基于深度学习的图像分析预测淋巴瘤对 CAR-T 细胞的反应

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas.

We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma.

The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0. 82+0. 05 with sensitivity 0. 87+0. 07, specificity 0. 77+0. 12, and AUC 0. 91+0. 03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0. 81, sensitivity 0. 75, and specificity 0. 88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.

54, sensitivity 0. 38, and specificity 0. 61 (p = 0. 046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.