预测口咽鳞癌淋巴结转移的基于CT的深度学习放射组学特征的开发与验证:一项多中心研究。

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2024-09-13 DOI:10.1093/dmfr/twae051
Tianzi Jiang, Hexiang Wang, Jie Li, Tongyu Wang, Xiaohong Zhan, Jingqun Wang, Ning Wang, Pei Nie, Shiyu Cui, Xindi Zhao, Dapeng Hao
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引用次数: 0

摘要

研究目的淋巴结转移(LNM)是影响口咽鳞癌(OPSCC)患者治疗策略和预后的关键因素。本研究旨在建立并验证一种深度学习(DL)放射组学模型,利用对比增强计算机断层扫描(CECT)预测口咽鳞癌的LNM:回顾性分析包括来自三家机构的279名OPSCC患者。CECT图像用于手工(HCR)和DL特征提取。HCR特征的降维使用了递归特征消除、最小绝对收缩和选择算子算法,而DL特征的降维使用了方差阈值和递归特征消除算法。使用支持向量机、决策树、随机森林、k-近邻、高斯天真贝叶斯分类器和轻梯度提升机构建了放射组学特征。然后利用筛选出的 DL、HCR 和临床特征构建了一个组合模型。接收者操作特征曲线下面积(AUC)用于量化模型的性能,校准曲线用于评估模型的校准情况:综合模型表现出强劲的性能,在训练队列中的AUC值为0.909(95% CI:0.861-0.957),在内部验证队列中的AUC值为0.884(95% CI:0.800-0.968),在外部验证队列中的AUC值为0.865(95% CI:0.791-0.939)。其表现优于临床模型和表现最好的放射组学模型。此外,校准结果也令人满意:结论:基于 CECT 的组合模型展示了在术前预测 OPSCC 中 LNM 的潜力,为更精确、更有针对性的治疗策略提供了有价值的工具:本研究提出了一种将临床因素与深度学习放射组学相结合的新型组合模型,大大提高了OPSCC的术前LNM预测能力。
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Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicenter study.

Objectives: Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT).

Methods: A retrospective analysis included 279 OPSCC patients from three institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination and least absolute shrinkage and selection operator algorithms, whereas DL feature dimensionality reduction used variance-threshold and recursive feature elimination algorithms. Radiomics signatures were constructed using support vector machine, decision tree, random forest, k-nearest neighbor, gaussian naive bayes classifiers and light gradient boosting machine. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration.

Results: The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI: 0.861-0.957) in the training cohort, 0.884 (95% CI: 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI: 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory.

Conclusions: The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies.

Advances in knowledge: This study presents a novel combined model integrating clinical factors with deep learning radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

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来源期刊
CiteScore
5.60
自引率
9.10%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X
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