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A nomogram incorporating CT-based peri-hematoma radiomics features to predict functional outcome in patients with
Xiaona Xia1, Jieqiong Liu1, Jiufa Cui2
1Department of Radiology, Qilu Hospital (Qingdao) of Shandong University, Qingdao, China.
Objective:
To evaluate the ability of non-contrast computed tomography based peri-hematoma and intra-hematoma radiomic features to predict the 90-day poor functional outcome for spontaneous intracerebral hemorrhage (sICH) and to present an effective clinically relevant machine learning system to assist in prognosis prediction.
Materials And Methods:
We retrospectively analyzed the data of 691 patients diagnosed with sICH at two medical centers. Fifteen radiomic features from the intra- and peri-hematoma regions were extracted and selected to build six radiomics models. The clinical-semantic model and nomogram model were constructed to compare prediction abilities. The areas under the curve (AUC) and decision curve analysis were used to evaluate discriminative performance.
Results:
Combining radiomics of the intra-hematoma with peri-hematoma regions significantly improved the AUC to 0.843 compared with radiomics of the intra-hematoma region (AUC = 0.780, P ＜ 0.001) in the test set. A similar trend was observed in the external validation cohort (AUC, 0.769 vs. 0.793, P = 0.709). The nomogram, which integrates clinical-semantic signatures with intra-hematoma and peri-hematoma radiomics signatures, accurately predicted a 90-day poor functional outcome in both the test and external validation sets (AUC 0.879 and 0.901, respectively).
Conclusion:
The nomogram constructed using clinical-semantic signatures and combined intra-hematoma and peri-hematoma radiomics signatures showed the potential to precisely predict 90-day poor functional outcomes for sICH.
