Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: A multicenter study

Jin, Zhicheng; Chen, Li; Zhong, Bin; Zhou, Haifeng; Zhu, Hai-Dong; Zhou, Hai Ying; Shen, Jingjing; Guo, Jun; Zhu, Xiaoli; Ji, Jiansong; Chen, Ni; Teng, Gao‐Jun

doi:10.1016/j.tranon.2021.101034

Cited by 30 publications

(18 citation statements)

References 30 publications

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“…Regarding clinical factors, we found that tumor size, but not AFP, was significantly associated with the initial treatment response, which was consistent with a previous study ( 31 ). The high baseline levels of AFP (> 20 ng/mL) may not be related to the initial therapy response, but several studies have reported that the AFP decline was associated with treatment response and significantly improved median survival in intermediate-stage HCC after TACE therapy ( 32 – 34 ).…”

Section: Discussionsupporting

confidence: 92%

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Peng

Huang

et al. 2021

Front. Oncol.

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ObjectivesWe aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE.MethodsThree hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated.ResultsGood predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each P < 0.001). The highest accuracies were achieved by integrating DL and the random forest algorithm in the training and validation cohorts (AUC = 0.995 and 0.994, respectively).ConclusionThe radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.

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Section: Discussionsupporting

confidence: 92%

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Peng

Huang

et al. 2021

Front. Oncol.

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“…Different researches have suggested the use of radiomic parameters to guide therapeutic decisions by response prediction of ablative therapies and immuno-oncological characteristics [ 124 – 126 ]. Therefore, if the therapeutic direction is considered inappropriate for ablation treatment, it should be changed with the targeted molecular agents use.…”

Section: Resultsmentioning

confidence: 99%

An update on radiomics techniques in primary liver cancers

Granata

Fusco

Setola

et al. 2022

Infect Agents Cancer

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Background Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. Methods This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. Results In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. Conclusions Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. Trial registration: Not applicable.

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“…A residual CNN was utilized in transfer learning to predict RECIST response to TACE based on pretreatment CT images of intermediate stage HCC, with AUCs above 0.90 in independent validation cohorts [87]. Jin et al created a nomogram of clinical features, radiological characteristics, and a pre-treatment CT radiomics signature to predict extrahepatic spread and macrovascular invasion in HCC patients who underwent TACE [88].…”

Section: Outcome Prediction For Malignant Diseasementioning

confidence: 99%

Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

et al. 2022

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: A multicenter study

Cited by 30 publications

References 30 publications

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

An update on radiomics techniques in primary liver cancers

Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

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