Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai, China.
Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Dentomaxillofac Radiol. 2023 Sep;52(6):20220301. doi: 10.1259/dmfr.20220301. Epub 2023 Feb 17.
To develop and validate a nomogram based on whole-tumour histograms of apparent diffusion coefficient (ADC) maps for predicting malignant transformation (MT) in sinonasal inverted papilloma (IP).
This retrospective study included 209 sinonasal IPs with and without MT, which were assigned into a primary cohort ( = 140) and a validation cohort ( = 69). Eight ADC histogram features were extracted from the whole-tumour region of interest. Morphological MRI features and ADC histogram parameters were compared between the two groups (with and without MT). Stepwise logistic regression was used to identify independent predictors and to construct models. The predictive performances of variables and models were assessed using the area under the curve (AUC). The optimal model was presented as a nomogram, and its calibration was assessed.
Four morphological features and seven ADC histogram parameters showed significant differences between the two groups in both cohorts (all < 0.05). Maximum diameter, loss of convoluted cerebriform pattern, ADC and ADC were identified as independent predictors to construct the nomogram. The nomogram showed significantly better performance than the morphological model in both the primary (AUC, 0.96 0.88; = 0.006) and validation (AUC, 0.96 0.88; = 0.015) cohorts. The nomogram showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram is clinically useful.
The developed nomogram, which incorporates morphological MRI features and ADC histogram parameters, can be conveniently used to facilitate the pre-operative prediction of MT in IPs.
开发并验证一种基于表观扩散系数（ADC）图全肿瘤直方图的列线图，用于预测鼻窦内翻性乳头状瘤（IP）的恶性转化（MT）。
本回顾性研究纳入了 209 例伴有和不伴有 MT 的鼻窦 IP，将其分为主要队列（n=140）和验证队列（n=69）。从全肿瘤感兴趣区提取 8 个 ADC 直方图特征。比较两组（有和无 MT）的形态 MRI 特征和 ADC 直方图参数。使用逐步逻辑回归识别独立预测因子并构建模型。使用曲线下面积（AUC）评估变量和模型的预测性能。最优模型表示为列线图，并评估其校准。
两组的四个形态学特征和七个 ADC 直方图参数在两个队列中均有显著差异（均<0.05）。最大直径、失去脑回样模式、ADC 和 ADC 被确定为独立预测因子，用于构建列线图。列线图在主要队列（AUC，0.96 vs. 0.88；=0.006）和验证队列（AUC，0.96 vs. 0.88；=0.015）中均表现出显著优于形态学模型的性能。列线图在两个队列中均表现出良好的校准。决策曲线分析表明，该列线图具有临床应用价值。
该列线图纳入了形态学 MRI 特征和 ADC 直方图参数，可方便地用于术前预测 IP 的 MT。
