Growth Pattern of Clear Cell Renal Cell Carcinoma in Patients with Delayed Surgical Intervention: Fast Growth Rate Correlates with High Grade and May Result in Poor Prognosis

An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome

PURPOSE

Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.

MATERIALS AND METHODS

A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT <  = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).

RESULTS

The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).

CONCLUSION

Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Natural history of Von Hippel-Lindau disease-associated and sporadic clear cell renal cell carcinoma: a comparative study

PURPOSE

To compare the tumor growth kinetics between sporadic clear cell renal cell carcinoma (ccRCC) and Von Hippel-Lindau disease-associated renal cell carcinoma (VHL-associated RCC). To analyze predictive markers for the growth rate of these two types of RCC.

METHODS

The clinical data of patients with renal tumors who received active surveillance were collected retrospectively. Immunohistochemical staining was utilized to analyze the expression levels of VHL, PBRM1, H3K36me3, and BAP1 in the postoperative specimens.

RESULTS

The age of the VHL group was significantly younger than that of the sporadic group (P < 0.0001). The mean linear growth rate (LGR) was significantly faster in the sporadic group (P = 0.0004). The tumors of those in the sporadic group tended to have a higher histologic grade (P = 0.0011). In the sporadic group, tumor histologic grade was an independent predictor for rapid mean LGR (P = 0.0022). In the VHL group, initial maximal tumor diameter (MTD) was the only independent predictor for rapid mean LGR (P < 0.0001). Tumors with low VHL expression and negative PBRM1 expression showed a faster growth rate in the sporadic group (P = 0.001 and P = 0.008, respectively). The expression levels of the four biomarkers showed no impact on the tumor growth rate in the VHL group.

CONCLUSION

Sporadic ccRCC grew faster than VHL-associated RCC. High histologic grade, low VHL expression and negative PBRM1 expression were predictors of faster growth in sporadic ccRCC. A large initial MTD was a predictor of faster growth for VHL-associated RCC.

Natural history and growth kinetics of clear cell renal cell carcinoma in sporadic and von Hippel-Lindau disease

Background

To evaluate and compare the natural history and growth kinetics of sporadic clear cell renal cell carcinoma (ccRCC) with those of ccRCC in von Hippel-Lindau disease (VHL).

Methods

Sixty patients in the sporadic group with 61 tumors and 15 patients in the VHL group with 30 tumors whom all underwent delayed surgery after at least 12 months of active surveillance (AS) were enrolled to conduct a retrospective cohort study. The growth rate was calculated, and the growth kinetics between the sporadic and VHL groups were compared. The patient and tumor characteristics were reviewed, and their correlation with growth rate was analyzed.

Results

The mean growth rate of sporadic ccRCC was 0.91 cm/year (ranging from 0–4.74 cm/year) and that of VHL ccRCC was 0.47 cm/year (ranging from 0.04–1.89 cm/year). The growth rate of sporadic ccRCC showed a tendency of being faster than that of VHL ccRCC but did not reach statistical significance (P=0.07). The factors affecting the growth rate were different between the two groups. For VHL ccRCC, the only factor that correlated with growth rate was initial tumor diameter (P<0.001), but for sporadic ccRCC, the only factor was pathological nuclear grade (P<0.001).

Conclusions

The growth rate of VHL-associated ccRCC might be slower than that of sporadic ccRCC. Furthermore, we identified a disparity in growth kinetics between sporadic and VHL-associated ccRCC.

Computed tomography features predicting aggressiveness of malignant parenchymal renal tumors suitable for partial nephrectomy

The aim of this study was to identify and standardize computed tomography (CT) features having a potential role in predicting aggressiveness of malignant parenchymal renal tumors suitable for partial nephrectomy (PN). We performed a non-systematic review of the recent literature to evaluate the potential impact of CT variables proposed by the Society of Abdominal Radiology Disease-Focused Panel on Renal Cell Carcinoma in predicting aggressiveness of newly diagnosed malignant parenchymal renal tumors. The analyzed variables were clinical tumor size, tumor growth rate, enhancement characteristics, amount of cystic component, polar and capsular location, tumor margins and distance between tumor and renal sinus. Unfavorable behavior was defined as: 1) renal cell carcinoma (RCC) with stage ≥pT3; 2) nuclear grade 3 or 4; 3) presence of sarcomatoid de-differentiation; or 4) non-clear cell subtypes with unfavorable prognosis (type 2 papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC). Beyond clinical tumor size, tumor growth rate, enhancement characteristics, amount of cystic component, tumor margins and distance between tumor and renal sinus are highly relevant features predicting an unfavorable behavior. Moreover, several studies supported the role of necrosis as preoperative predictor of tumor aggressiveness. Peritumoral and intratumoral vasculature as well as capsule status are emerging variables that need to be further evaluated. Tumor size, enhancement characteristics, tumor margins and distance to the renal sinus are highly relevant CT features predicting biological aggressiveness of malignant parenchymal renal tumors. Combination of these parameters might be useful to generate tools to predict the unfavorable behavior of renal tumors suitable for PN.

Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm

Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I-II) from high-grade (Fuhrman III-IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49-0.68), accuracy of 0.77 (95% CI 0.68-0.84), sensitivity of 0.38 (95% CI 0.29-0.48), and specificity of 0.86 (95% CI 0.78-0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50-0.69), accuracy of 0.81 (95% CI 0.72-0.88), sensitivity of 0.12 (95% CI 0.14-0.30), and specificity of 0.97 (95% CI 0.87-0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.

Specific Growth Rate as a Predictor of Survival in Pancreatic Neuroendocrine Tumors: A Multi-institutional Study from the United States Neuroendocrine Study Group

BACKGROUND

Pancreatic neuroendocrine tumors (PNETs) are often indolent; however, identifying patients at risk for rapidly progressing variants is critical, particularly for those with small tumors who may be candidates for expectant management. Specific growth rate (SGR) has been predictive of survival in other malignancies but has not been examined in PNETs.

METHODS

A retrospective cohort study of adult patients who underwent PNET resection from 2000 to 2016 was performed utilizing the multi-institutional United States Neuroendocrine Study Group database. Patients with ≥ 2 preoperative cross-sectional imaging studies at least 30 days apart were included in our analysis (N = 288). Patients were grouped as "high SGR" or "low SGR." Demographic and clinical factors were compared between the groups. Kaplan-Meier and log-rank analysis were used for survival analysis. Cox proportional hazard analysis was used to assess the impact of various clinical factors on overall survival (OS).

RESULTS

High SGR was associated with higher T stage at resection, shorter doubling time, and elevated HbA1c (all P ≤ 0.01). Patients with high SGR had significantly decreased 5-year OS (63 vs 80%, P = 0.01) and disease-specific survival (72 vs 91%, P = 0.03) compared to those with low SGR. In patients with small (≤ 2 cm) tumors (N = 106), high SGR predicted lower 5-year OS (79 vs 96%, P = 0.01). On multivariate analysis, high SGR was independently associated with worse OS (hazard ratio 2.67, 95% confidence interval 1.05-6.84, P = 0.04).

CONCLUSION

High SGR is associated with worse survival in PNET patients. Evaluating PNET SGR may enhance clinical decision-making, particularly when weighing expectant management versus surgery in patients with small tumors.

A novel prognostic nomogram based on 5 long non-coding RNAs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive histological subtype of all kidney malignancies, with high levels of incidence and mortality. In the present study, long non-coding (lnc)RNA expression profiles of patients with ccRCC from The Cancer Genome Atlas database were comprehensively analyzed to identify differentially expressed lncRNAs (DElncRNAs). The patients with ccRCC were then divided into training and validation cohorts. Univariate and LASSO regression analyses were performed to select the most significant survival-associated candidate DElncRNAs in the training cohort. Multivariate Cox regression analysis was then performed to develop a risk score formula and a prognostic nomogram for predicting 3- and 5-year overall survival (OS). The accuracies of the nomogram predictions were evaluated by determining the area under the receiver operating characteristic curve (AUC) and a calibration plot. Finally, functional enrichment analysis and protein-protein interaction network prediction were implemented to predict the functions and molecular mechanisms of the candidate DElncRNAs in ccRCC. A total of 1,553 DElncRNAs were identified, and 5 candidate DElncRNAs (AC026992.2, AC245041.2, LINC00524, LINC01956 and LINC02080) were included in the nomogram. The AUC values for 3- and 5-year overall survival in the training cohort were 0.768 and 0.814, respectively, which were increased compared with that based on the clinical index (0.760 and 0.694, respectively). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the 521 mRNAs highly associated with 5 DElncRNAs were primarily involved in 17 terms and 25 pathways, respectively. Based on the 5 DElncRNAs, a novel and convenient prognostic nomogram for predicting 3- and 5-year OS for patients with ccRCC was developed. The results of the present study may be conducive to the development of a precise predictive tool for the prognosis of ccRCC and may provide information regarding the molecular mechanisms of ccRCC. However, additional experimental in vitro and in vivo studies investigating lncRNAs may be required.

Histopathological Prognostic Factors in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (CCRCC) are the most frequent type of renal cell carcinoma. Fuhrman grade and tumor stage are prognostic factors with great importance in survival rate. This study was performed on 75 cases of CCRCC diagnosed by the Anatomical Pathology Laboratory of the County Clinical Emergency Hospital of Craiova between 2014 and 2017. The biological material was represented by pieces of nephrectomy. The cases were analyzed on two criteria: epidemiology (age, sex) and histopathology (Fuhrman grade, tumor stage, architectural pattern, sarcomatoid transformation, and necrosis). Statistical analysis was done using Chi Square tests in IBM SPSS software. Average diagnosis age of CCRCC was 58.8±10.2 years, predominantly in male patients (66.7%). Tumor sizes were between 2 and 14cm, with an average of 6.7±2.9cm. Most cases were determined to be tumor stage III (60%) and Fuhrman grade 2 (56%), followed, in order of frequency, by tumor stages I and II (28% and 10.7%) and Fuhrman grades 3 and 1 (21.3% and 20%). High Fuhrman grade CCRCC were significantly associated with advanced tumor stage (p<0.05, χ² test). Most cases presented a mixed pattern, significantly associated with advanced tumor stages (p<0.05, χ² test). Even though the presence of sarcomatoid transformation was more frequent in advanced tumor stages, it wasn’t significantly linked to them (p<0.05, χ² test). Conclusions: Analyzed histopathological parameters are useful for determining CCRCC aggressiveness. CCRCC in advanced tumor stages is associated with high Fuhrman grade and mixed architectural pattern.

Growth rates and outcomes of observed large renal masses

INTRODUCTION

The natural history of small renal masses has been well defined, leading to the recommendation of active surveillance in some patients with limited life expectancy. However, this information is less clear for large renal masses (LRM), leading to ambiguity for management in the older, comorbid patient. The objective of this study was to define the natural history, including the growth rate and metastatic risk, of LRM in order to better counsel patients regarding active surveillance.

METHODS

This was a retrospective review of patients with solid renal masses >4 cm that had repeated imaging identified from an institutional imaging database. Patient comorbidities and outcomes were obtained through retrospective chart analysis. Outcomes assessed included tumour growth and metastatic rates, as well as cancer-specific (CSS) and overall survival (OS) usimg Kaplan-Meier methodology.

RESULTS

We identified 69 patients between 2005 and 2016 who met the inclusion criteria. Mean age at study entry was 75.5 years; mean tumour maximal dimension at study entry was 5.6 cm. CSS was 83% and OS 63% for patients presenting without metastasis, with a mean followup of 57.5 months. The mean growth rate of those that developed metastasis during followup (n=15) was 0.98 cm/year (95% confidence interval [CI] 0.33-1.63) as compared to those that did not develop metastasis (n=46), with a growth rate of 0.67 cm/year (95% CI 0.34-1) (non-significant). Seven patients had evidence of metastasis at the baseline imaging of their LRM and had subsequent growth rate of 1.47 cm/year (95% CI 0.37-2.57) (non-significant) CONCLUSIONS: Compared to small renal masses, LRM are associated with higher metastasis rates and lower CSS and more rapid growth rates. Selection criteria for recommending observation of LRM in older, comorbid patients should be more conservative than for small renal masses.

Imaging Protocols for Active Surveillance in Renal Cell Carcinoma

PURPOSE OF REVIEW

To review the growth kinetics of small renal masses and available imaging modalities for mass characterization and surveillance, highlight current organizational recommendations for the active surveillance of small renal masses, and discuss the most recently reported oncological outcomes of patients as they relate to various surveillance imaging protocols and progression to delayed intervention.

RECENT FINDINGS

Overall, organizational guideline recommendations are broad and lack specifics regarding timing and modality for follow-up imaging of small renal masses. Additionally, despite general consensus in the literature about certain criteria to trigger delayed intervention, there exist no formal guidelines. Active surveillance of small renal masses is an acceptable management strategy for patients with prohibitive surgical risk; however, standardized imaging protocols for surveillance are lacking, as are randomized, prospective trials to evaluate the ideal follow-up protocol.

Renal Tumor Biopsy Technique

Objective:

To review hot issues and future direction of renal tumor biopsy (RTB) technique.

Data Sources:

The literature concerning or including RTB technique in English was collected from PubMed published from 1990 to 2015.

Study Selection:

We included all the relevant articles on RTB technique in English, with no limitation of study design.

Results:

Computed tomography and ultrasound were usually used for guiding RTB with respective advantages. Core biopsy is more preferred over fine needle aspiration because of superior accuracy. A minimum of two good-quality cores for a single renal tumor is generally accepted. The use of coaxial guide is recommended. For biopsy location, sampling different regions including central and peripheral biopsies are recommended.

Conclusion:

In spite of some limitations, RTB technique is relatively mature to help optimize the treatment of renal tumors.