can cause a diagnostic dilemma. Herein we describe two cases of PAX5-negative CHL and review of the English literature on this very rare entity. It is crucial to be aware of this phenomenon, which in some cases may lead to misdiagnosis and may ultimately adversely affect patient's management.

CD7 Positive Diffuse Large B-Cell Lymphoma Arising in a Background of Follicular Lymphoma: A Case Report and Review of the Literature.

Diffuse large B-cell lymphoma (DLBCL) is a neoplasm of large B-lymphocytes with a diffuse growth pattern. The neoplastic cells express B-cell markers such as CD20 and PAX-5 and there may be coexpression of BCL-2, BCL-6, CD10, and MUM-1. With the exception of CD5, other T-cell markers are not commonly expressed in this neoplasm. Here, we describe the first reported case of a DLBCL with abnormal expression CD7 arising in a background of follicular lymphoma in an 81-year-old male who presented with a nontender left axillary mass. Additionally, no other T-cell antigens were expressed in this B-cell lymphoma. Expression of CD7 in DLBCL is exceptionally rare and its prognostic significance is unknown. Here, we describe this rare case with review of literature of known DLBCLs with expression of T-cell antigens.