A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation
© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd..
BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA.
METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180).
RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence.
CONCLUSIONS: The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
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| Enthalten in: |
Liver international : official journal of the International Association for the Study of the Liver - 44(2024), 4 vom: 22. März, Seite 894-906 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Wentao [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/liv.15846 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367541327 |
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| 100 | 1 | |a Wang, Wentao |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Completed 25.03.2024 | ||
| 500 | |a Date Revised 25.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. | ||
| 520 | |a BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA | ||
| 520 | |a METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180) | ||
| 520 | |a RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence | ||
| 520 | |a CONCLUSIONS: The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a early‐stage hepatocellular carcinoma | |
| 650 | 4 | |a microvascular invasion | |
| 650 | 4 | |a radiofrequency ablation | |
| 700 | 1 | |a Wang, Yueyue |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Danjun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yingting |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Rongkui |e verfasserin |4 aut | |
| 700 | 1 | |a Ying, Siqi |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Jiabin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Bao, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Jiaping |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Mengsu |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Qiang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaoying |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Manning |e verfasserin |4 aut | |
| 700 | 1 | |a Shao, Guoliang |e verfasserin |4 aut | |
| 700 | 1 | |a Rao, Sheng-Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Kai |e verfasserin |4 aut | |
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