Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
BMC Cancer. 2024 Jan 30;24(1):150. doi: 10.1186/s12885-024-11917-3.
The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment Fluorine-fluorodeoxyglucose (F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients.
The data from 190 consecutive patients with LACC who underwent pretreatment F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram.
In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values.
In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.
现有的分期系统无法满足准确生存预测的需求。对接受同期放化疗（CCRT）的局部晚期宫颈癌（LACC）患者进行准确的生存预测，可以改善其治疗管理。因此，本研究旨在开发和验证基于预处理氟-氟代脱氧葡萄糖（F-FDG）正电子发射断层扫描（PET）-计算机断层扫描（CT）图像的放射组学模型，以准确预测患者的预后。
回顾性分析了来自两家癌症医院的 190 例接受 LACC 患者的预处理 F-FDG PET-CT 和 CCRT 的数据；来自同一家医院的 176 名患者被随机分为训练（n=117）和内部验证（n=50）队列。使用单变量和多变量 Cox 比例风险模型从训练队列中选择临床特征；从 PET 和 CT 图像中提取放射组学特征，并使用最小绝对收缩和选择算子和 Cox 比例风险回归进行过滤。然后，使用先前选择的临床、CT 和 PET 放射组学特征构建三个预测模型和一个列线图。使用来自另一家癌症医院的 23 例 LACC 患者的外部验证队列来验证模型。使用接收器工作特征曲线、Kaplan-Meier 曲线和列线图评估构建模型的预测性能。
在训练队列中，一个临床、一个 PET 放射组学和三个 CT 放射组学特征与无进展生存期显著相关。在所有三个队列中，与任何单一参数相比，联合模型在预测 3 年无进展生存期（曲线下面积：0.661、0.718 和 0.775；C 指数：0.698、0.724 和 0.705）和 5 年无进展生存期（曲线下面积：0.661、0.711 和 0.767；C 指数：0.698、0.722 和 0.676）方面表现出更好的效果和临床实用性。随后构建的列线图中，校准曲线显示实际观察值和列线图预测值之间具有良好的一致性。
本研究使用独立的外部验证队列开发并成功验证了一个临床放射组学预测模型。纳入放射组学和临床特征的列线图可以成为评估接受同期放化疗的局部晚期宫颈癌患者长期预后的有用临床工具。
