[The value of CT radiomics in predicting treatment outcomes in patients with nasal polyps].

Q4 Medicine
K H Wang, Y M Cui, J B Shi, Y Q Sun
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引用次数: 0

Abstract

Objective: To evaluate the predictive efficacy of sinus CT radiomics for treatment outcomes in nasal polyp patients undergoing endoscopic sinus surgery. Methods: A retrospective cohort study was conducted at the First Affiliated Hospital of Sun Yat-sen University, including 194 patients with nasal polyps treated between January 2015 and December 2019. The cohort comprised 132 males and 62 females, aged 16 to 75 years. Patients were divided into a training set (n=135) and an internal validation set (n=59). An external validation set (n=34), consisting of 22 males and 12 females aged 16 to 59 years, was included from January 2020 to December 2021. Disease control was evaluated using the criteria from the European Position Paper on Rhinosinusitis and Nasal Polyps 2020 (EPOS 2020). Radiomic features were extracted from sinus CT images and analyzed using the least absolute shrinkage and selection operator (LASSO) regression. Models combining radiomic and clinical features were developed to predict treatment efficacy. Results: The radiomics and combined models, based on four selected features, outperformed the clinical feature model in the training set, with AUC values of 0.901 and 0.915, versus 0.874, respectively. In the internal validation set, AUCs were 0.839, 0.832, and 0.716. Despite reduced AUCs in the external set, the radiomics model maintained good generalizability (0.748, 0.764, 0.620). Decision curve analysis showed significant clinical benefits in both radiomics and combined models. Conclusion: The CT-based radiomics model demonstrates significant predictive power in identifying refractory nasal polyps, suggesting its potential for clinical application in treatment outcome prediction.

[CT 放射组学在预测鼻息肉患者治疗结果中的价值]。
目的评估鼻窦 CT 放射组学对接受内窥镜鼻窦手术的鼻息肉患者治疗效果的预测功效。方法: 在中山大学附属第一医院进行了一项回顾性队列研究:中山大学附属第一医院开展了一项回顾性队列研究,纳入了2015年1月至2019年12月期间接受治疗的194例鼻息肉患者。其中男性 132 人,女性 62 人,年龄在 16 岁至 75 岁之间。患者被分为训练集(n=135)和内部验证集(n=59)。2020 年 1 月至 2021 年 12 月期间,纳入了外部验证集(n=34),其中包括 22 名男性和 12 名女性,年龄在 16 至 59 岁之间。采用《欧洲鼻炎和鼻息肉立场文件2020》(EPOS 2020)中的标准评估疾病控制情况。从鼻窦 CT 图像中提取放射学特征,并使用最小绝对收缩和选择算子(LASSO)回归法进行分析。结合放射学和临床特征建立模型,预测治疗效果。结果:基于四个选定特征的放射组学模型和组合模型在训练集中的表现优于临床特征模型,AUC 值分别为 0.901 和 0.915,而临床特征模型为 0.874。在内部验证集中,AUC 值分别为 0.839、0.832 和 0.716。尽管外部集的 AUC 值有所降低,但放射组学模型仍保持了良好的普适性(0.748、0.764 和 0.620)。决策曲线分析表明,放射组学模型和组合模型都具有明显的临床优势。结论基于 CT 的放射组学模型在识别难治性鼻息肉方面具有显著的预测能力,这表明该模型在治疗结果预测方面具有临床应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.40
自引率
0.00%
发文量
12432
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