Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

Prognostic value of CT scan-based radiomics in intracerebral hemorrhage patients: A systematic review and meta-analysis

OBJECTIVES

We conducted a systematic review and meta-analysis of current publications on the potential role of non-contrast-enhanced computed tomography (NCCT) radiomics as a prognostic indicator in patients with intracerebral hemorrhage (ICH).

METHODS

We systematically searched PubMed, EMBASE, and the Web of Science from inception until January 8, 2024. Studies with NCCT-based radiomics features for predicting the prognostic outcomes of ICH patients were included. We calculated the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under curve (AUC) values. The radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and the quality assessment of diagnostic accuracy studies (QUADAS-2) were used for quality assessment.

RESULTS

Twenty-two studies were included. The pooled sensitivity, specificity, DOR, and AUC of radiomics models were 0.73, 0.78, 10.03, and 0.83, respectively, while on the combined radiomics models with other non-radiomics features were 0.80, 0.80, 16.28, and 0.86. Subgroup analysis showed that studies with the following covariates have a higher accuracy: single center, modified Rankin Scale (mRS) criteria for the ICH outcomes assessment, following patients for evaluation of ICH outcomes for more than a month, automatic segmentation, capturing the radiomics feature from the only intra-hematomal region, using PyRadiomic tool for features extraction, and using non-logistic regression for modeling. The quality of literature using QUADAS-2 and METRICS tools was good and was under-average using the RQS tool. No publication bias was detected.

CONCLUSIONS

Radiomics features showed moderate to high accuracy for predicting ICH prognostic outcomes. Although the QUADAS-2 and METRICS assessments indicated good quality, the radiomics pipeline quality was under-average.

CLINICAL RELEVANCE

NCCT-based radiomics features can provide information about the prognostic outcomes of ICH patients after patient admission. This study exploits the value of current evidence on NCCT-based radiomics methodology in the prediction of ICH prognosis.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study

OBJECTIVES

To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.

MATERIALS AND METHODS

Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions.

RESULTS

The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort.

CONCLUSION

The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model.

CLINICAL RELEVANCE STATEMENT

Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Absolute Contusion Expansion Is Superior to Relative Expansion in Predicting Traumatic Brain Injury Outcomes: A Multi-Center Observational Cohort Study

Contusion expansion (CE) is a potentially treatable outcome predictor in traumatic brain injury (TBI), and a suitable end-point for hemostatic therapy trials. However, there is no consensus on the definition of clinically relevant CE, both in terms of measurement criteria (absolute vs. relative volume increase) and cutoff values. In light of this, the aim of this study was to assess the predictive abilities of different CE definitions on outcome. We performed a multi-center observational cohort study of adults with moderate-to-severe TBI treated in an intensive care unit. The exposure of interest was CE, defined as the absolute and relative volume change between the first and second computed tomography scan. The primary outcome was the Glasgow Outcome Scale (GOS) at 6-12 months post-injury, dichotomized into unfavorable (GOS ≤3) or favorable (GOS ≥4). The secondary outcome was all-cause mortality. In total, 798 patients were included, with a median duration of 7.0 h between the first and second CT scan. The median absolute and relative CE was 1.5 mL (interquartile range [IQR] 0.1-8.3 mL) and 100% (IQR 10-530%), respectively. Both CE forms were independently associated with unfavorable GOS. Absolute CE outperformed relative CE in predicting both unfavorable GOS (area under the curve [AUC]: 0.65 vs. 0.60, p = 0.002) and all-cause mortality (AUC: 0.66 vs. 0.60, p = 0.003). For dichotomized CE, absolute cutoffs of 1-10 mL yielded the best results. We conclude that absolute CE demonstrates stronger outcome correlation than relative CE. In studies focusing on lesion progression in TBI, it may be advantageous to use absolute CE as the primary outcome metric. For dichotomized outcomes, cutoffs between 1 and 10 mL are suggested, depending on the desired sensitivity-specificity balance.

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

PURPOSE

We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients.

METHODS

In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables.

RESULTS

Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis.

CONCLUSION

Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

A Clinical-Radiomics Based Nomogram to Predict Progressive Intraparenchymal Hemorrhage in Mild to Moderate Traumatic Injury Patients

PURPOSE

To develop a non-contrast computed tomography(NCCT)based radiomics model for predicting intraparenchymal hemorrhage progression in patients with mild to moderate traumatic brain injury(TBI).

METHODS

We retrospectively analyzed 166 mild to moderate TBI patients with intraparenchymal hemorrhage from January 2018 to December 2021. The enrolled patients were divided into training cohort and test cohort with a ratio of 6:4. Uni- and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration curve, the decision curve analysis, sensitivity, and specificity.

RESULTS

Eleven radiomics features, presence with SDH, and D-dimer > 5 mg/l were selected to construct the combined clinical-radiomic model for the prediction of TICH in mild to moderate TBI patients. The AUC of the combined model was 0.81(95% confidence interval (CI), 0.72 to 0.90) in the training cohort and 0.88 (95% CI 0.79 to 0.96) in the test cohort, which were superior to the clinical model alone (AUC_training = 0.72, AUC_test = 0.74). The calibration curve demonstrated that the radiomics nomogram had a good agreement between prediction and observation. Decision curve analysis confirmed clinically useful.

CONCLUSIONS

The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors can serve as a reliable and powerful tool for Predicting intraparenchymal hemorrhage progression for patients with mild to moderate TBI.