The Harms of Overdiagnosis and Overtreatment in Patients with Small Renal Masses: A Mini-review

A multi-cancer early detection blood test using machine learning detects early-stage cancers lacking USPSTF-recommended screening

US Preventive Services Task Force (USPSTF) guidelines recommend single-cancer screening for select cancers (e.g., breast, cervical, colorectal, lung). Advances in genome sequencing and machine learning have facilitated the development of blood-based multi-cancer early detection (MCED) tests intended to complement single-cancer screening. MCED tests can interrogate circulating cell-free DNA to detect a shared cancer signal across multiple tumor types. We report real-world experience with an MCED test that detected cancer signals in three individuals subsequently diagnosed with cancers of the ovary, kidney, and head/neck that lack USPSTF-recommended screening. These cases illustrate the potential of MCED tests to detect early-stage cancers amenable to cure.

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field is lagging behind other cancers. Despite increased detection of early disease as a consequence of the widespread use of diagnostic CT scans, 25% of patients have disseminated disease at diagnosis. Similarly, around 25% progress to metastatic disease following curatively intended surgery. Surgery is the cornerstone in the treatment of RCC; however, when the disease is disseminated, immunotherapy or immunotherapy in combination with a tyrosine kinase inhibitor is the patient's best option. Immunotherapy is a potent treatment, with durable treatment responses and potential to cure the patient, but only half of the patients benefit from the administered treatment, and there are currently no methods that can identify which patients will respond to immunotherapy. Moreover, there is a need to identify the patients in greatest risk of relapsing after surgery for localized disease and direct adjuvant treatment there. Even though several molecular biomarkers have been published to date, we are still lacking routinely used biomarkers to guide optimal clinical management. The purpose of this review is to highlight some of the most promising biomarkers, discuss the efforts made within this field to date, and describe the barriers needed to be overcome to have reliable and robust predictive and prognostic biomarkers in the clinic for renal cancer.

The role of [68 Ga]Ga-FAPI-04 PET/CT in renal cell carcinoma: a preliminary study

PURPOSE

We aimed to investigate the role of [68 Ga]Ga-FAPI-04 PET/CT and uptake patterns of primary and metastatic lesions in patients with renal cell carcinoma (RCC).

METHODS

Twenty patients with a suspicious lesion considered primary renal malignancy or a history of RCC were included in our study. Two patients were excluded from further analyses due to other confirmed malignancies. Six patients were newly diagnosed, while the indication of 12 patients was restaging. All patients underwent [68 Ga]Ga-FAPI-04 and [18F]F-FDG PET/CT. SUVmax and tumor-to-background ratio (TBR) of primary (n = 7) and local recurrent lesions (n = 6) and lymph node (n = 26), lung (n = 32), bone (n = 5), and other metastases (n = 14) were compared between the two tracers.

RESULTS

We detected 90 lesions in 18 patients with varying FAPI and FDG uptake values on both PET/CT. The median TBR of FAPI-PET/CT of all lesions was higher than TBR of FDG-PET/CT with statistically significance (5.6 vs. 2.1, p < 0.001). In primary and recurrent lesions, the median SUVmax, TBR, and tumor volume on FAPI-PET/CT were higher than FDG-PET/CT. The median SUVmax of lung lesions on FAPI-PET/CT was statistical significantly higher than FDG-SUVmax (3.8 vs. 1.8, p = 0.02). The median of FAPI-SUVmax on primary lesions was lower in the early stage based on TNM compared to the advanced stage. FAPI-SUVmax in 49% of all lesions were SUVmax ≥ 6, and 13% were SUVmax ≥ 10. In patient-based analyses, seven patients (39%) had at least one lesion with FAPI-SUVmax ≥ 10; 12 patients (67%) had at least one lesion with FAPI-SUVmax ≥ 6.

CONCLUSION

This study showed the potential utility of [68 Ga]Ga-FAPI-04 PET/CT showing promising results in RCC. We have presumed that FAPI-PET/CT may be performed for complementary imaging modality providing prognosis and possibility of theranostic application in selected patients.

External Validation of a Five-Tiered CT Algorithm for the Diagnosis of Clear Cell Renal Cell Carcinoma: A Retrospective Five-Reader Study

BACKGROUND. In 2022, a five-tiered CT algorithm was proposed for predicting whether a small (cT1a) solid renal mass represents clear cell renal cell carcinoma (ccRCC). OBJECTIVE. The purpose of this external validation study was to evaluate the proposed CT algorithm for diagnosis of ccRCC among small solid renal masses. METHODS. This retrospective study included 93 patients (median age, 62 years; 42 women, 51 men) with 97 small solid renal masses that were seen on corticomedullary phase contrast-enhanced CT performed between January 2012 and July 2022 and subsequently underwent surgical resection. Five readers (three attending radiologists, two clinical fellows) independently evaluated masses for the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score; these scores were used to derive the CT score by use of the previously proposed CT algorithm. The CT score's sensitivity, specificity, and PPV for ccRCC were calculated at threshold of 4 or greater, and the NPV for ccRCC was calculated at a threshold of 3 or greater (consistent with thresholds in studies of the MRI-based clear cell likelihood score and the CT algorithm's initial study). The CT score's sensitivity and specificity for papillary RCC were calculated at a threshold of 2 or less. Interreader agreement was assessed using the Gwet agreement coefficient (AC1). RESULTS. Overall, 61 of 97 masses (63%) were malignant and 43 of 97 (44%) were ccRCC. Across readers, CT score had sensitivity ranging from 47% to 95% (pooled sensitivity, 74% [95% CI, 68-80%]), specificity ranging from 19% to 83% (pooled specificity, 59% [95% CI, 52-67%]), PPV ranging from 48% to 76% (pooled PPV, 59% [95% CI, 49-71%]), and NPV ranging from 83% to 100% (pooled NPV, 90% [95% CI, 84-95%]), for ccRCC. A CT score of 2 or less had sensitivity ranging from 44% to 100% and specificity ranging from 77% to 98% for papillary RCC (representing nine of 97 masses). Interreader agreement was substantial for attenuation score (AC1 = 0.70), poor for heterogeneity score (AC1 = 0.17), fair for five-tiered CT score (AC1 = 0.32), and fair for dichotomous CT score at a threshold of 4 or greater (AC1 = 0.24 [95% CI, 0.14-0.33]). CONCLUSION. The five-tiered CT algorithm for evaluation of small solid renal masses was tested in an external sample and showed high NPV for ccRCC. CLINICAL IMPACT. The CT algorithm may be used for risk stratification and patient selection for active surveillance by identifying patients unlikely to have ccRCC.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Increasing Cancer in Adolescents and Young Adults: Cancer Types and Causation Implications

Purpose: This study aimed to identify cancer incidence trends in the United States and globally in adolescents and young adults (AYAs) 15-39 years of age, by sex, and to speculate on causes for trend changes. Methods: For the United States, SEER*Stat was used to obtain average annual percent change (AAPC) trends in cancer incidence during the period 2000-2019 among 395,163 AYAs. For global data, the source was the Institute of Health Metrics and Evaluation and its sociodemographic index (SDI) classification system. Results: In the United States, the invasive cancer incidence increased during the period 2000-2019 in both females (AAPC: 1.05, 95% CI: 0.90-1.20, p << 0.001) and males (AAPC: 0.56, 95% CI: 0.43-0.69, p << 0.001). A total of 25 and 20 types of cancers increased statistically significantly in female and male AYAs, respectively. Among potential causes for the increases, the obesity epidemic in the United States strongly correlates with the overall cancer increase in both its female (Pearson correlation coefficient R2 = 0.88, p = 0.0007) and male (R2 = 0.83, p = 0.003) AYAs, as does the most common malignancy in American AYAs, breast cancer (R2 = 0.83, p = 0.003). Worldwide, cancer incidence in the age group increased steadily during the period 2000-2019 among high-middle, middle, and low-middle SDI countries, but not in low SDI countries and with slowing of increase in high SDI countries. Conclusions: The increases and their age-dependent profiles implicate several causations that are preventable, including obesity, overdiagnosis, unnecessary diagnostic radiation, human papilloma virus infection, and cannabis avoidance. The United States is beginning to reverse the increasing incidence, and prevention efforts should be augmented accordingly.

A Review of the Recommendations and Strength of Evidence for Clinical Practice Guidelines on the Management of Small Renal Masses

Introduction With the rise in the detection of incidental small renal masses (SRM), the management paradigm for these patients has become an issue of increasing concern. We aim to identify areas of consensus, controversy, and opportunities for improvement among recently published guidelines and assess the strength of evidence for the management of SRMs. Methods We reviewed practice guidelines for SRMs promulgated by the American Urological Association, European Association of Urology, National Comprehensive Cancer Network, American Society of Clinical Oncology, European Society for Medical Oncology, and the Chinese Society of Clinical Oncology. Levels of evidence and strength of recommendations for evaluation, management and follow-up were analyzed with regard to consensus, conflict, and neglect. Results There is consensus among guidelines for the initial evaluation and treatment of SRMs, however, discrepancies exist with regard to indications for active surveillance, thermal ablation and timing/method of follow-up after treatment. Routine renal mass biopsy is not recommended by any guideline. Overwhelmingly, guideline statements are based on low to moderate levels of evidence; only 23% of the reviewed guidelines were based on high-level evidence; 38% based on moderate and 39% on low-level evidence or expert opinion. Conclusions Despite all six guidelines sharing a consensus on most management topics regarding SRMs, the ongoing lack of high-level evidence precludes gold standard recommendations in the areas of diagnosis, treatment, and follow-up. More high-quality studies are needed in order to develop stronger, data-supported universal guideline for the management of SRMs.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

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.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

Pre-operative Serum Albumin as a Potential Predictor of Benign Lesions in Renal Masses

BACKGROUND/AIM

We investigated pre-operative factors for predicting whether renal masses are benign in order to facilitate the selection of optimal candidates for pre-operative biopsy.

PATIENTS AND METHODS

We evaluated 278 patients with renal masses suspected to be clinically T1 or T2 renal cell carcinoma. All patients had undergone a partial or radical nephrectomy. Pre-operative parameters, including patient characteristics, tumor size, and blood tests, were utilized to predict which lesions were benign.

RESULTS

Twenty-five lesions (9.0%) were benign. Multivariate analysis showed that female sex [odds ratio (OR)=2.92, p=0.016], serum albumin ≥4.3 g/dl (OR=3.50, p=0.013), and tumor size <23 mm (OR=3.96, p=0.002) were significant independent factors for benign renal masses. The incidence of benign lesions in cases with all three factors (female sex, higher serum albumin, and smaller tumor size) was 4 of 16 (25.0%), which was significantly higher (p=0.037) than that in all cases (25/278; 9.0%).

CONCLUSION

Relatively high pre-operative serum albumin levels may be a predictor of benign lesions when associated with female sex and smaller tumor size.

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.

Growth Kinetics and Progression Rate of Bosniak Classification, Version 2019 III and IV Cystic Renal Masses on Imaging Surveillance

Background: Active surveillance is increasingly used as first-line management for localized renal masses. Triggers for intervention primarily reflect growth kinetics, which are poorly investigated for cystic masses defined by Bosniak classification version 2019 (v2019). Objective: To determine growth kinetics and incidence rates of progression of class III and IV cystic renal masses, as defined by Bosniak classification v2019. Methods: This retrospective study included 105 patients (68 men, 37 women; median age, 67 years) with 112 Bosniak v2019 class III or IV cystic renal masses on baseline renal-mass protocol CT or MRI examinations from January 2005 to September 2021. Mass dimensions were measured. Progression was defined as any of: linear growth rate (LGR) ≥5 mm per year (representing clinical guideline threshold for intervention), volume doubling time <1 year, T category increase, or N1 or M1 disease. Class III and IV masses were compared. Time-to-progression was estimated using Kaplan-Meier curve analysis. Results: At baseline, 58 masses were class III and 54 were class IV. Median follow-up was 406 days. Median LGR was for class III masses 0.0 mm per year [interquartile range (IQR) -1.3 to 1.8] and for class IV masses 2.3 mm per year (IQR 0.0¬¬-5.7) (p<.001). LGR exceeded 5 mm per year in 4 (7%) class 3 masses and 15 (28%) class IV masses (p=.005). Two patients, both with class IV masses, developed distant metastases. Incidence rate of progression was for class III masses 11.0 (95% CI 4.5-22.8) and for class IV masses 73.6 (95% CI 47.8-108.7) per 100,000 person-days of follow-up. Median time-to-progression was undefined for class III mases given small number of progression events and 710 days for class IV masses. Hazard ratio of progression for class IV relative to class III masses was 5.1 (95% CI 2.5-10.8) (p<.001). Conclusion: During active surveillance of cystic masses evaluated using Bosniak classification v2019, class IV masses grew faster and were more likely to progress than class III masses. Clinical Impact: In comparison with current active surveillance guidelines that treat class III and IV masses similarly, future iterations may incorporate relatively more intensive surveillance for class IV masses.

Clinical significance of novel DNA methylation biomarkers for renal clear cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor characterized by the highest mortality rate of the genitourinary cancers, and, therefore, new diagnostic and/or prognostic biomarkers are urgently needed.

METHODS

Based on genome-wide DNA methylation profiling in 11 pairs of ccRCC and non-cancerous renal tissues (NRT), the methylation at regulatory regions of ZNF677, FBN2, PCDH8, TFAP2B, TAC1, and FLRT2 was analyzed in 168 renal tissues and 307 urine samples using qualitative and quantitative methylation-specific PCR (MSP).

RESULTS

Significantly higher methylation frequencies for all genes were found in ccRCC tissues compared to NRT (33-60% vs. 0-11%). The best diagnostic performance demonstrated a panel of ZNF677, FBN2, PCDH8, TFAP2B & TAC1 with 82% sensitivity and 96% specificity. Hypermethylation of ZNF677 and PCDH8 in the tissue samples was significantly related to numerous adverse clinicopathologic parameters. For the urine-based ccRCC detection, the highest diagnostic power (AUC = 0.78) was observed for a panel of ZNF677 & PCDH8 (with or without FBN2 or FLRT2) with 69-78% sensitivity and 69-80% specificity, albeit with lower values in the validation cohort. Besides, methylation of PCDH8 was significantly related to higher tumor stage and fat invasion in the study and validation cohorts. Moreover, PCDH8 was strongly predictive for OS (HR, 5.7; 95% CI 1.16-28.12), and its prognostic power considerably increased in combination with ZNF677 (HR, 12.5; 95% CI 1.47-105.58).

CONCLUSION

In summary, our study revealed novel, potentially promising DNA methylation biomarkers of ccRCC with the possibility to be applied for non-invasive urine-based ccRCC detection and follow-up.

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.

Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.

METHODS

A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).

RESULTS

The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set.

CONCLUSION

Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.

Demographic Factors Associated With Non-Guideline-Based Treatment of Kidney Cancer in the United States

Importance

Significant demographic disparities have been found to exist in the delivery of health care. Demographic factors associated with clinical decision-making in kidney cancer have not been thoroughly studied.

Objective

To determine whether demographic factors, including sex and race/ethnicity, are associated with receipt of non-guideline-based treatment for kidney cancer.

Design, Setting, and Participants

This retrospective cohort study was conducted using data from the National Cancer Database for the years 2010 through 2017. Included patients were individuals aged 30 to 70 years with localized (ie, cT1-2, N0, M0) kidney cancer and no major medical comorbidities (ie, Charlson-Deyo Comorbidity Index score of 0 or 1) treated at Commission on Cancer-accredited health care institutions in the United States. Data were analyzed from November 2020 through March 2021.

Exposures

Demographic factors, including sex, race/ethnicity, and insurance status.

Main Outcomes and Measures

Receipt of non-guideline-based treatment (undertreatment or overtreatment) for kidney cancer, as defined by accepted clinical guidelines, was determined.

Results

Among 158 445 patients treated for localized kidney cancer, 99 563 (62.8%) were men, 120 001 individuals (75.7%) were White, and 91 218 individuals (57.6%) had private insurance. The median (interquartile range) age was 58 (50-64) years. Of the study population, 48 544 individuals (30.6%) received non-guideline-based treatment. Female sex was associated with lower adjusted odds of undertreatment (odds ratio [OR], 0.82; 95% CI, 0.77-0.88; P < .001) and higher adjusted odds of overtreatment (OR, 1.27; 95% CI, 1.24-1.30; P < .001) compared with male sex. Compared with White patients, Black and Hispanic patients had higher adjusted odds of undertreatment (Black patients: OR, 1.42; 95% CI, 1.29-1.55; P < .001; Hispanic patients: OR, 1.20; 95% CI, 1.06-1.36; P = .004) and overtreatment (Black patients: OR, 1.09; 95% CI, 1.05-1.13; P < .001; Hispanic patients: OR, 1.06; 95% CI, 1.01-1.11, P = .01). Individuals who were uninsured, compared with those who had insurance, had statistically significantly higher adjusted odds of undertreatment (OR, 2.63; 95% CI, 2.29-3.01; P < .001) and lower adjusted odds of overtreatment (OR, 0.72; 95% CI, 0.67-0.77; P < .001).

Conclusions and Relevance

This study found that there were significant disparities in treatment decision-making for patients with kidney cancer, with increased rates of non-guideline-based treatment for women and Black and Hispanic patients. These findings suggest that further research into the mechanisms underlying these disparities is warranted and that clinical and policy decision-making should take these disparities into account.

Biomarkers for Renal Cell Carcinoma Recurrence: State of the Art

PURPOSE OF REVIEW

We aim to summarize the current state of art about the possible use of biomarkers for predicting renal cell carcinoma (RCC) recurrence after curative treatment. In addition, we aim to provide a snapshot about the clinical implication of biomarkers use for follow-up planification.

RECENT FINDINGS

A wide variety of biomarkers have been proposed. RCC biomarkers have been individuated in tumoral tissue, blood, and urine. A variety of molecules, including proteins, DNA, and RNA, warrant a good accuracy for RCC recurrence and progression prediction. Their use in prediction models might warrant a better patients' risk stratification. Future prognostic models will probably include a combination of classical features (tumor grade, stage, etc.) and novel biomarkers. Such models might allow a more accurate treatment and follow-up planification.

Can we Avoid the Unnecessary Loss of nephrons in the Management of Small Solid Renal Masses? Additional Clinical Parameters to Predict Benign-malign Distinction

Objectives:

We aimed to investigate the predictive value of additional parameters for distinguishing benign-malign tumors and to prevent the loss of nephrons in small (≤4 cm) solid renal masses.

Methods:

The data of 56 patients underwent partial or radical nephrectomy between September 2009 and December 2017 due to diagnosis of localized renal cell carcinoma were retrospectively analyzed. Demographic datas, histopathological tumor types, neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), mean platelet volume (MPV), the Framingham risk score and its components, postoperative follow-up results were recorded. Patients were divided into two groups as benign and malign.

Results:

Among 56 patients with a median age of 60 (min: 35-max: 74) years, 13 patients had benign and 43 patients had malign pathologies. MLR (p=0.011), NLR (p=0.032), PLR (p=0.006), MPV (p=0.025), eGFR (p=0.019) and the Framingham score (p=0.008) were significantly higher in malign group. Among the components constituting the Framingham score, only presence of smoking (p=0.032), presence of hypertension (p=0.041) and total cholesterol values (p=0.021) were significantly higher. In multivariate analysis, NLR>2.02 (OR: 7.184, p=0.037), PLR>109.65 (OR: 12.692, p=0.002), MPV>3.44 (OR: 10.543, p=0.046) and Framingham score >10.5 (OR: 12.287, p=0.007) were found as predictive factors for distinguishing small solid renal masses concerning malignancy.

Conclusion:

We think that NLR, PLR, MPV and the Framingham scores may be used in the clinical evaluation of small solid renal masses. In this way, we may prevent the unnecessary loss of nephrons in benign masses with suspicion of malignancy.

Contrast-enhanced ultrasound (CEUS) imaging for active surveillance of small renal masses

PURPOSE

To assess the safety and efficacy of contrast-enhanced ultrasound (CEUS) imaging for monitoring small (< 4 cm) renal masses (SRM) in patients undergoing active surveillance (AS).

METHODS

We retrospectively selected all consecutive patients with SRMs who underwent AS for at least 6 months at our Institution between January 2014 and December 2018. CEUS imaging was performed by two experienced genitourinary radiologists at established time points. The accuracy of CEUS for monitoring SRM size was compared with that of CT scan. For solid SRMs, four enhancement patterns (EP) were recorded. Radiological progression was defined as SRM growth rate ≥ 5 mm/year.

RESULTS

Overall, 158/1049 (15.1%) patients with SRMs underwent AS. At a median follow-up of 25 months (IQR 13-39), no patient died due to renal cell carcinoma (RCC). No patients experienced CEUS-related adverse events. There was a large variability in the pattern of growth of SRMs (overall median growth rate: 0.40 mm/year), with 9.5% of SRMs showing radiological progression. The median SRM size was comparable between CEUS and CT scan examinations at all time points. The vast majority (92.7%) of SRMs did not show a change in their EP over time; and there was no association between the SRM's EP and radiological progression or SRM size. Overall, 43 (27.2%) patients underwent delayed intervention (DI); median SRM size, and median growth rate were significantly higher in these patients as compared to those continuing AS.

CONCLUSION

In experienced hands, CEUS is a safe and effective strategy for active monitoring of SRMs in well-selected patients undergoing AS.

Triggers for delayed intervention in patients with small renal masses undergoing active surveillance: a systematic review

INTRODUCTION

Patients with small renal masses (SRM) can be exposed to overdiagnosis and overtreatment. As such, active surveillance (AS) is recommended by all Guidelines for selected patients. However, it remains underutilized. One key reason is the lack of consensus on the factors prompting delayed intervention (DI). Herein we provide an updated overview of the triggers for DI in patients with SRMs initially undergoing AS.

EVIDENCE ACQUISITION

A systematic review of the English-language literature was performed according to the PRISMA statement recommendations using the MEDLINE, Cochrane Central Register of Controlled Trials and Web of Science databases.

EVIDENCE SYNTHESIS

Overall, 10 prospective studies including 1870 patients were included. Median patient age ranged between 64 and 75 years, while median tumor size between 1.7 cm to 2.3 cm. The proportion of cystic SRMs ranged from 0% to 30%. Baseline renal tumor biopsy was performed in 7-45.2% of patients. Among these, malignant histology was found in 28.5%-83.3% of cases. Overall, the median growth rate of SRMs ranged between 0.10 and 0.27 cm/year. The proportion of patients undergoing DI ranged between 7% and 44%, after a median AS period of 12-27 months. The most commonly performed type of DI was surgery. Of resected SRMs, 0% to 30% were benign. The actual triggers for DI were either tumor-related (renal mass growth, stage progression, development of local complications/symptoms) or patient-related (patient preference, improved medical conditions, or qualification for other surgical procedures). At a median follow-up of 21.7 - 57-6 months, the proportion of patients experiencing metastatic disease, cancer-specific and other-cause mortality was 0-3.1%, 0-4% and 0-45.6%, respectively.

CONCLUSIONS

The available evidence shows that both tumor-related and patient-related factors are ultimate triggers for DI in patients with SRMs undergoing AS. However, the level of evidence is still low and further research is needed to individualize AS strategies according to both tumor biology and patient-related characteristics and values.

Active surveillance for small renal masses in elderly patients does not increase overall mortality rates compared to primary intervention: a propensity score weighted analysis

BACKGROUND

The aim of the study was to test the effect of active surveillance (AS) versus primary intervention (PI) on overall mortality (OM) in elderly patients diagnosed with SRM.

METHODS

Elderly patients (75 years or older) diagnosed with SRMs (<4 cm) and treated with either PI (i.e. partial nephrectomy or kidney ablation) or AS between 2009 and 2018 were abstracted from the renal surgery in the elderly (RESURGE) and Delayed Intervention and Surveillance for small Renal Masses (DISSRM) datasets, respectively. OM rates were estimated among groups with Kaplan Meier method and Cox proportional hazards regression models after applying inverse probability of treatment weighting (IPTW). Multivariable logistic regression model was used to estimate IPTW. Covariates of interest were those unbalanced and/or significantly correlated with the treatment choice or with OM.

RESULTS

A total of 483 patients were included; 121 (25.1%) underwent AS. Sixty patients (12.4%) died. Overall, 6.7% of all deaths were related to cancer. IPTW-Kaplan Meier curves showed a 5-year overall survival rates of 70.0±3.5% and 73.2±4.8% in AS and PI groups, respectively (IPTW-Log-rank P value=0.308). IPTW-Cox regression model did not show meaningfully increased OM rates in AS group (HR: 1.31, 95% CI: 0.69-2.49).

CONCLUSIONS

AS represents an appealing treatment option for very elderly patients presenting with SRM, as it avoids the risks of a PI while not compromising the survival outcomes of these patients.

Development of a DNA Methylation-Based Diagnostic Signature to Distinguish Benign Oncocytoma From Renal Cell Carcinoma

PURPOSE

A challenge in the diagnosis of renal cell carcinoma (RCC) is to distinguish chromophobe RCC (chRCC) from benign renal oncocytoma, because these tumor types are histologically and morphologically similar, yet they require different clinical management. Molecular biomarkers could provide a way of distinguishing oncocytoma from chRCC, which could prevent unnecessary treatment of oncocytoma. Such biomarkers could also be applied to preoperative biopsy specimens such as needle core biopsy specimens, to avoid unnecessary surgery of oncocytoma.

METHODS

We profiled DNA methylation in fresh-frozen oncocytoma and chRCC tumors and adjacent normal tissue and used machine learning to identify a signature of differentially methylated cytosine-phosphate-guanine sites (CpGs) that robustly distinguish oncocytoma from chRCC.

RESULTS

Unsupervised clustering of Stanford and preexisting RCC data from The Cancer Genome Atlas (TCGA) revealed that of all RCC subtypes, oncocytoma is most similar to chRCC. Unexpectedly, however, oncocytoma features more extensive, overall abnormal methylation than does chRCC. We identified 79 CpGs with large methylation differences between oncocytoma and chRCC. A diagnostic model trained on 30 CpGs could distinguish oncocytoma from chRCC in 10-fold cross-validation (area under the receiver operating curve [AUC], 0.96 (95% CI, 0.88 to 1.00)) and could distinguish TCGA chRCCs from an independent set of oncocytomas from a previous study (AUC, 0.87). This signature also separated oncocytoma from other RCC subtypes and normal tissue, revealing it as a standalone diagnostic biomarker for oncocytoma.

CONCLUSION

This CpG signature could be developed as a clinical biomarker to support differential diagnosis of oncocytoma and chRCC in surgical samples. With improved biopsy techniques, this signature could be applied to preoperative biopsy specimens.

Reporting on Renal Masses, Recommendations for Terminology, and Sample Templates

Given the incidence of small renal masses, from benign cysts to malignancy, most radiologists encounter these lesions multiple times during their career. Radiologists have an opportunity to provide critical data that will further refine the understanding of the impact of these masses on patient outcomes. This article summarizes and describes recent updates and understanding of the critical observations and descriptors of renal masses. The templates and glossary of terms presented in this review article facilitate the radiology reporting of such data elements, giving radiologists the opportunity to improve diagnostic accuracy and influence management of small renal masses.

Preventing overuse of laboratory diagnostics: a case study into diagnosing anaemia in Dutch general practice

Background

More information is often thought to improve medical decision-making, which may lead to test overuse. This study assesses which out of 15 laboratory tests contribute to diagnosing the underlying cause of anaemia by general practitioners (GPs) and determines a potentially more efficient subset of tests for setting the correct diagnosis.

Methods

Logistic regression was performed to determine the impact of individual tests on the (correct) diagnosis. The statistically optimal test subset for diagnosing a (correct) underlying cause of anaemia by GPs was determined using data from a previous survey including cases of real-world anaemia patients.

Results

Only 9 (60%) of the laboratory tests, and patient age, contributed significantly to the GPs’ ability to diagnose an underlying cause of anaemia (CRP, ESR, ferritin, folic acid, haemoglobin, leukocytes, eGFR/MDRD, reticulocytes and serum iron). Diagnosing the correct underlying cause may require just five (33%) tests (CRP, ferritin, folic acid, MCV and transferrin), and patient age.

Conclusions

In diagnosing the underlying cause of anaemia a subset of five tests has most added value. The real-world impact of using only this subset should be further investigated. As illustrated in this case study, a statistical approach to assessing the added value of tests may reduce test overuse.

Outcomes and predictors of benign histology in patients undergoing robotic partial or radical nephrectomy for renal masses: a multicenter study

Introduction

Theaim of this study was to assess preoperative factors associated with benign histology in patients undergoing surgical removal of a renal mass and to analyze outcomes of robotic partial nephrectomy (PN) and radical nephrectomy (RN) for these masses.

Material and methods

Overall, 2,944 cases (543 benign and 2,401 malignant) who underwent robotic PN and RN between 2003–2018 at 10 institutions worldwide were included. The assessment of the predictors of benign histology was made at the final surgical pathology report. Descriptive statistics, Mann-Whitney U, Pearson’s χ², and logistic regression analysis were used.

Results

Patients in the benign group were mostly female (61 vs. 33%; p <0.001), with lower body mass index (BMI) (26.0 vs. 27.1 kg/m²; p <0.001). The benign group presented smaller tumor size (2.8 vs. 3.5 cm; p <0.001), R.E.N.A.L. score (6.0 vs. 7.0; p <0.001). There was a lower rate of hilar (11 vs.18%; p = 0.001), cT≥3 (1 vs. 4.5%; p <0.001) tumors in the benign group. There was a statistically significant higher rate of PN in the benign group (97 vs. 86%; p <0.001) as well as a statistically significant lower 30-day re-admission rate (2 vs. 5%; p = 0.081). Multivariable analysis showed male gender (OR: 0.52; p <0.001), BMI (OR: 0.95; p <0.001), and cT3a (OR: 0.22; p = 0.005) to be inversely associated to benign histology.

Conclusions

In 18% of cases, a benign histologic type was found. Only 3% of these tumors were treated with RN. Female gender, lower BMI, and higher T staging showed to be independent predictors of benign histology.