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Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images. | LitMetric

AI Article Synopsis

  • A study aimed to create a 2.5-dimensional deep learning model to predict early recurrence of hepatocellular carcinoma (HCC) using CT imaging data from patients who underwent HCC surgery.
  • The model was developed with data from 232 patients at one center and validated with an additional 91 patients from another center, incorporating various imaging views and learning techniques to enhance prediction accuracy.
  • Results showed that the 2.5D model outperformed a traditional 3D model and when combined with clinical features, it provided the best predictions for postoperative HCC recurrence across all patient cohorts.

Article Abstract

Purpose: To construct a 2.5-dimensional (2.5D) CT radiomics-based deep learning (DL) model to predict early postoperative recurrence of hepatocellular carcinoma (HCC).

Patients And Methods: We retrospectively analyzed the data of patients who underwent HCC resection at 2 centers. The 232 patients from center 1 were randomly divided into the training (162 patients) and internal validation cohorts (70 patients); 91 patients from center 2 formed the external validation cohort. We developed a 2.5D DL model based on a central 2D image with the maximum tumor cross-section and adjacent slices. Multiple views (transverse, sagittal, and coronal) and phases (arterial, plain, and portal) were incorporated. Multi-instance learning techniques were applied to the extracted data; the resulting comprehensive feature set was modeled using Logistic Regression, RandomForest, ExtraTrees, XGBoost, and LightGBM, with 5-fold cross validation and hyperparameter optimization with Grid-search. Receiver operating characteristic curves, calibration curves, DeLong test, and decision curve analysis were used to evaluate model performance.

Results: The 2.5D DL model performed well in the training (AUC: 0.920), internal validation (AUC: 0.825), and external validation cohorts (AUC: 0.795). The 3D DL model performed well in the training cohort and poorly in the internal and external validation cohorts (AUCs: 0.751, 0.666, and 0.567, respectively), indicating overfitting. The combined model (2.5D DL+clinical) performed well in all cohorts (AUCs: 0.921, 0.835, 0.804). The Hosmer-Lemeshow test, DeLong test, and decision curve analysis confirmed the superiority of the combined model over the other signatures.

Conclusion: The combined model integrating 2.5D DL and clinical features accurately predicts early postoperative HCC recurrence.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577935PMC
http://dx.doi.org/10.2147/JHC.S493478DOI Listing

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