Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model
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 treatmen...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-10-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/11769351241289719 |
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| author | Lujun Shen Yiquan Jiang Tao Zhang Fei Cao Liangru Ke Chen Li Gulijiayina Nuerhashi Wang Li Peihong Wu Chaofeng Li Qi Zeng Weijun Fan |
| author_facet | Lujun Shen Yiquan Jiang Tao Zhang Fei Cao Liangru Ke Chen Li Gulijiayina Nuerhashi Wang Li Peihong Wu Chaofeng Li Qi Zeng Weijun Fan |
| author_sort | Lujun Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-42a116ba8d25497fb24e000bb69f976e |
| institution | OA Journals |
| issn | 1176-9351 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj-art-42a116ba8d25497fb24e000bb69f976e2025-08-20T02:17:00ZengSAGE PublishingCancer Informatics1176-93512024-10-012310.1177/11769351241289719Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC ModelLujun Shen0Yiquan Jiang1Tao Zhang2Fei Cao3Liangru Ke4Chen Li5Gulijiayina Nuerhashi6Wang Li7Peihong Wu8Chaofeng Li9Qi Zeng10Weijun Fan11State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaDepartment of Information, Nanfang Hospital, Southern Medical University, Guangzhou, P.R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaDepartment of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaInformation Center, Sun Yat-sen University Cancer Center, Guangdong, ChinaCancer center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. ChinaObjectives: 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.https://doi.org/10.1177/11769351241289719 |
| spellingShingle | Lujun Shen Yiquan Jiang Tao Zhang Fei Cao Liangru Ke Chen Li Gulijiayina Nuerhashi Wang Li Peihong Wu Chaofeng Li Qi Zeng Weijun Fan Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model Cancer Informatics |
| title | Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model |
| title_full | Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model |
| title_fullStr | Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model |
| title_full_unstemmed | Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model |
| title_short | Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model |
| title_sort | machine learning for dynamic prognostication of patients with hepatocellular carcinoma using time series data survival path versus dynamic deephit hcc model |
| url | https://doi.org/10.1177/11769351241289719 |
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