Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images
© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd..
BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans.
METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110).
RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001).
CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Liver international : official journal of the International Association for the Study of the Liver - (2024) vom: 04. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wang, Fang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Deep learning |
|---|
| Anmerkungen: |
Date Revised 04.03.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1111/liv.15870 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369264193 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369264193 | ||
| 003 | DE-627 | ||
| 005 | 20240304233521.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240304s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/liv.15870 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1316.xml |
| 035 | |a (DE-627)NLM369264193 | ||
| 035 | |a (NLM)38436551 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Fang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 04.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. | ||
| 520 | |a BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans | ||
| 520 | |a METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110) | ||
| 520 | |a RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001) | ||
| 520 | |a CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a hepatocellular carcinoma | |
| 650 | 4 | |a magnetic resonance imaging | |
| 700 | 1 | |a Zhan, Gan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qing-Qing |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Hou-Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yuan-Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yin-Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Chu-Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Wen-Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jian-Bing |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yun-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Zhi-Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Li-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Lan-Fen |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yen-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Hong-Jie |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Liver international : official journal of the International Association for the Study of the Liver |d 2003 |g (2024) vom: 04. März |w (DE-627)NLM124147356 |x 1478-3231 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:04 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/liv.15870 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 04 |c 03 | ||