Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study

CYP19A1 polymorphisms and bladder cancer risk in the Chinese Han population

BACKGROUND

The expression of CYP19A1 has implications for the prognosis of female bladder cancer. However, this study aimed to explore the association between single nucleotide polymorphisms (SNPs) in CYP19A1 and bladder cancer risk, as no prior research has addressed this association.

RESEARCH DESIGN AND METHODS

We selected and genotyped five CYP19A1 SNPs (rs4646, rs6493487, rs1062033, rs17601876, and rs3751599) in 217 patients and 550 controls using the Agena MassARRAY system. Logistic regression analysis was employed to calculate the odds ratio (OR) and 95% confidence intervals (CIs). Bioinformatics predicted SNP functions and CYP19A1 involving pathways.

RESULTS

Our study revealed a significant association between bladder cancer risk and four SNPs (rs4646 (AC vs. CC: OR = 1.71, FDR-p = 0.005), rs6493487 (G vs. A: OR = 0.68, FDR-p = 0.011), rs1062033 (G vs. C: OR = 0.36, FDR-p < 0.001), and rs17601876 (GA vs. GG: OR = 1.66, FDR-p = 0.008)) in CYP19A1. The three SNPs (rs4646, rs1062033, and rs17601876) were significantly correlated with CYP19A1 expression levels in normal whole blood (p < 0.05). Moreover, CYP19A1 was found to primarily participate in the steroid hormone biosynthesis and metabolic pathways.

CONCLUSIONS

Consequently, CYP19A1 gene polymorphisms may play a crucial role in the genetic susceptibility to bladder cancer.

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer

BACKGROUND

The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups.

OBJECTIVE

To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms.

DESIGN, SETTING, AND PARTICIPANTS

Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined.

RESULTS AND LIMITATIONS

There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups.

CONCLUSIONS

The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer.

PATIENT SUMMARY

We previously developed a calculator that predicts patients' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly.

Hematuria Cancer Risk Score with Ultrasound Informs Cystoscopy Use in Patients with Hematuria

BACKGROUND

Hematuria is a cardinal symptom of urinary tract cancer and would require further investigations.

OBJECTIVE

To determine the ability of renal bladder ultrasound (RBUS) with the Hematuria Cancer Risk Score (HCRS) to inform cystoscopy use in patients with hematuria.

DESIGN, SETTING, AND PARTICIPANTS

The development cohort comprised 1984 patients with hematuria from 40 UK hospitals (DETECT 1; ClinicalTrials.gov: NCT02676180) who received RBUS. An independent validation cohort comprised 500 consecutive patients referred to secondary care for a suspicion of bladder cancer.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Sensitivity and true negative of the HCRS and RBUS were assessed.

RESULTS AND LIMITATIONS

A total of 134 (7%) and 36 (8%) patients in the development and validation cohorts, respectively, had a diagnosis of urinary tract cancer. Validation of the HCRS achieves good discrimination with an area under the receiver operating characteristic curve of 0.727 (95% confidence interval 0.648-0.800) in the validation cohort with sensitivity of 95% for the identification of cancer. Utilizing the cutoff of 4.500 derived from the HCRS in combination with RBUS in the development cohort, 680 (34%) patients would have been spared cystoscopy at the cost of missing a G1 Ta bladder cancer and a urinary tract cancer patient, while 117 (25%) patients would have avoided cystoscopy at the cost of missing a single patient of G1 Ta bladder cancer with sensitivity for the identification of cancer of 97% in the validation cohort.

CONCLUSIONS

The HCRS with RBUS offers good discriminatory ability in identifying patients who would benefit from cystoscopy, sparing selected patient cohorts from an invasive procedure.

PATIENT SUMMARY

The hematuria cancer risk score with renal bladder ultrasound allows for the triage of patients with hematuria who would benefit from visual examination of the bladder (cystoscopy). This resulted in 25% of patients safely omitting cystoscopy, which is an invasive procedure, and would lead to health care cost savings.

Prevalence of urinary tract cancer in the Spanish cohort of the IDENTIFY study

INTRODUCTION

Malignant tumors of the urinary tract are associated with high morbidity and mortality, and their prevalence can vary worldwide. Recently, the IDENTIFY study has published results on the prevalence of urinary tract cancer at a global level. This study evaluates the prevalence of cancer within the Spanish cohort of the IDENTIFY study to determine whether the published results can be extrapolated to our population.

PATIENTS AND METHODS

An analysis of the data from the Spanish cohort of patients in the IDENTIFY study was performed. This is a prospective cohort of patients referred to secondary care with suspected cancer, predominantly due to hematuria. Patients were recruited between December 2017 and December 2018.

RESULTS

A total of 706 patients from 9 Spanish centers were analyzed. Of these, 277 (39.2%) were diagnosed with cancer: 259 (36.7%) bladder cancer, 10 (1.4%) upper tract urothelial carcinoma, 9 (1.2%) renal cancer and 5 (0.7%) prostate cancer. Increasing age (OR 1.05 (95% CI 1.03-1.06; P < 0.001)), visible hematuria (VH) OR 2.19 (95% CI 1.13-4.24; P = 0.02)) and smoking (ex-smokers: OR 2.11(95% CI 1.30-3.40; P = 0.002); smokers: OR 2.36 (95% CI 1.40-3.95; P = 0.001)) were associated with higher probability of bladder cancer.

CONCLUSION

This study highlights the risk of bladder cancer in patients with VH and smoking habits. Bladder cancer presented the highest prevalence; higher than the prevalence reported in previous series and presented in the IDENTIFY study. Future work should evaluate other associated factors that allow us to create cancer prediction models to improve the detection of cancer in our patients.

Network pharmacology and in vitro experiments reveal sophoridine-induced apoptosis and G1 phase arrest via ROS-dependent PI3K/Akt/FoxO3a pathway activation in human bladder cancer cells

Bladder cancer (BLCA), a common primary malignancy, exhibits resistance to conventional chemotherapeutic agents. Sophoridine (SR) is a quinoline alkaloid derived from the traditional Chinese herb Sophora alopecuroides L., which belongs to the legume family Sophoraceae. SR is reported to exert growth-inhibitory effects against several cancers. However, the mechanisms underlying the growth-inhibitory effects of SR on BLCA have not been elucidated. This study performed molecular and cellular experiments to verify the growth-inhibitory effects of SR on BLCA and the underlying mechanisms. SR inhibited cell proliferation and promoted apoptosis and G1-phase arrest through the PI3K/AKT/FoxO3a signaling pathway. More interestingly, the effects of SR can be attributed to the accumulation of reactive oxygen species (ROS) in vivo. ROS may be the upstream factor of this pathway. Additionally, SR inhibited the migration and invasion of BLCA cells in a concentration-dependent or time-dependent manner. This is the first study to demonstrate the ROS-dependent PI3K/AKT/FoxO3a pathway-mediated anticancer effect of SR and the anticancer mechanism of SR in BLCA. The correlation between SR-induced ROS-dependent cell proliferation inhibition, apoptosis, cell cycle arrest, and PI3K/AKT/FoxO3a suggests that SR is a promising novel therapeutic for BLCA.

Development and Multicenter Case-Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction

PURPOSE

Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk.

EXPERIMENTAL DESIGN

This is a multicenter case-control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology.

RESULTS

uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%.

CONCLUSIONS

uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients.

Impact of COVID-19 on Uro-Oncological Patients: A Comprehensive Review of the Literature

Background: The aim of this paper is to discuss the impact of COVID-19 on patients with urological malignancies (prostate cancer, bladder and upper tract urothelial cancer, kidney cancer, penile and testicular cancer) and to review the available recommendations reported in the literature. Methods: A review was performed, through the PubMed database, regarding available recommendations reported in the literature, to identify studies examining the impact of COVID-19 on treatment and clinical outcomes (including upstaging, recurrence, and mortality) for uro-oncological patients. Results: The COVID-19 pandemic dramatically changed the urological guidelines and patients' access to screening programs and follow-up visits. Great efforts were undertaken to guarantee treatments to high-risk patients although follow up was not always possible due to recurrent surges, and patients with lower risk cancers had to wait for therapies. Conclusions: Physically and mentally, uro-oncological patients paid a heavy price during the COVID-19 pandemic. Long term data on the "costs" of clinical decisions made during the COVID-19 pandemic are still to be revealed and analyzed.

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

Long-Term Cardiovascular Mortality among 80,042 Older Patients with Bladder Cancer

Simple Summary

We conducted a large-scale population-based study with long-term follow-up to obtain a comprehensive assessment of death causes, especially cardiovascular disease death, among 80,042 older bladder cancer patients from a national cancer registry containing 44 years of data. To our knowledge, this was the first study to report the importance of CVD-related death as a competing risk among older patients with bladder cancer. CVD-related death surpassed BC as the leading cause of death 5–10 years after diagnosis among older BC patients, especially for patients with localized-stage and low-grade tumors. Furthermore, older BC patients had a higher risk of CVD-related death than the general population. Although BC management should be the primary focus of older BC patients, our results emphasized the importance of competing risks, the most prominent being CVD. Individual follow-up and management should focus not only on primary cancer but also on CVD-related death to minimize the risk of death in older patients with bladder cancer.

Abstract

Background: To identify the risk of death from cardiovascular disease (CVD) in older patients with bladder cancer (BC). Methods: This population-based study included 80,042 older BC patients (≥65 years) diagnosed between 1975 and 2018, with a mean follow-up of 17.2 years. The proportion of deaths, competing risk models, standardized mortality ratio (SMR), and absolute excess risk (AER) per 10,000 person-years were applied to identify the risk of CVD-related deaths among older BC patients. Results: For older patients with BC, CVD-related death was the chief cause of death, and cumulative CVD-related mortality also exceeded primary BC as the leading cause of death mostly 5–10 years after BC diagnosis, especially in localized-stage and low-grade subgroups. The risk of short- and long-term CVD-related death in older BC patients was higher than in the general older adult population (SMR = 1.30, 95% CI 1.28–1.32; AER = 105.68). The risk of sex-specific CVD-related deaths also increased compared to the general population of older adults, including heart disease, cerebrovascular diseases, hypertension without heart disease, atherosclerosis, aortic aneurysm and dissection, and other diseases of the arteries, arterioles, and capillaries. Conclusions: CVD-related death is an important competing risk among older BC patients and has surpassed primary BC as the chief cause of death, mainly 5–10 years after BC diagnosis. The risk of CVD-related death in older patients with BC was greater than in the general population. The management of older patients with BC should focus not only on the primary cancer but also on CVD-related death.

Can National Registries Contribute to Predict the Risk of Cancer? The Cancer Risk Assessment Model (CRAM)

Simple Summary

Early identification of individuals with an increased risk of cancer is an important challenge. Danish administrative registers may be useful in this respect because they cover the entire population and include comprehensive and consistently coded long-term data. We aimed to develop a predictive model based on Danish administrative registers to facilitate the automated identification of individuals at risk of any type of cancer. In addition to age, almost all the included factors contributed statistically significantly, but also only marginally, to the prediction models, which means that we have not overlooked obvious information available in the register. Future prediction studies should focus on specific cancer types where more precise risk estimations might be expected. It is our ultimate ambition that an effective model can be used at the point of care, integrated into electronic patient record systems to alert physicians of patients at a high risk of cancer.

Abstract

Purpose: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. Methods: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007–2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). Results: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81–0.82) for men and 0.75 (95% CI 0.74–0.75) for women. Conclusion: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice.