Clinical practice. Small renal mass

[Small renal mass: which criteria are decisive for a tumor board?]

Small renal masses (SRM) are a heterogeneous group of tumors with varying metastatic potential. The increasing use and improvement in the quality of abdominal imaging have led to an increasingly earlier diagnosis of incidental SRM, which are asymptomatic and confined to the organ. Despite these advances in imaging and the growing use of renal tumor biopsies, preoperative diagnosis of malignancy remains difficult. The treatment of SRM has shifted away from radical nephrectomy and now primarily includes organ-sparing surgery or active surveillance. The optimal strategy for treating SRM is continuously evolving as studies from prospective data registries can identify factors that influence both short- and long-term patient outcomes. Recent research on biomarkers, imaging techniques, and machine learning offer promising approaches to a deeper understanding of tumor biology and treatment options for this patient population.

Imaging of Chromophobe Renal Cell Carcinoma with 99mTc-Sestamibi SPECT/CT: Considerations Regarding Risk Stratification and Histologic Reclassification

PURPOSE

Indeterminate renal masses are increasingly incidentally found on cross-sectional imaging. 99mTc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) scans can be used to identify oncocytomas and oncocytic renal neoplasms, including a subset of chromophobe renal cell carcinomas (chRCCs), which are viewed as false-positive.

PROCEDURE

Patients imaged with renal sestamibi scans between 2014 and 2023 were reviewed. Those patients with solitary tumors that were originally classified as chRCC were included in the analysis. Imaging with SPECT/CT from the liver dome down had been carried out 75 min after the administration of 925 MBq of 99mTc-sestamibi. All available H&E and immunostained slides were re-reviewed and classified according to WHO 2022 criteria. Confirmatory immunohistochemical stains were performed in tumors considered morphologically suspicious for non-chRCC entities.

RESULT

A total of 18 patients with solitary tumors were included in the final analysis. 13/18 (72.2%) tumors in this cohort remained classified as chRCC, with 4/18 (22.2%) being eosinophilic-variant chRCC. The reclassified tumors (5/18 [27.8%]) included 2/18 (11.1%) low-grade oncocytic tumor (LOT), 1/18 (5.5%) eosinophilic vacuolated tumor (EVT), and 2/18 (11.1%) unclassified low-grade oncocytic neoplasms. As such, only 2/9 (22.2%) qualitatively "hot" tumors were chRCC other than eosinophilic-variant and only 1/9 (11.1%) "cold" tumors was a histology other than chRCC.

CONCLUSION

Based on current histopathologic classification methods, it is likely that the "false-positive" rate of uptake on renal sestamibi scans with chRCC has been over-stated. Further study is warranted to better refine the optimal utility of renal sestamibi scans for non-invasive risk stratification of indeterminate renal masses.

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.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment

OBJECTIVE

This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.

MATERIALS AND METHODS

For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.

RESULTS

Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.

CONCLUSIONS

This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.

CLINICAL RELEVANCE STATEMENT

These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.

KEY POINTS

• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

The evolving management of small renal masses

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis

BACKGROUND

Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT).

METHODS

The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC).

RESULTS

Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies.

CONCLUSIONS

To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.

Purely Off-Clamp Sutureless Robotic Partial Nephrectomy for Novice Robotic Surgeons: A Multi-Institutional Propensity Score-Matched Analysis

Objectives: To compare perioperative outcomes of patients treated with sutureless off-clamp robotic partial nephrectomy (sl-oc RAPN) by either a novice or an expert robotic surgeon at two different institutions. Methods: Data concerning two continuous series of patients with cT1-2N0M0 renal tumors treated with sl-oc RAPN either by a novice or an expert surgeon were extracted from prospectively populated institutional databases over the last 4 years. Perioperative outcomes as well as the baseline characteristics of patients and tumors were compared by using χ2 and Mann-Whitney tests for categorical and continuous variables, respectively. A 1:1 propensity match score analysis (PMSa) generated two homogeneous cohorts. Logistic regression analysis was performed to assess predictors of trifecta outcomes, defined as negative surgical margins, no Clavien-Dindo ≧ 3 grade complications, and no ≧ 30% postoperative eGFR reduction. Results: Overall, 328 patients were treated by an expert surgeon, while 40 were treated by a novice surgeon. After PMSa analysis, two cohorts of 23 patients each were generated, homogeneous for all baseline variables (p ≥ 0.07). Hospital stay was the only significantly different outcome observed between the two groups (5 days vs. 2 days; p < 0.001). No statistically significant differences were recorded when comparing trifecta outcomes (expert: 100% vs. novice: 87%; p = 0.07). In the logistic regression analysis, no statistically significant predictors of trifecta outcomes were recorded. Conclusions: sl-oc RAPN is a feasible and safe nephron sparing technique, even when performed by a novice robotic surgeon.

Comparison of perioperative outcomes of selective arterial clipping guided by near-infrared fluorescence imaging using indocyanine green versus undergoing standard robotic-assisted partial nephrectomy: a systematic review and meta-analysis

BACKGROUND

This study employs a meta-analytic approach to investigate the impact of robotic-assisted partial nephrectomy, with and without near-infrared fluorescence imaging (NIRF-RAPN vs S-RAPN), on patients' perioperative outcomes and postoperative changes in renal function.

MATERIALS AND METHODS

The authors conducted a comprehensive and rigorous systematic review and cumulative meta-analysis of primary outcomes following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), AMSTAR (Assessing the Methodological Quality of Systematic Reviews) Guidelines, and Risk-of-Bias Tool (RoB2). To ensure a thorough search, the authors systematically searched five major databases, including Medline, PubMed, Cochrane Library, Scopus, and Web of Science, from databases' inception to April 2023.

RESULTS

No significant differences were found between the two groups in terms of age ( P =0.19), right side ( P =0.54), BMI ( P =0.39), complexity score ( P =0.89), tumor size ( P =0.88), operating time ( P =0.39), estimated blood loss ( P =0.47), length of stay ( P =0.87), complications ( P =0.20), transfusion ( P =0.36), and positive margins ( P =0.38). However, it is noteworthy that the NIRF-RAPN group exhibited significant reductions in warm ischemia time ( P =0.001), the percentage change in estimated glomerular filtration rate at discharge ( P =0.01) compared to the S-RAPN group.

CONCLUSION

This meta-analysis provides evidence that the group undergoing NIRF-RAPN showed a statistically significant protective effect on the estimated glomerular filtration rate (eGFR).

Cancer Screening, Incidental Detection, and Overdiagnosis

BACKGROUND

In the past, patients were only diagnosed with cancer because they had symptoms. Now, because of screening and incidental detection, some patients are diagnosed with cancer when they are asymptomatic. While this shift is typically viewed as desirable, it has produced an unfortunate side-effect: it is now possible to be diagnosed with a cancer not destined to cause symptoms or death-a phenomenon labeled as overdiagnosis.

CONTENT

We begin with a brief introduction to the heterogeneity of cancer progression: at one extreme, some cancers are already systemic by the time they are detectable; at the other, some grow extremely slowly or even regress. The ensuing sections describe the evidence that the pursuit of earlier detection has led to overdiagnosis. Although rarely confirmed in an individual, overdiagnosis is readily identifiable in a long-term follow-up of a randomized trial of screening. Furthermore, 2 population signatures for overdiagnosis exist: (a) rising incidence coupled with stable mortality and (b) rising early-stage incidence coupled with stable late-stage incidence. Finally, we review the misleading feedback produced by overdiagnosis-such as rising 5-year survival rates and more cancer survivors. This feedback is erroneously interpreted as reinforcing the value of early detection, encourages more screening/incidental detection and, ironically, promotes more overdiagnosis.

SUMMARY

Overdiagnosis is an unintended consequence of the desire to detect cancer early. Given the evolving understanding that tumor biology and host response are more relevant to prognosis than early vs late diagnosis, it is time to challenge the assertion that early diagnosis is always the best approach to curing cancer.

Diagnostic accuracy of percutaneous core biopsy before cryoablation for small-sized renal cell carcinoma

PURPOSE

To retrospectively determine the diagnostic accuracy of a percutaneous core biopsy performed before cryoablation for small-sized renal cell carcinoma.

METHODS

In this study, 216 patients underwent a percutaneous core biopsy for 242 renal lesions suspected to be renal cell carcinoma on image findings before cryoablation at Kyushu University Hospital. We calculated the success rate of the histological diagnosis and investigated factors that may have contributed to the diagnostic success. Complications caused by the biopsy procedure were also evaluated.

RESULTS

The histological diagnosis was successful in 203 lesions (82.8%). The success rate of the histological diagnosis was 65.4% (34/52 cases) for tumors with a diameter of ≤15 mm and 88.9% (169/190 cases) for those >15 mm. Therefore, tumor diameter was a factor contributing to the histological diagnosis success rate in both univariate and multivariable analyses (P < 0.001). For lesions with a tumor diameter ≤15 mm, the histological diagnosis success rates increased from 50.0% to 76.2% in the presence of pre-lipiodol marking and to 85.7% when the biopsy procedure was performed separately from cryoablation; the latter was statistically significant (P = 0.039). Major complications that may have been caused by the biopsy procedure were grade 3 bleeding and tract seeding (one case each).

CONCLUSION

Percutaneous core biopsy in cryoablation for small-sized renal cell carcinoma had a high diagnostic rate and was safely performed. For lesions with a tumor diameter ≤15 mm, a separate biopsy procedure and pre-lipiodol marking may improve the diagnostic accuracy.

Potential therapeutic target secretogranin II might cooperate with hypoxia-inducible factor 1α in sunitinib-resistant renal cell carcinoma

Multitargeted receptor tyrosine kinase inhibitors, including vascular endothelial growth factor (VEGF) inhibitors, such as sunitinib, have been used as the primary targeted agents for patients with recurrent or distant metastasis of advanced renal cell carcinoma (RCC). However, endogenous or acquired sunitinib resistance has become a significant therapeutic problem. Therefore, we focused on mechanisms of sunitinib resistance in RCC. First, we undertook RNA sequencing analysis using previously established sunitinib-resistant RCC (SUR-Caki1, SUR-ACHN, and SUR-A498) cells. The results showed increased expression of secretogranin II (SCG2, chromogranin C) in SUR-RCC cells compared to parental cells. The Cancer Genome Atlas database showed that SCG2 expression was increased in RCC compared to normal renal cells. In addition, the survival rate of the SCG2 high-expression group was significantly lower than that of the RCC low-expression group. Thus, we investigated the involvement of SCG2 in sunitinib-resistant RCC. In vitro analysis showed that migratory and invasive abilities were suppressed by SCG2 knockdown SUR cells. As SCG2 was previously reported to be associated with angiogenesis, we undertook a tube formation assay. The results showed that suppression of SCG2 inhibited angiogenesis. Furthermore, coimmunoprecipitation assays revealed a direct interaction between SCG2 and hypoxia-inducible factor 1α (HIF1α). Expression levels of VEGF-A and VEGF-C downstream of HIF1α were found to be decreased in SCG2 knockdown SUR cells. In conclusion, SCG2 could be associated with sunitinib resistance through VEGF regulation in RCC cells. These findings could lead to a better understanding of the VHL/HIF/VEGF pathway and the development of new therapeutic strategies for sunitinib-resistant RCC.

Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy

PURPOSE

The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI).

MATERIAL AND METHODS

This retrospective study incorporated 48 tumors, 28 of which were ccRCCs and 20 were oncocytomas. All tumors were less than 4 cm in size and had undergone pre-biopsy or pre-surgery MRI. Following image pre-processing, 102 radiomics features were evaluated. A univariate analysis was performed using the Wilcoxon rank-sum test with Bonferroni correction. We compared multiple radiomics pipelines of normalization, feature selection, and machine learning (ML) algorithms, including random forest (RF), logistic regression (LR), AdaBoost, K-nearest neighbor, and support vector machine, using a supervised ML approach.

RESULTS

No statistically significant features were identified via the univariate analysis with Bonferroni correction. The most effective algorithm was identified using a pipeline incorporating standard normalization, RF-based feature selection, and LR, which achieved an area under the curve (AUC) of 83%, accuracy of 73%, sensitivity of 79%, and specificity of 65%. Subsequently, the most significant features were identified from this algorithm, and two groups of uncorrelated features were established based on Pearson correlation scores. Using these features, an algorithm was established after a pipeline of standard normalization and LR, achieving an AUC of 90%, an accuracy of 77%, sensitivity of 83%, and specificity of 69% for distinguishing ccRCCs from oncocytomas.

CONCLUSIONS

Radiomics analysis based on T2-weighted MRI can aid in distinguishing small ccRCCs from small oncocytomas. However, it is not superior to standard multiparameter renal MRI and does not yet allow us to dispense with percutaneous biopsy.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)

BACKGROUND

Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic.

PURPOSE

This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations.

MATERIALS AND METHODS

A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC.

RESULTS

A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74.

CONCLUSION

Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Radiologist's Disease: Imaging for Renal Cancer

There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.

The Impact of COVID-19 on New Kidney Cancer Diagnosis: Stage and Treatment in Northern Italy

This study aims to evaluate the impact of COVID-19 on new renal carcinoma (RC) diagnoses using data from the Reggio Emilia Cancer Registry in 2018-2020. A total of 293 RCs were registered, with roughly 100 cases yearly. The distribution by age shows a significant decrease in the 30-59 age group (33.7% in 2018, 24.8% in 2019, and 19.8% in 2020). The incidence of Stage I was 59.4%, 46.5%, and 58.2% in 2018, 2019, and 2020, respectively, whereas the Stage II rate had values of 6.9%, 7.9%, and 2.2% in the years 2018, 2019, and 2020, respectively. Slight non-significant variations were observed in Stages III and IV. Surgery was performed in 83.2% of cases in 2018, 78.2% in 2019, and 82.4% in 2020; the surgery distribution by stage showed no significant differences. Chemotherapy showed an increase in 2020, which was statistically significant only for Stage IV. The gender incidence trends over the last 25 years showed an increase in the male sex in the first period; then, a decline was documented, likely due to a decrease in cigarette consumption. In females, the trend was constant. The RC mortality trend significantly dropped in both genders over the entire study period.

Evaluation of oncological outcomes of robotic partial nephrectomy according to the type of hilar control approach (On-clamp vs Off-clamp), a multicentric study of the French network of research on kidney cancer-UROCCR 58-NCT03293563

PURPOSE

To compare off-clamp vs on-clamp robotic partial nephrectomy (RPN) for renal cell carcinoma (RCC) in terms of oncological outcomes, and to assess the impact of surgical experience (SE).

METHODS

We extracted data of a contemporary cohort of 1359 patients from the prospectively maintained database of the French national network of research on kidney cancer (UROCCR). The primary objective was to assess the positive surgical margin (PSM) rate. We also evaluated the oncological outcomes regardless of the surgical experience (SE) by dividing patients into three groups of SE as a secondary endpoints. SE was defined by the caseload of RPN per surgeon per year. For the continuous variables, we used Mann-Whitney and Student tests. We assessed survival analysis according to hilar control approach by Kaplan-Meier curves with log rank tests. A logistic regression multivariate analysis was used to evaluate the independent factors of PSM.

RESULTS

Outcomes of 224 off-clamp RPN for RCC were compared to 1135 on-clamp RPN. PSM rate was not statistically different, with 5.6% in the off-clamp group, and 11% in the on-clamp group (p = 0.1). When assessing survival analysis for overall survival (OS), local recurrence-free survival (LR), and metastasis-free survival (MFS) according to hilar clamping approach, there were no statistically significant differences between the two groups with p value log rank = 0.2, 0.8, 0.1, respectively. In multivariate analysis assessing SE, hilar control approach, hospital volume (HV), RENAL score, gender, Age, ECOG, EBL, BMI, and indication of NSS, age at surgery was associated with PSM (odds ratio [OR] 1.03 (95% CI 1.00-1.04), 0.02), whereas SE, HV, and type of hilar control approach were not predictive factors of PSM.

CONCLUSION

Hilar control approach seems to have no impact on PSM of RPN for RCC. Our findings were consistent with randomized trials.

A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm)

RATIONALE AND OBJECTIVES

Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM).

MATERIALS AND METHODS

One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness.

RESULTS

Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature.

CONCLUSIONS

The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies

OBJECTIVES

To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies.

METHODS

Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images. CatBoost was used for feature selection and ML model establishment. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML model.

RESULTS

The ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy (ACC) of 0.86. All three radiologists achieved better performance by referring to the classifier's decision, with accuracies increasing from 0.82 to 0.87, 0.82 to 0.88, and 0.76 to 0.87. The ML model achieved higher negative predictive values (NPV, 0.82-0.99), and the radiologists achieved higher positive predictive values (PPV, 0.91-0.95).

CONCLUSIONS

A ML classifier based on whole-tumor three-phase CT images can be a useful and promising tool for differentiating RCCs from benign renal tumors. The ML model also perfectly complements radiologist interpretations.

KEY POINTS

• A machine learning classifier based on CT images could be a reliable way to differentiate RCCs from benign renal tumors. • The machine learning model perfectly complemented the radiologists' interpretations. • Subtle variances in ROI delineation had little effect on the performance of the ML classifier.

Urine Molecular Biomarkers for Detection and Follow-Up of Small Renal Masses

Active surveillance (AS) is the best strategy for small renal masses (SRMs) management; however, reliable methods for early detection and disease aggressiveness prediction are urgently needed. The aim of the present study was to validate DNA methylation biomarkers for non-invasive SRM detection and prognosis. The levels of methylated genes TFAP2B, TAC1, PCDH8, ZNF677, FLRT2, and FBN2 were evaluated in 165 serial urine samples prospectively collected from 39 patients diagnosed with SRM, specifically renal cell carcinoma (RCC), before and during the AS via quantitative methylation-specific polymerase chain reaction. Voided urine samples from 92 asymptomatic volunteers were used as the control. Significantly higher methylated TFAP2B, TAC1, PCDH8, ZNF677, and FLRT2 levels and/or frequencies were detected in SRM patients' urine samples as compared to the control. The highest diagnostic power (AUC = 0.74) was observed for the four biomarkers panel with 92% sensitivity and 52% specificity. Methylated PCDH8 level positively correlated with SRM size at diagnosis, while TFAP2B had the opposite effect and was related to SRM progression. To sum up, SRMs contribute significantly to the amount of methylated DNA detectable in urine, which might be used for very early RCC detection. Moreover, PCDH8 and TFAP2B methylation have the potential to be prognostic biomarkers for SRMs.

Comparison of perinephric fat measurements between malignant and benign renal tumours

Objective

To investigate different parameters derived from the quantity and quality of perinephric fat, and to compare their effectiveness in predicting the malignant pathology of renal tumours.

Methods

Data from patients diagnosed with renal tumour between April 2014 and December 2020 were retrospectively reviewed, and patients were categorized into malignant or benign tumour groups. Fat parameters, including perinephric fat volume (PFV), perinephric fat area (PFA), perinephric fat thickness (PFT), and Mayo adhesive probability (MAP) score were measured using abdominal computed tomography scans. Between-group differences were assessed by analysis of variance and χ²-test. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of perinephric fat parameters in diagnosing malignancy.

Results

A total of 109 patients were included. MAP score, PFV, PFA, and PFT were significantly increased in the malignant versus benign tumour group, and after correction for body mass index (BMI), the indexed PFV/BMI, PFA/BMI, and PFT/BMI values remained significantly higher in the malignant tumour group. All parameters showed fair predictivity of malignancy, with comparable area under the curve values in the ROC curve.

Conclusion

An increased amount of perinephric fat is predictive of malignant pathology for renal tumours. The predictive accuracy for each perinephric fat parameter remained fair after correcting for BMI.

Novel Gerota-edge-sling technique facilitates retroperitoneal robot-assisted partial nephrectomy: a comparative study

Background

Retroperitoneal robotic partial nephrectomy is markedly restricted by limited space and visual field. We introduced a novel Gerota-edge-sling (GES) technique with self-designed traction devices to overcome these defects by attaching Gerota fascia to abdominal wall, and comparatively evaluated its utilization with routine technique.

Methods

A retrospective analysis was performed for consecutive patients who underwent routine (control group) or GES assisted (GES group) retroperitoneal robotic partial nephrectomy for localized renal tumors in our hospital between March 2018 and June 2020. Clinical data of perioperative outcomes and complications were collected and compared. Comparison of outcomes between anterior versus posterior tumor subgroups was also conducted. Linear regression analysis was used to define the relationship between dissection time and perinephric fat status in each group.

Results

Totally 103 patients were included, 48 in control and 55 in GES group respectively. All the procedures were completed successfully without conversion or positive surgical margin. GES group had significantly decreased console time (91 ± 36 min vs. 117 ± 41 min, p < 0.01) and dissection time (67 ± 35 min vs. 93 ± 38 min, p < 0.01) than control, while ischemia time, blood loss, and nephrometry score comparable between them. No major postoperative complications occurred. Dissection time of GES group was notably shorter than that of control in both anterior/posterior subgroups. Only in control group, dissection time was positively associated with perinephric fat status.

Conclusions

The GES technique acting as an adjunct to robotic arms with space-sparing feature, notably improves surgical exposure and facilitates dissection in retroperitoneal partial nephrectomy, while having great feasibility, efficacy and safety.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01079-4.

Trends in Incidence and Mortality of Kidney Cancer in a Northern Italian Province: An Update to 2020

The aim of this study was to examine the incidence and mortality trends for tumors and cardiovascular disease (CVD) in a province of northern Italy. The study included kidney cancers recorded in the period 1996−2020, divided by sex, age, year of incidence and years from diagnosis. The standardized incidence rate was calculated using the European population, and the Annual Percent Change (APC) was reported. In total, 2331 patients with kidney cancers were identified, mainly males (1504 cases) aged 60−79 years (1240 cases). There were 1257 deaths; there were no differences according sex but there were differences according to age (12.1% among younger adults and 80.4% among 80+). The incidence rate increased in males between 1996 and 2011 (APC = 2.3), while the mortality rate decreased in both males (APC = −3.3%) and females (APC = −4.5%). Comparing the same periods, kidney cancer-specific mortality decreased from 81.8% to 43.7%, while in the same period there was an increasing trend for CVD mortality. Moreover, the risk of CVD mortality increased as we moved away from the diagnosis (from 6.2% to 27.5%, p < 0.01). The same trend was observed for other causes of death (from 12.6% to 32.1%, p < 0.01). Thus, a multidisciplinary approach seems necessary during the follow-up and treatments of patients with kidney cancer.

Feasibility of interaortocaval clamping for renal artery during robot-assisted right partial nephrectomy: A propensity score-matching analysis

AIM

To evaluate the impact of the interaortocaval clamping technique for the right renal artery on perioperative outcomes of patients who underwent robot-assisted partial nephrectomy (RAPN).

METHODS

This study included 111 consecutive patients with right renal masses undergoing RAPN via the transperitoneal approach. In this series, standard and interaortocaval clamping techniques were defined as those for the right renal artery at the renal hilus and interaortocaval space, respectively. Based on the 3D images reconstructed from CT, interaortocaval clamping was preoperatively selected for patients in whom standard clamping of the main renal artery at the right hilum was judged to be technically difficult due to complicated vascular distribution, such as multiple branches of right renal arteries and veins and/or intertwining of these vessels.

RESULTS

Of 111 patients, 95 and 16 were classified into the standard and interaortocaval clamping groups, respectively, and interaortocaval clamping was uneventfully performed as planned in all 16. After adjusting patient variables by 1:3 propensity score-matching, 33 and 11 patients were included in the respective groups, and there were no significant differences in major clinical characteristics between them, while the incidences of multiple branches of right renal vessels as well as their intertwining beside the right renal hilus were significantly higher in the interaortocaval clamping group. However, no significant difference was noted in any of the perioperative outcomes, including operative time or intraoperative blood loss, between the two groups.

CONCLUSIONS

The interaortocaval clamping technique during RAPN is a feasible procedure with acceptable perioperative outcomes compared with standard hilar clamping, making it possible to more accurately resect renal tumors under clear visualization without unnecessary arterial bleeding from the tumor bed in patients with complex vascular distribution at the right renal hilus; however, special attention should be paid to the considerable individual variability of the interaortocaval anatomy.

Comparison of perioperative outcomes among patients with exophytic, mesophytic, and endophytic renal tumors undergoing robot-assisted partial nephrectomy

OBJECTIVES

It has been well documented that partial nephrectomy for completely endophytic renal tumors is a highly challenging procedure accompanied by several technical difficulties even with the assistance of a robotic surgical system. This study aimed to compare perioperative variables among patients with exophytic, mesophytic, and endophytic renal tumors undergoing robot-assisted partial nephrectomy.

METHODS

This study retrospectively included 265 consecutive patients with localized small renal masses undergoing robot-assisted partial nephrectomy at our institution. In this study, completely endophytic tumor was defined as the mass totally covered by renal healthy parenchyma, and according to the points for the 'E' domain of RENAL nephrometry score based on preoperative computed tomography, subjects were classified into three groups as follows: exophytic, mesophytic, and endophytic tumor groups, and perioperative outcomes among these groups were compared.

RESULTS

Of 265 patients, 127, 112, and 26 were classified into the exophytic, mesophytic, and endophytic tumor groups, respectively. A significantly smaller tumor diameter was observed in the endophytic group than in the other groups (P < 0.001), whereas the RENAL nephrometry score was significantly higher (P < 0.001). In addition, the warm ischemia time in the endophytic tumor group was significantly longer than that in other groups (P = 0.009); however, no significant difference in the trifecta achievement was noted among the three groups.

CONCLUSIONS

This study suggests that robot-assisted partial nephrectomy for patients with completely endophytic tumors can be regarded as a feasible approach without marked impairment of perioperative outcomes; however, further investigation of the long-term functional and oncological outcomes in these patients is required.

Artificial intelligence for renal cancer: From imaging to histology and beyond

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

Applied Change Management in Interventional Radiology—Implementation of Percutaneous Thermal Ablation as an Additional Therapeutic Method for Small Renal Masses

Interventional radiology (IR) has the potential to offer minimally invasive therapy. With this potential, new and arising IR methods may sometimes be in competition with established therapies. To introduce new methods, transformational processes are necessary. In organizations, structured methods of change management, such as the eight-step process of Kotter—(1) Establishing a sense of urgency, (2) Creating the guiding coalition, (3) Developing a vision and strategy, (4) Communicating the change vision, (5) Empowering employees for broad-based action, (6) Generating short-term wins, (7) Consolidating gains and producing more change, and (8) Anchoring new approaches in the culture—are applied based on considerable evidence. In this article, the application of Kotter’s model in the clinical context is shown through the structured transformational process of the organizational implementation of the percutaneous thermal ablation of small renal masses. This article is intended to familiarize readers in the medical field with the methods of structured transformational processes applicable to the clinical setting.

A pilot study investigating the feasibility of using a fully automatic software to assess the RENAL and PADUA score

PURPOSE

Image-based morphometric scoring systems such as the RENAL and PADUA scores are useful to evaluate the complexity of partial nephrectomy for renal cell carcinoma (RCC). The main aim of this study was to develop a new imaging software to enable an automatic detection and a 3D visualization of RCC from CT angiography (CTA) and to address the feasibility to use it to evaluate the features of the RENAL and the PADUA scores.

METHODS

A training dataset of 210 patients CTA-scans manually segmented was used to train a deep learning algorithm to develop the automatic detection and 3D-visualization of RCC. A trained operator blindly assessed the RENAL and PADUA scores on a testing dataset of 41 CTA from patients with RCC using a commercialized semi-automatic software (ground truth) and the new automatic software. Concordance between the two methods was evaluated.

RESULTS

The median PADUA score was 9 (7-11) and the renal score was 8 (5.5-9). The automatic software enabled to automatically detect the tumoral kidney and provided a 3D-visualization in all cases, with a computational time less than 20 seconds. Concordances for staging the anatomical features of the RENAL scores were respectively: 87.8% for radius, 85.4% for exophytic rate, 82.9% for location to the polar lines and 92.7% for the antero-posterior location. For the PADUA scores, concordances were 90.2% for tumor size, 85.4% for exophytic rate, 87.8% for polar location and 100% for renal rim.

CONCLUSION

By enabling an automatic 3D-visualization of tumoral kidney, this software could help to calculate morphometric scores, save time and improve reproducibility for clinicians.

LEVEL OF EVIDENCE: 4

Combination of holographic imaging with robotic partial nephrectomy for renal hilar tumor treatment

OBJECTIVES

To evaluate the clinical value of the holographic imaging technology in combination with robotic-assisted partial nephrectomy (RAPN) for renal hilar tumor treatment.

PATIENTS AND METHODS

From Dec. 2018 to Dec. 2021, patients diagnosed with renal hilar tumor were included in this retrospective study. Before the surgery, the engineers established the holographic image models based on the enhanced CT data. The models were used in patient consultation, pre-surgery planning and surgery simulation. During the RAPN, the navigation was achieved by real-time overlapping of the holographic images on the robotic surgery endoscopic views. The navigation technique helped the surgeon to identify the important anatomic structures such as tumor, renal vein, renal artery, and pelvis.

RESULTS

There were total of eight patients with renal hilar tumor who underwent RAPN combined with holographic imaging technique. The mean age was 57.3 years, the median ASA score was 2. The mean tumor size was 42.4 mm and the median RENAL Nephrometry score was 9.5. The clinical stages were cT1a (37.5%) and cT1b (62.5%). All the procedures were performed uneventfully by one surgeon. The mean operative time was 144.3 min, and the mean warm ischemia time was 27.9 min. The mean estimated blood loss was 86.3 ml. There was no conversion to open surgery or radical nephrectomy. There were no Clavien-Dindo ≥ 3 perioperative complications.

CONCLUSIONS

Using the holographic imaging technique, the pre-surgery planning, simulation of renal arterial clamp and excision of the tumor, and intraoperative navigation were feasible and helpful in facilitating RAPN.

A review of 99mTc-sestamibi SPECT/CT for renal oncocytomas: A modified diagnostic algorithm

99mTc-sestamibi SPECT/CT is a promising nuclear medicine imaging investigation for benign renal lesions such as renal oncocytomas. The purpose of this article is to i) review the current literature on 99mTc-sestamibi SPECT/CT, ii) to review to current application of 99mTc-sestamibi SPECT/CT for indeterminate renal lesion imaging, and iii) to discuss present limitations and areas for future research. The literature has been reviewed up to April 2022 for articles relating to the application of 99mTc-sestamibi SPECT/CT for benign renal lesions including a recently published systematic review and meta-analysis performed by the authors. One study evaluating 99mTc-sestamibi SPECT alone and five studies evaluating 99mTc-sestamibi SPECT/CT have been performed to date. 99mTc-sestamibi SPECT/CT demonstrates high sensitivity and specificity for detecting benign renal lesions, particularly renal oncocytomas. 99mTc-sestamibi SPECT/CT demonstrates near-perfect specificity for benign and low-grade renal lesions. The optimal quantified threshold ratio for tumor-to-background renal parenchyma radiotracer uptake for a positive result is > 0.6. In this article, we propose a modified diagnostic algorithm for small enhancing renal masses measuring 1-4 cm in which suspected benign lesions after conventional imaging are considered for 99mTc-sestamibi SPECT-CT. In this algorithm, positive studies can be monitored with active surveillance rather than requiring invasive biopsy and/or targeted therapy.

Functional, oncological outcomes and safety of laparoscopic partial nephrectomy versus open partial nephrectomy in localized renal cell carcinoma patients with high anatomical complexity

PURPOSE

Partial nephrectomy (PN) is the main treatment strategy for localized renal cell carcinoma (RCC). However, for RCC with high anatomical complexity, PN remains a challenge for urologists. Therefore, this study aimed to evaluate the functional oncological outcomes and safety of laparoscopic partial nephrectomy (LPN) versus open partial nephrectomy (OPN) in localized RCC patients with highly anatomical complexity (R.E.N.A.L. score ≥ 10).

PATIENTS AND METHODS

We retrospectively studied 575 patients who underwent PN at our center between January 2007 and December 2017. After propensity score-matching (PSM), 137 patients treated with LPN and 54 patients treated with OPN were balanced into 97 and 44 pairs. Patient demographics, and extensive perioperative and prognostic data were recorded and compared.

RESULTS

In the matched group, the OPN group had significantly less eGFR loss than the LPN group (2.57 ml/min/1.73 m² vs. 31.59 ml/min/1.73 m², P < 0.001). The recurrence-free survival (P = 0.287), overall survival (P = 0.296), cancer-specific survival (P = 0.664), and cardiocerebrovascular disease-specific survival (P = 0.341) were equivalent between groups. The rates of minor (P = 0.621) and major (P = 0.647) complications were also similar between groups.

CONCLUSIONS

This PSM cohort study showed that OPN resulted in better renal function preservation than LPN in localized RCC patients with high anatomical complexity, and had comparable oncological and safety outcomes after long-term follow-up. These findings may help improve clinical decision-making for localized RCC patients with high anatomical complexity.

Shared decision-making in urology and female pelvic floor medicine and reconstructive surgery

Shared decision-making (SDM) is a hallmark of patient-centred care that uses informed consent to help guide patients with making complex health-care decisions. In SDM, patients and providers work together to determine the best course of action based on both the current available evidence and the patient's values and preferences. SDM not only provides a framework for the legal and ethical obligations providers need to fulfil for informed consent, but also leads to improved knowledge of treatment options and satisfaction of decision-making for patients. Tools such as decision aids have been developed to support SDM for complex decisions. Several decision aids are available for use in the field of urology and female pelvic medicine and reconstructive surgery, but these decision aids are also associated with barriers to SDM implementation including patient, provider and systematic challenges. However, solutions to such barriers to SDM include continued development of SDM tools to improve patient engagement, expand training of providers in SDM communication models and a process to encourage implementation of SDM.

Oncosurgery-Related Acute Kidney Injury

Oncosurgery is a surgical specialty that focuses on the diagnosis, staging, and management of cancer and cancer-related complications. Acute kidney injury is a common and important complication related to oncologic surgery, associated with longer hospital length of stay, greater costs, increased risk of incident or progressive chronic kidney disease (CKD), and higher mortality. The pathogenesis of oncosurgery-related acute kidney injury is multifactorial and determined by different variables, including patient characteristics (comorbidities, volume status, age, pre-existing CKD), specific cancer type or location, surgical procedure involved, as well as intrinsic neuroendocrine and hemodynamic responses to anesthesia and/or surgery. Early nephrology evaluation may be helpful to assist with preservation of kidney function and prevention of further kidney injury.

The impact of tumor size on the survival of patients with small renal masses: A population-based study

Background

Active surveillance (AS) with delayed intervention has gained acceptance as a management strategy for small renal masses (SRMs). However, during AS, there is a risk of tumor growth. Thus, we aim to investigate whether tumor growth in patients with SRMs leads to tumor progress.

Methods

In this study, we enrolled 16,070 patients from the Surveillance, Epidemiology, and End Results database with T1a renal cell carcinoma (RCC) between 2004 and 2017. The 16,070 patients were divided into three groups: 10,526 in the partial nephrectomy (PN) group, 2768 in the local ablation (LA) group, and 2776 in the AS group. Associations of tumor size with all‐cause and cancer‐specific mortality were evaluated using Kaplan–Meier analyses and Cox regression models.

Results

Four tumor size categories were delineated (≤1, >1–2, >2–3, and > 3–4 cm in diameter), and 10‐year all‐cause and cancer‐specific mortality both significantly increased with increasing tumor size in the PN, LA, and AS groups (all p < 0.05). Tumors were substaged based on diameter: T1aA (≤2 cm) and T1aB (>2–4 cm). All‐cause and cancer‐specific mortality were significantly higher in T1aB tumors than T1aA tumors in each group (hazard ratio = 1.395 and 1.538, respectively; all p < 0.05).

Conclusions

Tumor growth relates to worse prognosis of T1a RCC, and 2 cm serves as a size threshold that is prognostically relevant for patients with T1a RCC. Because of the lack of accurate predictors of tumor growth rate, AS for patients with SRMs incurs a risk of tumor progression.

Predictive Factors Affecting Metastasis of Small Renal Mass and Its Prognostic Analysis

BACKGROUND

The incidence of small renal mass (SRM) increases, and the prognosis of SRM is poor once metastasized. Therefore, we conducted this study to assess the clinical and pathological characteristics of SRM to determine the risk factors that influence the metastasis and prognosis of SRM.

METHODS

A small renal mass is defined as a solid tumor mass with the largest diameter of 4 cm or less on the pathological diagnosis. The metastasis is confirmed by imaging or pathological examination. We retrospectively included 40 patients with metastatic SRM (mSRM) treated in the department of urology of Peking University Third Hospital from October 2002 to October 2020. Meanwhile, 358 patients with nonmetastatic SRM treated in our hospital from January 2015 to December 2017 were selected as controls. Clinicopathologic features were compiled.

RESULTS

Multivariate logistic regression analysis showed that age (P = .027, odds ratio [OR] = 1.037, 95% confidence interval [CI] 1.004-1.070), clinical symptoms (P < .001, OR = 4.311, 95% CI 1.922-9.672), World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grade 3/4 (P = .004, OR = 7.637, 95% CI 1.943-30.012; P = .004, OR = 20.523, 95% CI 2.628-160.287), and lymphatic invasion (P = .030, OR = 15.844, 95% CI 1.314-191.033) were risk factors for distant metastasis of SRM. Once metastasis occurs, the prognosis of SRM is poor. Multivariate Cox regression analysis of the prognosis of mSRM showed that age (P = .016, hazard ratio [HR] = 1.125, 95% CI 1.022-1.239), preoperative serum creatinine (P = .041, HR = 1.003, 95% CI 1.000-1.005), vascular invasion (P = .041, HR = 1.003, 95% CI 1.000-1.005), and metastasis (P < .001, HR = 24.069, 95% CI 4.549-127.356) were risk factors for overall survival (OS), and only metastasis (P < .001, HR = 9.52, 95% CI 5.43-16.7) was a risk factor for progression-free survival (PFS) of SRM.

CONCLUSIONS

SRM with advanced age, clinical symptoms, high pathological nuclear grade, and lymphatic invasion are more likely to have distant metastasis. And SRM with older age, poor preoperative basic renal function, pathological vascular invasion, and metastasis have worse OS.

Tumor enucleation for the treatment of T1 renal tumors: A systematic review and meta-analysis

Purpose

To evaluate the clinical efficacy and safety of tumor enucleation (TE) compared with partial nephrectomy (PN) for T1 renal cell carcinoma.

Materials and Methods

According to protocol, we searched multiple data sources for published and unpublished randomized controlled trials and nonrandomized studies (NRSs) in any language. We performed systematic review and meta-analysis according to the Cochrane Handbook for Systematic Reviews of Interventions and rated the certainty of the evidence (CoE) using the GRADE framework.

Results

We are uncertain about the effects of TE on perioperative (mean difference [MD] 3.38, 95% CI 1.52 to 5.23; I²=68%; 4 NRSs; 942 participants; very low CoE) and long-term (MD 2.31, 95% CI -1.40 to 6.01; I²=57%; 4 NRSs; 542 participants; very low CoE) residual renal function. TE may result in little to no difference in short-term residual renal function (MD 1.04, 95% CI 0.25 to 1.83; I²=0%; 2 NRSs; 256 participants; low CoE). We are uncertain about the effects of TE on cancer-specific mortality (risk ratio [RR] 0.90, 95% CI: 0.11 to 7.28; I²=0%; 2 NRSs; 551 participants; very low CoE) and major adverse events (RR 0.48, 95% CI: 0.30 to 0.79; I²=0%; 10 NRS; 2,360 participants; very low CoE).

Conclusions

While TE appears to have similar effects on short term postoperative residual renal function, there were uncertainties on mortality and major adverse events. However, we need rigorous RCTs to elucidate the effects of TE as the evidence stems mostly from NRSs.

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

SIMPLE SUMMARY

Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.

BACKGROUND

Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.

METHODS

Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.

RESULTS

The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.

CONCLUSIONS

The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

Endoscopic ultrasound-guided trans-duodenal fine-needle biopsy of a small renal mass: case report and review of the literature

Background

The incidental detection of small renal masses (SRMs) is increasing and biopsy to obtain pathological diagnosis is increasingly proposed as a diagnostic tool to guide further management. Renal mass biopsies are traditionally performed via a percutaneous approach. However, this is not always feasible due to anatomical limitations. A rarely reported alternative biopsy approach for SRMs is endoscopic ultrasound (EUS)-guided fine-needle biopsy (FNB). Herein, we describe a case of EUS-guided trans-duodenal FNB for a SRM that was not amenable to standard percutaneous biopsy.

Case presentation

A 48-year-old man was incidentally found to have a right-sided SRM measuring 2.9 × 2.2 × 2.4 cm during evaluation for a hernia. It was anterior, interpolar, completely endophytic and near the renal hilum. The tumor was not amenable to traditional percutaneous biopsy due to its anterior location. However, the renal mass was in close proximity to the descending duodenum and so it was felt that an EUS-guided trans-duodenal FNB would be feasible. The procedure was successful without any complications. The specimen adequacy was satisfactory for evaluation and consistent with renal papillary carcinoma with WHO/ISUP grade 3 nuclear changes.

Conclusion

Our case report demonstrated that EUS-guided trans-duodenal FNB was a safe and feasible approach to obtaining biopsy tissue diagnosis of a SRM that was not amenable to percutaneous biopsy.

Identifying Preoperative Predictors of Operative Time and Their Impact on Outcomes in Robot-Assisted Partial Nephrectomy

Objective: To identify preoperative characteristics in patients with renal masses that influence operative time during robot-assisted partial nephrectomy (RAPN) and evaluate the relationship between operative time and length of stay (LOS), complication rates, and overall outcome. Materials and Methods: We queried our institutional database to identify a cohort of patients who underwent RAPN by two experienced robotic surgeons between 2012 and 2019. A multivariable regression model was developed to analyze operative time, LOS, and any grade complication within 30 days postoperatively using the bootstrap resampling technique. Results: A total of 392 patients were included. On multivariable analyses, prior abdominal surgery (p = 0.001) was associated with 22 minutes of increase in operating room time, as well as adhesive perirenal fat (22 minutes, p = 0.001). For each one unit increase in nephrometry score, there was a 4-minute increase in operating room time (p = 0.028), and for each one-cm increase in tumor size, there was an associated 12-minute increase in operating room time (p < 0.001). For each 1 year increase in age, there was an associated 0.024-day increase in LOS [odds ratio (OR) (0.013-0.035)]; in addition, for every one-cm increase in tumor size there was a 0.18-day associated increase in LOS [OR (0.070-0.28)]. Each 1-hour increase in operating room time was associated with a 0.25-day increased LOS [OR (0.092-0.41)]. Only tumor size was found to be associated with any grade complication. Conclusions: Patients with a history of abdominal surgery, larger complex tumors, and significant Gerota's fat undergoing robotic partial nephrectomy should anticipate longer operative times. Older patients with larger tumors and longer operative times can anticipate a longer LOS. Tumor size appears to be the common determinant of all three outcomes: operative time, LOS, and any grade Clavien complication.

Diagnostic accuracy of core biopsies of renal masses: Experience in a real-life setting from a tertiary center

OBJECTIVE

To document and analyze diagnostic accuracy of renal core biopsy (RCB), its diagnostic correlation with resection specimens, and to question the need for immunohistochemistry (IHC) in the preoperative diagnosis of renal masses.

MATERIAL AND METHOD

RCBs performed at a reference center between 2007 and 2017 were included. Pathological, clinical, and radiological data were obtained from medical records.

RESULTS

Among 302 biopsies included in this study, 274 (90.7%) were diagnostic. Two hundred sixty-six were neoplastic and 179 were of primary renal origin. The most common secondary neoplasms were hematolymphoid (n = 35) and metastatic (n = 17). Sixty-nine tumors were classified as small renal masses (SRMs) (≤4 cm in diameter) and 53 of them were malignant. Nephrectomy was performed in 58 patients. Overall diagnostic accuracy between resections and RCBs was 88.7%. IHC was performed in 160 (53%) cases. In 15 of those, a definite diagnosis could not be rendered. Renal cell origin and subtype were determined by histomorphology alone in 81 and 75 cases, respectively. Sixty primary neoplasms of renal cell origin required IHC for diagnosis.

CONCLUSION

RCB is a safe and highly accurate method for the diagnosis of both primary and secondary renal neoplasms. IHC is mostly required for the diagnosis of secondary tumors. Histomorphology is still the primary diagnostic tool, highly dependent on the experience of the surgical pathologist.

Holographic reconstruction technology used for intraoperative real-time navigation in robot-assisted partial nephrectomy in patients with renal tumors: a single center study

Background

To explore the efficacy and advantages of real-time navigation using holographic reconstruction (HR) technology combined with da VinciTM robotic system for partial nephrectomy (PN) in patients with renal tumor.

Methods

The clinical data of 41 patients with totally intrarenal tumors receiving robot-assisted partial nephrectomy (RAPN) from April 2018 to October 2020 in our department were collected and retrospectively analyzed. All operations were performed by the same surgeon. HR technology and three-dimensional (3D) reconstruction techniques were applied for real-time navigation to resect tumors using the da VinciTM robotic system. The relevant clinical parameters and surgical outcomes of the patients were recorded and analyzed.

Results

HR technology allowed accurate evaluation of tumors, renal hilus vessels, and surrounding organs during the operation. With real-time navigation HR, all cases were performed by RAPN. The mean operative time was 115.3±20.3 (range, 70–153) minutes, and the warm ischemia time (WIT) was 18.7±3.9 (range, 13–28) minutes. The estimated blood loss (EBL) was 98.8±18.7 (range, 60–141) mL. Negative surgical margins were reported in all cases. Patients with absence of grade ≤1 Clavien-Dindo complications. Compared with the clinical outcomes of standard RAPN, as reported in the literature, HR-assisted technology reduced the mean operative time, the WIT, and the EBL in patients undergoing RAPN. Therefore, combining HR with robotic abdominal surgery can enhance the efficiency of locating blood vessels and allow for more accurate resection of tumors.

Conclusions

As a novel and promising computer digital technology, HR can significantly improve the success of RAPN operations. This retrospective study demonstrated that HR-assisted operations resulted in shorter operation times and less perioperative complications and were thus safer and more effective in patients with renal tumors compared with RAPN not used HR.

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

BACKGROUND

A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate.

OBJECTIVE

To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses.

DESIGN, SETTING, AND PARTICIPANTS

A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography-proven clinically localized renal mass who underwent partial or radical nephrectomy.

INTERVENTION

Radiomic analysis of preoperative scans was performed. Clinical and radiomic variables of importance were identified through decision tree analysis, which were incorporated into Random Forest and REAL Adaboost predictive models.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was the degree of congruity between the virtual diagnosis and final pathology. Subanalyses were performed for small renal masses and patients who had percutaneous renal mass biopsies as part of their workup. Receiver operating characteristic curves were used to evaluate each model's discriminatory function.

RESULTS AND LIMITATIONS

A total of 684 patients met the selection criteria. Of them, 76% had renal cell carcinoma; 57% had small renal masses, of which 73% were malignant. Predictive modeling differentiated benign pathology from malignant with an area under the curve (AUC) of 0.84 (95% confidence interval [CI] 0.79-0.9). In small renal masses, radiomic analysis yielded a discriminatory AUC of 0.77 (95% CI 0.69-0.85). When negative and nondiagnostic biopsies were supplemented with radiomic analysis, accuracy increased from 83.3% to 93.4%.

CONCLUSIONS

Radiomic-based predictive modeling may distinguish benign from malignant renal masses. Clinical factors did not substantially improve the diagnostic accuracy of predictive models. Enhanced diagnostic predictability may improve patient selection before surgery and increase the utilization of active surveillance protocols.

PATIENT SUMMARY

Not all kidney tumors are cancerous, and some can be watched. We evaluated a new method that uses radiographic features invisible to the naked eye to distinguish benign masses from true cancers and found that it can do so with acceptable accuracy.

A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI

PURPOSE

To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma.

METHODS

In this retrospective study, 74 patients (49 male, mean age 59.3) with 243 renal masses (203 ccRCC and 40 oncocytoma) that had undergone MR imaging 6 months prior to pathologic confirmation of the lesions were included. Segmentation using seed placement and bounding box selection was used to extract the lesion patches from T2-WI, and T1-WI pre-contrast, post-contrast arterial and venous phases. Then, a deep convolutional neural network (AlexNet) was fine-tuned to distinguish the ccRCC from oncocytoma. Five-fold cross validation was used to evaluate the AI algorithm performance. A subset of 80 lesions (40 ccRCC, 40 oncocytoma) were randomly selected to be classified by two radiologists and their performance was compared to the AI algorithm. Intra-class correlation coefficient was calculated using the Shrout-Fleiss method.

RESULTS

Overall accuracy of the AI system was 91% for differentiation of ccRCC from oncocytoma with an area under the curve of 0.9. For the observer study on 80 randomly selected lesions, there was moderate agreement between the two radiologists and AI algorithm. In the comparison sub-dataset, classification accuracies were 81%, 78%, and 70% for AI, radiologist 1, and radiologist 2, respectively.

CONCLUSION

The developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs.

Minimally Invasive Partial Nephrectomy: Are All Clamps Created Equal? An Ex Vivo Model Simulation of Vascular Clampage

REASON FOR REVIEW

During the partial nephrectomy, clamping of the vascular pedicle before exision of the tumor is a key step in minimizing blood loss and maintaining adequate visualization. Different vascular clamping devices have been developed for minimal invasive surgery. However, there are no reports comparing them in turn of efficiency RECENT FINDINGS: We present an ex vivo experimental model, designed to demonstrate differences between the clamping devices. All clamps proved to function properly without any leakage at 90 and 120 mmHg, respectively. Our study and the ex vivo model prove that all available clamps are equally efficient at physiologic pressures.

Renal mass biopsy: A strategy to reduce associated costs and morbidity when managing localized renal masses

INTRODUCTION AND OBJECTIVES

Renal mass biopsy (RMB) has not been widely adopted in evaluating small renal mass due to concerns for safety, efficacy, and its perceived lack of consequence on management decisions. We assess the potential cost savings and morbidity avoidance of routine RMB on cT1 renal masses undergoing robotic-assisted partial nephrectomy (RAPN).

METHODS

We identified n = 920 consecutive RAPN pT1 renal masses and n = 429 consecutive RMBs for cT1 renal masses over 12 years. Using a novel pathological-based risk classification system for cT1 renal masses, we evaluated the morbidity and costs of our RAPN and RMB cohorts. We then define four clinical scenarios where RMB could potentially delay and/or avoid intervention in our pT1 RAPN cohort and model potential complications prevented and cost savings utilizing common clinical scenarios.

RESULTS

Using our risk stratification system in RAPN patients, final histology was classified as benign in n=174 (18.9%) cases, very low-risk (n = 62 [7%]), low-risk (n = 383 [42%]), and high-risk (n = 301 [33%]), respectively. We identified n = 116 (12.6%) Clavien graded peri-operative complications. In our RMB patients, 120 (27.9%), 17 (3.9%), 240 (55.9%), 52(12.1%) were benign, very low, low and high-risk tumors. The median total direct cost for RAPN was $6955/case compared to $1312/case for RMB. If we established a primary goal to avoid immediate extirpative surgery in benign renal tumors, in the elderly (>70 y) with very low-risk tumors and/or those with high renal functional risks (≥ CKD3b), or competing risks (ASA ≥ 3), RMB could have reduced direct costs by approximately 20% and avoided n = 39 Clavien graded complications, seven readmissions, three transfusions, and two returns to the OR. With the additional cost of performing RMB on those not initially biopsied, the net cost saving would be approximately $1.2 million with minimal added complications while still treating high-risk tumors.

CONCLUSIONS

Routine RMB before intervention results in cost-saving and complication avoidance. Given the limitations of biopsy, shared decision-making is mandatory. Biopsy should be considered prior to intervention in at-risk populations.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools

Background

To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).

Methods

Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.

Results

Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986).

Conclusion

The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.

Perioperative outcomes in patients undergoing robot-assisted partial nephrectomy: Comparative assessments between complex and non-complex renal tumors

INTRODUCTION

The aim of this study was to investigate the effects of renal tumor complexity on perioperative outcomes in patients receiving robot-assisted partial nephrectomy (RAPN).

METHODS

This study included 153 consecutive patients with cT1 renal masses undergoing RAPN and analyzed their perioperative outcomes, particularly tumor complexity. In this series, cT1b, completely endophytic, hilar, and cystic tumors were considered complex tumors. Patients with tumors that met at least one of the complex criterion were placed in the complex tumor group; patients with tumors that did not meet any of the complex criteria were placed in the non-complex tumor group.

RESULTS

Of the 153 patients, 54 (35.3%) had complex tumors; specifically, 18 (11.8%) had cT1b tumors, 15 (9.8%) had completely endophytic tumors, 28 (18.3%) had hilar tumors, and 8 (5.2%) had cystic tumors. The non-complex group included 99 patients (64.7%). The complex tumor group had significantly longer warm ischemia and console times than the non-complex tumor group, but there was no significant difference between them in the achievement of the trifecta. Both warm ischemia and console times were significantly correlated with the number of complex factors. Multivariate analyses of complex factors demonstrated that completely endophytic and cT1b tumors were independently associated with warm ischemia time and console time, respectively.

CONCLUSIONS

For patients with complex tumors, RAPN may be a feasible procedure with acceptable perioperative outcomes. However, special attention should be paid to long warm ischemia and console times, particularly in those with completely endophytic and/or cT1b tumors.

When More Is Better: Underused Advanced Imaging Exams That Can Improve Outcomes and Reduce Cost of Care

Appropriate use of resources is a tenet of care transformation efforts, with a national campaign to reduce low-value imaging. The next level of performance improvement is to bolster evidence-based screening, imaging surveillance, and diagnostic innovation, which can avert more costly, higher-risk elements of unnecessary care like emergent interventions. Clinical scenarios in which underused advanced imaging can improve outcomes and reduce total cost of care are reviewed, including abdominal aortic aneurysm surveillance, coronary artery disease diagnosis, and renal mass characterization. Reliable abdominal aortic aneurysm surveillance imaging reduces emergency surgery and can be driven by radiologists incorporating best practice standardized recommendations in imaging interpretations. Coronary computed tomography angiography in patients with stable and unstable chest pain can reduce downstream resource use while improving outcomes. Preoperative ^99mTc-sestamibi single-photon emission computed tomography (SPECT) reliably distinguishes oncocytoma from renal cell carcinoma to obviate unnecessary nephrectomy. As technological advances in diagnostic, molecular, and interventional radiology improve our ability to detect and cure disease, analyses of cost effectiveness will be critical to radiology leadership and sustainability in the transition to a value-based reimbursement model.