Purpose. This study aimed to investigatie the feasibility of pretherapeutic CT radiomics-based nomograms to predict the overall survival (OS) of patients with nondistant metastatic Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT). Methods. A retrospective review of 137 patients with nondistant metastatic BCLC-C HCC who underwent SBRT was made. Radiomics features distilled from pretherapeutic CT images were selected by the method of LASSO regression for radiomics signature construction. Then, the clinical model was constructed based on clinical characteristics. A radiomics nomogram was constructed using the radiomics score (Rad-score) and clinical characteristics to predict post-SBRT OS in BCLC-C HCC patients. An analysis of discriminatory ability and calibration was performed to confirm the efficacy of the radiomics nomogram. Results. In order to construct the radiomic signature, seven significant features were selected. Patients were divided into low-risk (Rad-score < −0.03) and high-risk (Rad-score ≥ −0.03) groups based on the best Rad-score cutoff value. There were statistically significant differences in OS both in the training set ( p < 0.0001 ) and the validation set ( p = 0.03 ) after stratification. The C-indexes of the radiomics nomogram were 0.77 (95% CI: 0.72–0.82) in the training set and 0.71 (95% CI: 0.61–0.81) in the validation set, which outperformed the clinical model and radiomics signature. An AUC of 0.76, 0.79, and 0.84 was reached for 6-, 12-, and 18-month survival predictions, respectively. Conclusions. The predictive nomogram that combines radiomic features with clinical characteristics has great prospects for application in the prediction of post-SBRT OS in nondistant metastatic BCLC-C HCC patients.
Background The process of initiating and completing clinical drug trials in hospital settings is highly complex, with numerous institutional, technical, and record-keeping barriers. In this study, we independently developed an integrated clinical trial management system (CTMS) designed to comprehensively optimize the process management of clinical trials. The CTMS includes system development methods, efficient integration with external business systems, terminology, and standardization protocols, as well as data security and privacy protection. Methods The development process proceeded through four stages, including demand analysis and problem collection, system design, system development and testing, system trial operation, and training the whole hospital to operate the system. The integrated CTMS comprises three modules: project approval and review management, clinical trial operations management, and background management modules. These are divided into seven subsystems and 59 internal processes, realizing all the functions necessary to comprehensively perform the process management of clinical trials. Efficient data integration is realized through extract-transform-load, message queue, and remote procedure call services with external systems such as the hospital information system (HIS), laboratory information system (LIS), electronic medical record (EMR), and clinical data repository (CDR). Data security is ensured by adopting corresponding policies for data storage and data access. Privacy protection complies with laws and regulations and de-identifies sensitive patient information. Results The integrated CTMS was successfully developed in September 2015 and updated to version 4.2.5 in March 2021. During this period, 1388 study projects were accepted, 43,051 electronic data stored, and 12,144 subjects recruited in the First Affiliated Hospital, Zhejiang University School of Medicine. Conclusion The developed integrated CTMS realizes the data management of the entire clinical trials process, providing basic conditions for the efficient, high-quality, and standardized operation of clinical trials.
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