Ahmad FB, Anderson RN. The leading causes of death in the US for 2020. JAMA. 2021;325(18):1829–30. https://doi.org/10.1001/jama.2021.5469.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schwartz SM. Epidemiology of cancer. Clin Chem. 2024;70(1):140–9. https://doi.org/10.1093/clinchem/hvad202.

Article  CAS  PubMed  Google Scholar 

Soria F, Shariat SF, Lerner SP, et al. Epidemiology, diagnosis, preoperative evaluation and prognostic assessment of upper-tract urothelial carcinoma (UTUC). World J Urol. 2017;35(3):379–87. https://doi.org/10.1007/s00345-016-1928-x.

Article  PubMed  Google Scholar 

Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49. https://doi.org/10.3322/caac.21820.

Article  PubMed  Google Scholar 

Al Hussein Al Awamlh B, Chang SS. Novel therapies for high-risk non-muscle invasive bladder cancer. Curr Oncol Rep. 2023;25(2):83–91. https://doi.org/10.1007/s11912-022-01350-9.

Article  CAS  PubMed  Google Scholar 

Szybalski W, Iyer VN. Crosslinking of DNA by enzymatically or chemically activated mitomycins and porfiromycins, bifunctionally “alkylating” antibiotics. Fed Proc. 1964;23:946–57.

CAS  PubMed  Google Scholar 

Verweij J, Pinedo HM. Mitomycin C: mechanism of action, usefulness and limitations. Anticancer Drugs. 1990;1(1):5–13.

Article  CAS  PubMed  Google Scholar 

Rosen GH, Nallani A, Muzzey C, et al. Antegrade instillation of UGN-101 (mitomycin for pyelocalyceal solution) for low-grade upper tract urothelial carcinoma: initial clinical experience. J Urol. 2022;207(6):1302–11. https://doi.org/10.1097/JU.0000000000002454.

Article  PubMed  Google Scholar 

Gottlieb J, Linehan J, Murray KS. Advances in chemoablation in upper tract urothelial carcinoma: overview of indications and treatment patterns. Transl Androl Urol. 2023;12(9):1449–55. https://doi.org/10.21037/tau-23-69.

Article  PubMed  PubMed Central  Google Scholar 

Prasad SM, Shishkov D, Mihaylov NV, et al. Primary chemoablation of recurrent low-grade intermediate-risk nonmuscle-invasive bladder cancer with UGN-102: a single-arm, open-label, phase 3 trial (ENVISION). J Urol. 2025;213(2):205–16. https://doi.org/10.1097/JU.0000000000004296.

Article  PubMed  Google Scholar 

Kadoyama K, Kuwahara A, Yamamori M, et al. Hypersensitivity reactions to anticancer agents: data mining of the public version of the FDA adverse event reporting system, AERS. J Exp Clin Cancer Res. 2011;30(1):93. https://doi.org/10.1186/1756-9966-30-93.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lindquist M, Stahl M, Bate A, et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf. 2000;23(6):533–42. https://doi.org/10.2165/00002018-200023060-00004.

Article  CAS  PubMed  Google Scholar 

Tantikul C, Dhana N, Jongjarearnprasert K, et al. The utility of the World Health Organization-The Uppsala Monitoring Centre (WHO-UMC) system for the assessment of adverse drug reactions in hospitalized children. Asian Pac J Allergy Immunol. 2008;26(2–3):77–82.

PubMed  Google Scholar 

Anand K, Ensor J, Trachtenberg B, et al. Osimertinib-induced cardiotoxicity: a retrospective review of the FDA adverse events reporting system (FAERS). JACC Cardio Oncol. 2019;1(2):172–8. https://doi.org/10.1016/j.jaccao.2019.10.006.

Article  Google Scholar 

Shin YE, Rojanasarot S, Hincapie AL, et al. Safety profile and signal detection of phosphodiesterase type 5 inhibitors for erectile dysfunction: a food and drug administration adverse event reporting system analysis. Sex Med. 2023;11(5):qfad059. https://doi.org/10.1093/sexmed/qfad059.

Article  PubMed  PubMed Central  Google Scholar 

Shu Y, Ding Y, Liu Y, et al. Post-marketing safety concerns with secukinumab: a disproportionality analysis of the FDA adverse event reporting system. Front Pharmacol. 2022;13:862508. https://doi.org/10.3389/fphar.2022.862508.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zou F, Zhu C, Lou S, et al. A real-world pharmacovigilance study of mepolizumab in the FDA adverse event reporting system (FAERS) database. Front Pharmacol. 2023;14:1320458. https://doi.org/10.3389/fphar.2023.1320458.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Aronson JK, Hauben M. Anecdotes that provide definitive evidence. BMJ. 2006;333(7581):1267–9. https://doi.org/10.1136/bmj.39036.666389.94.

Article  PubMed  PubMed Central  Google Scholar 

Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012;4(125):125ra31. https://doi.org/10.1126/scitranslmed.3003377.

Article  PubMed  PubMed Central  Google Scholar 

Zou F, Cui Z, Lou S, et al. Adverse drug events associated with linezolid administration: a real-world pharmacovigilance study from 2004 to 2023 using the FAERS database. Front Pharmacol. 2024;15:1338902. https://doi.org/10.3389/fphar.2024.1338902.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hu H, Zhao Y, Mao J, et al. A real-world pharmacovigilance study of adverse drug reactions associated with lecanemab and aducanumab based on WHO-VigiAccess and FAERS databases. Front Pharmacol. 2025;16:1561020. https://doi.org/10.3389/fphar.2025.1561020.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483–6. https://doi.org/10.1002/pds.677.

Article  CAS  PubMed  Google Scholar 

van Puijenbroek EP, Bate A, Leufkens HG, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11(1):3–10. https://doi.org/10.1002/pds.668.

Article  CAS  PubMed  Google Scholar 

Bate A, Lindquist M, Edwards IR, et al. A bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54(4):315–21. https://doi.org/10.1007/s002280050466.

Article  CAS  PubMed  Google Scholar 

Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug Saf. 2002;25(6):381–92. https://doi.org/10.2165/00002018-200225060-00001.

Article  CAS  PubMed  Google Scholar 

Sakaeda T, Tamon A, Kadoyama K, et al. Data mining of the public version of the FDA adverse event reporting system. Int J Med Sci. 2013;10(7):796–803. https://doi.org/10.7150/ijms.6048.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fornasier G, Taborelli M, Francescon S, et al. Targeted therapies and adverse drug reactions in oncology: the role of clinical pharmacist in pharmacovigilance. Int J Clin Pharm. 2018;40(4):795–802. https://doi.org/10.1007/s11096-018-0653-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ye Y, Fan B, Hu X. A real-world pharmacovigilance study of belzutifan in renal cell carcinoma and von Hippel–Lindau disease: insights from the FDA adverse event reporting system database. Int J Clin Pharm. 2025. https://doi.org/10.1007/s11096-025-01953-9.

Article  PubMed  Google Scholar 

Zhao Y, Jiang H, Xue L, et al. Exploring the safety profile of tremelimumab: an analysis of the FDA adverse event reporting system. Int J Clin Pharm. 2024;46(2):480–7.