Li Jie, Zhou Chenran, Qu Xiaoxia, Du Lianze, Yuan Qinghai, Han Qinghe, Xian Junfang
Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin Province, 130041, China.
Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
BMC Med Imaging. 2025 Jul 1;25(1):238. doi: 10.1186/s12880-025-01771-5.
To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametric MRI (mpMRI) for distinguishing IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.
This multicenter retrospective study analyzed 214 histopathologically confirmed cases (68 IgG4-ROD, 146 MALT lymphoma) from two institutions (2019-2024). A LASSO-SVM classifier was optimized through comparative evaluation of seven machine learning models, incorporating fused radiomic features (1,197 features) from ILN/PLN regions. Diagnostic performance was benchmarked against two subspecialty radiologists (10-20 years' experience) using receiver operating characteristics - area under the curve (AUC), precision-recall AUC (PR-AUC), and decision curve analysis (DCA), adhering to CLEAR/METRICS guidelines.
The fusion model (FR_RAD) achieved state-of-the-art performance, with an AUC of 0.927 (95% CI 0.902-0.958) and a PR-AUC of 0.901 (95% CI 0.862-0.940) in the training set, and an AUC of 0.907 (95% CI 0.857-0.965) and a PR-AUC of 0.872 (95% CI 0.820-0.924) on external testing. In contrast, subspecialty radiologists achieved lower AUCs of 0.671-0.740 (95% CI 0.630-0.780) and PR-AUCs of 0.553-0.632 (95% CI 0.521-0.664) (all p < 0.001). FR_RAD also outperformed radiologists in accuracy (88.6% vs. 66.2% and 71.3%; p < 0.01). DCA demonstrated a net benefit of 0.18 at a high-risk threshold of 30%, equivalent to avoiding 18 unnecessary biopsies per 100 cases.
The fusion model integrating multi-regional radiomics from mpMRI achieves precise differentiation between IgG4-ROD and orbital MALT lymphoma, outperforming subspecialty radiologists. This approach highlights the transformative potential of spatial radiomics analysis in resolving diagnostic uncertainties and reducing reliance on invasive procedures for orbital lesion characterization.
开发并验证一种诊断框架，该框架整合来自多参数磁共振成像（mpMRI）的瘤内（ILN）和瘤周（PLN）影像组学特征，以区分IgG4相关眼病（IgG4-ROD）与眼眶黏膜相关淋巴组织（MALT）淋巴瘤。
这项多中心回顾性研究分析了来自两个机构（2019 - 2024年）的214例经组织病理学确诊的病例（68例IgG4-ROD，146例MALT淋巴瘤）。通过对七种机器学习模型的比较评估，优化了一种套索支持向量机（LASSO-SVM）分类器，该模型纳入了来自ILN/PLN区域的融合影像组学特征（1197个特征）。根据接受者操作特征曲线下面积（AUC）、精确召回率AUC（PR-AUC）和决策曲线分析（DCA），以两名专科放射科医生（10 - 二十年经验）为基准评估诊断性能，遵循CLEAR/METRICS指南。
融合模型（FR_RAD）取得了先进的性能，在训练集中AUC为0.927（95%CI 0.902 - 0.958），PR-AUC为0.901（95%CI 0.862 - 0.940），在外部测试中AUC为0.907（95%CI 0.857 - 0.965），PR-AUC为0.872（95%CI 0.820 - 0.924）。相比之下，专科放射科医生的AUC较低，为0.671 - 0.740（95%CI 0.630 - 0.780），PR-AUC为0.553 - 0.632（95%CI 0.521 - 0.664）（所有p < 0.001）。FR_RAD在准确性方面也优于放射科医生（分别为88.6%对66.2%和71.3%；p < 0.01）。DCA显示在30%的高风险阈值下净效益为0.18，相当于每100例可避免18次不必要的活检。
整合来自mpMRI的多区域影像组学的融合模型实现了IgG4-ROD与眼眶MALT淋巴瘤之间的精确区分，优于专科放射科医生。这种方法凸显了空间影像组学分析在解决诊断不确定性和减少对眼眶病变特征性诊断的侵入性程序依赖方面的变革潜力。
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