| Autor: |
Xin-Cheng Mao, Shuo Shi, Lun-Jie Yan, Han-Chao Wang, Zi-Niu Ding, Hui Liu, Guo-Qiang Pan, Xiao Zhang, Cheng-Long Han, Bao-Wen Tian, Dong-Xu Wang, Si-Yu Tan, Zhao-Ru Dong, Yu-Chuan Yan, Tao Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Biomarker Research, Vol 11, Iss 1, Pp 1-16 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2050-7771 |
| DOI: |
10.1186/s40364-023-00527-z |
| Popis: |
Abstract Background and aim The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC. Methods One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72–0.86) and 0.75 (95%CI: 0.62–0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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