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Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models.

作者信息

Vosshenrich Jan, Zech Christoph J, Heye Tobias, Boldanova Tuyana, Fucile Geoffrey, Wieland Stefan, Heim Markus H, Boll Daniel T

机构信息

Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.

Clarunis - University Center for Gastrointestinal and Liver Diseases, Petersgraben 4, 4031, Basel, Switzerland.

出版信息

Eur Radiol. 2021 Jun;31(6):4367-4376. doi: 10.1007/s00330-020-07511-3. Epub 2020 Dec 3.


DOI:10.1007/s00330-020-07511-3
PMID:33274405
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8128820/
Abstract

OBJECTIVES: To investigate if nested multiparametric decision tree models based on tumor size and CT texture parameters from pre-therapeutic imaging can accurately predict hepatocellular carcinoma (HCC) lesion response to transcatheter arterial chemoembolization (TACE). MATERIALS AND METHODS: This retrospective study (January 2011-September 2017) included consecutive pre- and post-therapeutic dynamic CT scans of 37 patients with 92 biopsy-proven HCC lesions treated with drug-eluting bead TACE. Following manual segmentation of lesions according to modified Response Evaluation Criteria in Solid Tumors criteria on baseline arterial phase CT images, tumor size and quantitative texture parameters were extracted. HCCs were grouped into lesions undergoing primary TACE (VT-lesions) or repeated TACE (RT-lesions). Distinct multiparametric decision tree models to predict complete response (CR) and progressive disease (PD) for the two groups were generated. AUC and model accuracy were assessed. RESULTS: Thirty-eight of 72 VT-lesions (52.8%) and 8 of 20 RT-lesions (40%) achieved CR. Sixteen VT-lesions (22.2%) and 8 RT-lesions (40%) showed PD on follow-up imaging despite TACE treatment. Mean of positive pixels (MPP) was significantly higher in VT-lesions compared to RT-lesions (180.5 vs 92.8, p = 0.001). The highest AUC in ROC curve analysis and accuracy was observed for the prediction of CR in VT-lesions (AUC 0.96, positive predictive value 96.9%, accuracy 88.9%). Prediction of PD in VT-lesions (AUC 0.88, accuracy 80.6%), CR in RT-lesions (AUC 0.83, accuracy 75.0%), and PD in RT-lesions (AUC 0.86, accuracy 80.0%) was slightly inferior. CONCLUSIONS: Nested multiparametric decision tree models based on tumor heterogeneity and size can predict HCC lesion response to TACE treatment with high accuracy. They may be used as an additional criterion in the multidisciplinary treatment decision-making process. KEY POINTS: • HCC lesion response to TACE treatment can be predicted with high accuracy based on baseline tumor heterogeneity and size. • Complete response of HCC lesions undergoing primary TACE was correctly predicted with 88.9% accuracy and a positive predictive value of 96.9%. • Progressive disease was correctly predicted with 80.6% accuracy for lesions undergoing primary TACE and 80.0% accuracy for lesions undergoing repeated TACE.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/e24e126f9b15/330_2020_7511_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/97c4faa829a0/330_2020_7511_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/1310403ec466/330_2020_7511_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/05945c151f61/330_2020_7511_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/716e49036b62/330_2020_7511_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/5fb36f3aa63f/330_2020_7511_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/e24e126f9b15/330_2020_7511_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/97c4faa829a0/330_2020_7511_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/1310403ec466/330_2020_7511_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/05945c151f61/330_2020_7511_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/716e49036b62/330_2020_7511_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/5fb36f3aa63f/330_2020_7511_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c58b/8128820/e24e126f9b15/330_2020_7511_Fig6_HTML.jpg

相似文献

[1]
Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models.

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[2]
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[8]
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引用本文的文献

[1]
Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma.

World J Clin Cases. 2025-10-6

[2]
Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review.

Dig Dis Sci. 2025-2

[3]
Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms.

Quant Imaging Med Surg. 2024-7-1

[4]
A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram.

Radiol Med. 2024-1

[5]
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score.

Discov Oncol. 2023-10-17

[6]
Computed tomography texture analysis combined with preoperative clinical factors serve as a predictor of early efficacy of transcatheter arterial chemoembolization in hepatocellular carcinoma.

Abdom Radiol (NY). 2023-6

[7]
CT texture analysis in predicting treatment response and survival in patients with hepatocellular carcinoma treated with transarterial chemoembolization using random forest models.

BMC Cancer. 2023-3-3

[8]
The Prognostic Value of Preoperative Serum Markers and Risk Classification in Patients with Hepatocellular Carcinoma.

Ann Surg Oncol. 2023-5

[9]
Study on the changes of CT texture parameters before and after HCC treatment in the efficacy evaluation and survival predication of patients with HCC.

Front Oncol. 2022-10-28

[10]
Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC.

Diagnostics (Basel). 2022-5-24

本文引用的文献

[1]
Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

Eur Radiol. 2020-1-3

[2]
Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer.

Sci Rep. 2019-11-22

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Establishment of a predictive model for short-term efficacy of transcatheter arterial chemoembolization treatment in hepatocellular carcinoma and its clinical application.

J Cancer Res Ther. 2019

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Hepatocellular Carcinoma.

N Engl J Med. 2019-4-11

[5]
Hepatocellular carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

Ann Oncol. 2018-10-1

[6]
Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer.

J Magn Reson Imaging. 2018-8-13

[7]
Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

J Vasc Interv Radiol. 2018-6

[8]
Drug-eluting beads transarterial chemoembolization for hepatocellular carcinoma: Current state of the art.

World J Gastroenterol. 2018-1-14

[9]
Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings.

AJR Am J Roentgenol. 2017-8-16

[10]
CT texture analysis using the filtration-histogram method: what do the measurements mean?

Cancer Imaging. 2013-9-23

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