The British journal of radiology · 2026
Cox regression model for predicting the surgical risk and timing of necrotizing enterocolitis based on abdominal radiographs radiomics features.
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Lu Chaogang, Yang Mingshu, Zhu Yinghao, Luo Siqi, Xia Yaqin, Wang Laishuan, Bai Mei, Qiao Zhongwei
Аннотация
OBJECTIVES: To evaluate the ability of abdominal radiographs-based radiomics features to predict surgical risk and timing in neonates with necrotizing enterocolitis (NEC), providing an objective basis for clinical decision-making.
METHODS: A retrospective cohort of 262 NEC patients (2016-2022) was divided into surgery and nonsurgery groups. Radiomics features were extracted from abdominal radiographs at NEC diagnosis using LASSO and Cox regression to generate a radiomics risk score (Riskscore). The cohort was split into training (n = 184, 42 surgical cases) and test sets (n = 78, 18 surgical cases). Kaplan-Meier survival curves and Cox proportional hazards models were used to assess surgical risk. A nomogram with AUC was developed to predict surgical exemption over 1-7 weeks.
RESULTS: The surgical group had lower gestational age and birth weight (P < .001). Significant differences in survival curves were seen between high- and low-risk groups in both sets (P < .001, P = .0047). Riskscore was a significant factor for surgical interventions (P < .05). The nomogram model showed good performance, with AUCs of 0.716-0.744 in the training set and 0.668-0.733 in the test set.
CONCLUSIONS: Abdominal radiographs-based radiomics feature analysis shows potential as a valuable tool for predicting surgical risk and timing in NEC patients.
ADVANCES IN KNOWLEDGE: This study is the first to assess abdominal radiographs' value in predicting surgical risk and timing for NEC.
METHODS: A retrospective cohort of 262 NEC patients (2016-2022) was divided into surgery and nonsurgery groups. Radiomics features were extracted from abdominal radiographs at NEC diagnosis using LASSO and Cox regression to generate a radiomics risk score (Riskscore). The cohort was split into training (n = 184, 42 surgical cases) and test sets (n = 78, 18 surgical cases). Kaplan-Meier survival curves and Cox proportional hazards models were used to assess surgical risk. A nomogram with AUC was developed to predict surgical exemption over 1-7 weeks.
RESULTS: The surgical group had lower gestational age and birth weight (P < .001). Significant differences in survival curves were seen between high- and low-risk groups in both sets (P < .001, P = .0047). Riskscore was a significant factor for surgical interventions (P < .05). The nomogram model showed good performance, with AUCs of 0.716-0.744 in the training set and 0.668-0.733 in the test set.
CONCLUSIONS: Abdominal radiographs-based radiomics feature analysis shows potential as a valuable tool for predicting surgical risk and timing in NEC patients.
ADVANCES IN KNOWLEDGE: This study is the first to assess abdominal radiographs' value in predicting surgical risk and timing for NEC.