Yu Baoting, Huang Chencui, Fan Xiaofei, Liu Dongyao, Zhang Yuting, Ding Jun
Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China.
Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd, Beijing, China.
Ann Surg Oncol. 2025 May 13. doi: 10.1245/s10434-025-17399-2.
Differentiating between parotid adenolymphoma and malignant tumors remains challenging.
This study aims to improve preoperative diagnosis accuracy by evaluating the role of multimodal functional magnetic resonance imaging (MRI) and advanced radiomics analysis.
We retrospectively analyzed 124 patients with adenolymphoma and malignant parotid tumors, divided into primary (n = 84) and test (n = 40) cohorts. Tumor regions were manually labeled on susceptibility-weighted imaging (SWI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). Seven radiomics models were constructed using logistic regression. We also incorporated intratumoral susceptibility signal (ITSS) grading and performed histogram analysis of apparent diffusion coefficient (ADC) maps.
The united radiomics model combining SWI, DWI, and CE-T1WI showed the highest diagnostic performance (area under the curve (AUC) = 0.95, accuracy = 0.93, specificity = 0.93) in the primary cohort, outperforming single-sequence and double-sequence models. The test set validated the model's good diagnostic performance (AUC = 0.9). ITSS grading significantly differed between adenolymphomas and malignant tumors (p < 0.001). ADC histogram analysis revealed significant differences in mean, 10th percentile, and kurtosis values between the two groups.
The multisequence radiomics model combining DWI, SWI, and CE-T1WI provides a comprehensive and accurate noninvasive approach for differentiating parotid adenolymphoma from malignant tumors. This method helps avoid the risks associated with invasive procedures, such as tumor cell implantation and metastasis, while guiding personalized surgical decision-making. By offering a novel diagnostic tool, this study enhances the precision of preoperative tumor characterization and supports more effective treatment planning and prognosis assessment for patients with parotid gland tumors.
鉴别腮腺腺淋巴瘤与恶性肿瘤仍然具有挑战性。
本研究旨在通过评估多模态功能磁共振成像（MRI）和先进的放射组学分析的作用来提高术前诊断准确性。
我们回顾性分析了124例腺淋巴瘤和腮腺恶性肿瘤患者，分为初级队列（n = 84）和测试队列（n = 40）。在磁敏感加权成像（SWI）、扩散加权成像（DWI）和对比增强T1加权成像（CE-T1WI）上手动标记肿瘤区域。使用逻辑回归构建了7种放射组学模型。我们还纳入了瘤内磁敏感信号（ITSS）分级，并对表观扩散系数（ADC）图进行了直方图分析。
结合SWI、DWI和CE-T1WI的联合放射组学模型在初级队列中显示出最高的诊断性能（曲线下面积（AUC）= 0.95，准确率 = 0.93，特异性 = 0.93），优于单序列和双序列模型。测试集验证了该模型良好的诊断性能（AUC = 0.9）。腺淋巴瘤和恶性肿瘤之间的ITSS分级有显著差异（p < 0.001）。ADC直方图分析显示两组之间的平均值、第10百分位数和峰度值有显著差异。
结合DWI、SWI和CE-T1WI的多序列放射组学模型为鉴别腮腺腺淋巴瘤与恶性肿瘤提供了一种全面、准确的非侵入性方法。该方法有助于避免与侵入性手术相关的风险，如肿瘤细胞种植和转移，同时指导个性化手术决策。通过提供一种新颖的诊断工具，本研究提高了术前肿瘤特征描述的精度，并支持对腮腺肿瘤患者进行更有效的治疗规划和预后评估。
