Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma

Abstract The presence of microvascular invasion (MVI) is a critical determinant of early hepatocellular carcinoma (HCC) recurrence and prognosis. We developed a nomogram model integrating clinical laboratory examinations and radiological imaging results from our clinical database to predict microvas...

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Autores principales:	Shuqi Mao, Xi Yu, Yong Yang, Yuying Shan, Joseph Mugaanyi, Shengdong Wu, Caide Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ba2eb80de14c4681b2eb36ff4ce45404
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spelling	oai:doaj.org-article:ba2eb80de14c4681b2eb36ff4ce454042021-12-02T15:23:08ZPreoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma10.1038/s41598-021-93528-72045-2322https://doaj.org/article/ba2eb80de14c4681b2eb36ff4ce454042021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93528-7https://doaj.org/toc/2045-2322Abstract The presence of microvascular invasion (MVI) is a critical determinant of early hepatocellular carcinoma (HCC) recurrence and prognosis. We developed a nomogram model integrating clinical laboratory examinations and radiological imaging results from our clinical database to predict microvascular invasion presence at preoperation in HCC patients. 242 patients with pathologically confirmed HCC at the Ningbo Medical Centre Lihuili Hospital from September 2015 to January 2021 were included in this study. Baseline clinical laboratory examinations and radiological imaging results were collected from our clinical database. LASSO regression analysis model was used to construct data dimensionality reduction and elements selection. Multivariate logistic regression analysis was performed to identify the independent risk factors associated with MVI and finally a nomogram for predicting MVI presence of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities. Survival analysis indicated that the probability of overall survival (OS) and recurrence-free survival (RFS) were significantly different between patients with MVI and without MVI (P < 0.05). Histopathologically identified MVI was found in 117 of 242 patients (48.3%). The preoperative factors associated with MVI were large tumor diameter (OR = 1.271, 95%CI: 1.137–1.420, P < 0.001), AFP level greater than 20 ng/mL (20–400 vs. ≤ 20, OR = 2.025, 95%CI: 1.056–3.885, P = 0.034; > 400 vs. ≤ 20, OR = 3.281, 95%CI: 1.661–6.480, P = 0.001), total bilirubin level greater than 23 umol/l (OR = 2.247, 95%CI: 1.037–4.868, P = 0.040). Incorporating tumor diameter, AFP and TB, the nomogram achieved a better concordance index of 0.725 (95%CI: 0.661–0.788) in predicting MVI presence. Nomogram analysis showed that the total factor score ranged from 0 to 160, and the corresponding risk rate ranged from 0.20 to 0.90. The DCA showed that if the threshold probability was > 5%, using the nomogram to diagnose MVI could acquire much more benefit. And the net benefit of the nomogram model was higher than single variable within 0.3–0.8 of threshold probability. In summary, the presence of MVI is an independent prognostic risk factor for RFS. The nomogram detailed here can preoperatively predict MVI presence in HCC patients. Using the nomogram model may constitute a usefully clinical tool to guide a rational and personalized subsequent therapeutic choice.Shuqi MaoXi YuYong YangYuying ShanJoseph MugaanyiShengdong WuCaide LuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuqi Mao Xi Yu Yong Yang Yuying Shan Joseph Mugaanyi Shengdong Wu Caide Lu Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
description	Abstract The presence of microvascular invasion (MVI) is a critical determinant of early hepatocellular carcinoma (HCC) recurrence and prognosis. We developed a nomogram model integrating clinical laboratory examinations and radiological imaging results from our clinical database to predict microvascular invasion presence at preoperation in HCC patients. 242 patients with pathologically confirmed HCC at the Ningbo Medical Centre Lihuili Hospital from September 2015 to January 2021 were included in this study. Baseline clinical laboratory examinations and radiological imaging results were collected from our clinical database. LASSO regression analysis model was used to construct data dimensionality reduction and elements selection. Multivariate logistic regression analysis was performed to identify the independent risk factors associated with MVI and finally a nomogram for predicting MVI presence of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities. Survival analysis indicated that the probability of overall survival (OS) and recurrence-free survival (RFS) were significantly different between patients with MVI and without MVI (P < 0.05). Histopathologically identified MVI was found in 117 of 242 patients (48.3%). The preoperative factors associated with MVI were large tumor diameter (OR = 1.271, 95%CI: 1.137–1.420, P < 0.001), AFP level greater than 20 ng/mL (20–400 vs. ≤ 20, OR = 2.025, 95%CI: 1.056–3.885, P = 0.034; > 400 vs. ≤ 20, OR = 3.281, 95%CI: 1.661–6.480, P = 0.001), total bilirubin level greater than 23 umol/l (OR = 2.247, 95%CI: 1.037–4.868, P = 0.040). Incorporating tumor diameter, AFP and TB, the nomogram achieved a better concordance index of 0.725 (95%CI: 0.661–0.788) in predicting MVI presence. Nomogram analysis showed that the total factor score ranged from 0 to 160, and the corresponding risk rate ranged from 0.20 to 0.90. The DCA showed that if the threshold probability was > 5%, using the nomogram to diagnose MVI could acquire much more benefit. And the net benefit of the nomogram model was higher than single variable within 0.3–0.8 of threshold probability. In summary, the presence of MVI is an independent prognostic risk factor for RFS. The nomogram detailed here can preoperatively predict MVI presence in HCC patients. Using the nomogram model may constitute a usefully clinical tool to guide a rational and personalized subsequent therapeutic choice.
format	article
author	Shuqi Mao Xi Yu Yong Yang Yuying Shan Joseph Mugaanyi Shengdong Wu Caide Lu
author_facet	Shuqi Mao Xi Yu Yong Yang Yuying Shan Joseph Mugaanyi Shengdong Wu Caide Lu
author_sort	Shuqi Mao
title	Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
title_short	Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
title_full	Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
title_fullStr	Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
title_full_unstemmed	Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
title_sort	preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ba2eb80de14c4681b2eb36ff4ce45404
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Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma

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