Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Ultrasound Med Biol. 2024 Nov;50(11):1619-1627. doi: 10.1016/j.ultrasmedbio.2024.06.004. Epub 2024 Aug 3.
To explore the performance of ultrasound image-based radiomics in predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of clear-cell renal cell carcinoma (ccRCC).
A retrospective study was conducted via histopathological examination on participants with ccRCC from January 2021 to August 2023. Participants were randomly allocated to a training set and a validation set in a 3:1 ratio. The maximum cross-sectional image of the lesion on the preoperative ultrasound image was obtained, with the region of interest (ROI) delineated manually. Radiomic features were computed from the ROIs and subsequently normalized using Z-scores. Wilcoxon test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature reduction and model development. The performance of the model was estimated by indicators including area under the curve (AUC), sensitivity and specificity.
A total of 336 participants (median age, 57 y; 106 women) with ccRCC were finally included, of whom 243 had low-grade tumors (grade 1-2) and 93 had high-grade tumors (grade 3-4). A total of 1163 radiomic features were extracted from the ROIs for model construction and 117 informative radiomics features selected by Wilcoxon test were submitted to LASSO. Our ultrasound-based radiomics model included 51 features and achieved AUCs of 0.90 and 0.79 for the training and validation sets, respectively. Within the training set, the sensitivity and specificity measured 0.75 and 0.92, respectively, whereas in the validation set, the sensitivity and specificity measured 0.65 and 0.84, respectively. In the subgroup analysis, for the training and validation sets Philips AUCs were 0.91 and 0.75, Toshiba AUCs were 0.82 and 0.90, and General Electric AUCs were 0.95 and 0.82, respectively.
Ultrasound-based radiomics can effectively predict the WHO/ISUP grading of ccRCC.
探讨基于超声图像的放射组学在预测世界卫生组织（WHO）/国际泌尿病理学会（ISUP）分级的透明细胞肾细胞癌（ccRCC）中的性能。
通过对 2021 年 1 月至 2023 年 8 月的 ccRCC 患者进行组织病理学检查，进行回顾性研究。患者按 3:1 的比例随机分配到训练集和验证集中。从术前超声图像的病变最大横截面上获得，手动勾画感兴趣区（ROI）。从 ROI 中计算放射组学特征，并使用 Z 分数进行归一化。采用 Wilcoxon 检验和最小绝对收缩和选择算子（LASSO）回归进行特征降维和模型建立。通过曲线下面积（AUC）、灵敏度和特异性等指标来评估模型的性能。
最终纳入 336 例 ccRCC 患者（中位年龄 57 岁；106 例女性），其中低级别肿瘤（1-2 级）243 例，高级别肿瘤（3-4 级）93 例。从 ROI 中提取了 1163 个放射组学特征用于模型构建，通过 Wilcoxon 检验筛选出 117 个有意义的放射组学特征，提交给 LASSO。我们的基于超声的放射组学模型包括 51 个特征，在训练集和验证集的 AUC 分别为 0.90 和 0.79。在训练集中，灵敏度和特异性分别为 0.75 和 0.92，在验证集中，灵敏度和特异性分别为 0.65 和 0.84。在亚组分析中，对于训练集和验证集，Philips 的 AUC 分别为 0.91 和 0.75，Toshiba 的 AUC 分别为 0.82 和 0.90，而 General Electric 的 AUC 分别为 0.95 和 0.82。
基于超声的放射组学可有效预测 ccRCC 的 WHO/ISUP 分级。
