Probabilistic ratiocination of hepatocellular carcinoma after resection: evaluation of expected to be promising approaches
| العنوان: | Probabilistic ratiocination of hepatocellular carcinoma after resection: evaluation of expected to be promising approaches |
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| المؤلفون: | Dong Wei, Qiang Xia, Fuchen Liu, Weiping Zhou, Guojun Hou, Qi-fei Tao, Tao Tian, Guo Xinggang, Wenli Zhang, Hui Liu, Zongyan Wang, Seogsong Jeong |
| المصدر: | Ann Transl Med |
| بيانات النشر: | AME Publishing Company, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Prothrombin time, Oncology, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Probabilistic logic, Area under the curve, General Medicine, Nomogram, medicine.disease, Internal medicine, Hepatocellular carcinoma, Genetic model, Covariate, medicine, Original Article, business, Predictive modelling |
| الوصف: | Background Precise prediction of survival after treatment is of great importance for patients with diseases with high mortality. RNA sequencing data and deep learning (DL) methods are expected to become promising approaches in the development of prediction models in the future. We aimed to evaluate the optimal covariates and methodology for patients with hepatocellular carcinoma (HCC) undergoing surgical resection. Methods The Cox proportional hazards regression model and the DL approach were used to develop prediction models incorporating clinical, genetic, and combined clinical and genetic variables for survival prediction in patients with HCC after resection. A total of 1,114 patients and 184 patients were enrolled in the present study from 2,163 and 601 patients from Eastern Hepatobiliary Surgery Hospital and Renji Hospital, respectively. The models were internally validated through random sampling and externally validated in clinical cohorts. Between-model comparisons were carried out in terms of the integrated discrimination improvement and net reclassification index. Results The Cox and DL clinical models were developed by adopting 7 independent prognostic factors (total bilirubin, prothrombin time, tumor size, tumor number, lymph node metastasis, and vascular invasion) and 22 clinical factors, respectively. Both the Cox clinical model and the DL clinical model showed excellent performances in the derivation [area under the curve (AUC): 0.75 vs. 0.77] and validation (AUC: 0.83 vs. 0.80) sets. The derived Cox genetic model with 6 significant prognostic genes was not as effective as the DL approach involving 686 genes. A combined clinical and genetic approach modified the performances of both the Cox and DL models. The integrated discrimination improvement and net reclassification index of the DL clinical model were generally better than those of the Cox clinical model. Conclusions Our Cox clinical model sufficiently provided precise survival prediction in patients with HCC after resection. It may serve as an accurate and cost-effective tool for predicting survival in such patients. |
| تدمد: | 2305-5847 2305-5839 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f00cc8af173f45b49018ce2b4f8beb71 https://doi.org/10.21037/atm-20-4828 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f00cc8af173f45b49018ce2b4f8beb71 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23055847 23055839 |
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