In this study, we combined the simple variables obtained in routine clinical practice and the ML algorithm to establish a model for predicting the rupture risk of small aneurysms. The best models, SVM, carried out a satisfying ability of discrimination in screening IAs with high risk of rupture, with an AUC value of 0.817 and 0.893 in the internal and external validation. In SHAP analyze, size, location, shape and presence of hypertension exerted great influences on predicting outcome.
Physicians and patients are often caught in a dilemma when making treatment decisions for unruptured IAs, especially in small ones. On the one hand, the low rupture risk of small IAs makes conservative treatment seem more reasonable. On the other hand, the disastrous consequences lead by rupture make many patients incline to receive preventive treatment. Nevertheless, treatment is always accompanied by risks. The former study indicates that among patients without history of hemorrhage, the total morbidity and mortality rate of 1 year after open surgery and endovascular treatment are 12.6% and 9.8%, respectively [1]. Accordingly, accurately and quickly screening IAs with high risk of rupture for preventive treatment of these IAs is extremely crucial.
Traditional statistical methods have been widely employed to correlate ruptured aneurysms with related risk factors. However, the fact of the complex relationship between various features and the outcome would bring some problems to the analysis based on the assumption of a simple linear relationship. ML has shown great potential in dealing with variables with nonlinear relationships and missing values [8,9]. It could enable us to have a more comprehensive understanding of the relationships from different perspectives. Furthermore, with the wide application of electronic medical record system and the progress of technology, ML model could be integrated into some systems that could automatically process a large amount of data, and bring great convenience in aiding clinical decision for doctors and providing individualized diagnosis and treatment for patients.
The prime advantage of our model was convenient to apply and serve for physicians and patients. Considering that it could be a difficult task for physicians to spend much time on collecting complex additional information in their busy work, we only collect patient and morphological characteristics that can be accessed in routine clinical practice for modeling. This design could improve the convenience of our model in clinical environment well. On the contrary, two previous studies constructed ML models based on complex hemodynamics and pyradiomics-derived morphological features, which may limit their clinical promotion [19,20]. At the same time, another two researches employed convolutional neural networks to develop prediction model, which worked by identifying information from 3D-DSA [21,22]. However, ignoring important patient characteristics could exert some impact on the clinical efficacy of their models in the real world.
Another advantage of our model is the interpretability by introducing SHAP algorithm to rank the importance of the selected input features of IAs patients. ML has gradually become a research hotspot because of its excellent ability to handle large samples and nonlinear relationships. However, a significant defect of ML models is that they tend to operate like “black boxes”, which makes them seem less reliable for experts. What we did to conquer this flaw was to interpret the predictions made by our models according to the SHAP method [18]. By this way, the rules behind prediction of our ML model could be better revealed; and physicians could validate the interpretation of the ML model based on professional knowledge.
Researchers have extensively studied and discussed various factors related to ruptured IAs, in which larger size [3,23], irregular shape [24], or location at ACOA and PCOA [25] associated with higher rupture risk have been recognized by most studies. Same results could be concluded in our study. Interestingly, patients with history of hypertension in our cohort showed a lower risk of rupture, which were different from some studies. This may be attributed to the changes brought about by the use of antihypertensive drugs. In a previous animal model study, they found that the normalization of blood pressure by antihypertensive drugs can reduce the rupture rate of aneurysms in mice [26]. In addition, one Finland research pointed that drug-treated hypertension may relate to the formulation of IAs instead of the rupture, and bring higher rupture risk only if not be treated [27]. Similarly, several studies regarded DM as a protective factor, and attributed it to the consume of hypoglycemic agents [28,29]. More well-designed researches were required to sufficiently investigate the connection between IAs rupture and drug-treated hypertension.
There are still certain limitations in our study. First and foremost, the retrospective nature of this study may introduce impacts to our analysis. Second, most IAs of the patients had ruptured during the study period. Although ruptured IAs were indeed unstable, there were reports considered that post rupture morphology should not be considered as an adequate alternative indicator in evaluating the rupture risk [30]. Third, we only took into account clinically accessible factors. Some complex factors, such as morphology and hemodynamics parameters, were rarely included in the current study. Finally, although our model is satisfying in external validation, it remains problematic that the external validation dataset is relatively small. Going forward, prospective multicenter validation and long-term follow-up is needed to better improve our results.