In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Objective To retrospectively analyze the risk factors for recurrence within 1 year after discontinuation of antiepileptic drugs (AEDs) in epilepsy patients, and to construct and validate a columnar diagram model for individualized prediction of recurrence risk. The aim is not only to provide decision-making assistance for clinicians but, more importantly, to offer a quantitative tool for nursing staff to identify high-risk patients, implement stratified management, and deliver precision interventions. Methods A retrospective study was conducted on 271 epilepsy patients treated at Beijing Tiantan Hospital, Capital Medical University, from October 2018 to October 2023, who ultimately attempted AED withdrawal. Patients were divided into a recurrence group (n=67) and a non-recurrence group (n=204) based on whether they experienced recurrence within 1 year after AED discontinuation. Key predictive factors were screened from candidate clinical variables using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression analysis to identify independent risk factors. A columnar diagram predictive model was constructed based on these independent factors. Internal validation was performed using bootstrap repeated sampling (1000 times), and the model's discriminative power, calibration, and clinical utility were comprehensively evaluated by calculating the consistency index (C-index), plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curve (DCA). Results LASSO regression and multivariate analysis ultimately identified three independent predictors: epileptiform discharges on electroencephalogram (EEG) (OR=3.15, 95%CI (1.72, 5.78)], presence of structural etiology [OR=2.89, 95%CI (1.51, 5.54)], and duration of epilepsy before drug withdrawal (≥5 years) OR=2.46, 95%CI (1.30, 4.66)]. The columnar diagram model constructed based on these predictors achieved a C-index of 0.786 [95%CI (0.728, 0.844)] in the original cohort. After internal validation, the adjusted C-index was 0.773. ROC curve analysis demonstrated an area under the curve (AUC) of 0.786. Calibration curves showed good consistency between predicted and observed probabilities, with a Hosmer-Lemeshow test P-value of 0.452. Decision curve analysis indicated that the model had clinical net benefit when applied within a threshold probability range of 2% to 58%, with the maximum net benefit observed at a threshold probability of approximately 30%. Conclusion This study established a columnar diagram model incorporating three readily available clinical indicators, enabling rapid calculation of individualized relapse risk during outpatient drug withdrawal assessments. The model identifies three core targets for nursing stratification (EEG discharges, structural etiology, and long disease duration), providing objective evidence for transitioning from "routine education" to "risk-based precision intervention" for nursing staff. This approach contributes to optimizing the entire withdrawal process management and enhancing patient safety.