A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method
Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine. We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection....
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| Veröffentlicht in: | Computers in biology and medicine 2024-08, Vol.178, p.108663, Article 108663 |
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| creator | Xin, Haibei Li, Yuanfeng Wang, Quanlei Liu, Ren Zhang, Cunzhen Zhang, Haidong Su, Xian Bai, Bin Li, Nan Zhang, Minfeng |
| description | Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.
We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).
We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p 0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).
The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
•We construct a novel risk scoring system for the prognosis of HCC patients.•We include two cohorts with large sample sizes and comprehensive clinical information.•The immune-related features might be helpful in HCC prognosis prediction. |
| doi_str_mv | 10.1016/j.compbiomed.2024.108663 |
| format | Article |
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We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).
We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10−7 and 3.4 × 10−5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).
The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
•We construct a novel risk scoring system for the prognosis of HCC patients.•We include two cohorts with large sample sizes and comprehensive clinical information.•The immune-related features might be helpful in HCC prognosis prediction.</description><identifier>ISSN: 0010-4825</identifier><identifier>ISSN: 1879-0534</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2024.108663</identifier><identifier>PMID: 38905890</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Aged ; Biomarkers, Tumor - metabolism ; Carcinoma, Hepatocellular - mortality ; CD163 antigen ; CD8 antigen ; Cell culture ; Cox proportional hazards ; Female ; Hazard identification ; Hepatitis B ; Hepatocellular carcinoma ; Humans ; Immunohistochemistry ; Learning algorithms ; Liver cancer ; Liver Neoplasms - mortality ; Liver Neoplasms - pathology ; Machine Learning ; Male ; Medical prognosis ; Metastases ; Middle Aged ; Multiplex immunohistochemistry ; Overall survival ; PD-1 protein ; PD-L1 protein ; Precision medicine ; Prediction models ; Prognosis ; Proportional Hazards Models ; Proteins ; Recurrence ; Regression analysis ; Risk ; Risk Assessment ; Risk groups ; Scoring models ; Support vector machines ; Surgical equipment ; Survival ; Therapeutic applications</subject><ispartof>Computers in biology and medicine, 2024-08, Vol.178, p.108663, Article 108663</ispartof><rights>2024 Elsevier Ltd</rights><rights>Copyright © 2024 Elsevier Ltd. All rights reserved.</rights><rights>2024. Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c262t-eeb8b64b9d8acf9226fe8f70399e5ed67a264fe1e5187f82b9175d0fea50ae853</cites><orcidid>0009-0000-6102-6544 ; 0009-0007-9746-5101</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0010482524007480$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38905890$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xin, Haibei</creatorcontrib><creatorcontrib>Li, Yuanfeng</creatorcontrib><creatorcontrib>Wang, Quanlei</creatorcontrib><creatorcontrib>Liu, Ren</creatorcontrib><creatorcontrib>Zhang, Cunzhen</creatorcontrib><creatorcontrib>Zhang, Haidong</creatorcontrib><creatorcontrib>Su, Xian</creatorcontrib><creatorcontrib>Bai, Bin</creatorcontrib><creatorcontrib>Li, Nan</creatorcontrib><creatorcontrib>Zhang, Minfeng</creatorcontrib><title>A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.
We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).
We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10−7 and 3.4 × 10−5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).
The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
•We construct a novel risk scoring system for the prognosis of HCC patients.•We include two cohorts with large sample sizes and comprehensive clinical information.•The immune-related features might be helpful in HCC prognosis prediction.</description><subject>Aged</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Carcinoma, Hepatocellular - mortality</subject><subject>CD163 antigen</subject><subject>CD8 antigen</subject><subject>Cell culture</subject><subject>Cox proportional hazards</subject><subject>Female</subject><subject>Hazard identification</subject><subject>Hepatitis B</subject><subject>Hepatocellular carcinoma</subject><subject>Humans</subject><subject>Immunohistochemistry</subject><subject>Learning algorithms</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - mortality</subject><subject>Liver Neoplasms - pathology</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Middle Aged</subject><subject>Multiplex immunohistochemistry</subject><subject>Overall survival</subject><subject>PD-1 protein</subject><subject>PD-L1 protein</subject><subject>Precision medicine</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Proteins</subject><subject>Recurrence</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>Risk Assessment</subject><subject>Risk groups</subject><subject>Scoring models</subject><subject>Support vector machines</subject><subject>Surgical equipment</subject><subject>Survival</subject><subject>Therapeutic applications</subject><issn>0010-4825</issn><issn>1879-0534</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc1q3DAUhUVpaSZpXyEIuunGkyvZluVlGtIkEOimXQtZvupoIluuZA9Jn6CPXZlJKHSThRBI37k_5xBCGWwZMHGx35owTJ0LA_ZbDrzKz1KI8g3ZMNm0BdRl9ZZsABgUleT1CTlNaQ8AFZTwnpyUsoU6nw35c0nHcEBPo0sPNJkQ3fiTpqc040CniL0zc6KZiNp7mpZ4cAftabB0h5Oeg0HvF68jNToaN4ZB0yWtJUx4zPowhTi7MGbJTv_WsU900GbnRqQedRxXcsB5F_oP5J3VPuHH5_uM_Ph6_f3qtrj_dnN3dXlfGC74XCB2shNV1_ZSG9tyLixK20DZtlhjLxrNRWWRYZ2NsJJ3LWvqHizqGjTKujwjn49183C_FkyzGlxat9AjhiWpEprsZQNQZvTTf-g-LDHvslKSNVA1gmVKHikTQ0oRrZqiG3R8UgzUmpbaq39pqTUtdUwrS8-fGyzd-vcifIknA1-OAGZHDg6jSsbhaHIsEc2s-uBe7_IXLTKuog</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Xin, Haibei</creator><creator>Li, Yuanfeng</creator><creator>Wang, Quanlei</creator><creator>Liu, Ren</creator><creator>Zhang, Cunzhen</creator><creator>Zhang, Haidong</creator><creator>Su, Xian</creator><creator>Bai, Bin</creator><creator>Li, Nan</creator><creator>Zhang, Minfeng</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0009-0000-6102-6544</orcidid><orcidid>https://orcid.org/0009-0007-9746-5101</orcidid></search><sort><creationdate>202408</creationdate><title>A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method</title><author>Xin, Haibei ; Li, Yuanfeng ; Wang, Quanlei ; Liu, Ren ; Zhang, Cunzhen ; Zhang, Haidong ; Su, Xian ; Bai, Bin ; Li, Nan ; Zhang, Minfeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c262t-eeb8b64b9d8acf9226fe8f70399e5ed67a264fe1e5187f82b9175d0fea50ae853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aged</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Carcinoma, Hepatocellular - mortality</topic><topic>CD163 antigen</topic><topic>CD8 antigen</topic><topic>Cell culture</topic><topic>Cox proportional hazards</topic><topic>Female</topic><topic>Hazard identification</topic><topic>Hepatitis B</topic><topic>Hepatocellular carcinoma</topic><topic>Humans</topic><topic>Immunohistochemistry</topic><topic>Learning algorithms</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - mortality</topic><topic>Liver Neoplasms - pathology</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Middle Aged</topic><topic>Multiplex immunohistochemistry</topic><topic>Overall survival</topic><topic>PD-1 protein</topic><topic>PD-L1 protein</topic><topic>Precision medicine</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Proportional Hazards Models</topic><topic>Proteins</topic><topic>Recurrence</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>Risk Assessment</topic><topic>Risk groups</topic><topic>Scoring models</topic><topic>Support vector machines</topic><topic>Surgical equipment</topic><topic>Survival</topic><topic>Therapeutic applications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xin, Haibei</creatorcontrib><creatorcontrib>Li, Yuanfeng</creatorcontrib><creatorcontrib>Wang, Quanlei</creatorcontrib><creatorcontrib>Liu, Ren</creatorcontrib><creatorcontrib>Zhang, Cunzhen</creatorcontrib><creatorcontrib>Zhang, Haidong</creatorcontrib><creatorcontrib>Su, Xian</creatorcontrib><creatorcontrib>Bai, Bin</creatorcontrib><creatorcontrib>Li, Nan</creatorcontrib><creatorcontrib>Zhang, Minfeng</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biochemistry Abstracts 1</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xin, Haibei</au><au>Li, Yuanfeng</au><au>Wang, Quanlei</au><au>Liu, Ren</au><au>Zhang, Cunzhen</au><au>Zhang, Haidong</au><au>Su, Xian</au><au>Bai, Bin</au><au>Li, Nan</au><au>Zhang, Minfeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2024-08</date><risdate>2024</risdate><volume>178</volume><spage>108663</spage><pages>108663-</pages><artnum>108663</artnum><issn>0010-4825</issn><issn>1879-0534</issn><eissn>1879-0534</eissn><abstract>Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.
We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).
We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10−7 and 3.4 × 10−5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).
The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.
•We construct a novel risk scoring system for the prognosis of HCC patients.•We include two cohorts with large sample sizes and comprehensive clinical information.•The immune-related features might be helpful in HCC prognosis prediction.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>38905890</pmid><doi>10.1016/j.compbiomed.2024.108663</doi><orcidid>https://orcid.org/0009-0000-6102-6544</orcidid><orcidid>https://orcid.org/0009-0007-9746-5101</orcidid></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Aged Biomarkers, Tumor - metabolism Carcinoma, Hepatocellular - mortality CD163 antigen CD8 antigen Cell culture Cox proportional hazards Female Hazard identification Hepatitis B Hepatocellular carcinoma Humans Immunohistochemistry Learning algorithms Liver cancer Liver Neoplasms - mortality Liver Neoplasms - pathology Machine Learning Male Medical prognosis Metastases Middle Aged Multiplex immunohistochemistry Overall survival PD-1 protein PD-L1 protein Precision medicine Prediction models Prognosis Proportional Hazards Models Proteins Recurrence Regression analysis Risk Risk Assessment Risk groups Scoring models Support vector machines Surgical equipment Survival Therapeutic applications |
| title | A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method |
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