Zheng Bozhong, Yu Baoting, Zheng Xuewei, Qu Xiaolong, Li Tong, Zhang Yun, Ding Jun
Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China.
Front Oncol. 2025 Jul 15;15:1588358. doi: 10.3389/fonc.2025.1588358. eCollection 2025.
Oral squamous cell carcinoma is a highly invasive tumor. The degree of histological differentiation and lymph node metastasis are important factors in the treatment and prognosis of patients. There is a lack of non-invasive and accurate preoperative risk prediction model in the existing clinical work.
This study sought to develop and validate a combined model including MRI radiomics and morphological analysis to predict lymph node metastasis and degree of tumor differentiation prior to surgical intervention for oral squamous cell carcinoma (OSCC).
This study retrospectively included 119 patients which were divided into a training cohort (n=83) and a validation cohort (n=36). To predict lymph node metastasis (LNM) and degree of tumor differentiation, both univariate and multivariate analyses were performed to identify significant features and develop morphological prediction models. Radiomics features were extracted from T2-FS and DWI sequences, followed by feature selection and the establishment of Rad-scores using the LASSO method. Two nomograms was constructed by integrating MRI morphological features with radiomics features. The performance of the models was assessed using the AUC and the Delong test. Calibration curves and DCA were employed to further evaluate the models' practical applicability.
Nine radiomics features were selected to develop the Rad-scores. The morphological features for predicting LNM are depth of invasion and tumor thickness. The morphological features for predicting the degree of tumor differentiation are ADC value and intratumoral necrosis.In the validation cohort, the nomogram for predicting LNM achieved an area under the curve (AUC) of 0.90 (95% CI: 0.84, 0.97), while the nomogram for tumor grade prediction achieved an AUC of 0.87 (95% CI: 0.76, 0.98), demonstrating excellent diagnostic performance. Calibration curve and decision curve further confirmed the accuracy of nomograms prediction.
Nomograms derived from MRI radiomics and morphological characteristics offer a noninvasive and precise method for predicting degree of tumor differentiation and LNM in OSCC preoperatively. The combined model is an accurate risk prediction model with good clinical benefits and prediction accuracy.
口腔鳞状细胞癌是一种具有高度侵袭性的肿瘤。组织学分化程度和淋巴结转移是患者治疗和预后的重要因素。在现有的临床工作中，缺乏无创且准确的术前风险预测模型。
本研究旨在开发并验证一种包含MRI影像组学和形态学分析的联合模型，以预测口腔鳞状细胞癌（OSCC）手术干预前的淋巴结转移和肿瘤分化程度。
本研究回顾性纳入了119例患者，分为训练队列（n = 83）和验证队列（n = 36）。为了预测淋巴结转移（LNM）和肿瘤分化程度，进行了单变量和多变量分析以识别显著特征并建立形态学预测模型。从T2-FS和DWI序列中提取影像组学特征，随后进行特征选择并使用LASSO方法建立Rad分数。通过将MRI形态学特征与影像组学特征相结合构建了两个列线图。使用AUC和德龙检验评估模型的性能。采用校准曲线和决策曲线分析进一步评估模型的实际适用性。
选择了9个影像组学特征来建立Rad分数。预测LNM的形态学特征是浸润深度和肿瘤厚度。预测肿瘤分化程度的形态学特征是ADC值和瘤内坏死。在验证队列中，预测LNM的列线图曲线下面积（AUC）为0.90（95%CI：0.84，0.97），而预测肿瘤分级的列线图AUC为0.87（95%CI：0.76，0.98），显示出优异的诊断性能。校准曲线和决策曲线进一步证实了列线图预测的准确性。
源自MRI影像组学和形态学特征的列线图为术前预测OSCC的肿瘤分化程度和LNM提供了一种无创且精确的方法。联合模型是一种准确的风险预测模型，具有良好的临床效益和预测准确性。
