Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (12): 78-85.doi: 10.6040/j.issn.1671-7554.0.2023.0659

• The innovation and challenge of artificial intelligence in medical imaging-Clinical Research • Previous Articles    

Texture analysis based on CT to predict the short-term outcomes of acute pulmonary embolism

CHEN Rong1, YANG Yue1, YANG Zhixiang1, SU Yaying2, PANG Zhiying3, WANG Dawei4, CUI Shujun3, YANG Fei3   

  1. 1. Graduate School, Hebei North University, Zhangjiakou 075000, Hebei, China;
    2. Department of Nuclear Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, Hebei, China;
    3. Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, Hebei, China;
    4. Department of Cardiothoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, Hebei, China
  • Published:2024-01-11

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Abstract: Objective To explore the value of quantitative prediction of the short-term outcomes in patients with acute pulmonary embolism(APE)based on CT texture analysis. Methods The CT pulmonary angiography(CTPA)images and clinical data of 79 APE patients were retrospectively collected. The patients were divided into good prognosis group(n=56)and poor prognosis group(n=23). After the ratio of the maximum diameter of the right ventricle to the left ventricle of the heart(RV/LV)and Qanadli index were recorded, and texture features were extracted, univariate and multivariate Logistic regression was conducted with R software. The receiver operating characteristic(ROC)curve, calibration curve and clinical decision curve were drawn to evaluate the efficacy of texture feature parameters, RV/LV ratio and Qanadli index in predicting poor prognosis of APE patients. Results RV/LV≥1.0, Qanadli index, and texture characterization parameters GLSZM-GLN were independent predictors of short-term poor prognosis in APE patients, with the area under the ROC curve(AUC)being 0.826(0.725-0.902), 0.922(0.839-0.970), and 0.867(0.772-0.933), respectively. The AUC of the combined curve was 0.958(0.887-0.990). The clinical decision curve showed the combined curve had the best efficacy in predicting poor prognosis of APE and the highest clinical application value. Conclusion Texture analysis based on CT thrombi, RV/LV≥1.0 and Qanadli index are able to assess the short-term poor prognosis of APE patients, which have certain clinical application value.

Key words: Acute pulmonary embolism, CT pulmonary angiography, Textured features, RV/LV, Qanadli index, Prognosis

CLC Number: 

  • R445.3
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