Predicting the recurrence of hepatocellular carcinoma (≤ 5 cm) after resection surgery with promising risk factors: habitat fraction of tumor and its peritumoral micro-environment
Purpose Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment. Material and Methods...
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Published in | Radiologia medica Vol. 128; no. 10; pp. 1181 - 1191 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Milan
Springer Milan
01.10.2023
Springer Nature B.V |
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Abstract | Purpose
Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment.
Material and Methods
A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan–Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit.
Results
Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f
3
-P
4
) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5–549.2,
p
< 0.001) together with other two clinical variables. Integrating both clinical variables and f
3
-P
4
, a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617–0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f
3
-P
4
or risk score given by nomogram have far longer RFS than those with high f
3
-P
4
or risk score (stratification by f
3
-P
4
: 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months).
Conclusion
Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making. |
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AbstractList | Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment.PURPOSECharacterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment.A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit.MATERIAL AND METHODSA total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit.Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f3-P4) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f3-P4, a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f3-P4 or risk score given by nomogram have far longer RFS than those with high f3-P4 or risk score (stratification by f3-P4: 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months).RESULTSQualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f3-P4) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f3-P4, a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f3-P4 or risk score given by nomogram have far longer RFS than those with high f3-P4 or risk score (stratification by f3-P4: 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months).Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making.CONCLUSIONHabitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making. PurposeCharacterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment.Material and MethodsA total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan–Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit.ResultsQualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f3-P4) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5–549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f3-P4, a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617–0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f3-P4 or risk score given by nomogram have far longer RFS than those with high f3-P4 or risk score (stratification by f3-P4: 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months).ConclusionHabitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making. Purpose Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment. Material and Methods A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan–Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit. Results Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f 3 -P 4 ) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5–549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f 3 -P 4 , a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617–0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f 3 -P 4 or risk score given by nomogram have far longer RFS than those with high f 3 -P 4 or risk score (stratification by f 3 -P 4 : 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months). Conclusion Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making. Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment. A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit. Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f -P ) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f -P , a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f -P or risk score given by nomogram have far longer RFS than those with high f -P or risk score (stratification by f -P : 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months). Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making. |
Author | Zhang, Yunfei Sheng, Ruofan Yang, Chun Zeng, Mengsu Dai, Yongming |
Author_xml | – sequence: 1 givenname: Yunfei surname: Zhang fullname: Zhang, Yunfei organization: Shanghai Institute of Medical Imaging, Fudan University, Department of Radiology, Zhongshan Hospital, Fudan University – sequence: 2 givenname: Chun surname: Yang fullname: Yang, Chun organization: Shanghai Institute of Medical Imaging, Fudan University, Department of Radiology, Zhongshan Hospital, Fudan University – sequence: 3 givenname: Ruofan surname: Sheng fullname: Sheng, Ruofan organization: Department of Radiology, Zhongshan Hospital, Fudan University – sequence: 4 givenname: Yongming surname: Dai fullname: Dai, Yongming email: daiyongming5555@163.com organization: School of Biomedical Engineering, ShanghaiTech University – sequence: 5 givenname: Mengsu surname: Zeng fullname: Zeng, Mengsu email: zengmengsu111@163.com organization: Shanghai Institute of Medical Imaging, Fudan University, Department of Radiology, Zhongshan Hospital, Fudan University |
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Cites_doi | 10.1016/j.jhep.2017.04.024 10.1148/ryai.2020190168 10.1080/15548627.2018.1474994 10.1016/j.jhep.2019.05.005 10.3892/or.2017.5738 10.1186/s12964-020-0530-4 10.1007/s12072-020-10032-2 10.1007/s00330-014-3108-y 10.1148/radiol.2017170845 10.1007/s00261-012-9952-9 10.1016/j.mri.2011.05.007 10.3390/brainsci11020200 10.1007/s11605-018-3927-2 10.3748/wjg.v20.i20.5935 10.1111/cas.15609 10.1016/j.mri.2014.02.017 10.1056/NEJMra1713263 10.1016/j.ejrad.2015.12.015 10.1148/radiol.2017170554 10.1002/cac2.12010 10.1109/ICCIMA.2007.328 |
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Keywords | Habitat imaging Hepatocellular carcinoma Recurrence-free survival Peritumoral micro-environment |
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Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to... Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the... PurposeCharacterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict... |
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SubjectTerms | Abdominal Radiology Adult Aged Biomarkers Carcinoma, Hepatocellular - diagnostic imaging Carcinoma, Hepatocellular - pathology Carcinoma, Hepatocellular - surgery Contrast Media Decision analysis Decision making Diagnostic Radiology Female Habitats Hepatectomy Humans Imaging Interventional Radiology Liver cancer Liver Neoplasms - diagnostic imaging Liver Neoplasms - pathology Liver Neoplasms - surgery Magnetic Resonance Imaging - methods Male Mathematical analysis Medical imaging Medical prognosis Medicine Medicine & Public Health Middle Aged Neoplasm Recurrence, Local - diagnostic imaging Neuroradiology Nomograms Performance prediction Predictive Value of Tests Prognosis Radiology Regression analysis Retrospective Studies Risk Factors Spatial distribution Survival Tumor Microenvironment Ultrasound |
Title | Predicting the recurrence of hepatocellular carcinoma (≤ 5 cm) after resection surgery with promising risk factors: habitat fraction of tumor and its peritumoral micro-environment |
URI | https://link.springer.com/article/10.1007/s11547-023-01695-6 https://www.ncbi.nlm.nih.gov/pubmed/37597123 https://www.proquest.com/docview/2872245481/abstract/ https://www.proquest.com/docview/2853944865/abstract/ |
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