为了开发一种基于MRI肿瘤内部和周围区域的辐射组学标志和独立危险因素的综合性标签示图，用于预测早期宫颈腺癌（AC）和腺鳞癌（ASC）患者的子宫旁侵袭（PMI）。共回顾性纳入了接受术前MRI检查和激光子宫颈切除术/子宫切除术的460例IB至IIB期宫颈AC和ASC患者，并分为原始（Ori）、内验证和外验证队列。将从肿瘤感兴趣区域（ROI-T）和3mm和5mm肿瘤周围环（ROI-3和ROI-5）中提取原始（Ori）和小波（Wav）转换特征。然后独立建立由原始（Ori）和原始-小波（Ori-Wav）特征构成的肿瘤（RST）和3mm和5mm周围区域（RS3和RS5）的辐射组学标志，并比较其诊断表现以选择最佳标志。最后，根据多变量逻辑回归分析，将最佳内部和周围区域标志以及临床独立危险因素整合，开发出示图。FIGO分期、MRI检查中宫颈基底环的破坏（DCSRMR）、MRI检查中子宫旁侵袭（PMIMR）和血清CA-125被确定为独立危险因素。将独立危险因素、基于Ori-Wav特征的RST和RS5整合到示图中，在原始、内部验证和外部验证队列中预测PMI的曲线下面积分别为0.874（0.810-0.922）、0.885（0.834-0.924）和0.966（0.887-0.995）。此外，示图在预测PMI方面优于辐射组学标志和临床模型在三个队列中的表现。示图可以术前准确、非创伤地预测早期宫颈AC和ASC患者的PMI。示图可以术前准确、非创伤地预测早期宫颈AC和ASC患者的PMI，并帮助对于放化疗或激光子宫切除术的精确治疗决策。早期宫颈AC和ASC患者中PMI的准确术前预测可以帮助对于放化疗或激光子宫切除术的精确治疗决策。将独立危险因素、基于Ori-Wav特征的RST和RS5整合到示图中，可以术前准确、非创伤地预测早期宫颈AC和ASC患者中的PMI。示图在早期宫颈AC和ASC患者中预测PMI方面优于辐射组学标志和临床模型。© 2023年. 作者（们）对欧洲放射学会拥有独家许可。

To develop a comprehensive nomogram based on MRI intra- and peritumoral radiomics signatures and independent risk factors for predicting parametrial invasion (PMI) in patients with early-stage cervical adenocarcinoma (AC) and adenosquamous carcinoma (ASC).A total of 460 patients with IB to IIB cervical AC and ASC who underwent preoperative MRI examination and radical trachelectomy/hysterectomy were retrospectively enrolled and divided into primary, internal validation, and external validation cohorts. The original (Ori) and wavelet (Wav)-transform features were extracted from the volumetric region of interest of the tumour (ROI-T) and 3mm- and 5mm-peritumoral rings (ROI-3 and ROI-5), respectively. Then the Ori and Ori-Wav feature-based radiomics signatures from the tumour (RST) and 3 mm- and 5 mm-peritumoral regions (RS3 and RS5) were independently built and their diagnostic performances were compared to select the optimal ones. Finally, the nomogram was developed by integrating optimal intra- and peritumoral signatures and clinical independent risk factors based on multivariable logistic regression analysis.FIGO stage, disruption of the cervical stromal ring on MRI (DCSRMR), parametrial invasion on MRI (PMIMR), and serum CA-125 were identified as independent risk factors. The nomogram constructed by integrating independent risk factors, Ori-Wav feature-based RST, and RS5 yielded AUCs of 0.874 (0.810-0.922), 0.885 (0.834-0.924), and 0.966 (0.887-0.995) for predicting PMI in the primary, internal and external validation cohorts, respectively. Furthermore, the nomogram was superior to radiomics signatures and clinical model for predicting PMI in three cohorts.The nomogram can preoperatively, accurately, and noninvasively predict PMI in patients with early-stage cervical AC and ASC.The nomogram can preoperatively, accurately, and noninvasively predict PMI and facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy in patients with early-stage cervical AC and ASC.The accurate preoperative prediction of PMI in early-stage cervical AC and ASC can facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy. The nomogram integrating independent risk factors, Ori-Wav feature-based RST, and RS5 can preoperatively, accurately, and noninvasively predict PMI in early-stage cervical AC and ASC. The nomogram was superior to radiomics signatures and clinical model for predicting PMI in early-stage cervical AC and ASC.© 2023. The Author(s), under exclusive licence to European Society of Radiology.