Lin Yun, Sun Yang, Li Chunyuan, Zhang Yongyue, Zhang Rongjin, Wang Shumin, Jing Hongmei, Cui Ligang
Department of Ultrasound, Peking University Third Hospital, Beijing, 100191, China.
Department of Ultrasound, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, 361000, China.
Ann Hematol. 2025 Mar;104(3):1697-1704. doi: 10.1007/s00277-025-06299-w. Epub 2025 Mar 15.
To develop a baseline predictive model for refractory diffuse large B-cell lymphoma (DLBCL) utilizing imaging data including ultrasound findings and PET-CT in conjunction with clinical parameters. We retrospectively analyzed data from 140 patients with newly diagnosed DLBCL treated at Peking University Third Hospital between January 2018 and January 2023. All patients underwent ultrasound, histopathological examinations and PET-CT examinations. After completing 6-8 cycles of standardized chemotherapy, patients were categorized into refractory and non-refractory groups according to the Lugano International Response Assessment Criteria. Univariate analyses were performed using T-tests and Chi-Squared Tests, and independent risk factors for refractory DLBCL were identified through logistic regression. A nomogram predictive model was constructed using the R package "rms," and its predictive performance was subsequently validated. Univariate analysis and logistic regression identified that blurred margins of the affected lymph nodes in ultrasound images (P < 0.001, OR = 18.238) and IPI score(P = 0.051, OR = 3.131) were significant risk factors for disease progression. The predictive nomogram established for refractory diffuse large B-cell lymphoma demonstrated an area under the receiver operating characteristic curve (AUC) of 0.835, with a sensitivity of 85.5% and specificity of 79.5%. Following internal validation, the predictive model exhibited a high degree of alignment between the estimated risk of refractory diffuse large B-cell lymphoma and the actual observed progression events. The prediction model of the R-DLBCL prediction model, amalgamating ultrasonic characterizations and clinical indicators, proves instrumental in identifying high-risk DLBCL groups.
利用包括超声检查结果和PET-CT在内的影像数据并结合临床参数，开发难治性弥漫性大B细胞淋巴瘤（DLBCL）的基线预测模型。我们回顾性分析了2018年1月至2023年1月在北京大学第三医院接受治疗的140例新诊断DLBCL患者的数据。所有患者均接受了超声、组织病理学检查和PET-CT检查。在完成6-8个周期的标准化化疗后，根据卢加诺国际反应评估标准将患者分为难治组和非难治组。使用T检验和卡方检验进行单因素分析，并通过逻辑回归确定难治性DLBCL的独立危险因素。使用R包“rms”构建列线图预测模型，随后对其预测性能进行验证。单因素分析和逻辑回归确定，超声图像中受累淋巴结边界模糊（P < 0.001，OR = 18.238）和国际预后指数（IPI）评分（P = 0.051，OR = 3.131）是疾病进展的重要危险因素。为难治性弥漫性大B细胞淋巴瘤建立的预测列线图显示，受试者工作特征曲线（AUC）下面积为0.835，敏感性为85.5%，特异性为79.5%。经过内部验证，预测模型在难治性弥漫性大B细胞淋巴瘤的估计风险与实际观察到的进展事件之间表现出高度一致性。融合超声特征和临床指标的R-DLBCL预测模型有助于识别高危DLBCL组。
