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Updated: Jan 11, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Automatic segmentation and CT-based deep learning radiomics nomogram for predicting overall survival in patients with
Xiaomin Zheng1, Kaicai Liu1, Mengmeng Zhao1
1Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China.
Background And Objective:
Accurate prediction of overall survival (OS) in patients with small cell lung cancer (SCLC) is crucial for personalized treatment. This study aimed to create a three-dimensional (3D) automatic segmentation model for identifying SCLC lesions in computed tomography (CT) images. Moreover, we sought to develop and validate a deep learning radiomics nomogram (DLRN) utilizing pretreatment CT images to predict OS in SCLC patients.
Methods:
A total of 1061 SCLC patients from four hospitals in China were retrospectively enrolled. A 3D automatic segmentation model for SCLC lesions was developed using the nnU-Net neural network. Radiomics and deep learning features were extracted from the 3D tumor volume on the basis of pretreatment CT images of arterial phase (AP) and venous phase (VP). Subsequently, the radiomics score (Rad-score) and deep learning score (DL-score) were constructed. An integrated DLRN was constructed by combining the Rad-score and DL-score, followed by assessments of its discrimination, calibration, reclassification, and clinical usefulness.
Results:
The Dice similarity coefficients of the 3D automatic segmentation model on the AP and VP image test sets were 0.878 and 0.872, respectively. The DLRN showed satisfactory predictive performance for OS and yielded concordance indices of 0.892, 0.873, and 0.872 for the internal validation cohort, external validation cohort 1, and external validation cohort 2, respectively, with good calibration in all cohorts. Furthermore, the DLRN outperformed the single model and significantly outperformed the clinical nomogram (all P < 0.05). However, the addition of clinical factors did not improve the predictive efficacy of the DLRN on the basis of the net reclassification improvement and integrated discrimination improvement (all P > 0.05).
Conclusion:
The 3D automated segmentation model performed highly accurate segmentation of SCLC lesions, and a CT-based DLRN exhibited strong potential in predicting clinical outcomes for SCLC patients, potentially offering valuable insights for individualized therapy.
