Development and Validation of a Diagnostic Model for Identifying Clear Cell Renal Cell Carcinoma in Small Renal Masses Based on CT Radiological Features: A Multicenter Study

RATIONALE AND OBJECTIVES

This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs).

MATERIAL AND METHODS

This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model.

RESULTS

There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001).

CONCLUSION

The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.

Sonographic Features of Small (< 4 cm) Renal Tumors With Low Signal Intensity on T2-Weighted MR Images: Differentiating Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma

OBJECTIVE

The purpose of this study is to characterize and assess the diagnostic utility of sonographic features of minimal-fat angiomyolipoma (AML) and renal cell carcinoma (RCC) with regard to small (< 4 cm) renal masses with a predominantly low signal intensity (SI) on T2-weighted MR images.

MATERIALS AND METHODS

Fifty small renal masses with a predominantly low SI on T2-weighted MR images and no macroscopic fat, all of which had US images available, were assessed. MRI variables (T2 ratio, signal intensity index [SII], and tumor-to-spleen ratio on chemical-shift images), CT features (enhancement patterns and attenuations values on unenhanced images and images obtained in the corticomedullary and nephrographic phases), and sonographic features (echogenicity, heterogeneity, and the presence of acoustic shadowing, a hypoechoic rim, or an intratumoral cyst) were recorded in a blinded manner. Echo-genicity was classified as hypo-, iso-, or hyperechoic compared with the renal parenchyma or markedly hyperchoic when equivalent to that of the renal sinus fat.

RESULTS

Minimal-fat AML and RCC were confirmed in 22 and 28 patients, respectively. T2 ratios were significantly lower for minimal-fat AML versus RCCs (p = 0.044). Minimal-fat AMLs exhibited echogenicities that were considered hypoechoic (31.8%), isoechoic (4.5%), hyperechoic (18.2%), or markedly hyperechoic (45.5%). No RCC showed marked hyperechogenicity. CT attenuation values were significantly higher for the minimal-fat AMLs seen in all imaging phases. When the combination of the T2 ratio, nephrographic phase attenuation, and echogenicity was assessed, the AUC value was 0.93 (95% CI, 0.81-0.98), which was a significant increase over the AUC value of 0.83 (95% CI, 0.69-0.92) for noted the combination of the T2 ratio and nephrographic phase attenuation.

CONCLUSION

Additional reviews of the echogenicity of small renal masses with low SI on T2-weighted MR images may aid the diagnosis of minimal-fat AML.

Declining incidence of benign lesions among small renal masses treated with surgery: Effect of diagnostic tests for characterization

PURPOSE

We evaluated the changes in the incidence of benign lesions in surgically removed small renal masses (SRMs) and the effect of diagnostic tests for characterizing SRMs.

METHODS

We included 2,707 patients receiving surgery for SRMs (<4cm). Trends in the incidence of benign histology were evaluated according to the surgery year (period 1: 2001-2005, 2: 2006-2010, and 3: 2011-2015). Multivariable logistic regression analysis was performed to identify factors associated with benign lesions. Additionally, the number of surgeries prevented due to benign histological findings on renal mass biopsies (RMB) done on 206 patients with SRM during study period was evaluated.

RESULTS

Benign histology was identified in 192 (7.1%) patients. Incidence of benign histology was 9.7%, 7.0%, and 6.3% for period 1, 2 and 3, respectively. The uses of multiphase computed tomography and magnetic resonance imaging were more common in periods 2 and 3 than in period 1 (P<0.001). The use of RMB in period 3 was higher than in periods 1 and 2 (0.8 vs. 0.9 vs. 9.0%, P<0.001). In multivariable analysis, older age, male sex, larger tumor size, and recent surgery year (period 3 vs. 1, odds ratio = 0.62, P = 0.028) were independently associated with decreased odds of benign lesions. The number of prevented surgeries by performing RMB was 0, 10, and 39 in periods 1, 2, and 3, respectively.

CONCLUSIONS

Incidence of benign histology after surgery for SRMs declined during recent years, which might be associated with the recent increased use of RMB.

CT and MR imaging for solid renal mass characterization

As our understanding has expanded that relatively large fraction of incidentally discovered renal masses, especially in small size, are benign or indolent even if malignant, there is growing acceptance of more conservative management including active surveillance for small renal masses. As for advanced renal cell carcinomas (RCCs), nonsurgical and subtype specific treatment options such as immunotherapy and targeted therapy is developing. On these backgrounds, renal mass characterization including differentiation of benign from malignant tumors, RCC subtyping and prediction of RCC aggressiveness is receiving much attention and a variety of imaging techniques and analytic methods are being investigated. In addition to conventional imaging techniques, integration of texture analysis, functional imaging (i.e. diffusion weighted and perfusion imaging) and multivariate diagnostic methods including machine learning have provided promising results for these purposes in research fields, although standardization and external, multi-institutional validations are needed.

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography

OBJECTIVE

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML).

MATERIALS AND METHODS

We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method.

RESULTS

The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase.

CONCLUSION

A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.