Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma
Objective To construct a preoperative prediction model for proliferative hepatocellular carcinoma(HCC) based on enhanced CT image features, and to analyze the prognosis of the disease. Methods A retrospective case-control study was conducted on 603 patients with pathologically confirmed HCC. Amo...
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Editorial Office of Journal of Army Medical University
2025-04-01
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| Series: | 陆军军医大学学报 |
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| author | CHENG Jie CHENG Jie WEI Xiaofan CHEN Wei |
| author_facet | CHENG Jie CHENG Jie WEI Xiaofan CHEN Wei |
| author_sort | CHENG Jie |
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| description | Objective To construct a preoperative prediction model for proliferative hepatocellular carcinoma(HCC) based on enhanced CT image features, and to analyze the prognosis of the disease. Methods A retrospective case-control study was conducted on 603 patients with pathologically confirmed HCC. Among them, 519 cases from the First Affiliated Hospital of Army Medical University were randomly divided into a training group(n=363) and an internal verification group(n=156) in a ratio of 7:3. Another 84 patients from the Second Affiliated Hospital of Chongqing Medical University served as an external validation group. All patients underwent abdominal CT scan with contrast before surgery. The clinical data and CT imaging characteristics of proliferative and non-proliferative HCC patients were observed. Binary logistic regression analysis was used to identify the independent risk factors of proliferative HCC, and a nomogram prediction model was constructed. Receiver operating characteristic(ROC) curve was plotted to evaluate its diagnostic performance, and calibration curve and decision curve analysis(DCA) were applied to evaluate its calibration performance and clinical application value. The model was validated in both the internal and external validation groups. Kaplan-Meier survival curves were employed to compare the prognosis between proliferative and non-proliferative HCC. Results Multivariate analysis showed that negative result of HBV-DNA quantification, incomplete tumor capsule, intratumoral necrosis or ischemia(≥20%), and annular hyperenhancement in arterial phase were independent predictors in predicting proliferative HCC(P<0.05). Our nomogram model for predicting proliferative HCC based on the above clinical imaging features had an AUC value of 0.805(95%CI: 0.756~0.854), a sensitivity of 83.19% and a specificity of 64.80% in the training group. For the internal validation group, the AUC value was 0.793(95%CI: 0.721~0.854), the sensitivity was 67.86%, and the specificity was 75.00%. In the external validation group, the AUC value was 0.842(95%CI: 0.746~0.912), the sensitivity was 72.41%, and the specificity was 90.91%. Calibration curve and DCA showed that the model had good calibration performance and clinical applicability. The disease free survival(DFS) of the patients with proliferative HCC diagnosed by pathologically confirmed/predictive models was significantly shorter than that of non-proliferative HCC patients(training group: P=0.005, P<0.001; internal validation group: P=0.006, P=0.006; external validation group: P=0.002, P=0.015). Conclusion Our prediction model based on clinical and imaging features can accurately diagnose proliferative HCC before surgery, and the prognosis of proliferative HCC is generally poor.
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| format | Article |
| id | doaj-art-e6ca8172302e463997f799afac63bc28 |
| institution | OA Journals |
| issn | 2097-0927 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj-art-e6ca8172302e463997f799afac63bc282025-08-20T02:11:49ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272025-04-0147770871910.16016/j.2097-0927.202411115Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinomaCHENG Jie0CHENG Jie1WEI Xiaofan2CHEN Wei3Department of Radiology， the Second Affiliated Hospital of Chongqing Medical University， ChongqingDepartment of Radiology， Research Center for 7T Magnetic Resonance Translational MedicineDepartment of Radiology， the Second Affiliated Hospital of Chongqing Medical University， ChongqingDepartment of Radiology， Research Center for 7T Magnetic Resonance Translational MedicineObjective To construct a preoperative prediction model for proliferative hepatocellular carcinoma(HCC) based on enhanced CT image features, and to analyze the prognosis of the disease. Methods A retrospective case-control study was conducted on 603 patients with pathologically confirmed HCC. Among them, 519 cases from the First Affiliated Hospital of Army Medical University were randomly divided into a training group(n=363) and an internal verification group(n=156) in a ratio of 7:3. Another 84 patients from the Second Affiliated Hospital of Chongqing Medical University served as an external validation group. All patients underwent abdominal CT scan with contrast before surgery. The clinical data and CT imaging characteristics of proliferative and non-proliferative HCC patients were observed. Binary logistic regression analysis was used to identify the independent risk factors of proliferative HCC, and a nomogram prediction model was constructed. Receiver operating characteristic(ROC) curve was plotted to evaluate its diagnostic performance, and calibration curve and decision curve analysis(DCA) were applied to evaluate its calibration performance and clinical application value. The model was validated in both the internal and external validation groups. Kaplan-Meier survival curves were employed to compare the prognosis between proliferative and non-proliferative HCC. Results Multivariate analysis showed that negative result of HBV-DNA quantification, incomplete tumor capsule, intratumoral necrosis or ischemia(≥20%), and annular hyperenhancement in arterial phase were independent predictors in predicting proliferative HCC(P<0.05). Our nomogram model for predicting proliferative HCC based on the above clinical imaging features had an AUC value of 0.805(95%CI: 0.756~0.854), a sensitivity of 83.19% and a specificity of 64.80% in the training group. For the internal validation group, the AUC value was 0.793(95%CI: 0.721~0.854), the sensitivity was 67.86%, and the specificity was 75.00%. In the external validation group, the AUC value was 0.842(95%CI: 0.746~0.912), the sensitivity was 72.41%, and the specificity was 90.91%. Calibration curve and DCA showed that the model had good calibration performance and clinical applicability. The disease free survival(DFS) of the patients with proliferative HCC diagnosed by pathologically confirmed/predictive models was significantly shorter than that of non-proliferative HCC patients(training group: P=0.005, P<0.001; internal validation group: P=0.006, P=0.006; external validation group: P=0.002, P=0.015). Conclusion Our prediction model based on clinical and imaging features can accurately diagnose proliferative HCC before surgery, and the prognosis of proliferative HCC is generally poor. https://aammt.tmmu.edu.cn/html/202411115.htmlproliferative typehepatocellular carcinomax-ray computed tomographyprognostic study |
| spellingShingle | CHENG Jie CHENG Jie WEI Xiaofan CHEN Wei Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma 陆军军医大学学报 proliferative type hepatocellular carcinoma x-ray computed tomography prognostic study |
| title | Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma |
| title_full | Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma |
| title_fullStr | Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma |
| title_full_unstemmed | Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma |
| title_short | Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma |
| title_sort | clinical value and prognosis analysis of enhanced ct preoperative diagnosis for proliferative hepatocellular carcinoma |
| topic | proliferative type hepatocellular carcinoma x-ray computed tomography prognostic study |
| url | https://aammt.tmmu.edu.cn/html/202411115.html |
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