Background: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. Objectives: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. Methods: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. Results: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. Conclusions: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.
背景：肾癌是导致癌症特异性死亡的十大原因之一，其中透明细胞肾细胞癌（ccRCC）亚型占大多数病例。ccRCC的分级对于评估肿瘤侵袭性和制定临床管理方案至关重要。目的：本研究旨在通过影像组学与机器学习模型，术前预测ccRCC的WHO/ISUP分级，并表征肿瘤亚区的异质性，包括在一个亚组中与术前活检确定的分级进行比较。方法：数据来源于两个国家的多家机构，共纳入391例经病理证实的ccRCC患者。为进行分析，数据被分为四个队列：队列1和队列2分别包含来自两国各自机构的数据，队列3为队列1和队列2的合并数据，队列4为队列1的一个子集，该子集同时具备活检及后续切除（部分或全肾切除）的组织学结果。通过三维图像分割获取感兴趣体素（VOI）掩模。随后从增强图像中提取影像组学特征并进行数据标准化。采用皮尔逊相关系数和XGBoost模型进行特征降维。之后，运用11种机器学习算法预测ccRCC分级并表征肿瘤内亚区的异质性。结果：在队列1中，50%肿瘤核心区和25%肿瘤边缘区表现最佳，平均AUC分别为77.9%和78.6%。在队列2和队列3中，50%肿瘤核心区性能最优，平均AUC值分别为87.6%和76.9%。队列4采用25%边缘区时，通过内部与外部验证获得的分级预测AUC值分别为95.0%和80.0%，而以切除组织学最终分级为参考标准时，活检组织学的分类AUC仅为31.0%。CatBoost分类器在四个队列中均表现最佳，队列1至4的平均AUC分别为80.0%、86.5%、77.0%和90.3%。结论：与术前活检相比，影像组学特征联合机器学习模型在预测ccRCC的WHO/ISUP分级方面具有更优的潜力。此外，肿瘤亚区包含重要信息，在确定肿瘤分级时应独立分析。因此，术前区分ccRCC分级以改善患者诊疗与管理具有可行性。
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