Natural history and management of small renal masses

A comprehensive analysis of renal cell carcinoma as first and second primary cancers

Objective

Second primary renal cell carcinoma (2nd RCC) refers to renal cell carcinoma (RCC) diagnosed after another unrelated malignancy. This study aims to compare the clinical manifestation, pathology, treatment, and prognostic features of patients with 2nd RCC and first primary renal cell carcinoma (1st RCC).

Materials and methods

Data of the patients with localized RCC were retrospectively collected. They were classified as 2nd RCC or 1st RCC according to a previously diagnosed cancer, including 113 cases of 2nd RCC and 749 cases of 1st RCC.

Results

The most common types of extrarenal malignancies in patients with 2nd RCC include lung, colorectal, breast, gynecological, and gastric cancers. The age and smoking rate of 2nd RCC patients were significantly higher than in those of 1st RCC patients. For 2nd RCC patients, fewer had clinical symptoms and renal masses tend to be smaller. One hundred and eight (95.6%) patients with 2nd RCC received surgical interventions. All patients with 1st RCC underwent renal surgery. More patients with 2nd RCC underwent a partial nephrectomy. Pathologically, there was no significant difference in postoperative pathological types between the 2nd and 1st RCCs. However, the 2nd RCCs were commonly identified in the early stages. The median overall survival (OS) of 2nd RCC patients was 117 months, which was shorter than that of 1st RCC patients.

Conclusions

Second RCC is not uncommon. More attention should be paid to screening for 2nd RCC in cancer survivors. There are some differences between patients with 2nd and 1st RCCs that should be viewed separately.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02493-6.

Second Primary Renal Cell Carcinoma With Nonrenal Malignancies: An Analysis of 118 Cases and a Review of Literature

Objectives

To evaluate the nature, diagnosis, treatment and prognosis of second primary renal cell carcinoma (SPRCC).

Materials and Methods

We retrospectively collected data from 118 patients with SPRCC. Clinical characteristics, imaging features and treatments were analyzed and comparisons between SPRCC and renal metastases (RM) were made.

Results

SPRCC accounts for 11.4% of all RCC. The most common types of extrarenal malignancies included lung, colorectal, breast and gynecological cancers. The median age was 58.5 years old, and 61.0% (72/118) of the patients were male. About 5.1% of the patients presented with symptoms. The average tumor diameter was 4.4 cm (1-8.4 cm). The diagnostic specificity of enhanced computed tomography (CT) was 80.1%. When comparing with RM, more patients with stage I–II extrarenal malignancy and less patients with bilateral, multiple, and endogenic renal masses on computed tomography were found in the SPRCC group. A total of 110 SPRCC patients underwent surgery, including 48 radical nephrectomies and 62 partial nephrectomies. The median overall survival time was 117 months. Female, asymptomatic status, no distant metastasis, and surgical treatment predicted a better survival.

Conclusions

SPRCC are not uncommon, and it should be considered during the follow-up of patients with nonrenal malignancy. The differential diagnosis between SPRCC and RM was mainly based on imaging and puncture biopsy.

Study on the correlation between preoperative inflammatory indexes and adhesional perinephric fat before laparoscopic partial nephrectomy

Objective

This study was aimed to evaluate the effect of preoperative composite inflammatory index on adhesional perinephric fat (APF), providing a help for preoperative risk assessment of laparoscopic partial nephrectomy (LPN) in patients with renal cell carcinoma.

Materials and methods

A retrospective study was conducted on 231 patients with renal cell carcinoma, who underwent laparoscopic partial nephrectomy. They were divided into two groups according to whether there was APF during operation. Relevant clinical data, laboratory parameters and imaging examination were obtained before operation to calculate the composite inflammatory index and MAP score. The composite inflammatory index was divided into high value group and low value group by ROC curve method. The related predictive factors of APF were analyzed by logistic regression method.

Results

The APF was found in 105 patients (45.5%). In multivariate analysis, systemic immune inflammation index (SII) (high/low), MAP score, tumor size and perirenal fat thickness were independent predictors of APF. The operation time of patients with APF was longer, and the difference of blood loss was not statistically significant.

Conclusion

SII is an independent predictor of APF before laparoscopic partial nephrectomy.

Trial registration ChiCTR, ChiCTR2100045944. Registered 30 April 2021—Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=125703.

Active Surveillance for Small Renal Masses

Active surveillance (AS) is a safe and reasonable management strategy for many patients with small renal masses (SRM) suspicious for a clinical T1a renal cell carcinoma based on excellent metastasis-free and cancer-specific survival. However, the expansion of robotic extirpation of SRM has outpaced the adoption of AS, resulting in the possibility of overtreatment for select patients with SRM, especially the elderly and comorbid. In this review of AS for SRM, with a focus on the Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) Registry, we detail the rationale behind AS, review lessons learned from the past decades of literature, and offer suggestions for appropriate patient selection and follow-up. An improved understanding of the data supporting AS will empower physicians and patients to more comfortably pursue AS to avoid over-treatment and provide individualized care to patients with SRM.

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade

OBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p > 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

OBJECTIVE

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

MATERIALS AND METHODS

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.

RESULTS

Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.

CONCLUSIONS

The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

KEY POINTS

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Diagnostic Performance of DWI for Differentiating High- From Low-Grade Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of our study was to review the diagnostic performance of DWI for differentiating high- from low-grade clear cell renal cell carcinoma (RCC).

MATERIALS AND METHODS

MEDLINE, EMBASE, and Cochrane library databases were searched up to March 15, 2017. We included diagnostic accuracy studies that used DWI for differentiating high- from low-grade clear cell RCC compared with histopathologic results of Fuhrman grade based on nephrectomy or biopsy specimens in original research articles. Two independent reviewers assessed methodologic quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Sensitivity and specificity of the included studies were pooled and graphically presented using a hierarchic summary ROC plot. For heterogeneity exploration, we assessed the presence of a threshold effect and performed meta-regression analyses.

RESULTS

Eight retrospective studies (394 patients with 397 clear cell RCCs) were included. Pooled sensitivity was 0.78 (95% CI, 0.68-0.85) with a specificity of 0.86 (95% CI, 0.70-0.94). A considerable threshold effect was observed with a correlation coefficient of 0.811 (95% CI, 0.248-0.964) between the sensitivity and false-positive rate. At meta-regression analysis, apparent diffusion coefficient (× 10 mm²/s) cutoff value (< 1.57 vs ≥ 1.57; p = 0.03) and location of ROI (solid portion vs whole tumor; p = 0.04) were significant factors affecting heterogeneity. Other factors with regard to patients and tumors, study, and MRI characteristics were not significant (p = 0.17-0.91).

CONCLUSION

DWI shows moderate diagnostic performance for differentiating high-from low-grade clear cell RCC. Substantial heterogeneity was observed because of a threshold effect. Further prospective studies may be needed; all included studies were retrospective.

Adherent perinephric fat at minimally invasive partial nephrectomy is associated with adverse peri-operative outcomes and malignant renal histology

OBJECTIVES

To predict adherent perinephric fat (APF) at minimally invasive partial nephrectomy (MIPN) using the Mayo adhesive probability (MAP) score and to determine the impact of MAP score and APF on MIPN outcomes.

PATIENTS AND METHODS

A total of 245 patients undergoing MIPN were included in the study. The presence of APF was determined through keywords in operating notes, and radiographic data were obtained from preoperative cross-sectional imaging. Posterior fat thickness was measured between the renal capsule and the posterior abdominal wall at the level of the renal vein. Perinephric stranding was graded on a 0-3 severity scale.

RESULTS

The study included 123 men and 122 women, with a median age of 55 years, body mass index of 31.7, tumour size of 2.7 cm and nephrometry score of 6. The median posterior fat thickness was 1.79 cm and MAP score 2.63. In all, 26 patients (10.6%) had evidence of APF at the time of renal surgery. Factors predictive of APF included increasing age (P = 0.001), male gender (P = 0.045), perinephric stranding (P = 0.002), posterior fat thickness (P < 0.001) and MAP score (P < 0.001). APF was associated with adverse pathological and peri-operative outcomes including malignant renal histology (P = 0.04), longer operating time (P = 0.005) and greater estimated blood loss (EBL; P = 0.025).

CONCLUSIONS

Specific clinical and radiographic factors predict APF at MIPN. The presence of APF is associated with adverse peri-operative outcomes including longer operating time and greater EBL. APF was also associated with renal malignancy on final pathology, but further studies are necessary to elucidate the precise underlying mechanism.

MRI features of renal cell carcinoma that predict favorable clinicopathologic outcomes

OBJECTIVE

The purpose of this article is to determine whether MRI features of renal cell carcinoma (RCC), such as enhancing solid component and T1 signal intensity, are associated with clinicopathologic outcomes.

MATERIALS AND METHODS

This retrospective study included 241 RCCs in 230 patients who underwent preoperative MRI, had pathologic analysis results available, and were monitored for at least 3 months. A radiologist assessed tumor features on MRI, including unenhanced T1 signal relative to renal cortex and the percentage of solid enhancing components. The electronic medical record or follow-up images were reviewed to assess for the development of local recurrence or metastases. Statistical analysis was performed to correlate imaging features at MRI with pathologic and clinical outcome.

RESULTS

The following tumor features were observed: predominantly cystic morphologic features (defined as solid component≤25%, n=33), solid component greater than 25% (n=208), T1 hypointensity (n=97), and T1 intermediate intensity or hyperintensity (n=144). Local recurrence or metastases were observed in 14 patients. Compared with T1-intermediate or -hyperintense lesions, T1-hypointense RCCs were more likely to be low stage (90.7% vs 74.3%; p=0.001) and low grade (78.9% vs 41.8%; p<0.001) and had a lower rate of recurrence or metastases (3.3% vs 8%; p=0.167). Compared with lesions with greater than 25% solid enhancement, predominantly cystic RCCs were more likely to be lower stage (93.9% vs 78.8%; p=0.053) and lower grade (94.7 vs 56.5%; p<0.001) and to have no incidence of recurrence or metastasis (0% vs 6.9%; p=0.227). RCCs that were both cystic and T1 hypointense (n=14) were lower stage (100% vs 79.6%; p=0.047) and lower grade (92.9% vs 58.1%; p=0.01) and had no recurrence or metastases on follow-up.

CONCLUSION

Cystic and T1-hypointense RCC show less-aggressive pathologic features and favorable clinical behavior.

Growth pattern of renal cell carcinoma (RCC) in patients with delayed surgical intervention

PURPOSE

Few studies have evaluated the growth pattern of renal cell carcinoma (RCC) in patients with delayed treatment. This report investigated the growth rate and stage progression of incidentally discovered RCC following a long period of active surveillance.

METHODS

Thirty-two patients who did not receive immediate surgical treatment for renal solid masses that later proved to be RCC were reviewed retrospectively. Annual tumor growth rates were calculated according to changes in the maximal diameter on CT or MRI. Clinical and pathological characteristics associated with tumor growth rate and stage progression were analyzed.

RESULTS

The median tumor size grow from 2.14 (range, 0.30-6.70) cm to 4.33 (range, 1.40-8.80) cm after a median 46.0 months observation period. The average tumor growth rate was 0.80 (range, 0.16-3.80) cm/year. Clear cell carcinoma (0.86 cm/year) tended to grow faster than papillary cell carcinoma (0.28 cm/year) (P = 0.066). The mean growth rate of grade 2 tumors (0.88 cm/year) was faster than that of grade 1 tumors (0.36 cm/year) (P = 0.041). Thirteen tumors (40.6%) were upstaged at a median 48 months after initial presentation. Cox regression analysis revealed initial tumor size as the only risk factor for upstaging (P = 0.018). No local and systemic recurrences were noted in our cohort after the intervention at a median of 47 (range, 6-248) months of follow-up.

CONCLUSIONS

RCCs were found to be slow growing in a group of untreated renal cell carcinoma patients. However, some tumors progressed in stage under observation. The growth rate of RCC tended to correlate with histologic grade and histologic subtype.