【摘要】 目的：探讨MRI影像学特征联合扩散加权成像（DWI）序列对舌鳞状细胞癌（TSCC）病理分化程度的应用研究价值。方法：回顾性收集重庆医科大学附属第一医院2020年2月至2024年10月经手术病理证实的TSCC 41例，术前均行常规MRI平扫+增强及DWI序列扫描。对术前TSCC一般临床资料、影像学特征、ADC值与术后病理分级进行统计学分析，影像学特征包括：肿瘤大小、形态、边界、边缘、囊变或坏死、供血血管、骨受侵、肌肉受侵、淋巴结转移、其他受侵（会厌、扁桃体、颌下腺、腭舌弓）。41例TSCC，根据分化程度分为低中分化组（n=20例，包括低分化、低-中分化和中分化）与中高分化组（n=21例，包括中-高分化和高分化）。比较两组在临床资料、影像学特征、表观扩散系数（ADC）值上的差异，分析肿瘤影像学特征、ADC值和病理分级的相关性。采用ROC曲线分别分析肿瘤影像学特征、ADC值、影像学特征联合ADC值对肿瘤病理分型的评估效能。结果：两组MRI影像学特征比较，大小（χ~2=4.188,P=0.041）、边缘（χ~2=4.193,P=0.041）、供血血管（χ~2=4.188,P=0.041）有统计学差异。两组ADC值有统计学差异（Z=-4.986,P<0.001）。肿瘤大小与病理分级呈负相关（r为-0.401、P=0.009）。肿瘤ADC值与病理分级呈正相关性（r为0.783、P<0.001）。对肿瘤大小、ADC值、肿瘤大小联合ADC值的病理分型评估效能进行ROC曲线分析，显示曲线下面积分别为0.683(95%CI:0.517～0.849)、0.955(95%CI:0.896～1.000)、0.964(95%CI:0.916～1.000)。结论：肿瘤大小、边缘、供血血管和ADC值对TSCC病理分化程度有重要的参考研究价值，肿瘤大小联合ADC值可提高TSCC病理分型评估效能，为临床精准治疗提供依据。更多还原

【Abstract】 Objective: To investigate the value of MRI imaging features combined with diffusionweighted imaging(DWI) sequences in assessing the pathological differentiation degree of tongue squamous cell carcinoma(TSCC). Methods: Forty-one patients with surgically and histopathologically confirmed TSCC between February 2020 and October 2024 at the First Affiliated Hospital of Chongqing Medical University, were retrospectively collected. All patients underwent routine MRI plain scan+enhancement and DWI sequence scan before surgery. Preoperative clinical data, MRI imaging features, apparent diffusion coefficient(ADC) values, and postoperative pathological grades were statistically analyzed. MRI imaging features included: tumor size, shape, boundary, margin, cystic change or necrosis,blood-supplying vessels, bone invasion, muscle invasion, lymph node metastasis, and invasion of other structures(epiglottis, tonsil, submandibular gland, palatoglossal arch). According to the degree of differentiation, 41 cases of TSCC were divided into low and medium differentiation group(n=20 cases, including low differentiation, low-medium differentiation and medium differentiation) and medium and high differentiation group(n=21 cases, including medium-high differentiation and high differentiation). Differences in clinical data, MRI imaging features, and ADC values between the two groups were compared. The correlation between tumor imaging features, ADC values, and pathological grade was analyzed. Receiver operating characteristic(ROC) curves were used to evaluate the diagnostic efficacy of imaging features,ADC values, and their combination for predicting pathological differentiation. Results: Comparison of MRI imaging features between the two groups showed statistically significant differences in size(χ~2 =4.188, P=0.041), margin(χ~2=4.193, P=0.041), and blood-supplying vessels(χ~2=4.188, P=0.041). A statistically significant difference in ADC values was found between the two groups(Z=-4.986, P<0.001).Tumor size showed a negative correlation with pathological grade(r=-0.401, P=0.009). Tumor ADC value showed a positive correlation with pathological grade(r=0.783, P<0.001). ROC curve analysis for assessing pathological differentiation using tumor size, ADC value, and their combination showed areas under the curve(AUC) of 0.683(95%CI: 0.517～0.849), 0.955(95%CI: 0.896～1.000), and 0.964(95%CI:0.916～1.000), respectively. Conclusion: Tumor size, margin, blood-supplying vessels, and ADC values serve as valuable imaging biomarkers for evaluating the pathological differentiation degree of TSCC. Combining tumor size with ADC values enhances the assessment efficacy for pathological differentiation, providing a basis for precise clinical treatment planning.更多还原