Assessing the predictive value of smoking history for immunotherapy outcomes in bladder cancer patients.

Background: The therapeutic effectiveness of immune checkpoint inhibitors (ICIs) in bladder cancer varies among individuals. Identifying reliable predictors of response to these therapies is crucial for optimizing patient outcomes. Methods: This retrospective study analyzed 348 bladder cancer patients treated with ICIs, with additional validation using data from 248 patients at our institution who underwent PD-L1 immunohistochemical staining. We examined patient smoking history, clinicopathological characteristics, and immune phenotypes. The main focus was the correlation between smoking history and immunotherapy outcomes. Multivariate logistic and Cox proportional hazard regressions were used to adjust for confounders. Results: The study cohort comprised 348 bladder cancer patients receiving ICIs. Among them, 116 (33.3%) were never smokers, 197 (56.6%) were former smokers (median pack-years = 28), and 35 (10.1%) were current smokers (median pack-years = 40). Analysis revealed no statistically significant difference in overall survival across different smoking statuses (objective response rates were 11.4% for current smokers, 17.2% for never smokers, and 22.3% for former smokers; P = 0.142, 0.410, and 0.281, respectively). However, a notable trend indicated a potentially better response to immunotherapy in former smokers compared to current and never smokers. In the validation cohort of 248 patients from our institution, immunohistochemical analysis showed that PD-L1 expression was significantly higher in former smokers (55%) compared to current smokers (37%) and never smokers (47%). This observation underscores the potential influence of smoking history on the tumor microenvironment and its responsiveness to ICIs. Conclusion: In conclusion, our study demonstrates the importance of incorporating smoking history in predicting the response to immunotherapy in bladder cancer patients, highlighting its role in personalized cancer treatment approaches. Further research is suggested to explore the comprehensive impact of lifestyle factors on treatment outcomes.

Bibliometric insights into drug resistance in bladder cancer: Two decades of progress (1999-2022).

Aims: To provide a comprehensive bibliometric overview of drug resistance in bladder cancer (BC) from 1999 to 2022, aiming to illuminate its historical progression and guide future investigative avenues. Methods: Literature on BC drug resistance between 1999 and 2022 was sourced from the Web of Science. Visual analyses were executed using Vosviewer and Citespace software, focusing on contributions by countries, institutions, journals, authors, references, and keywords. Results: From 2727 publications, a marked growth in BC drug resistance studies was discerned over the two decades. Prominent among all institutions is the University of Texas System. The majority of top-ranked journals were American. In authorship significance, McConkey DJ led in publications, while Bellmunt J dominated in citations. Research topics predominantly spanned cancer demographics, drug efficacy evaluations, molecular features, oncology subtypes, and individualized treatment strategies, with a notable contemporary emphasis on molecular mechanisms behind drug resistance and nuances of ICIs. Conclusions: Our bibliometric analysis charts the landscape of BC drug resistance research from 1999 to 2022. While the study of resistance mechanisms has been robust, there's an evident need for deeper exploration into the molecular intricacies and the potential of ICIs and targeted therapeutic strategies.

A bibliometric insight into neoadjuvant chemotherapy in bladder cancer: trends, collaborations, and future avenues.

Background: Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) remains the cornerstone of treatment for muscle-invasive bladder cancer (MIBC). While platinum-based regimens have demonstrated benefits in tumor downstaging and improved long-term survival for selected patients, they may pose risks for those who are ineligible or unresponsive to chemotherapy. Objective: We undertook a bibliometric analysis to elucidate the breadth of literature on NAC in bladder cancer, discern research trajectories, and underscore emerging avenues of investigation. Methods: A systematic search of the Web of Science Core Collection (WoSCC) was conducted to identify articles pertaining to NAC in bladder cancer from 1999 to 2022. Advanced bibliometric tools, such as VOSviewer, CiteSpace, and SCImago Graphica, facilitated the examination and depicted the publication trends, geographic contributions, institutional affiliations, journal prominence, author collaborations, and salient keywords, emphasizing the top 25 citation bursts. Results: Our analysis included 1836 publications spanning 1999 to 2022, indicating a growing trend in both annual publications and citations related to NAC in bladder cancer. The United States emerged as the predominant contributor in terms of publications, citations, and international collaborations. The University of Texas was the leading institution in publication output. "Urologic Oncology Seminars and Original Investigations" was the primary publishing journal, while "European Urology" boasted the highest impact factor. Shariat, Shahrokh F., and Grossman, H.B., were identified as the most prolific and co-cited authors, respectively. Keyword analysis revealed both frequency of occurrence and citation bursts, highlighting areas of concentrated study. Notably, the integration of immunochemotherapy is projected to experience substantial growth in forthcoming research. Conclusions: Our bibliometric assessment provides a panoramic view of the research milieu surrounding neoadjuvant chemotherapy for bladder cancer, encapsulating the present state, evolving trends, and potential future directions, with a particular emphasis on the promise of immunochemotherapy.

Immune regulation and the tumor microenvironment in anti-PD-1/PDL-1 and anti-CTLA-4 therapies for cancer immune evasion: A bibliometric analysis.

This study aims to conduct a bibliometric analysis, employing visualization tools to examine literature pertaining to tumor immune evasion related to anti-CTLA-4 and anti-PD-1/PD-L1 therapy from 1999 to 2022. A special emphasis is placed on the interplay between tumor microenvironment, signaling pathways, immune cells and immune evasion, with data sourced from the Web of Science core collection (WoSCC). Advanced tools, including VOSviewer, Citespace, and Scimago Graphica, were utilized to analyze various parameters, such as co-authorship/co-citation patterns, regional contributions, journal preferences, keyword co-occurrences, and significant citation bursts. Out of 4778 publications reviewed, there was a marked increase in research focusing on immune evasion, with bladder cancer being notably prominent. Geographically, China, the USA, and Japan were the leading contributors. Prestigious institutions like MD Anderson Cancer Center, Harvard Medical School, Fudan University, and Sun Yat Sen University emerged as major players. Renowned journals in this domain included Frontiers in Immunology, Cancers, and Frontiers in Oncology. Ehen LP and Wang W were identified as prolific authors on this topic, while Topalian SL stood out as one of the most cited. Research current situation is notably pivoting toward challenges like immunotherapy resistance and the intricate signaling pathways driving drug resistance. This bibliometric study seeks to provide a comprehensive overview of past and current research trends, emphasizing the potential role of tumor microenvironment, signaling pathways and immune cells in the context of immune checkpoint inhibitors (ICIs) and tumor immune evasion.

Evaluation of renal ischemia-reperfusion injury using CEUS in mice.

BACKGROUND: Renal ischemia-reperfusion injury (IRI) frequently occurs clinically. We investigated the value of contrast-enhanced ultrasonography (CEUS) in the evaluation of renal IRI levels in mice. METHODS: Thirty-six healthy adult male C57BL/6 mice (20-22 g) were randomly divided into the sham, 10 min, 20 min, 30 min, 40 min, and 50 min groups based on the time of renal warm ischemia by blocking the left renal pedicle, approved by the Institutional Animal Ethics Committee. Time-intensity curve (TIC)-derived parameters such as peak enhancement (PE) and wash-in perfusion index (WiPI) were produced using CEUS at 1 h and 24 h after IRI. The severity of kidney injury was detected by the renal tubular necrosis rate which was analyzed by hematoxylin and eosin staining at 24 h after IRI. The Spearman correlation coefficient was used to describe the correlations between PE and WiPI values and the renal tubular necrosis rate. RESULTS: The PE and WiPI values decreased after IRI in the groups with a warm ischemia time ≥ 20 min. The renal tubular necrosis rate was significantly correlated with the PE value at 1 h (ρ = -0.802) and 24 h (ρ = -0.861) after IRI and the WiPI value at 1 h (ρ = -0.814) and 24 h (ρ = -0.853) after IRI (all p < 0.001). CONCLUSION: TIC-derived parameters, including PE and WiPI values, can be used to evaluate the severity of renal IRI in mice. CEUS is a safe and effective technology for the detection of renal IRI. RELEVANCE STATEMENT: CEUS can evaluate the severity of renal ischemia-reperfusion injury by peak enhancement and wash-in perfusion index values selected from various time-intensity curve-derived parameters. KEY POINTS: • Contrast-enhanced ultrasonography can evaluate the level of renal ischemia-reperfusion injury. • Peak enhancement and wash-in perfusion index are correlated with the renal tubular necrosis rate. • CEUS can detect changes in unilateral renal function without radiation.

Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo: A remedy for the diagnostic pitfall of dual-energy computed tomography.

BACKGROUND: Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them. METHODS: This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated. RESULTS: When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899-0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796-0.995) and 0.870 (95% CI, 0.769-0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model. CONCLUSIONS: DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.

Preoperative fluorescence in situ hybridization analysis as a predictor of tumor recurrence in patients with non-muscle invasive bladder cancer: a bi-institutional study.

BACKGROUND: Non-muscle invasive bladder cancer (NMIBC) is known for its elevated recurrence rate, necessitating an enhancement in the current risk stratification for recurrence. The urine-based fluorescence in situ hybridization (FISH) assay has emerged as a noninvasive auxiliary tool for detecting bladder cancer. The aim of this study was to explore the potential relationship between the preoperative FISH assay and recurrence, and to develop a FISH-clinical nomogram for predicting the recurrence-free survival (RFS) in NMIBC patients. METHODS: In total, 332 eligible patients were enrolled from two hospitals. The SYSMH cohort was randomly assigned to the training set (n = 168) and the validation set I (n = 72) at a ratio of 7:3, while the SYSUTH cohort was allocated to the validation set II (n = 92). The correlation between the preoperative FISH assay and recurrence was determined through the Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used for model construction. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: We uncovered that chromosome 7 aneuploidy, p16 locus loss, number of the positive FISH sites, and the FISH test result were significantly associated with tumor recurrence. Then, a FISH-clinical nomogram incorporating the FISH test result, T stage, associated CIS, tumor grade, and tumor status was developed. It showed favorable calibration and discrimination with a C-index of 0.683 (95%CI, 0.611-0.756) in the training set, which was confirmed in the validation set I and validation set II with C-indexes of 0.665 (95%CI, 0.565-0.765) and 0.778 (95%CI, 0.665-0.891), respectively. Decision curve analysis revealed the clinical usefulness of the nomogram. Moreover, our proposed nomogram significantly outperformed the guideline-recommended EORTC and CUETO scoring models. CONCLUSION: Our study confirmed the prognostic value of the preoperative FISH assay and proposed a FISH-clinical nomogram to predict RFS in NMIBC patients. Our nomogram can serve as a more precise tool for recurrence risk stratification, which may optimize disease management in bladder cancer and improve patient prognosis.

Identification of Novel Phenotypes Correlated with CKD: A Phenotype-Wide Association Study.

Background: A comprehensive understanding of phenotypes related to CKD will facilitate the identification and management of CKD. We aimed to panoramically test and validate associations between multiple phenotypes and CKD using a phenotype-wide association study (PheWAS). Methods: 15,815 subjects from cross-sectional cohorts of the National Health and Nutrition Examination Survey (1999-2006) were randomly 50:50 split into training and testing sets. CKD was defined as eGFR < 60 mL/min/1.73m2. We performed logistic regression analyses between each of 985 phenotypes with CKD in the training set (false discovery rate < 1%) and validated in the testing set (false discovery rate < 1% ). Random forest (RF) model, Nagelkerke's Pseudo-R2, and the area under the receiver operating characteristic (AUROC) were used to validate the identified phenotypes. Results: We identified 18 phenotypes significantly related to CKD, among which retinol, red cell distribution width (RDW), and C-peptide were less researched. The top 5 identified phenotypes were blood urea nitrogen (BUN), homocysteine (HCY), retinol, parathyroid hormone (PTH), and osmolality in RF importance ranking. Besides, BUN, HCY, PTH, retinol, and uric acid were the most important phenotypes based on Pseudo-R2. AUROC of the RF model was 0.951 (full model) and 0.914 (top 5 phenotypes). Conclusion: Our study demonstrated associations between multiple phenotypes with CKD from a holistic view, including 3 novel phenotypes: retinol, RDW, and C-peptide. Our findings provided valid evidence for the identification of novel biomarkers for CKD.

m6A methylation regulators as predictors for treatment of advanced urothelial carcinoma with anti-PDL1 agent.

Purpose: Immune checkpoint blockade agents were shown to provide a survival advantage in urothelial carcinoma, while some patients got minimal benefit or side effects. Therefore, we aimed to investigate the prognostic value of m6A methylation regulators, and developed a nomogram for predicting the response to atezolizumab in urothelial carcinoma patients. Methods: A total of 298 advanced urothelial carcinoma patients with response data in the IMvigor210 cohort were included. Differential expressions of 23 m6A methylation regulators in different treatment outcomes were conducted. Subsequently, a gene signature was developed in the training set using the least absolute shrinkage and selection operator (LASSO) regression. Based on the multivariable logistic regression, a nomogram was constructed by incorporating the gene signature and independent clinicopathological predictors. The performance of the nomogram was assessed by its discrimination, calibration, and clinical utility with internal validation. Results: Six m6A methylation regulators, including IGF2BP1, IGF2BP3, YTHDF2, HNRNPA2B1, FMR1, and FTO, were significantly differentially expressed between the responders and non-responders. These six regulators were also significantly correlated with the treatment outcomes. Based on the LASSO regression analysis, the gene signature consisting of two selected m6A methylation regulators (FMR1 and HNRNPA2B1) was constructed and showed favorable discrimination. The nomogram integrating the gene signature, TMB, and PD-L1 expression on immune cells, showed favorable calibration and discrimination in the training set (AUC 0.768), which was confirmed in the validation set (AUC 0.755). Decision curve analysis confirmed the potential clinical usefulness of the nomogram. Conclusions: This study confirmed the prognostic value of FMR1 and HNRNPA2B1, and constructed a nomogram for individualized prediction of the response to atezolizumab in patients with urothelial carcinoma, which may aid in making treatment strategies.

Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation.

BACKGROUND: Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. METHODS: In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. RESULTS: Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. CONCLUSION: We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

Kidney damage causally affects the brain cortical structure: A Mendelian randomization study.

BACKGROUND: Alterations in the brain cortical structures of patients with chronic kidney disease (CKD) have been reported; however, the cause has not been determined yet. Herein, we used Mendelian randomization (MR) to reveal the causal effect of kidney damage on brain cortical structure. METHODS: Genome-wide association studies summary data of estimated glomerular filtration rate (eGFR) in 480,698 participants from the CKDGen Consortium were used to identify genetically predicted eGFR. Data from 567,460 individuals from the CKDGen Consortium were used to assess genetically determined CKD; 302,687 participants from the UK Biobank were used to evaluate genetically predicted albuminuria. Further, data from 51,665 patients from the ENIGMA Consortium were used to assess the relationship between genetic predisposition and reduced eGFR, CKD, and progressive albuminuria with alterations in cortical thickness (TH) or surficial area (SA) of the brain. Magnetic resonance imaging was used to measure the SA and TH globally and in 34 functional regions. Inverse-variance weighted was used as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger and weighted median were used to detect heterogeneity and pleiotropy. FINDINGS: At the global level, albuminuria decreased TH (ß = -0.07 mm, 95% CI: -0.12 mm to -0.02 mm, P = 0.004); at the functional level, albuminuria reduced TH of pars opercularis gyrus without global weighted (ß = -0.11 mm, 95% CI: -0.16 mm to -0.07 mm, P = 3.74×10-6). No pleiotropy was detected. INTERPRETATION: Kidney damage causally influences the cortex structure which suggests the existence of a kidney-brain axis. FUNDING: This study was supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2020A1515111119 and 2017B020227007), the National Key Research and Development Program of China (Grant No. 2018YFA0902803), the National Natural Science Foundation of China (Grant No. 81825016, 81961128027, 81772719, 81772728), the Key Areas Research and Development Program of Guangdong (Grant No. 2018B010109006), Guangdong Special Support Program (2017TX04R246), Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and Grants from the Guangdong Science and Technology Department (2020B1212060018).

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.

A Model for Identifying Optimal Patients for Primary Tumor Resection in Patients With Metastatic Bladder Cancer.

BACKGROUND: A survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confusing which patients are suitable for the surgery. For this purpose, we developed a model to screen stage M1 patients who would benefit from primary tumor resection. METHODS: Patients with metastatic bladder cancer were screened from the Surveillance, Epidemiology, and End Results database (2004-2016) and then were divided into surgery (partial or complete cystectomy) group and non-surgery group. To balance the characteristics between them, a 1:1 propensity score matching analysis was applied. A hypothesis was proposed that the received primary tumor resection group has a more optimistic prognosis than the other group. The multivariable Cox model was used to explore the independent factors of survival time in two groups (beneficial and non-beneficial groups). Logistic regression was used to build a nomogram based on the significant predictive factors. Finally, a variety of methods are used to evaluate our model. RESULTS: A total of 7,965 patients with metastatic bladder cancer were included. And 3,314 patients met filtering standards, of which 545 (16.4%) received partial or complete cystectomy. Plots of the Kaplan-Meier and subgroup analyses confirmed our hypothesis. After propensity score matching analysis, a survival benefit was still observed that the surgery group has a longer median overall survival time (11.0 vs. 6.0 months, p < 0.001). Among the surgery cohort, 303 (65.8%) patients lived longer than 6 months (beneficial group). Differentiated characteristics included age, gender, TNM stage, histologic type, differentiation grade, and therapy, which were integrated as predictors to build a nomogram. The nomogram showed good discrimination in both training and validation cohorts (area under the receiver operating characteristic curve (AUC): 0.806 and 0.742, respectively), and the calibration curves demonstrated good consistency. Decision curve analysis showed that the nomogram was clinically useful. Compared with TNM staging, our model shows a better predictive value in identifying optimal patients for primary tumor resection. CONCLUSIONS: A practical predictive model was created and verified, which might be used to identify the optimal candidates for the partial or complete cystectomy group of the primary tumor among metastatic bladder cancer.

Depression and prostate cancer risk: A Mendelian randomization study.

BACKGROUND: The association between depression and prostate carcinogenesis has been reported in observational studies but the causality from depression on prostate cancer (PCa) remained unknown. We aimed to assess the causal effect of depression on PCa using the two-sample Mendelian randomization (MR) method. METHODS: Two sets of genetics instruments were used for analysis, derived from publicly available genetic summary data. One was 44 single-nucleotide polymorphisms (SNPs) robustly associated with major depressive disorder (MDD) and the other was two SNPs related with depressive status as ever depressed for a whole week. Inverse-variance weighted method, weighted median method, MR-Egger regression, MR Pleiotropy RESidual Sum, and Outlier test were used for MR analyses. RESULTS: No evidence for an effect of MDD on PCa risk was found in inverse-variance weighted (OR: 1.12, 95% CI: 0.97-1.30, p = 0.135), MR-Egger (OR 0.89, 95% CI: 0.29-2.68, p = 0.833), and weighted median (OR: 1.08, 95% CI: 0.92-1.27, p = 0.350). Also, no strong evidence for an effect of depressive status on PCa incidence was found using the inverse-variance weighted method (OR 0.72, 95% CI: 0.35-1.47, p = 0.364). CONCLUSIONS: The large MR analysis indicated that depression may not be causally associated with a risk of PCa.

A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy.

PURPOSE: Bevacizumab is considered a promising therapy for brain necrosis after radiotherapy, while some patients fail to derive benefit or even worsen. Hence, we developed and validated a radiomics model for predicting the response to bevacizumab in patients with brain necrosis after radiotherapy. EXPERIMENTAL DESIGN: A total of 149 patients (with 194 brain lesions; 101, 51, and 42 in the training, internal, and external validation sets, respectively) receiving bevacizumab were enrolled. In total, 1,301 radiomic features were extracted from the pretreatment MRI images of each lesion. In the training set, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm. Multivariable logistic regression analysis was then used to develop a radiomics model incorporated in the radiomics signature and independent clinical predictors. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with internal and external validation. RESULTS: The radiomics signature consisted of 18 selected features and showed good discrimination performance. The model, which integrates the radiomics signature, the interval between radiotherapy and diagnosis of brain necrosis, and the interval between diagnosis of brain necrosis and treatment with bevacizumab, showed favorable calibration and discrimination in the training set (AUC 0.916). These findings were confirmed in the validation sets (AUC 0.912 and 0.827, respectively). Decision curve analysis confirmed the clinical utility of the model. CONCLUSIONS: The presented radiomics model, available as an online calculator, can serve as a user-friendly tool for individualized prediction of the response to bevacizumab in patients with brain necrosis after radiotherapy.

Association of chromosome 7 aneuploidy measured by fluorescence in situ hybridization assay with muscular invasion in bladder cancer.

BACKGROUND: The preoperative prediction of muscular invasion status is important for adequately treating bladder cancer (BC) but nevertheless, there are some existing dilemmas in the current preoperative diagnostic accuracy of BC with muscular invasion. Here, we investigated the potential association between the fluorescence in situ hybridization (FISH) assay and muscular invasion among patients with BC. A cytogenetic-clinical nomogram for the individualized preoperative differentiation of muscle-invasive BC (MIBC) from non-muscle-invasive BC (NMIBC) is also proposed. METHODS: All eligible BC patients were preoperatively tested using a FISH assay, which included 4 sites (chromosome-specific centromeric probe [CSP] 3, 7, and 17, and gene locus-specific probe [GLP]-p16 locus). The correlation between the FISH assay and BC muscular invasion was evaluated using the Chi-square tests. In the training set, univariate and multivariate logistic regression analyses were used to develop a cytogenetic-clinical nomogram for preoperative muscular invasion prediction. Then, we assessed the performance of the nomogram in the training set with respect to its discriminatory accuracy and calibration for predicting muscular invasion, and clinical usefulness, which were then validated in the validation set. Moreover, model comparison was set to evaluate the discrimination and clinical usefulness between the nomogram and the individual variables incorporated in the nomogram. RESULTS: Muscular invasion was more prevalent in BC patients with positive CSP3, CSP7 and CSP17 status (OR [95% CI], 2.724 [1.555 to 4.774], P < 0.001; 3.406 [1.912 to 6.068], P < 0.001 and 2.483 [1.436 to 4.292], P = 0.001, respectively). Radiology-determined tumor size, radiology-determined clinical tumor stage and CSP7 status were identified as independent risk factors of BC muscular invasion by the multivariate regression analysis in the training set. Then, a cytogenetic-clinical nomogram incorporating these three independent risk factors was constructed and was observed to have satisfactory discrimination in the training (AUC 0.784; 95% CI: 0.715 to 0.853) and validation (AUC 0.743; 95% CI: 0.635 to 0.850) set. The decision curve analysis (DCA) indicated the clinical usefulness of our nomogram. In models comparison, using the receiver operator characteristic (ROC) analyses, the nomogram showed higher discriminatory accuracy than any variables incorporated in the nomogram alone and the DCAs also identified the nomogram as possessing the highest net benefits at wide range of threshold probabilities. CONCLUSION: CSP7 status was identified as an independent factor for predicting muscular invasion in BC patients and was successfully incorporated in a clinical nomogram combining the results of the FISH assay with clinical risk factors.

Nomograms for the Prediction of Survival for Patients with Pediatric Adrenal Cancer after Surgery.

Purpose: To develop and validate a nomogram to postoperatively evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with pediatric adrenal cancer. Methods: In total, 847 eligible patients diagnosed between 1988 and 2015 form the Surveillance Epidemiology, and End Results (SEER) database were enrolled in this study according to the specified inclusion and exclusion criteria. They were divided into a training set (n = 661) and a validation set (n = 186). Multivariate Cox proportional hazards regression algorithm was used to identify the independent predictors of OS and CSS in the training set, and develop the predicting models, which were presented two nomograms. The performance of the nomograms (discrimination, calibration and clinical usefulness) was assessed in the training set and validated in the validation set. Results: Based on the multivariate Cox proportional hazards regression analyses, three independent predictors including age at diagnosis, tumor size and M stage were identified for both OS and CSS. Then, an OS nomogram and a CSS nomogram were developed incorporating these three predictors, respectively. The OS nomogram showed good calibration and discrimination in the training set (C-index [95% CI], 0.744 [0.711-0.777]), which was confirmed in the validation set (C-index [95% CI], 0.746 [0.656-0.836]). Favorable calibration and discrimination of the CSS nomogram were also observed in the training set (C-index [95% CI], 0.749 [0.715-0.783]) and validation set (C-index [95% CI], 0.789 [0.710-0.868]). Moreover, the nomograms successfully distinguished patients with high risk of all-cause and cancer-specific mortality in all patients and in the stratified analyses. Decision curve analysis demonstrated the usefulness of the nomograms. Conclusion: The presented nomograms show favorable predictive accuracy for OS and CSS in patients with pediatric adrenal cancer after surgery. Further validation is warranted prior to clinical implementation.

A nomogram for individualized estimation of survival among adult patients with adrenocortical carcinoma after surgery: a retrospective analysis and multicenter validation study.

BACKGROUND: Clinical outcome of adrenocortical carcinoma (ACC) varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited. The objective of this study was to develop and externally validate a nomogram for overall survival (OS) prediction in adult patients with ACC after surgery. METHODS: Based on the data from the Surveillance Epidemiology, and End Results (SEER) database, adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set, comprised of 404 patients diagnosed between January 2007 and December 2015, and an internal validation set, comprised of 318 patients diagnosed between January 1988 and December 2006. The endpoint of this study was OS. The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability, calibration, and clinical usefulness. The nomogram was then validated using the internal SEER validation, also externally validated using the Cancer Genome Atlas set (TCGA, 82 patients diagnosed between 1998 and 2012) and a Chinese multicenter cohort dataset (82 patients diagnosed between December 2002 and May 2018), respectively. RESULTS: Age at diagnosis, T stage, N stage, and M stage were identified as independent predictors for OS. A nomogram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination (C-index 95% confidence interval [CI], 0.715 [0.679-0.751]), which was validated in the internal validation set (C-index [95% CI], 0.672 [0.637-0.707]), the TCGA set (C-index [95% CI], 0.810 [0.732-0.888]) and the Chinese multicenter set (C-index [95% CI], 0.726 [0.633-0.819]), respectively. Encouragingly, the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses. Decision curve analysis indicated that the nomogram was clinically useful and applicable. CONCLUSIONS: The study presents a nomogram that incorporates clinicopathological predictors, which can accurately predict the OS of adult ACC patients after surgery. This model and the corresponding risk classification system have the potential to guide therapy decisions after surgery.

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.

BACKGROUND: Bladder cancer (BCa) can be divided into muscle-invasive BCa (MIBC) and non-muscle-invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic-clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC. METHODS: In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2-weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic-clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69). RESULTS: The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging-determined tumor size, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram. CONCLUSIONS: The proposed noninvasive radiomic-clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.