Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Cancer Immunol Immunother. 2024 Mar 30;73(5):87. doi: 10.1007/s00262-024-03644-2.
To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy.
105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model.
The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively.
The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.
构建基于 MRI 特征和临床数据的预测模型，以评估接受化疗免疫治疗的肝转移胰腺癌患者的无进展生存期（PFS）、总生存期（OS）和客观缓解率（ORR）。
将 105 例接受化疗免疫治疗的肝转移胰腺癌患者分为训练集（n=52）、验证集（n=22）和测试集（n=31）。勾画多病灶感兴趣区，提取多序列放射组学特征，分别构建预测 PFS、OS 和 ORR 的放射组学模型。提取临床变量，分别构建预测 PFS、OS 和 ORR 的临床模型。由放射组学模型和临床模型联合构建列线图。
ORR 与 PFS 或 OS 无显著相关性。预测 6 个月 PFS 的列线图的曲线下面积（AUC）在训练集、验证集和测试集中分别达到 0.847（0.737-0.957）、0.786（0.566-1.000）和 0.864（0.735-0.994）。预测 1 年 OS 的列线图 AUC 在训练集、验证集和测试集中分别达到 0.770（0.635-0.906）、0.743（0.479-1.000）和 0.818（0.630-1.000）。预测 ORR 的列线图 AUC 在训练集、验证集和测试集中分别达到 0.914（0.828-1.00）、0.938（0.840-1.00）和 0.846（0.689-1.00）。
基于 MRI 影像特征和临床数据的预测模型可有效预测肝转移胰腺癌患者接受化疗免疫治疗后的 PFS、OS 和 ORR，可用于评估患者的预后。
