Preoperative prediction of renal fibrous capsule invasion in clear cell renal cell carcinoma using CT-based radiomics model

Abstract

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.

Keywords: CT; clear cell renal cell carcinoma; radiomics; renal fibrous capsule invasion.

Figure 1.

Criteria of patient enrolment in the study. Abbreviations: ccRCC = clear cell renal cell carcinoma; CECT = contrast-enhanced CT.

Figure 2.

Unenhanced, corticomedullary, and nephrographic phases of ccRCC in 2 groups (noncapsule invasion vs capsule invasion). The tumour invasion is difficult to distinguish by naked eye. Abbreviation: ccRCC = clear cell renal cell carcinoma.

Figure 3.

Overall pipeline of the study design.

Figure 4.

The illustration of segmentation model (A), and examples of the kidney and tumour segmentation results (B).

Figure 5.

Performance of different classifiers for preoperative prediction of renal fibrous capsule invasion in ccRCC for (A) batch 1 and (B) batch 2. The AdaBoost classifier showed good discrimination ability in 2 batch datasets. Abbreviation: ccRCC = clear cell renal cell carcinoma.

Figure 6.

The ROC curves of AdaBoost for (A) batch 1, (B) batch 2 and (C) the combined evaluation of batched 1 and 2. Abbreviations: ccRCC = clear cell renal cell carcinoma; ROC = receiver operating characteristic.

Figure 7.

The SHAP values and weights of features importance. Abbreviations: GLCM = grey-level co-occurrence matrix; GLDM = grey-level dependence matrix; GLN = grey-level non-uniformity; GLNN = grey-level non-uniformity normalized; GLSZM = grey-level scale zone matrix; LDHGLE = large dependence high grey-level emphasis; IMC = informational measure of correlation; SHAP = SHapley Additive exPlanations; SZN = size-zone non-uniformity.