Comparison of Percutaneous Renal Mass Biopsy and R.E.N.A.L. Nephrometry Score Nomograms for Determining Benign Vs Malignant Disease and Low-risk Vs High-risk Renal Tumors

Overview of clinical management for older patients with renal cell carcinoma

The rapidly increasing pool of older patients being diagnosed with and surviving their cancer is creating many challenges. Regarding localized renal cell carcinoma, surgery is considered as gold standard treatment options even in older men, whereas active surveillance and ablation therapy are alternative options for a proportion of these patients. With regard to advanced disease, anti-vascular endothelial growth factor tyrosine kinase inhibitors (VEGFR-TKI) and immune check point inhibitor are standard treatment modalities, although treatment choice from multiple regimens and prevention of adverse events need to be considered. Better assessment techniques, such as comprehensive geriatric assessment to meet the unique needs of older patients, are a central focus in the delivery of high-quality geriatric oncology care. Through this process, shared decision-making should be adopted in clinical care to achieve optimal goals of care that reflect patient and caregiver hopes, needs and preferences. It is necessary to continue investigating oncological outcomes and complications associated with treatment in this population to ensure appropriate cancer care. In this narrative review, we completed a literature review of the various treatments for renal cell carcinoma in older patients that aimed to identify the current evidence related to the full range of the treatments including active surveillance, surgery, ablation therapy and systemic therapy. Prospectively designed studies and studies regarding geriatric assessment were preferentially added as references. Our goals were to summarize the real-world evidence and provide a decision framework that guides better cancer practices for older patients with renal cell carcinoma.

Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal malignant tumor. Preoperative imaging boasts advantages in diagnosing and choosing treatment methods for ccRCC.

Purpose

This study is aimed at building models based on R.E.N.A.L. nephrometry score (RNS) and CT texture analysis (CTTA) to estimate the Fuhrman grade of ccRCC and comparing the advantages and disadvantages of the two models.

Materials and Methods

143 patients with pathologically confirmed ccRCC were enrolled. All patients were stratified into Fuhrman low-grade and high-grade groups with complete CT data and R.E.N.A.L. nephrometry scores. CTTA features were extracted from the ROI delineated at the largest tumor level, and RNS and CTTA features were included in the logistic regression model, respectively.

Results

RNS model constructed based on multivariate logistic regression analysis showed that 3 pts for R-scores, 2 pts for E-scores, and 3 pts for L-scores were significant indicators to predict high-grade ccRCC, the AUC of RNS model was 0.911, and the sensitivity and specificity were 71.11% and 83.67%, respectively. The CTTA-model confirmed energy, kurtosis, and entropy as independent predictive factors, and the AUC of CTTA model was 0.941, with an optimal sensitivity and specificity of 84.44% and 93.88%.

Conclusions

R.E.N.A.L. nephrometry score has a certain provocative effect on the Fuhrman pathological grading of ccRCC. As a potential emerging technology, CTTA is expected to replace R.E.N.A.L. nephrometry score in evaluating patients' Fuhrman classification, and this approach might become an available method for assisting clinicians in choosing appropriate operation.

Machine learning applications to enhance patient specific care for urologic surgery

PURPOSE

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

METHODS

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used.

RESULTS

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

CONCLUSION

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.

Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists

OBJECTIVE. The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. MATERIALS AND METHODS. This retrospective study included a cohort of 254 renal cell carcinomas (RCCs) (190 clear cell RCCs [ccRCCs], 38 chromophobe RCCs [chrRCCs], and 26 papillary RCCs [pRCCs]), 26 fat-poor angioleiomyolipomas, and 10 oncocytomas with preoperative CT examinations. Lesions identified by four expert-level radiologists (> 3000 genitourinary CT and MRI studies) were manually segmented for radiologicradiomic analysis. Disease-specific support vector machine radiologic-radiomic ML models for classification of renal masses were trained and validated using a 10-fold cross-validation. Performance values for the expert-level radiologists and radiologic-radiomic ML models were compared using the McNemar test. RESULTS. The performance values for the four radiologists were as follows: sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 48.4-71.9% (median, 61.8%; variance, 161.6%) for differentiating ccRCCs from pRCCs and chrRCCs; sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 52.8-88.9% for differentiating ccRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 80.6%; variance, 269.1%); and sensitivity of 28.1-60.9% (median, 84.5%; variance, 122.7%) and specificity of 75.0-88.9% for differentiating pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 50.0%; variance, 191.1%). After a 10-fold cross-validation, the radiologic-radiomic ML model yielded the following performance values for differentiating ccRCCs from pRCCs and chrRCCs, ccRCCs from fat-poor angioleiomyolipomas and oncocytomas, and pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas: a sensitivity of 90.0%, 86.3%, and 73.4% and a specificity of 89.1%, 83.3%, and 91.7%, respectively. CONCLUSION. Expert-level radiologists had obviously large variances in performance for differentiating benign from malignant solid renal masses. Radiologic-radiomic ML can be a potential way to improve interreader concordance and performance.

Change in Nephrometry Scoring in Small Renal Masses (<4 cm) on Active Surveillance: Preliminary Observations From Tayside Active Surveillance Cohort (TASC) Study

RATIONALE AND OBJECTIVES

Prediction of growth, in particular knowing the possibility of aggressive cancer in small renal masses on active surveillance, remains poorly understood. The study was designed to determine whether serial nephrometry score measurements could predict possibility of aggressive malignancy (grade of cancer) in patients with small renal masses opting for active surveillance initially.

MATERIALS AND METHODS

One hundred sixteen patients between January 2000 and December 2016 undergoing partial nephrectomy were recruited. Out of these, 97 were analyzed using different nephrometry scoring systems. Measurement of nephrometry scores (Radius of tumors, Exo/Endophytic; Nearness of tumors to the collecting system or sinus; Anterior/posterior; Location in relation to polar lines, Preoperative Aspects and Dimensions Used for Anatomical, Centrality Index) was performed by two researchers. Among the patients opting for partial nephrectomy, 40 were on active surveillance for at least 12 months (mean 32; 12-60 months) before partial nephrectomy. Computed tomography scan images of these patients were retrieved and analyzed including comparison to histopathology.

RESULTS

Nephrometry scores measured on serial computed tomography scan images showed a significant correlation between change in score and grade of cancer on multivariate analysis (P value .001). Addition of multivariate analysis to nomogram based on change in size alone did not improve predictive value of area under the curve significantly.

CONCLUSIONS

Change in nephrometry scoring measurements correlates with grade of cancer in small renal masses but falls short of significantly predicting presence of malignancy or grade of cancer on nomogram in patients opting for active surveillance for small renal masses. At present, this approach may be inadequate for decision-making.

Role of R.E.N.A.L. Nephrometry Score in Laparoscopic Partial Nephrectomy

Background:

Preoperative anatomical scoring system is conducive to comparison between treatment options and evaluation of postoperative outcomes in patients with small renal tumors. This study aimed to evaluate the clinical application efficacy of the R.E.N.A.L. nephrometry score (RNS) in predicting perioperative outcomes in patients with renal tumor who underwent laparoscopic partial nephrectomy (LPN).

Methods:

The clinical data of 139 patients with renal tumors who underwent LPN between 2009 and 2015 were collected and analyzed. Patients were divided into three groups according to their RNS (low, moderate, and high). Clinical characteristics including perioperative variables, complications, and RNS were compared to evaluate the differences between the three groups. Multivariable logistic regression analysis was used to analyze the risk factors of postoperative complications.

Results:

According to the RNS, there were 74, 50, and 15 patients in the low, moderate, and high RNS groups, respectively. There were significant differences in estimated blood loss (EBL; χ² = 7.285, P = 0.026), warm ischemia time (WIT; χ² = 13.718, P = 0.001), operation time (OT; χ² = 6.882, P = 0.032), perioperative creatinine clearance change (PCCC; χ² = 6.206, P = 0.045), and number of patients with complications (NPC; P = 0.002) among the three groups. The values for EBL, WIT, OT, PCCC, and NPC for patients in the high RNS group were higher than those for patients in the low RNS group. After adjustment for OT, WIT, and EBL, RNS was statistically significantly associated with the risk of postoperative complications in a multivariable logistic regression model (odds ratio = 1.541, 95% confidence interval: 1.059–2.242, P = 0.024).

Conclusions:

The RNS is a valuable tool for evaluating the complexity of renal tumor anatomy. It can aid surgeons in preoperative decision-making concerning management therapy. Future multicenter, large sample size studies are warranted for evaluating its predicting performance of perioperative outcomes.

Accuracy and safety of ultrasound-guided percutaneous needle core biopsy of renal masses: A single center experience in China

Our aim is to determine the sufficiency, accuracy, and safety of ultrasound-guided percutaneous needle core biopsy of renal masses in Chinese patients.Patients who had undergone ultrasound-guided needle core renal mass biopsy from June 2012 to June 2016 at West China Hospital, China were retrospectively reviewed. The information obtained included demographics, mass-related parameters, biopsy indications, technique, complications, pathologic results, and follow-up. Concordance of surgical resection pathology and follow-up data were assessed.Renal mass biopsies were performed in 106 patients. Thirty-nine (36.8%) were asymptomatic. The male/female ratio was 60/46, with a median age of 49.5 years. Median mass size was 8.1 cm (range 1.8-20). Biopsy was performed through a 16-gauge needle, with median cores of 2 taken (range 1-5). Only one significant biopsy-related complication (hemorrhage requiring transfusion) was encountered. An adequate tissue sample was obtained in 97.2% (103/106) of biopsies. Eighty-seven biopsies (82.1%) showed malignant neoplasms, 16 (15.1%) yielded benignity, and 3 (2.8%) were nondiagnostic. After biopsy, 46 patients (43.4%) underwent surgery. Compared with the subsequent mass resection pathology, the biopsy diagnoses were identical in 43 cases. The accuracy rate of biopsy distinguishing malignant from benign lesions was 99.1%, and the rate for determining tumor histological type (excluding the nondiagnostic biopsies) was 95.1%. The sensitivity and specificity in detecting malignancy were 98.9% and 100%, respectively.In several situations, there is still a role for biopsy before intervention. Percutaneous needle core biopsy under ultrasonography guidance is highly accurate and safe, and can determine the proper management of undefinable masses.

R.E.N.A.L. Nephrometry Score: A Preoperative Risk Factor Predicting the Fuhrman Grade of Clear-Cell Renal Carcinoma

Objective: The purpose of this study was to evaluate the efficacy and feasibility of the R.E.N.A.L. Nephrometry Score to postoperatively predict high-grade clear-cell renal carcinoma (ccRCC). Methods: The study included 288 patients diagnosed with ccRCC who had complete CT/CTA data and R.E.N.A.L. Nephrometry Scores and underwent renal surgery at our center between January 2012 and December 2015. The relationship between the pathological grade of renal masses and R.E.N.A.L. Nephrometry Score was evaluated. Results: Univariate analysis indicated that diagnostic modality, cystic necrosis, enlargement of the regional lymph node, distant metastasis, clinical T stage, TNM stage, surgical modality, tumor size, nearness of the tumor to the collecting system or sinus, total Nephrometry Score and individual anatomic descriptor components were significantly associated with postoperative tumor grade (P < 0.05). Multivariate analysis showed that tumor size, the maximal diameter (R score), exophytic/endophytic properties (E score) and the location relative to the polar lines (L score) were independent prognostic factors to preoperatively predicting ccRCC pathological grade. The areas under the ROC curve with respect to the multi-parameter regression model (0.935, 95%CI: 0.904-0.966), tumor size (0.901, 95%CI: 0.866-0.937), R score (0.868, 95%CI: 0.825-0.911), E score (0.511, 95%CI: 0.442-0.581) and L score (0.842, 95%CI: 0.791-0.892) were calculated and compared. Conclusion: Tumor size, as well as R, E, and L scores were independent prognostic factors for high-grade pathology. Lager tumor sizes and higher R, E and L scores were more likely to be associated with high-grade pathological outcomes. Thus, the R.E.N.A.L. Score is of practical significance in facilitating urologists to make therapeutic decisions.

Update on Renal Mass Biopsy

Renal masses are diagnosed with an increasing frequency. However, a significant proportion of these masses are benign, and the majority of malignant tumors are biologically indolent. Furthermore, renal tumors are often harbored by the elderly and comorbid patients. As such, matching of renal tumor biology to appropriate treatment intensity is an urgent clinical need. Renal mass biopsy is currently a very useful clinical tool that can assist with critical clinical decision-making in patients with renal mass. Yet, renal mass biopsy is associated with limitations and, as such, may not be appropriate for all patients.