RCMNet: A deep learning model assists CAR-T therapy for leukemia

TL;DR

This paper introduces RCMNet, a deep learning model combining CNN and Transformer, to improve the identification of CAR-T cells in leukemia treatment, achieving high accuracy on public and clinical datasets.

Contribution

The study presents a novel integrated deep learning model, RCMNet, that enhances CAR-T cell identification accuracy for leukemia therapy applications.

Findings

RCMNet achieves 99.63% accuracy on public dataset.

Transfer learning improves accuracy to 83.36% on clinical dataset.

Model outperforms previous state-of-the-art methods.

Abstract

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.