Development and validation of a clinical-radiomics nomogram for predicting a poor outcome and 30-day mortality after a spontaneous intracerebral hemorrhage.

BACKGROUND

Noncontrast computed tomography (NCCT) is often performed for patients with a suspected spontaneous intracerebral hemorrhage (ICH) at the time of admission. Both clinical and radiomic features on the initial NCCT can predict the outcomes of those with ICH, but satisfactory model performance remains challenging.

METHODS

A total of 258 acute ICH patients from the Central Hospital of Wuhan (CHW) between January 2018 and December 2020 were retrospectively assigned to training and internal validation cohorts at a ratio of 7:3. An independent external testing cohort of 87 patients from January 2021 to July 2021 from the Fifth Affiliated Hospital of Nanchang University (FAHNU) was also used. Based on the least absolute shrinkage and selection operator (LASSO) algorithm, radiomics (rad)-scores were generated from 9 quantitative features on the initial NCCT images. Three models (radiomics, clinical, and hybrid) were established using stepwise logistic regression analysis. The Akaike information criterion and the likelihood ratio test were used to compare the goodness of fit of the three models. Receiver operating characteristic (ROC) curve analysis was performed and bar charts were constructed to evaluate the discrimination of constructed model for predicting a poor outcome following ICH.

RESULTS

The three cohorts had similar baseline clinical characteristics, including demographic features and outcomes. In the clinical model, hematoma expansion [2.457 (0.297, 2.633); P=0.014], intracerebral ventricular hemorrhage [2.374 (0.180, 1.882); P=0.018], and location [-2.268 (-2.578, -0.188); P=0.023] were independently associated with a poor clinical outcome. In the hybrid model, location [-2.291 (-2.925, -0.228); P=0.022], and rad-score [5.255 (0.680, 11.460); P<0.001] were independently associated with a poor outcome. The hybrid model achieved satisfactory discriminability, with areas under curve (AUCs) of 0.892 [95% confidence interval (CI): 0.847 to 0.937], 0.893 (95% CI: 0.820 to 0.966), and 0.838 (95% CI: 0.755 to 0.920) in the training, internal validation, and external testing cohorts, respectively. The hybrid model also achieved good discriminability in the prediction of 30-day mortality, with AUCs of 0.840, 0.823, and 0.883 in the training, internal validation, and external testing cohorts, respectively. The rad-score [2.861 (1.940, 4.220); P<0.001] was the predominant risk factor associated with 30-day mortality.

CONCLUSIONS

Radiomic analysis based on initial NCCT scans showed added value in predicting a poor outcome after ICH. A clinical-radiomics model yielded improved accuracy in predicting a poor outcome and 30-day death following ICH compared with radiomics alone.