Assessing lymph node status in patients with kidney cancer

Sub-regional Radiomics Analysis for Predicting Metastasis Risk in Clear Cell Renal Cell Carcinoma: A Multicenter Retrospective Study

RATIONALE AND OBJECTIVES

Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates.

MATERIAL AND METHODS

A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models.

RESULTS

In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI: 0.902-0.968] and 0.934 [95%CI: 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI: 0.794-0.988] and 0.903 [95%CI: 0.819-0.987], respectively.

CONCLUSION

We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study

PURPOSE

To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.

MATERIALS AND METHODS

In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve.

RESULTS

A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model.

CONCLUSION

The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.

PSMA-Expression Is Highly Associated with Histological Subtypes of Renal Cell Carcinoma: Potential Implications for Theranostic Approaches

In renal cell carcinoma (RCC), accurate imaging methods are required for treatment planning and response assessment to therapy. In addition, there is an urgent need for new therapeutic options, especially in metastatic RCC. One way to combine diagnostics and therapy in a so-called theranostic approach is the use of radioligands directed against surface antigens. For instance, radioligands against prostate-specific membrane antigen (PSMA) have already been successfully used for diagnosis and radionuclide therapy of metastatic prostate cancer. Recent studies have demonstrated that PSMA is expressed not only in prostate cancer but also in the neovasculature of several solid tumors, which has raised hopes to use PSMA-guided theranostic approaches in other tumor entities, too. However, data on PSMA expression in different histopathological subtypes of RCC are sparse. Because a better understanding of PSMA expression in RCC is critical to assess which patients would benefit most from theranostic approaches using PSMA-targeted ligands, we investigated the expression pattern of PSMA in different subtypes of RCC on protein level. Immunohistochemical staining for PSMA was performed on formalin-fixed, paraffin-embedded archival material of major different histological subtypes of RCC (clear cell RCC (ccRCC)), papillary RCC (pRCC) and chromophobe RCC (cpRCC). The extent and intensity of PSMA staining were scored semi-quantitatively and correlated with the histological RCC subtypes. Group comparisons were calculated with the Kruskal-Wallis test. In all cases, immunoreactivity was detected only in the tumor-associated vessels and not in tumor cells. Staining intensity was the strongest in ccRCC, followed by cpRCC and pRCC. ccRCC showed the most diffuse staining pattern, followed by cpRCC and pRCC. Our results provide a rationale for PSMA-targeted theranostic approaches in ccRCC and cpRCC.

The Current Role of Lymph Node Dissection in Nonmetastatic Localized Renal Cell Carcinoma

PURPOSE

To explore the current role of lymph node dissection (LND) in the management of nonmetastatic localized renal cell carcinoma (RCC).

BACKGROUND

There is currently no proven benefit of LND in the setting of RCC, and its role remains controversial because of conflicting evidence. Patients who may benefit from LND are those at greatest risk of nodal disease, but the tools used to predict nodal involvement are limited due to unpredictable retroperitoneal lymphatics. The indications, templates, and extent of LND are also not standardized, adding to the ambiguity of current guidelines surrounding its use.

EVIDENCE ACQUISITION

A PubMed search of the literature from January 2017 to December 2022 was conducted using the search terms "renal cell carcinoma" or "renal cancer" in combination with "lymph node dissection" or "lymphadenectomy". Case studies and editorials were excluded, whereas studies investigating the therapeutic effect of LND were classified as either demonstrating a benefit or no benefit. References of the studies and review articles were also searched for notable studies and findings that were outside the five-year literature search. The studies in this review were restricted to the English language.

RESULTS

Only a number of studies in recent years have found an association between the extent of LND and increased survival. Most studies do not indicate an associated benefit, and some even suggest a negative effect on survival. Most of these studies are retrospective.

CONCLUSION

The therapeutic value of LND in RCC is still unclear, and although prospective data are needed, its declining rates and emerging new therapies make this unlikely. A better understanding of renal lymphatics and improved detection of nodal disease may help determine the role of LND in nonmetastatic localized RCC.

ACR Appropriateness Criteria® Staging of Renal Cell Carcinoma: 2022 Update

Renal cell carcinoma is a complex group of highly heterogenous renal tumors demonstrating variable biological behavior. Pretreatment imaging of renal cell carcinoma involves accurate assessment of the primary tumor, presence of nodal, and distant metastases. CT and MRI are the key imaging modalities used in the staging of renal cell carcinoma. Important imaging features that impact treatment include tumor extension into renal sinus and perinephric fat, involvement of pelvicalyceal system, infiltration into adrenal gland, involvement of renal vein and inferior vena cava, as well as the presence of metastatic adenopathy and distant metastases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Integrating Surgery in the Multidisciplinary Care of Advanced Renal Cell Carcinoma

The role of surgery for patients with locally advanced and metastatic renal cell carcinoma (RCC) is not precisely defined in our contemporary era of systemic therapies. Research in this field is focused on the role of regional lymphadenectomy, along with indications and timing of cytoreductive nephrectomy and metastasectomy. As our understanding of the molecular and immunological basis of RCC continues to develop along with the advent of novel systemic therapies, prospective clinical trials will be critical in defining how surgery should be integrated into the treatment paradigm of advanced RCC.

Sarcomatoid renal cell carcinoma: MRI features and their association with survival

OBJECTIVE

To evaluate MRI features of sarcomatoid renal cell carcinoma (RCC) and their association with survival.

METHODS

This retrospective single-center study included 59 patients with sarcomatoid RCC who underwent MRI before nephrectomy during July 2003-December 2019. Three radiologists reviewed MRI findings of tumor size, non-enhancing areas, lymphadenopathy, and volume (and percentage) of T2 low signal intensity areas (T2LIA). Clinicopathological factors of age, gender, ethnicity, baseline metastatic status, pathological details (subtype and extent of sarcomatoid differentiation), treatment type, and follow-up were extracted. Survival was estimated using Kaplan-Meier method and Cox proportional-hazards regression model was used to identify factors associated with survival.

RESULTS

Forty-one males and eighteen females (median age 62 years; interquartile range 51-68) were included. T2LIAs were present in 43 (72.9%) patients. At univariate analysis, clinicopathological factors associated with shorter survival were: greater tumor size (> 10 cm; HR [hazard ratio] = 2.44, 95% CI 1.15-5.21; p = 0.02), metastatic lymph nodes (present; HR = 2.10, 95% CI 1.01-4.37; p = 0.04), extent of sarcomatoid differentiation (non-focal; HR = 3.30, 95% CI 1.55-7.01; p < 0.01), subtypes other than clear cell, papillary, or chromophobe (HR = 3.25, 95% CI 1.28-8.20; p = 0.01), and metastasis at baseline (HR = 5.04, 95% CI 2.40-10.59; p < 0.01). MRI features associated with shorter survival were: lymphadenopathy (HR = 2.24, 95% CI 1.16-4.71; p = 0.01) and volume of T2LIA (> 3.2 mL, HR = 4.22, 95% CI 1.92-9.29); p < 0.01). At multivariate analysis, metastatic disease (HR = 6.89, 95% CI 2.79-16.97; p < 0.01), other subtypes (HR = 9.50, 95% CI 2.81-32.13; p < 0.01), and greater volume of T2LIA (HR = 2.51, 95% CI 1.04-6.05; p = 0.04) remained independently associated with worse survival.

CONCLUSION

T2LIAs were present in approximately two thirds of sarcomatoid RCCs. Volume of T2LIA along with clinicopathological factors were associated with survival.

A Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma

Introduction: Renal cell carcinoma (RCC) is one of the most common urinary tumors. The risk of metastasis for patients with RCC is about 1/3, among which 30−40% have lymph node metastasis, and the existence of lymph node metastasis will greatly reduce the survival rate of patients. However, the necessity of lymph node dissection is still controversial at present. Therefore, a new predictive model is urgently needed to judge the risk of lymph node metastasis and guide clinical decision making before operation. Method: We retrospectively collected the data of 189 patients who underwent retroperitoneal lymph node dissection or enlarged lymph node resection due to suspected lymph node metastasis or enlarged lymph nodes found during an operation in Tongji Hospital from January 2016 to October 2021. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (lasso) regression analyses were used to identify preoperative predictors of pathological lymph node positivity. A nomogram was established to predict the probability of lymph node metastasis in patients with RCC before surgery according to the above independent predictors, and its efficacy was evaluated with a calibration curve and a DCA analysis. Result: Among the 189 patients, 54 (28.60%) were pN1 patients, and 135 (71.40%) were pN0 patients. Three independent impact factors were, finally, identified, which were the following: age (OR = 0.3769, 95% CI = 0.1864−0.7622, p < 0.01), lymph node size according to pre-operative imaging (10−20 mm: OR = 15.0040, 95% CI = 1.5666−143.7000, p < 0.05; >20 mm: OR = 4.4013, 95% CI = 1.4892−7.3134, p < 0.01) and clinical T stage (cT1−2 vs. cT3−4) (OR = 3.1641, 95% CI = 1.0336−9.6860, p < 0.05). The calibration curve and DCA (Decision Curve Analysis) showed the nomogram of this predictive model had good fitting. Conclusions: Low age, large lymph node size in pre-operative imaging and high clinical T stage can be used as independent predictive factors of pathological lymph node metastasis in patients with RCC. Our predictive nomogram using these factors exhibited excellent discrimination and calibration.

Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study

Background

Lymph node (LN) metastasis is strongly associated with distant metastasis of renal cell carcinoma (RCC) and indicates an adverse prognosis. Accurate LN-status prediction is essential for individualized treatment of patients with RCC and to help physicians make appropriate surgical decisions. Thus, a prediction model to assess the hazard index of LN metastasis in patients with RCC is needed.

Methods

Partial data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data of 492 individuals with RCC, collected from the Southwest Hospital in Chongqing, China, were used for external validation. Eight indicators of risk of LN metastasis were screened out. Six machine learning (ML) classifiers were established and tuned, focused on predicting LN metastasis in patients with RCC. The models were integrated with big data analytics and ML algorithms. Based on the optimal model, we developed an online risk calculator and plotted overall survival using Kaplan-Meier analysis.

Results

The extreme gradient-boosting (XGB) model was superior to the other models in both internal and external trials. The area under the curve, accuracy, sensitivity, and specificity were 0.930, 0.857, 0.856, and 0.873, respectively, in the internal test and 0.958, 0.935, 0.769, and 0.944, respectively, in the external test. These parameters show that XGB has an excellent ability for clinical application. The survival analysis showed that patients with predicted N1 tumors had significantly shorter survival (p < 0.0001).

Conclusion

Our study shows that integrating ML algorithms and clinical data can effectively predict LN metastasis in patients with confirmed RCC. Subsequently, a freely available online calculator (https://xinglinyi.shinyapps.io/20221004-app/) was built, based on the XGB model.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database

Objectives

Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).

Patients and Methods

Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.

Results

Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.

Conclusion

In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.

Methods

The clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.

Results

A total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.

Conclusion

This study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

Contrast enhanced computed tomography assessment of superficial inguinal lymph node metastasis in canine mammary gland tumors

Mammary gland neoplasms are predominant in dogs. However, sentinel lymph node (SLN) status assessment criteria have not been established for these cases. In this retrospective, secondary analysis, diagnostic case control study, CT images of 65 superficial inguinal SLNs were obtained before and 1, 3, 5, and 10 min after intravenous administration of contrast agent (iopamidol 370 mgI/mL). The presence and degree of postcontrast enhancement were assessed, by means of the median absolute density value and the maximum absolute density value at any time point in the center and in the periphery of each SLN measured in Hounsfield units (HU), before and after contrast agent administration. These values were compared with histopathological findings postsurgical excision. Receiver operating characteristic analysis was conducted. The absolute density values ranged widely at each time point and within each group of nodes (negative, positive, control group). At all time points, the median density value in the center and in the periphery was significantly higher in metastatic than in non-metastatic SLNs (P ≤ .014). Among the parameters tested, the median absolute density value measured in the periphery of the SLN 3 min after injection showed the highest sensitivity, specificity, and accuracy (AUC) (87.5%, 82.1%, and 92.1% respectively), with a cutoff value of 50.9 HU. The maximum absolute density value at any time point in the center and periphery of the SLNs was also significantly higher in metastatic SLNs compared to non-metastatic (P ≤ .001). With a cutoff value of 59.5 HU, the maximum absolute density value in the periphery of the SLN displayed high sensitivity and specificity (87.5% and 89.3%, respectively). The results of this study support the hypothesis that contrast enhanced CT imaging may aid in the assessment of SLN metastasis in dogs with mammary gland neoplasms.

Long Non-Coding PROX1-AS1 Expression Correlates with Renal Cell Carcinoma Metastasis and Aggressiveness

Long non-coding RNAs (lncRNAs) can be specifically expressed in different tissues and cancers. By controlling the gene expression at the transcriptional and translational levels, lncRNAs have been reported to be involved in tumor growth and metastasis. Recent data demonstrated that multiple lncRNAs have a crucial role in renal cell carcinoma (RCC) progression-the most common malignant urogenital tumor. In the present study, we found a trend towards increased PROX1 antisense RNA 1 (PROX1-AS1) expression in RCC specimens compared to non-tumoral margins. Next, we found a positive correlation between PROX1-AS1 expression and the occurrence of distant and lymph node metastasis, higher tumor stage (pT1 vs. pT2 vs. pT3-T4) and high-grade (G1/G2 vs. G3/G4) clear RCC. Furthermore, global demethylation in RCC-derived cell lines (769-P and A498) and human embryonic kidney 293 (HEK293) cells induced a significant increase of PROX1-AS1 expression level, with the most remarkable change in HEK293 cells. In line with this evidence, bisulfite sequencing analysis confirmed the specific demethylation of bioinformatically selected CpG islands on the PROX1-AS1 promoter sequence in the HEK293 cell line but not in the tumor cells. Additionally, the human specimen analysis showed the hemimethylated state of CG dinucleotides in non-tumor kidney tissues, whereas the tumor samples presented the complete, partial, or no demethylation of CpG-islands. In conclusion, our study indicated that PROX1-AS1 could be associated with RCC progression, and further investigations may define its role as a new diagnostic marker and therapeutic target.

Multiomic Analysis of Cereblon Expression and Its Prognostic Value in Kidney Renal Clear Cell Carcinoma, Lung Adenocarcinoma, and Skin Cutaneous Melanoma

Cereblon (CRBN) is a component of the E3 ubiquitin ligase complex that plays crucial roles in various cellular processes. However, no systematic studies on the expression and functions of CRBN in solid tumors have been conducted to date. Here, we analyzed CRBN expression and its clinical value using several bioinformatic databases. CRBN mRNA expression was downregulated in various cancer types compared to normal cells. Survival analysis demonstrated that overall survival was significantly positively correlated with CRBN expression in some cancer types including lung adenocarcinoma (LUAD), kidney renal clear cell carcinoma (KIRC), and skin cutaneous melanoma (SKCM). CRBN expression was downregulated regardless of clinicopathological characteristics in LUAD and KIRC. Analysis of genes that are commonly correlated with CRBN expression among KIRC, LUAD, and SKCM samples elucidated the potential CRBN-associated mechanisms of cancer progression. Overall, this study revealed the prognostic value of CRBN and its potential associated mechanisms, which may facilitate the development of anti-cancer therapeutic agents.

Update on the Role of Imaging in Clinical Staging and Restaging of Renal Cell Carcinoma Based on the AJCC 8th Edition, From the AJR Special Series on Cancer Staging

This article reviews the essential role of imaging in clinical staging and restaging of renal cell carcinoma (RCC). To completely characterize and stage an indeterminate renal mass, renal CT or MRI without and with IV contrast administration is recommended. The critical items for initial clinical staging of an indeterminate renal mass or of a known RCC according to the TNM staging system are tumor size, renal sinus fat invasion, urinary collecting system invasion, perinephric fat invasion, venous invasion, adrenal gland invasion, invasion of the perirenal (Gerota) fascia, invasion into other adjacent organs, the presence of enlarged or pathologic regional (retroperitoneal) lymph nodes, and the presence of distant metastatic disease. Larger tumor size is associated with higher stage disease and invasiveness, lymph node spread, and distant metastatic disease. Imaging practice guidelines for clinical staging of RCC, as well as the role of renal mass biopsy, are highlighted. Specific findings associated with response of advanced cancer to antiangiogenic therapy and immunotherapy are discussed, as well as limitations of changes in tumor size after targeted therapy. The accurate clinical staging and restaging of RCC using renal CT or MRI provides important prognostic information and helps guide the optimal management of patients with RCC.

Patterns of distant metastases in patients with clear cell renal cell carcinoma--A population-based analysis

We developed this study to describe the patterns of distant metastasis (DM) and explore the predictive and prognostic factors of DM in clear cell renal cell carcinoma (ccRCC) patients. We collected the eligible patients from the Surveillance, Epidemiology, and End Result (SEER) database from 2010 to 2015. Then, comparisons of baseline characteristics between patients in different metastatic patterns were made. In addition, proportional mortality ratios (PMRs) and proportion trends of different patterns were calculated. Afterward, survival outcomes were explored by Kaplan–Meier (KM) analyses. Finally, predictive and prognostic factors of DM were investigated. A total of 33,449 ccRCC patients were eventually identified, including 2931 patients with DM and 30,518 patients without DM. 8.76% of patients suffered DM at their initial diagnosis, 35.01% of them had multiple metastases. Generally, lung (6.19%) was the most common metastatic site in patients with DM, and brain (1.20%) was the least frequent metastatic organ. The proportion trends of different metastatic patterns tended to be stable between 2010 and 2015. Moreover, higher tumor grade, T stage, and N stage were identified as risk factors of DM. Finally, age at diagnosis, grade, T stage, N stage, the administration of surgery, the number of metastatic sties, marital status, and household income were found to be significantly associated with prognosis. Lung was the most common metastatic site in ccRCC patients. Different survival outcomes and prognostic factors were identified for different metastatic patterns. Hence, our study would have great value for clinical practice in the future.