Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study
| العنوان: | Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study |
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| المؤلفون: | Nam-Joon Yi, Jae-Won Joh, Jeong Hoon Lee, Kwang-Woong Lee, Junho Bae, Yuri Cho, Jung Hwan Yoon, Joong-Won Park, Young Chang, Bohyun Kim, Yoon Jun Kim, Kyung-Suk Suh, Seoung Hoon Kim, Jong Man Kim, Dong Hyun Sinn, Joon Yeul Nam |
| المصدر: | Cancers, Vol 12, Iss 2791, p 2791 (2020) Cancers Volume 12 Issue 10 |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Oncology, Cancer Research, medicine.medical_specialty, medicine.medical_treatment, Milan criteria, Liver transplantation, lcsh:RC254-282, Article, 03 medical and health sciences, 0302 clinical medicine, Primary outcome, Internal medicine, medicine, Derivation, liver transplantation, business.industry, deep learning, hepatocellular carcinoma, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Regression, Tumor recurrence, Multicenter study, 030220 oncology & carcinogenesis, Hepatocellular carcinoma, 030211 gastroenterology & hepatology, business |
| الوصف: | Several models have been developed using conventional regression approaches to extend the criteria for liver transplantation (LT) in hepatocellular carcinoma (HCC) beyond the Milan criteria. We aimed to develop a novel model to predict tumor recurrence after LT by adopting artificial intelligence (MoRAL-AI). This study included 563 patients who underwent LT for HCC at three large LT centers in Korea. Derivation (n = 349) and validation (n = 214) cohorts were independently established. The primary outcome was time-to-recurrence after LT. A MoRAL-AI was derived from the derivation cohort with a residual block-based deep neural network. The median follow-up duration was 74.7 months (interquartile-range, 18.5&ndash 107.4) 204 patients (36.2%) had HCC beyond the Milan criteria. The optimal model consisted of seven layers including two residual blocks. In the validation cohort, the MoRAL-AI showed significantly better discrimination function (c-index = 0.75) than the Milan (c-index = 0.64), MoRAL (c-index = 0.69), University of California San Francisco (c-index = 0.62), up-to-seven (c-index = 0.50), and Kyoto (c-index = 0.50) criteria (all p < 0.001). The largest weighted parameter in the MoRAL-AI was tumor diameter, followed by alpha-fetoprotein, age, and protein induced by vitamin K absence-II. The MoRAL-AI had better predictability of tumor recurrence after LT than conventional models. The MoRAL-AI can also evolve with further data. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2072-6694 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::067a55ce58f15a03fb4835f1f8d1e1b8Test https://www.mdpi.com/2072-6694/12/10/2791Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....067a55ce58f15a03fb4835f1f8d1e1b8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20726694 |
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