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Objectives: To develop radiomics-based classifiers for preoperative prediction of fibrous capsule invasion in renal cell carcinoma (RCC) patients by CT images., Methods: In this study, clear cell RCC (ccRCC) patients who underwent both preoperative abdominal contrast-enhanced CT and nephrectomy surgery at our hospital were analysed. By transfer learning, we used base model obtained from Kidney Tumour Segmentation challenge dataset to semi-automatically segment kidney and tumours from corticomedullary phase (CMP) CT images. Dice similarity coefficient (DSC) was measured to evaluate the performance of segmentation models. Ten machine learning classifiers were compared in our study. Performance of the models was assessed by their accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC). The reporting and methodological quality of our study was assessed by the CLEAR checklist and METRICS score., Results: This retrospective study enrolled 163 ccRCC patients. The semiautomatic segmentation model using CMP CT images obtained DSCs of 0.98 in the training cohort and 0.96 in the test cohort for kidney segmentation, and DSCs of 0.94 and 0.86 for tumour segmentation in the training and test set, respectively. For preoperative prediction of renal capsule invasion, the AdaBoost had the best performance in batch 1, with accuracy, precision, recall, and F1-score equal to 0.8571, 0.8333, 0.9091, and 0.8696, respectively; and the same classifier was also the most suitable for this classification in batch 2. The AUCs of AdaBoost for batch 1 and batch 2 were 0.83 (95% CI: 0.68-0.98) and 0.74 (95% CI: 0.51-0.97), respectively. Nine common significant features for classification were found from 2 independent batch datasets, including morphological and texture features., Conclusions: The CT-based radiomics classifiers performed well for the preoperative prediction of fibrous capsule invasion in ccRCC., Advances in Knowledge: Noninvasive prediction of renal fibrous capsule invasion in RCC is rather difficult by abdominal CT images before surgery. A machine learning classifier integrated with radiomics features shows a promising potential to assist surgical treatment options for RCC patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC)., Methods: Two-hundred and one patients (training cohort: n = 126; internal validation cohort: n = 39; external validation cohort: n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis, then the performance of the nomogram (discrimination and clinical usefulness) was evaluated and validated subsequently. Moreover, the prediction efficacy for SDM in ccRCC subgroups of different sizes was also assessed., Results: Incorporating Rad-score derived from 9 optimal MR radiomics features (age, pseudocapsule and regional lymph node), the radiomics-based nomogram was capable of predicting SDM in the training cohort (area under the ROC curve (AUC) = 0.914) and validated in both the internal and external cohorts (AUC = 0.854 and 0.816, respectively) and also showed a convincing predictive power in ccRCC subgroups of different sizes (≤ 4 cm, AUC = 0.875; 4-7 cm, AUC = 0.891; 7-10 cm, 0.908; > 10 cm, AUC = 0.881). Decision curve analysis indicated that the radiomics-based nomogram is of clinical usefulness., Conclusions: The multiparametric MRI radiomics-based nomogram could achieve precise individualised prediction of SDM in patients with ccRCC, potentially improving the management of ccRCC., Key Points: • Radiomics features derived from multiparametric magnetic resonance images showed relevant association with synchronous distant metastasis in clear cell renal cell carcinoma. • MRI radiomics-based nomogram may serve as a potential tool for the risk prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

Objective: The aim of this study was to assess the utility of lesion size and iodine quantification using dual-energy spectral computed tomography to distinguish between low-grade and high-grade clear cell renal cell carcinomas (ccRCCs)., Methods: Spectral parameters of 75 patients with pathologically proven ccRCCs who underwent preoperative dual-energy spectral computed tomography examinations were divided into low-grade and high-grade groups. Independent sample t test, receiver operating characteristic curve analysis, and Spearman rank correlation were analyzed., Results: The lesion size was significantly smaller, and spectral parameters were significantly higher in the low-grade ccRCC. The significant correlation (r = -0.412, P < 0.001) by the Spearman rank correlation was between the normalized iodine concentration and lesion size. The receiver operating characteristic analysis demonstrated that 0.710 was the optimal cutoff value, which yielded the following: sensitivity, 97.6%; specificity, 97.1%; positive predictive value, 97.6%; negative predictive value, 97.1%; and accuracy, 97.3%., Conclusions: Iodine quantification can play an important role in distinguishing low-grade from high-grade ccRCC.

Objective: To study the CT findings of cystic nephroma (CN) and multilocular cystic renal cell carcinoma (MCRCC) and to improve the accuracy of preoperative diagnosis of these two diseases., Methods: The CT findings of nine CN cases and 19 MCRCC cases confirmed by pathology were blindly reviewed and compared with their pathological results. Fisher's exact test and independent-samples T test were applied to statistically analyze some of the CT features of the CN and MCRCC lesions., Results: The thickness of cystic walls and partitions in the nine CN cases ranged from 0.5 to 5 mm. Cystic walls and partitions were slightly thicker in some parts without visible mural nodules. Varying amounts of solid tissue could be found in all the 19 MCRCC tumors, and the cystic walls and partitions were found partially thickened ranging from 3 mm to 13 mm. Eight cases were with mural nodules (nodule diameter: 4.5-16 mm). Nine cases of CN tumors were lobulated and 7 protruded into the renal sinus. Three out of the 19 MCRCC presented shallow lobulation, and 7 tumors protruded into the renal sinus. The CT contrast-enhancement scanning displayed moderate delayed enhancement in the cystic walls and partitions in 8 cases. The enhanced scanning revealed that all the nine cases showed enhancement of the cystic walls and partitions, while 8 cases of them had mild to moderate delayed enhancement. The cystic walls, partitions and nodules were enhanced in 19 MRCC cases, among them 17 cases displayed obvious enhancement in the cortical phase. Among the differences of CT findings between MC and MRCC, the shallow lobulation, protruding into the renal sinus, mural nodules, cystic wall and partition thickness, and net growth in the cortical and nephrographic phase were statistically significantly different (P<0.05 for all)., Conclusions: CT scan can provide significant evidence for CN and MCRCC diagnosis. CN cases usually present relatively thin and even cystic walls and partitions without mural nodules and with shallow lobulation and protruding into the renal sinus. The enhancement is mild to moderate, dynamic and delayed, while the opposite CT findings may indicate a higher possibility of MCRCC.