Catalog of exogenous risk factors for bladder carcinogenesis

Jahrreiss, Victoria; Pradère, Benjamin; Laukhtina, Ekaterina; Mori, Keiichiro; Shariat, Shahrokh F.

doi:10.1097/mou.0000000000000753

Cited by 16 publications

(24 citation statements)

References 60 publications

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“…Metabolic syndrome, diabetes mellitus, UTIs, and parasitic infection, especially schistosomiasis, are all linked with an elevated risk of bladder cancer. 17,18 In recent years, with the development of technology, studies have used deep learning (DL) and ML models to come up with programs that can help in the diagnosis of cancers, predict their prognosis and survival outcomes, and selection the best route of treatment for patients. 19 Considering DL and ML studies on bladder cancer, Tsai et al predicted the neoplasm by utilizing laboratory data and ML methods.…”

Section: Discussionmentioning

confidence: 99%

“…According to the results, we discovered recurrent UTI, neurogenic bladder, and bladder stones were all significantly associated with bladder cancer in Table 1 and had an important position in Figure 3, respectively. Metabolic syndrome, diabetes mellitus, UTIs, and parasitic infection, especially schistosomiasis, are all linked with an elevated risk of bladder cancer 17,18 …”

Section: Discussionmentioning

confidence: 99%

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Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

et al. 2023

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BackgroundBladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation.AimsIn the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer.MethodsThis population‐based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated.ResultsThe RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = .78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer.ConclusionMachine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

et al. 2023

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“…Sufficient or convincing evidence about UBC carcinogenesis is associated with genetic susceptibility, cigarette smoking, diet, endemic Schistosoma haematobium infestation, and environmental pollution exposure 31 . However, the biological mechanism of the urinary tract carcinogenesis related to ambient air pollution remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer

Zhang

Tsai

Kok

et al. 2022

Sci Rep

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Particulate matter and volatile organic compounds, including total hydrocarbons (THCs), are major ambient air pollutants. Primary nonmethane hydrocarbons (NMHCs) originate from vehicle emissions. The association between air pollution and urinary bladder cancer (UBC) is debatable. We investigated whether long-term exposure to ambient hydrocarbons increases UBC risk among people aged ≥ 20 years in Taiwan. Linkage dataset research with longitudinal design was conducted among 589,135 initially cancer-free individuals during 2000–2013; 12 airborne pollutants were identified. Several Cox models considering potential confounders were employed. The study outcomes were invasive or in situ UBC incidence over time. The targeted pollutant concentration was divided into three tertiles: T1/T2/T3. The mean age of individuals at risk was 42.5 (SD 15.7), and 50.5% of the individuals were men. The mean daily average over 10 years of airborne THC concentration was 2.25 ppm (SD 0.13), and NMHC was 0.29 ppm (SD 0.09). Both pollutants show long-term monotonic downward trend over time using the Mann–Kendall test. There was a dose-dependent increase in UBC at follow-up. UBC incidence per 100,000 enrollees according to T1/T2/T3 exposure to THC was 60.9, 221.2, and 651.8, respectively; it was 170.0/349.5/426.7 per 100,000 enrollees, corresponding to T1/T2/T3 exposure to NMHC, respectively. Without controlling for confounding air pollutants, the adjusted hazard ratio (adj.HR) was 1.83 (95% CI 1.75–1.91) per 0.13-ppm increase in THC; after controlling for PM2.5, adj.HR was even higher at 2.09 (95% CI 1.99–2.19). The adj.HR was 1.37 (95% CI 1.32–1.43) per 0.09-ppm increase in ambient NMHC concentration. After controlling for SO2 and CH4, the adj.HR was 1.10 (95% CI 1.06–1.15). Sensitivity analyses showed that UBC development risk was not sex-specific or influenced by diabetes status. Long-term exposure to THC and NMHC may be a risk factor for UBC development. Acknowledging pollutant sources can inform risk management strategies.

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“…Sufficient or convincing evidence about UBC carcinogenesis is associated with genetic susceptibility, cigarette smoking, diet, Schistosoma haematobium infestation, and environmental pollution exposure 31 . However, the biological mechanism of carcinogenesis related to ambient air pollution remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer

Zhang

Tsai

Kok

et al. 2022

Preprint

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BackgroundParticulate matter and volatile organic compounds, including total hydrocarbons (THCs), are major ambient air pollutants. The primary nonmethane hydrocarbons (NMHCs) originate from vehicle emissions. Studies on the association between air pollution and urinary bladder cancer (UBC) have revealed contradictory results.ObjectivesThe present study investigated whether long-term exposure to ambient hydrocarbons increases UBC risk among people aged ≥20 years in Taiwan.MethodsLinkage dataset research with longitudinal design was conducted on 600,666 cancer-free individuals from 2000–2013; 12 airborne pollutants were determined. Several Cox models considering potential confounders were employed. The study outcomes were invasive or in situ UBC incidence over time. The targeted pollutant concentration was divided into three tertiles: T1/T2/T3.ResultsThe mean age of the individuals at risk was 42.8 (SD, 15.7), and 50.2% were men. The mean daily average over 10-years of airborne THC concentration was 2.24 ppm (SD, 0.14), and NMHC was 0.29 ppm (SD, 0.09). There was a dose-dependent increase in UBC at follow-up. The incidence of UBC cases per 100,000 people by T1/T2/T3 exposure to THC was 60.3, 203.7, and 450.8, respectively; it was 180.2/202.4/453.8 per 100,000, corresponding to T1/T2/T3 exposure to NMHC, respectively. Without controlling for confounding air pollutants, the adjusted hazard ratio (adj.HR) was 1.77 (95% CI, 1.69–1.87) per 0.14 ppm increase of THC; after controlling for PM2.5, adj.HR was even higher at 2.10 (95% CI, 1.99– 2.22). The adj.HR was 1.38 (95% CI, 1.31–1.44) per 0.09 ppm increase in ambient NMHC concentration. After controlling for SO2 and CH4, the adj.HR was 1.16 (95% CI, 1.11–1.21). Sensitivity analyses showed that the UBC development risk was not sex-specific or influenced by diabetes status.DiscussionLong-term exposure to THC and NMHC may be a risk factor for UBC development. Acknowledging the pollutant sources can inform risk management strategies.

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Catalog of exogenous risk factors for bladder carcinogenesis

Cited by 16 publications

References 60 publications

Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer

Long-term ambient hydrocarbon exposure and incidence of urinary bladder cancer

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