التفاصيل البيبلوغرافية
| العنوان: |
Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI. |
| المؤلفون: |
Kwong, Jethro C. C., Wu, Jeremy, Malik, Shamir, Khondker, Adree, Gupta, Naveen, Bodnariuc, Nicole, Narayana, Krishnateja, Malik, Mikail, van der Kwast, Theodorus H., Johnson, Alistair E. W., Zlotta, Alexandre R., Kulkarni, Girish S. |
| المصدر: |
NPJ Digital Medicine; 4/18/2024, Vol. 7 Issue 1, p1-11, 11p |
| مصطلحات موضوعية: |
NON-muscle invasive bladder cancer, RISK assessment, MEDICAL information storage & retrieval systems, DIGITAL technology, CANCER invasiveness, ARTIFICIAL intelligence, DIGITAL health, SYSTEMATIC reviews, MEDLINE, HEALTH outcome assessment, EVALUATION, DISEASE risk factors |
| مستخلص: |
Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32−93) and median cohort size of 125 (IQR 93−309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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