卷积神经网络（CNN）可能改善弥漫大B细胞淋巴瘤（DLBCL）中的反应预测。本研究的目的是调查使用最大强度投影（MIP）图像从18F-氟脱氧葡萄糖（18F-FDG）正电子发射断层扫描（PET）基线扫描中的CNN来预测2年内疾病进展时间（TTP）的可行性，并将其与国际预后指数（IPI）进行比较，即临床使用的评分。分析了从前瞻性临床试验（HOVON-84）收集的296个DLBCL 18F-FDG PET / CT基线扫描。使用冠状和矢状MIP进行交叉验证。使用外部数据集（340例DLBCL患者）验证模型。评估了概率、代谢肿瘤体积和最大体积相关性。还评估了合成移除肿瘤的PET扫描的概率。CNN以0.74的曲线下面积（AUC）提供了2年TTP预测，优于基于IPI的模型（AUC = 0.68）。此外，在去除肿瘤后，原始MIP的高概率（>0.6）显着降低（<0.4，一般来说）。这些发现表明，基于MIP的CNN能够预测DLBCL的治疗结果。© 2023. Springer Nature Limited.

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.© 2023. Springer Nature Limited.