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Prediction of local tumor progression after microwave ablation for early-stage hepatocellular carcinoma with machine learning.
Ren, He; An, Chao; Fu, Wanxi; Wu, Jingyan; Yao, Wenhuan; Yu, Jie; Liang, Ping.
Afiliação
  • Ren H; Department of Ultrasound, The Sixth Medical Center of PLA General Hospital; Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China.
  • An C; Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China.
  • Fu W; Department of Ultrasound, The Sixth Medical Center of PLA General Hospital, Beijing, China.
  • Wu J; Department of Medical Image, Yangfangdian Community Healthcare Centre, Beijing, China.
  • Yao W; Department of Ultrasound, The Sixth Medical Center of PLA General Hospital, Beijing, China.
  • Yu J; Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China.
  • Liang P; Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China.
J Cancer Res Ther ; 19(4): 978-987, 2023 Aug.
Article em En | MEDLINE | ID: mdl-37675726
ABSTRACT

Objectives:

Local tumor progression (LTP) is a major constraint for achieving technical success in microwave ablation (MWA) for the treatment of early-stage hepatocellular carcinoma (EHCC). This study aims to develop machine learning (ML)-based predictive models for LTP after initial MWA in EHCC. Materials and

Methods:

A total of 607 treatment-naïve EHCC patients (mean ± standard deviation [SD] age, 57.4 ± 10.8 years) with 934 tumors according to the Milan criteria who subsequently underwent MWA between August 2009 and January 2016 were enrolled. During the same period, 299 patients were assigned to the external validation datasets. To identify risk factors of LTP after MWA, clinicopathological data and ablation parameters were collected. Predictive models were developed according to 21 variables using four ML algorithms and evaluated based on the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs).

Results:

After a median follow-up time of 28.7 months (range, 7.6-110.5 months), 6.9% (42/607) of patients had confirmed LTP in the training dataset. The tumor size and number were significantly related to LTP. The AUCs of the four models ranged from 0.791 to 0.898. The best performance (AUC 0.898, 95% CI [0.842 0.954]; SD 0.028) occurred when nine variables were introduced to the CatBoost algorithm. According to the feature selection algorithms, the top six predictors were tumor number, albumin and alpha-fetoprotein, tumor size, age, and international normalized ratio.

Conclusions:

Out of the four ML models, the CatBoost model performed best, and reasonable and precise ablation protocols will significantly reduce LTP.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Neoplasias Hepáticas Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Aged / Humans / Middle aged Idioma: En Revista: J Cancer Res Ther Assunto da revista: NEOPLASIAS / TERAPEUTICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Neoplasias Hepáticas Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Aged / Humans / Middle aged Idioma: En Revista: J Cancer Res Ther Assunto da revista: NEOPLASIAS / TERAPEUTICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China