Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Stumpo, Vittorio; Staartjes, Victor E.; Esposito, Giuseppe; Serra, Carlo; Regli, Luca; Olivi, Alessandro; Sturiale, Carmelo Lucio

doi:10.1007/978-3-030-85292-4_36

Cited by 6 publications

(6 citation statements)

References 80 publications

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“…(1) As regards the first question, some authors reported that the presence of MIAs is associated with a higher rate of post-treatment stroke and new focal neurological deficits compared with patients suffering from SAH associated with a single aneurysm; however, no significant differences in overall functional status and survival were observed [ 9 , 10 ]. Conversely, other studies did not find any association between the detection of MIAs at the time of the SAH and a worse outcome after treatment [ 11 , 12 ].…”

Section: Discussionmentioning

confidence: 99%

Timing and outcome of bystanders treatment in patients with subarachnoid hemorrhage associated with multiple aneurysms

et al. 2022

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In case of subarachnoid hemorrhage (SAH) associated with multiple intracranial aneurysms (MIAs), the main goal of acute treatment is securing the source of bleeding (index aneurysm). Indications and timing of bystanders treatment are instead still debated as the risk of new SAHs in patients harboring MIAs is not yet established. However, even if technically feasible, a simultaneous management of all aneurysms remains questionable, especially for safety issues. We retrospectively reviewed our last 5-year experience with SAH patients harboring MIAs entered in a clinic-radiological monitoring for bystanders follow-up in order to evaluate the occurrence of morphological changes, bleeding events, and safety and efficacy of a delayed treatment. We included 39 patients with mean age of 59.5 ± 12.2 years who survived a SAH. Among them, 14 underwent treatment, whereas 25 continued follow-up. The mean time between index and bystanders treatment was 14.3 ± 19.2 months. Patients undergoing bystanders treatment were mainly female and in general younger than patients undergoing observation. No cases of growth or bleeding were observed among bystanders within the two groups during the follow-up, which was longer than 1 year for the intervention group, and almost 40 months for the observation group. No major complications and mRS modifications were observed after bystanders treatment. Our data seem to suggest that within the short follow-up, intervention and observation seem to be likewise safe for bystander aneurysms, showing at the same time that a delayed management presents a similar risk profile of treating unruptured aneurysms in patients with no previous history of SAH.

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Section: Discussionmentioning

confidence: 99%

Timing and outcome of bystanders treatment in patients with subarachnoid hemorrhage associated with multiple aneurysms

et al. 2022

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“…ML algorithms can analyze large amounts of data and identify complex patterns which might not be achieved by ordinary classifications or logistic regression analysis (LR). A range of ML models have been applied to generate patient-specific predictive analytics for outcomes in neurosurgery, and some studies have demonstrated excellent performance in outcome prediction for a range of neurosurgical conditions 4 – 6 , particularly cerebrovascular neurosurgery 7 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Stroh,

Stefanits,

Maletzky

et al. 2023

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Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.

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“…It has been used in many different scientific and medical applications in recent years because of its powerful data mining and analysis capabilities. 12 – 14 Marostica et al 15 used deep convolutional neural networks to detect and diagnose kidney cancer with good results. Zhu et al 17 used convolutional neural networks for blood cell classification 16 and malaria image classification, and their models were superior to existing methods.…”

Section: Introductionmentioning

confidence: 99%

The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm

Guo

2023

Technol Cancer Res Treat

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Background This study aimed to develop and validate predictive models based on machine learning (ML) algorithms for patients with bone metastases (BM) from clear cell renal cell carcinoma (ccRCC) and to identify appropriate models for clinical decision-making. Methods In this retrospective study, we obtained information on ccRCC patients diagnosed with bone metastasis (ccRCC-BM), from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 ( n = 1490), and collected clinicopathological information on ccRCC-BM patients at our hospital ( n = 42). We then applied four ML algorithms: extreme gradient boosting (XGB), logistic regression (LR), random forest (RF), and Naive Bayes model (NB), to develop models for predicting the overall survival (OS) of patients with bone metastasis from ccRCC. In the SEER dataset, 70% of the patients were randomly divided into training cohorts and the remaining 30% were used as validation cohorts. Data from our center were used as an external validation cohort. Finally, we evaluated the model performance using receiver operating characteristic curves (ROC), area under the ROC curve (AUC), accuracy, specificity, and F1-scores. Results The mean survival times of patients in the SEER and Chinese cohort were 21.8 months and 37.0 months, respectively. Age, marital status, grade, T stage, N stage, tumor size, brain metastasis, liver metastasis, lung metastasis, and surgery were included in the ML model. We observed that all four ML algorithms performed well in predicting the 1-year and 3-year OS of patients with ccRCC-BM. Conclusion ML is useful in predicting the survival of patients with ccRCC-BM, and ML models can play a positive role in clinical applications.

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Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Cited by 6 publications

References 80 publications

Timing and outcome of bystanders treatment in patients with subarachnoid hemorrhage associated with multiple aneurysms

Timing and outcome of bystanders treatment in patients with subarachnoid hemorrhage associated with multiple aneurysms

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm

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