Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.1177/11769351241289719
Lujun Shen, Yiquan Jiang, Tao Zhang, Fei Cao, Liangru Ke, Chen Li, Gulijiayina Nuerhashi, Wang Li, Peihong Wu, Chaofeng Li, Qi Zeng, Weijun Fan
{"title":"Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model.","authors":"Lujun Shen, Yiquan Jiang, Tao Zhang, Fei Cao, Liangru Ke, Chen Li, Gulijiayina Nuerhashi, Wang Li, Peihong Wu, Chaofeng Li, Qi Zeng, Weijun Fan","doi":"10.1177/11769351241289719","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Patients with intermediate or advanced hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment and treatment planning. In 2018, a novel machine learning methodology \"survival path\" (SP) was developed to facilitate dynamic prognosis prediction and treatment planning. One year after, a deep learning approach called Dynamic Deephit was developed. The performance of the two state-of-art models in dynamic prognostication have not been compared.</p><p><strong>Methods: </strong>We trained and tested the SP and Dynamic DeepHit models in a large cohort of 2511 HCC patients using time-series data. The time-series data were converted into data of time slices, with an interval of three months. The time-dependent c-index for OS at given prediction time (<i>t</i> = 1, 6, 12, 18 months) and evaluation time (∆<i>t</i> = 3, 6, 9, 12, 18, 24, 36, 48 months) were compared.</p><p><strong>Results: </strong>The comparison between SP model and Dynamic DeepHit-HCC model showed the latter had significant better performance at the time of initial admission. The time-dependent c-index of Dynamic DeepHit-HCC model gradually decreased with the extension of time (from 0.756 to 0.639 in the training set; from 0.787 to 0.661 in internal testing set; from 0.725 to 0.668 in multicenter testing set); while the time-dependent c-index of SP model displayed an increased trend (from 0.665 to 0.748 in the training set; from 0.608 to 0.743 in internal testing set; from 0.643 to 0.720 in multicenter testing set). When the prediction time comes to 6 months or later since initial treatment, the survival path model outperformed the dynamic DeepHit model at late evaluation times (∆<i>t</i> > 12 months).</p><p><strong>Conclusions: </strong>This research highlighted the unique strengths of both models. The SP model had advantage in long term prediction while the Dynamic DeepHit-HCC model had advantages in prediction at near time points. Fine selection of models is needed in dealing with different scenarios.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"23 ","pages":"11769351241289719"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483769/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/11769351241289719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Objectives: Patients with intermediate or advanced hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment and treatment planning. In 2018, a novel machine learning methodology "survival path" (SP) was developed to facilitate dynamic prognosis prediction and treatment planning. One year after, a deep learning approach called Dynamic Deephit was developed. The performance of the two state-of-art models in dynamic prognostication have not been compared.

Methods: We trained and tested the SP and Dynamic DeepHit models in a large cohort of 2511 HCC patients using time-series data. The time-series data were converted into data of time slices, with an interval of three months. The time-dependent c-index for OS at given prediction time (t = 1, 6, 12, 18 months) and evaluation time (∆t = 3, 6, 9, 12, 18, 24, 36, 48 months) were compared.

Results: The comparison between SP model and Dynamic DeepHit-HCC model showed the latter had significant better performance at the time of initial admission. The time-dependent c-index of Dynamic DeepHit-HCC model gradually decreased with the extension of time (from 0.756 to 0.639 in the training set; from 0.787 to 0.661 in internal testing set; from 0.725 to 0.668 in multicenter testing set); while the time-dependent c-index of SP model displayed an increased trend (from 0.665 to 0.748 in the training set; from 0.608 to 0.743 in internal testing set; from 0.643 to 0.720 in multicenter testing set). When the prediction time comes to 6 months or later since initial treatment, the survival path model outperformed the dynamic DeepHit model at late evaluation times (∆t > 12 months).

Conclusions: This research highlighted the unique strengths of both models. The SP model had advantage in long term prediction while the Dynamic DeepHit-HCC model had advantages in prediction at near time points. Fine selection of models is needed in dealing with different scenarios.

使用时间序列数据对肝细胞癌患者进行动态诊断的机器学习:生存路径与动态深度HCC模型的比较
目标:中晚期肝细胞癌(HCC)患者需要反复进行疾病监测、预后评估和治疗规划。2018 年,一种新颖的机器学习方法 "生存路径"(SP)被开发出来,以促进动态预后预测和治疗规划。一年后,又开发出一种名为 "动态 Deephit "的深度学习方法。这两种最先进的模型在动态预后方面的表现尚未进行过比较:我们使用时间序列数据,在 2511 名 HCC 患者的大型队列中训练和测试了 SP 模型和动态 DeepHit 模型。时间序列数据被转换为时间片数据,每片间隔为三个月。比较了特定预测时间(t = 1、6、12、18 个月)和评估时间(Δt = 3、6、9、12、18、24、36、48 个月)下与时间相关的 OS c 指数:结果:SP 模型与动态 DeepHit-HCC 模型的比较结果表明,后者在初始入院时的表现明显更好。随着时间的延长,动态 DeepHit-HCC 模型与时间相关的 c 指数逐渐下降(训练集从 0.756 降至 0.639;内部测试集从 0.787 降至 0.661;多中心测试集从 0.725 降至 0.668)。在多中心测试集中从 0.725 降至 0.668);而 SP 模型随时间变化的 c 指数呈上升趋势(在训练集中从 0.665 升至 0.748;在内部测试集中从 0.608 升至 0.743;在多中心测试集中从 0.643 升至 0.720)。当预测时间达到初始治疗后 6 个月或更晚时,生存路径模型在晚期评估时间(Δt > 12 个月)的表现优于动态 DeepHit 模型:这项研究凸显了两种模型的独特优势。SP模型在长期预测方面具有优势,而动态DeepHit-HCC模型在近时间点的预测方面具有优势。在处理不同情况时,需要对模型进行精细选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信