Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No.277, West Yanta Road, Xi'an, 710061, Shaanxi, People's Republic of China.
Department of Radiology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, People's Republic of China.
Eur Radiol. 2020 Jun;30(6):3585-3593. doi: 10.1007/s00330-019-06655-1. Epub 2020 Feb 17.
To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).
All 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated.
For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.
The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.
• No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.
开发并确定一种基于 MRI 的放射组学列线图，用于预测早期宫颈癌（ECC）患者的宫旁侵犯（PMI）。
所有 137 例 ECC（FIGO 分期 IB-IIA）患者在根治性子宫切除术前行 T2WI 和 DWI 扫描。通过最小绝对值收缩和选择操作回归选择从 T2WI 和 DWI 提取的放射组学特征来计算放射组学特征。分别使用源自 T2WI 和联合 T2WI 和 DWI 的放射组学特征构建支持向量机（SVM）模型，以评估放射组学特征区分 PMI 患者的性能。基于性能更好的放射组学特征、患者年龄和病理分级绘制放射组学列线图，并对其鉴别和校准性能进行评估。
对于 T2WI 和联合 T2WI 和 DWI，放射组学特征在主队列和验证队列中的 AUC 分别为 0.797（95%CI，0.682-0.911）与 0.946（95%CI，0.899-0.994），以及 0.780（95%CI，0.641-0.920）与 0.921（95%CI，0.832-1）。整合来自联合 T2WI 和 DWI、患者年龄和病理分级的放射组学特征的放射组学列线图在主队列和验证队列中均具有出色的鉴别能力，C 指数值分别为 0.969（95%CI，0.933-1）和 0.941（95%CI，0.868-1）。校准曲线显示出良好的一致性。
放射组学列线图在预测 ECC 患者的 PMI 方面表现良好，可作为辅助工具，为 ECC 患者提供个体化的治疗方案。
尚无先前的研究报告利用放射组学列线图预测 ECC 患者的 PMI。
放射组学模型涉及从联合 T2WI 和 DWI 提取的放射组学特征，这些特征可描绘 ECC 患者肿瘤之间的异质性。
放射组学列线图可以帮助临床医生为 ECC 患者制定个体化的治疗决策。
