Discussion
NMIBC, malignancy, high-grade tumour, tumour, Tumour-related, CUETO
CPH, DISEASES, BLADDER CANCER, TUMOUR
In this population-based study involving 32 060 patients with T1 bladder cancer, we aimed to identify predictive factors for survival and compare the accuracy of 5-year CSS and OS predictions with the use of ANN and LR-based models. Firstly and most importantly, multiple factors are associated with unfavourable CSS including clinical and histopathological features: older age, higher histological grade, non-urothelial histology variants, tumour size ≥ 3 cm, recurrent tumour, tumour location, and sociodemographic features such as black race, non-married status and lower annual income.Secondly, the analysis showed that ANN is a feasible prediction tool with acceptable, moderate accuracy in survival estimation. Thirdly, ANN and conventional machine learning method—LR-based model exhibit the same discrimination and prediction accuracy for 5-year CSS and OS in T1 NMIBC. Forth, pursuit for better prognostic tools in T1 BC is of great significance as the reported real-world survival outcomes remain poor.The above results raise the question if the neural network is a valid approach for survival model creation. The accuracy was no better than the one of the conventional LR models. Moreover, identical prediction factors as for LR were provided for ANN and yielded the same accuracy. The obvious drawback of the ANN is the ‘black-box’ problem, which means that we cannot exactly know which factors play the most significant role and how the ANN learning process goes on. However, as ANN seem to be not inferior to LR it must be considered a valid approach to be used in future studies and web applications for clinical use. Perhaps the optimization (e.g. hyperparameters modification) might be a clue to the success and ANN models could become a valuable tool for clinical implementation. Disadvantages coming from the so-called ‘black-box’ problem are also possible to overcome as suggested by some authors [Artificial intelligence with the underscored role of deep-learning techniques such as neural networks has an undeniable role to play in current and future medicine [Widely used EORTC and CUETO risk scores or EAU risk groups were not designed to estimate the risk of cancer-specific or overall mortality. Above classifications were established to stratify patients according to the progression- and/or recurrence-free survival, but not CSS [We identified clinical, histopathological and sociodemographic factors that were independently associated with CSS. These included obvious factors such as high-grade tumour, history of previous NMIBC and non-urothelial histology variants. High-grade histology is the strongest risk factor for progression in NMIBC [Factors influencing the OS were similar to those predicting CSS. The following factors were associated with worse OS: high-grade tumour, tumour size ≥ 3 cm, non-urothelial histology, tumour location, older age, black race, male gender, the presence of other malignancy, non-married status, lower annual income and non-metropolitan citizenship. Tumour-related factors such as high-grade histology, variants of histology, and large size contributed to worse OS, most probably due to their association with higher CSM. Older age and the presence of other malignancy increase the risk of all-cause mortality. The male gender was associated with worse OS, which supports the general rule of shorter life expectancy in men. Conversely, consistently other authors show poorer bladder cancer-specific survival in women than in men [Limitations of the study include the lack of detailed data on the patient comorbidities, smoking habits, and some tumour characteristics (e.g. lymphovascular invasion) and most importantly the quality of surgery (e.g. detrusor muscle in the specimen, macroscopically radical resection) which all have been proven to influence the prognosis [Analysis of 5-year OS and CSS required the exclusion of patients with insufficient follow-up which must be regarded as bias. Since both cancer-specific and overall mortality are time-dependent variables, analysing them as binary end-point might raise some methodological concerns and it should be clearly reported as a limitation. We have, however, performed consecutive analyses to confirm the utility of such an approach. Performed CPH analysis yielded the same predictors for CSS as LR. Among patients excluded from survival prediction, only 6.7% were lost to follow-up before 60 months. Other patients which were excluded from CSS analysis, died from other diseases than BC as reported in the results section. The bias of excluding the patients who did not reach the 5-year follow-up was limited by the performed comparison between the analyzed and excluded patients (supplementary table 1). The only main clinically significant difference between included and excluded patients was more advanced age in the latter ones contributing to higher other-cause mortality (supplementary table 1). A binary endpoint (5-year CSM) seems therefore acceptable for the comparison of LR and ANN performance. The accuracy of prediction in the training and validation datasets was very similar which indicates a rather small risk of model overfitting.To conclude, available predictive factors for CSS and OS might be useful to estimate the risk of 5-year CSS and OS in patients with T1 BC. The accuracy of mortality prediction is still moderate, also when ANN is applied. Patients with T1 BC and adverse features should receive more aggressive treatment after initial TURBT.