基于超声放射组学的动态图预测软组织肉瘤的组织学分级:一项多中心队列研究。

IF 3.4 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Mengjie Wu, Boyang Zhou, Ao Li, Hailing Zha, Xinyue Wang, Hongjin Hua, Tiantian Zhang, Shuping Wei, Wei Zhang, Huixiong Xu
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引用次数: 0

摘要

目的:通过术前超声图像预测软组织肉瘤(STS)的组织学分级,帮助选择个性化的治疗方案,改善远期预后。方法:回顾性研究2016年4月至2023年12月期间经组织学证实的STS患者238例,分为训练组和内部验证组。在2024年1月至2024年12月期间,从三个中心前瞻性地招募了70名患者作为外部验证队列。从术前灰度超声图像中提取放射组学特征。采用多变量logistic回归分析,建立了动态模态图(DynNom)。采用接收工作特征曲线、校准曲线、Hosmer-Lemeshow检验、决策曲线分析(DCA)和临床影响曲线(CIC)评价预测效果。结果:DynNom基于临床- us特征(转移状态、回声性、膜层和血管)和放射组学特征,在训练、内部和外部验证队列中预测STS组织学分级的最佳AUC分别为0.915 (95% CI, 0.873-0.947)、0.87 (95% CI, 0.79-0.93)和0.90 (95% CI, 0.80-0.96)。结论:动态图是一种实用的工具,可以预测STS的组织学分级,有助于临床医生筛选组织学上高度的STS作为新辅助治疗的候选者。知识进展:动态图有可能准确预测术前STS患者的组织学分级。动态图确定的高危患者是术前放疗和新辅助化疗的潜在候选者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultrasound radiomics-based dynamic nomogram to predict histologic grade in soft tissue sarcoma: a multi-center cohort study.

Objectives: To predict histologic grade of soft tissue sarcoma (STS) with preoperative ultrasound images, aiding in the selection of personalized treatment plans and improving long-term prognosis.

Methods: In total, 238 patients with histologically proven STS were retrospectively enrolled from April 2016 to December 2023 and divided into the training and internal validation cohorts. 70 patients were prospectively enrolled from three centers between January 2024 and December 2024 as the external validation cohort. Radiomics features were extracted from preoperative grayscale ultrasound images. The dynamic nomogram (DynNom) was developed by using multivariable logistic regression analysis. Predictive performance was evaluated with the receiving operating characteristic curve, calibration curve, Hosmer-Lemeshow test, decision curve analysis (DCA), and clinical impact curve (CIC).

Results: The DynNom based on clinical-US characteristics (metastasis status, echogenicity, fascia layer, and vascularity) and radiomics features yielded an optimal AUC of 0.915 (95% CI, 0.873-0.947), 0.87 (95% CI, 0.79-0.93), and 0.90 (95% CI, 0.80-0.96) for predicting the STS histologic grade in the training, internal and external validation cohorts, respectively. The DynNom outperformed the conventional model and radiomics model (P < 0.05). Calibration curves and Hosmer-Lemeshow tests indicated its satisfactory calibration ability. DCA confirmed that the DynNom outperformed other models in overall net benefit, meanwhile CIC suggested that the DynNom had great clinical applicability in predicting histologic grade.

Conclusions: The dynamic nomogram is a practical tool that could predict the histologic grade of STS, which might help clinicians to screen histologic high-grade STSs as neoadjuvant treatment candidates.

Advances in knowledge: The dynamic nomogram had the potential to accurately predict histologic grade in STS patients before surgery. High-risk patients defined by the dynamic nomogram were potential candidates for preoperative radiotherapy and neoadjuvant chemotherapy.

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来源期刊
British Journal of Radiology
British Journal of Radiology 医学-核医学
CiteScore
5.30
自引率
3.80%
发文量
330
审稿时长
2-4 weeks
期刊介绍: BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option
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