Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population
Abstract Aims With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a...
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-022-09812-w |
| _version_ | 1817976600547819520 |
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| author | Zonglin Xie Zhenpeng Peng Yujian Zou Han Xiao Bin Li Qian Zhou Shuling Chen Lixia Xu Jingxian Shen Yunxian Mo Sui Peng Ming Kuang Jianting Long Shi-Ting Feng |
| author_facet | Zonglin Xie Zhenpeng Peng Yujian Zou Han Xiao Bin Li Qian Zhou Shuling Chen Lixia Xu Jingxian Shen Yunxian Mo Sui Peng Ming Kuang Jianting Long Shi-Ting Feng |
| author_sort | Zonglin Xie |
| collection | DOAJ |
| description | Abstract Aims With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP. Methods A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance. Results Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P < 0.01). A clinical model (including 3 major features, TNSI, gender and AFP) was built to diagnose HCC and achieved good diagnostic performance (area under curve values were 0.954 in the training cohort, 0.931 in the internal validation cohort and 0.902 in the external cohort). Conclusions The clinical model in this study developed a satisfied non-invasive diagnostic performance for HCC in LRP. |
| first_indexed | 2024-04-13T22:05:48Z |
| format | Article |
| id | doaj.art-04d409b7dc0445b6bbec7a7674d5cda3 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-13T22:05:48Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-04d409b7dc0445b6bbec7a7674d5cda32022-12-22T02:27:57ZengBMCBMC Cancer1471-24072022-06-0122111010.1186/s12885-022-09812-wNon-invasive diagnosis strategy of hepatocellular carcinoma in low-risk populationZonglin Xie0Zhenpeng Peng1Yujian Zou2Han Xiao3Bin Li4Qian Zhou5Shuling Chen6Lixia Xu7Jingxian Shen8Yunxian Mo9Sui Peng10Ming Kuang11Jianting Long12Shi-Ting Feng13Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu DistinctDepartment of Radiology, The Affiliated Dongguan Hospital, Southern Medical UniversityDivision of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen UniversityClinical Trial Unit, The First Affiliated Hospital of Sun Yat-Sen UniversityClinical Trial Unit, The First Affiliated Hospital of Sun Yat-Sen UniversityDivision of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Oncology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu DistinctDepartment of Medical Imaging, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineDepartment of Medical Imaging, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineDepartment of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-Sen UniversityDivision of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Oncology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu DistinctDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu DistinctAbstract Aims With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP. Methods A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance. Results Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P < 0.01). A clinical model (including 3 major features, TNSI, gender and AFP) was built to diagnose HCC and achieved good diagnostic performance (area under curve values were 0.954 in the training cohort, 0.931 in the internal validation cohort and 0.902 in the external cohort). Conclusions The clinical model in this study developed a satisfied non-invasive diagnostic performance for HCC in LRP.https://doi.org/10.1186/s12885-022-09812-wHepatocellular carcinomaLI-RADSDiagnosisLow-riskNon-invasive |
| spellingShingle | Zonglin Xie Zhenpeng Peng Yujian Zou Han Xiao Bin Li Qian Zhou Shuling Chen Lixia Xu Jingxian Shen Yunxian Mo Sui Peng Ming Kuang Jianting Long Shi-Ting Feng Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population BMC Cancer Hepatocellular carcinoma LI-RADS Diagnosis Low-risk Non-invasive |
| title | Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population |
| title_full | Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population |
| title_fullStr | Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population |
| title_full_unstemmed | Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population |
| title_short | Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population |
| title_sort | non invasive diagnosis strategy of hepatocellular carcinoma in low risk population |
| topic | Hepatocellular carcinoma LI-RADS Diagnosis Low-risk Non-invasive |
| url | https://doi.org/10.1186/s12885-022-09812-w |
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