Guo Qinhao, Lin Zijing, Lu Jing, Li Rong, Wu Lei, Deng Lin, Qiang Jinwei, Wu Xiaohua, Gu Yajia, Li Haiming
Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Abdom Radiol (NY). 2023 Mar;48(3):1119-1130. doi: 10.1007/s00261-023-03802-7. Epub 2023 Jan 18.
To develop and validate an MRI-based radiomics nomogram for the preoperative prediction of miliary changes in the small bowel mesentery (MCSBM) in advanced high-grade serous ovarian cancer (HGSOC).
One hundred and twenty-eight patients with pathologically proved advanced HGSOC (training cohort: n = 91; validation cohort: n = 37) were retrospectively included. All patients were initially evaluated as MCSBM-negative by preoperative imaging modalities but were finally confirmed by surgery and histopathology (MCSBM-positive: n = 53; MCSBM-negative: n = 75). Five radiomics signatures were built based on the features from multisequence magnetic resonance images. Independent clinicoradiological factors and radiomics-fusion signature were further integrated to construct a radiomics nomogram. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and clinical utility.
Radiomics signatures, ascites, and tumor size were independent predictors of MCSBM. A nomogram integrating radiomics features and clinicoradiological factors demonstrated satisfactory predictive performance with areas under the curves (AUCs) of 0.871 (95% CI 0.801-0.941) and 0.858 (95% CI 0.739-0.976) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) revealed that the nomogram had a significantly improved ability compared with the clinical model in the training cohort (NRI = 0.343, p = 0.002; IDI = 0.299, p < 0.001) and validation cohort (NRI = 0.409, p = 0.015; IDI = 0.283, p = 0.001).
Our proposed nomogram has the potential to serve as a noninvasive tool for the prediction of MCSBM, which is helpful for the individualized assessment of advanced HGSOC patients.
开发并验证基于磁共振成像(MRI)的影像组学列线图,用于术前预测晚期高级别浆液性卵巢癌(HGSOC)中小肠系膜粟粒样改变(MCSBM)。
回顾性纳入128例经病理证实的晚期HGSOC患者(训练队列:n = 91;验证队列:n = 37)。所有患者术前影像学检查初步评估为MCSBM阴性,但最终经手术和组织病理学确诊(MCSBM阳性:n = 53;MCSBM阴性:n = 75)。基于多序列磁共振图像特征构建了5个影像组学特征。进一步整合独立的临床放射学因素和影像组学融合特征,构建影像组学列线图。使用受试者工作特征(ROC)曲线、校准曲线和临床效用评估列线图的性能。
影像组学特征、腹水和肿瘤大小是MCSBM的独立预测因素。整合影像组学特征和临床放射学因素的列线图在训练队列和验证队列中的曲线下面积(AUC)分别为0.871(95%CI 0.801 - 0.941)和0.858(95%CI 0.739 - 0.976),显示出令人满意的预测性能。净重新分类指数(NRI)和综合判别改善(IDI)表明,在训练队列(NRI = 0.343,p = 0.002;IDI = 0.299,p < 0.001)和验证队列(NRI = 0.409,p = 0.015;IDI = 0.283,p = 0.001)中,列线图与临床模型相比具有显著提高的能力。
我们提出的列线图有潜力作为预测MCSBM的非侵入性工具,有助于对晚期HGSOC患者进行个体化评估。