Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Abstract

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.

[Retracted] Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

“…The CNN has an overfitting problem that affects the performance of the prediction method. Nawabi et al [ 55 ] applied random forest with filter and texture-derived features for the prediction process. The developed method with the selected features achieved higher performance in the analysis.…”

Neuroimaging of Acute Intracerebral Hemorrhage

“…The same algorithm also identified subtle ICHs that were overlooked by radiologists further underlining the potential of machine-learning aided decision making in ICH diagnosis, especially in times of increasing imaging workloads [72]. Another group found that machine learning-based evaluation of image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems and that the integration of conventional scores and image features had synergistic effects with an increase of accuracy for prediction functional outcome in patients with ICH [73]. Further, mobile stroke units may not only help in the early diagnosis of ischemic stroke but also add to rapid diagnosis of ICH and to correctly triaging these patients to the appropriate hospital, which may especially be important in rural areas [74][75][76].…”

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

“…In patients with intracerebral hemorrhage (ICH), the ICHscore is one of the most widely used clinical prediction scores (85)(86)(87)(88). Although ML technology for outcome prediction has rapidly advanced for ischemic stroke, recent ML studies predicting functional outcomes after ICH have also demonstrated high-discriminating power (63,89). A recent study by Sennfält et al tracked long-term functional dependence and mortality after an acute ischemic stroke of more than 20,000 Swedish patients (90).…”