Construction and validation of a novel prognostic nomogram for patients with sarcomatoid renal cell carcinoma: a SEER-based study

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan-Meier (K-M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K-M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.

The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma

AIMS

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis.

METHODS

All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity.

RESULTS

Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS (p < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group.

CONCLUSIONS

Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.

Clinicopathologic factors linked to oncologic outcomes for renal cell carcinoma with sarcomatoid dedifferentiation: A PRISMA-compliant systematic review and meta-analysis

Background

There are still differences in the prognostic factors of renal cell carcinoma with sarcomatoid dedifferentiation (sRCC). The aim of this study was to evaluate important predictors of survival in patients with sRCC.

Patients and methods

A comprehensive search of PubMed, Embase, and Cochrane Library was conducted to identify eligible studies. The endpoints embraced overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS). Hazard ratios (HRs) and related 95% confidence intervals (CIs) were extracted.

Results

A total of 13 studies were included for analyses. The pooled results showed that high European Cooperative Oncology Group performance score (HR 2.39, 95% CI 1.32–4.30; P = 0.004), high T stage (HR 2.18, 95% CI 1.66–2.86; P < 0.001), positive lymph node (HR 1.54, 95% CI 1.40–1.69; P < 0.001), distant metastasis (HR 2.52, 95% CI 1.99–3.21; P < 0.001), lung metastases (HR 1.45, 95% CI 1.16–1.80; P < 0.001), liver metastases (HR 1.71, 95% CI 1.30–2.25; P < 0.001), tumor necrosis (HR 1.78, 95% CI 1.14–2.80; P = 0.010), and percentage sarcomatoid ≥50% (HR 2.35, 95% CI 1.57–3.52; P < 0.001) were associated with unfavorable OS. Positive lymph node (HR 1.57, 95% CI 1.33–1.85; P < 0.001) and high neutrophil to lymphocyte ratio (HR 1.16, 95% CI 1.04–1.29; P = 0.008) were associated with unfavorable CSS. High T stage (HR 1.93 95% CI 1.44–2.58; P < 0.001) was associated with unfavorable progression-free survival.

Conclusions

A meta-analysis of available data identified important prognostic factors for CSS, OS, and PFS of sRCC, which should be systematically evaluated for patient counseling, risk stratification, and treatment selection.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=249449.

Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis

We aimed to construct and validate nomogram models that predict the incidence of lung metastasis (LM) in patients with renal cell carcinoma (RCC) and evaluate overall survival (OS) and cancer-specific survival (CSS) among RCC patients with LM. The Surveillance, Epidemiology, and End Results database was analyzed for RCC patients diagnosed between 2010 and 2015. The X-tile program was used to determine the best cutoff values for age at initial diagnosis and tumor size. Logistic regression analysis was performed to explore independent risk factors for LM, and COX regression analysis was used to identify prognostic indicators for OS and CSS in lung metastatic RCC patients. Subsequently, 3 nomograms were established, and receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were utilized to validate their accuracy. We randomly assigned 10,929 patients with RCC to 2 groups with 1:1 allocation. Multivariate logistic analyses revealed that pathology, tumor (T) stage, nodes (N) stage, race, grade, surgery, metastatic sites, and tumor size were independent risk factors for LM. Multivariate Cox analyses showed that pathology, T stage, N stage, age, surgery, metastatic sites, and residence were independent prognostic factors for OS and CSS in patients with LM. Then, nomograms were developed based on the multivariate logistic and Cox regression analyses results. The ROC and DCA curves confirmed that these nomograms achieved satisfactory discriminative power. Three effective nomograms were constructed and validated that can be used to assist clinicians in predicting the incidence of LM and evaluating the prognosis of lung metastatic RCC.

Construction and validation of a novel prognostic nomogram for patients with metastatic renal cell carcinoma: a SEER-based study

Objective

We aimed to establish and validate nomograms to evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with metastatic renal cell carcinoma (MRCC).

Methods

Between 2010 and 2015, the clinical information of patients with MRCC was selected using the Surveillance, Epidemiology, and End Results database. Two nomograms were constructed based on Cox regression analysis, and their prediction accuracy was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

After propensity score matching, there were 568 patients with MRCC in the training group and 568 in the validation group. Multivariate analyses revealed that age, residence, pathology, T stage, N stage, surgery, and metastatic sites were independent prognostic factors for the OS and CSS of MRCC. The C-index and ROC curves indicated that the two nomograms of OS and CSS showed satisfactory discriminative power. Furthermore, DCA displayed that the nomograms achieved more clinical net benefit than the American Joint Committee on Cancer staging system.

Conclusion

We constructed and validated two effective prognostic nomograms for patients with MRCC that accurately predicted the probabilities of 1-, 2-, and 3-year OS and CSS.

The first competing risk survival nomogram in patients with papillary renal cell carcinoma

There is still a lack of competing risk analysis of patients with papillary renal cell carcinoma (pRCC) following surgery. We performed the cumulative incidence function (CIF) to estimate the absolute risks of cancer-specific mortality (CSM) and other-cause mortality (OCM) of pRCC over time, and constructed a nomogram predicting the probability of 2-, 3- and 5-year CSM based on competing risk regression. A total of 5993 pRCC patients who underwent nephrectomy between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The 2-, 3-, 5-year CSM rates were 3.2%, 4.4% and 6.5%, respectively, and that of OCM were 3.2%, 5.0% and 9.3%, respectively. The estimates of 5-year cumulative mortality were most pronounced among patients aged > 75 years in OCM (17.0%). On multivariable analyses, age, tumor grade, T stage, N stage, and with or without bone, liver and lung metastases were identified as independent predictors of CSM following surgery and were integrated to generate the nomogram. The nomogram achieved a satisfactory discrimination with the AUC_t of 0.730 at 5-year, and the calibration curves presented impressive agreements. Taken together, age-related OCM is a significant portion of all-cause mortality in elderly patients and our nomogram can be used for decision-making and patient counselling.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

An Internally Validated Nomogram for Predicting the Likelihood of Improvement of Clinical Global Impression in Patients With Lifelong Premature Ejaculation Treated With Dapoxetine

BACKGROUND

Although the introduction of dapoxetine has ushered in a new era in the treatment of premature ejaculation, many patients with lifelong premature ejaculation (LPE) exhibit an unimproved clinical global impression even after treatment with dapoxetine.

AIM

To investigate independent predictors of the improvement of Clinical Global Impression (iCGI) in patients with LPE treated with dapoxetine and develop a nomogram to predict a patient's likelihood of achieving iCGI.

METHODS

Data of 243 patients with LPE diagnosed at Xijing Hospital (Xi'an, China) and Northwest Women's and Children's Hospital (Xi'an, China) from January 2019 to May 2020 were analyzed. Independent predictors of iCGI were identified, and a nomogram was developed using R software based on a multivariate logistic regression model. The predictive accuracy of the nomogram was measured using the area under the receiver operating characteristic curve. The nomogram was calibrated by comparing predictions with observations.

MAIN OUTCOME MEASURES

The primary outcome was the patient-rated Clinical Global Impression of Change scale score after a 4-week course of dapoxetine treatment, which was collected via an online questionnaire. A Clinical Global Impression of Change score of ≥1 was defined as iCGI in this study.

RESULTS

Patients with LPE with at least a bachelor's degree, a self-reported intravaginal ejaculation latency time of >1 minute, and an International Index of Erectile Function question 5 score of ≥3 were independent factors associated with achieving iCGI, whereas a Premature Ejaculation Diagnostic Tool question 1 score of ≥2 was an independent factor negatively associated with achieving iCGI. The predictive accuracy of the nomogram, which was developed by integrating all variables with independent predictive significance, was 0.710 (95% confidence interval: 0.702-0.718). In addition, the calibration plot demonstrated excellent agreement between predictions and observations.

CLINICAL IMPLICATIONS

If the predictive performance of our nomogram is further proven in multiple external validations, it can be used to select suitable patients for dapoxetine treatment, thereby reducing the number of patients discontinuing treatment.

STRENGTHS & LIMITATIONS

This study developed the first nomogram for predicting the likelihood of achieving iCGI in patients with LPE treated with dapoxetine. However, our nomogram was not externally validated using independent cohorts from other institutions.

CONCLUSION

This study identified several independent predictors of iCGI in patients with LPE treated with dapoxetine. An effective nomogram was developed to predict their likelihood of achieving iCGI. External validations using data of Western patients with LPE are required to test the broader applicability of this Chinese patient-based tool. Hou G, Gao M, Zhang L, et al. An Internally Validated Nomogram for Predicting the Likelihood of Improvement of Clinical Global Impression in Patients With Lifelong Premature Ejaculation Treated With Dapoxetine. J Sex Med 2020;17:2341-2350.

Survival nomogram for patients with upper tract recurrence after resection for localized bladder urothelial carcinoma

Aim: To develop a survival nomogram for patients with upper tract recurrence (UTR) after resection for localized bladder urothelial carcinoma (BUC). Methods: The data of 361 patients with UTR after resection for BUC registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. The nomogram was established using the Fine and Gray method and its predictive accuracy was assessed using the concordance index. The nomogram was calibrated by comparing the predicted and actual survival. Results: The concordance index of the nomogram was 0.746 (95% CI: 0.733-0.759). Excellent agreement was observed between the predicted and actual survival in all calibration plots. Conclusion: This study describes the first survival nomogram for patients experienced UTR after resection for BUC.