Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Peng Zhou; Quanye Sun; Gesheng Song; Zexiang Liu; Jianfeng Qi; Xuhui Yuan; Xu Wang; Shaofeng Yan; Jianyang Du; Zhengjun Dai; Jianjun Wang; Shaoshan Hu

doi:10.3389/fneur.2022.982928

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Front Neurol. 2022 Nov 8:13:982928. doi: 10.3389/fneur.2022.982928. eCollection 2022.

Authors

Peng Zhou¹, Quanye Sun², Gesheng Song³, Zexiang Liu¹, Jianfeng Qi¹, Xuhui Yuan¹, Xu Wang¹, Shaofeng Yan¹, Jianyang Du⁴, Zhengjun Dai⁵, Jianjun Wang¹, Shaoshan Hu⁶

Affiliations

¹ Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
² Research Center of Translational Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
³ Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
⁴ Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
⁵ Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.
⁶ Department of Neurosurgery, Emergency Medicine Center, Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.

Abstract

Objective: We developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.

Methods: Retrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.

Results: The clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.

Conclusion: Radiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.

Keywords: basal ganglia hemorrhage; machine learning model; perihematomal edema; prognosis; radiomics.