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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.
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The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.
RESUMO
BACKGROUND: The existence and prognosis of T1LG (T1 low-grade) bladder cancer is controversial. Also, because of data paucity, it remains unclear what is the clinical history of bacillus Calmette-Guérin (BCG) treated T1LG tumors and if it differs from other NMIBC (non-muscle-invasive bladder cancer) representatives. The aim of this study was to analyse recurrence-free survival (RFS) and progression-free survival (PFS) in patients with T1LG bladder cancers treated with BCG immunotherapy. METHODS: A multi-institutional and retrospective study of 2510 patients with Ta/T1 NMIBC with or without carcinoma in situ (CIS) treated with BCG (205 T1LG patients) was performed. Kaplan-Meier estimates and log-rank test for RFS and PFS to compare the survival between TaLG, TaHG, T1LG, and T1HG NMIBC were used. Also, T1LG tumors were categorized into EAU2021 risk groups and PFS analysis was performed, and Cox multivariate model for both RFS and PFS were constructed. RESULTS: The median follow-up was 52 months. For the T1LG cohort, the estimated RFS and PFS rates at 5-year were 59.3% and 89.2%, respectively. While there were no differences in RFS between NMIBC subpopulations, a slightly better PFS was found in T1LG NMIBC compared to T1HG (5-year PFS; T1LG vs. T1HG: 82% vs. 89%; P<0.001). A heterogeneous classification of patients with T1LG NMIBC was observed when EAU 2021 prognostic model was applied, finding a statistically significant worse PFS in patients classified as high-risk T1LG (5-year PFS; 81.8%) compared to those in intermediate (5-year PFS; 93,4%), and low-risk T1LG tumors (5-year PFS; 98,1%). CONCLUSIONS: The RFS of T1LG was comparable to other NMIBC subpopulations. The PFS of T1LG tumors was significantly better than of T1HG NMIBC. The EAU2021 scoring model heterogeneously categorized the risk of progression in T1LG tumors and the high-risk T1LG had the worst PFS.