Ma Dongshen, Yuan Yuqing, Miao Xiaodan, Gu Ying, Wang Yubo, Luo Dan, Fan Meiting, Shi Xiaoli, Xi Shuxue, Ji Binbin, Xiang Chenxi, Liu Hui
Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China.
Front Oncol. 2025 Mar 3;15:1480645. doi: 10.3389/fonc.2025.1480645. eCollection 2025.
Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL) in humans, and it is a highly heterogeneous malignancy with a 40% to 50% risk of relapsed or refractory (R/R), leading to a poor prognosis. So early prediction of R/R risk is of great significance for adjusting treatments and improving the prognosis of patients.
We collected clinical information and H&E images of 227 patients diagnosed with DLBCL in Xuzhou Medical University Affiliated Hospital from 2015 to 2018. Patients were then divided into R/R group and non-relapsed & non-refractory group based on clinical diagnosis, and the two groups were randomly assigned to the training set, validation set and test set in a ratio of 7:1:2. We developed a model to predict the R/R risk of patients based on clinical features utilizing the random forest algorithm. Additionally, a prediction model based on histopathological images was constructed using CLAM, a weakly supervised learning method after extracting image features with convolutional networks. To improve the prediction performance, we further integrated image features and clinical information for fusion modeling.
The average area under the ROC curve value of the fusion model was 0.71±0.07 in the validation dataset and 0.70±0.04 in the test dataset. This study proposed a novel method for predicting the R/R risk of DLBCL based on H&E images and clinical features.
For patients predicted to have high risk, follow-up monitoring can be intensified, and treatment plans can be adjusted promptly.
弥漫性大B细胞淋巴瘤（DLBCL）是人类非霍奇金淋巴瘤（NHL）最常见的类型，是一种高度异质性的恶性肿瘤，复发或难治（R/R）风险为40%至50%，预后较差。因此，早期预测R/R风险对于调整治疗方案和改善患者预后具有重要意义。
我们收集了2015年至2018年在徐州医科大学附属医院诊断为DLBCL的227例患者的临床信息和苏木精-伊红（H&E）图像。然后根据临床诊断将患者分为R/R组和非复发及非难治组，并将两组按7:1:2的比例随机分配到训练集、验证集和测试集。我们利用随机森林算法开发了一个基于临床特征预测患者R/R风险的模型。此外，在使用卷积网络提取图像特征后，采用弱监督学习方法CLAM构建了一个基于组织病理学图像的预测模型。为了提高预测性能，我们进一步将图像特征和临床信息进行融合建模。
融合模型在验证数据集中的ROC曲线下平均面积值为0.71±0.07，在测试数据集中为0.70±0.04。本研究提出了一种基于H&E图像和临床特征预测DLBCL患者R/R风险的新方法。
对于预测为高风险的患者，可以加强随访监测，并及时调整治疗方案。
