Haematologists usually over-estimate the percentage of CD138+ plasma cells in marrow biopsies

Treatment Strategies for Multiple Myeloma Treatment and the Role of High-Throughput Screening for Precision Cancer Therapy

In the past few years, development of approved drug candidates has improved the disease management of multiple myeloma (MM). However, due to drug resistance, some of the patients do not respond positively, while some of the patients acquire drug resistance, thereby these patients eventually relapse. Hence, there are no other therapeutic options for multiple myeloma patients. Therefore, this necessitates a precision-based approach to multiple myeloma therapy. The use of patient's samples to test drug sensitivity to increase efficacy and reduce treatment-related toxicities is the goal of functional precision medicine. Platforms such as high-throughput-based drug repurposing technology can be used to select effective single drug and drug combinations based on the efficacy and toxicity studies within a time frame of couple of weeks. In this article, we describe the clinical and cytogenetic features of MM. We highlight the various treatment strategies and elaborate on the role of high-throughput screening platforms in a precision-based approach towards clinical treatment.

Deep Learning Accurately Quantifies Plasma Cell Percentages on CD138-Stained Bone Marrow Samples

The diagnosis of plasma cell neoplasms requires accurate, and ideally precise, percentages. This plasma cell percentage is often determined by visual estimation of CD138-stained bone marrow biopsies and clot sections. While not necessarily inaccurate, estimates are by definition imprecise. For this study, we hypothesized that deep learning can be used to improve precision. We trained a semantic segmentation-based convolutional neural network (CNN) using annotations of CD138+ and CD138- cells provided by one pathologist on small image patches of bone marrow and validated the CNN on an independent test set of image patches using annotations from two pathologists and a non-deep learning commercial software. On validation, we found that the intraclass correlation coefficients for plasma cell percentages between the CNN and pathologist #1, a non-deep learning commercial software and pathologist #1, and pathologists #1 and #2 were 0.975, 0.892, and 0.994, respectively. The overall results show that CNN labels were almost as accurate as pathologist labels at a cell-by-cell level. Once satisfied with performance, we scaled-up the CNN to evaluate whole slide images (WSIs), and deployed the system as a workflow friendly web application to measure plasma cell percentages using snapshots taken from microscope cameras.

Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells

AIMS

The objective of this study was to develop and validate an open-source digital pathology tool, QuPath, to automatically quantify CD138-positive bone marrow plasma cells (BMPCs).

METHODS

We analysed CD138-scanned slides in QuPath. In the initial training phase, manual positive and negative cell counts were performed in representative areas of 10 bone marrow biopsies. Values from the manual counts were used to fine-tune parameters to detect BMPCs, using the positive cell detection and neural network (NN) classifier functions. In the testing phase, whole-slide images in an additional 40 cases were analysed. Output from the NN classifier was compared with two pathologist's estimates of BMPC percentage.

RESULTS

The training set included manual counts ranging from 2403 to 17 287 cells per slide, with a median BMPC percentage of 13% (range: 3.1%-80.7%). In the testing phase, the quantification of plasma cells by image analysis correlated well with manual counting, particularly when restricted to BMPC percentages of <30% (Pearson's r=0.96, p<0.001). Concordance between the NN classifier and the pathologist whole-slide estimates was similarly good, with an intraclass correlation of 0.83 and a weighted kappa for the NN classifier of 0.80 with the first rater and 0.90 with the second rater. This was similar to the weighted kappa between the two human raters (0.81).

CONCLUSIONS

This represents a validated digital pathology tool to assist in automatically and reliably counting BMPC percentage on CD138-stained slides with an acceptable error rate.