Radiomic Analysis Based on Gd-EOB-DTPA Enhanced MRI for the Preoperative Prediction of Ki-67 Expression in Hepatocellular Carcinoma
Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved..
RATIONALE AND OBJECTIVES: To develop and validate a random forest model based on radiomic features in Gd-EOB-DTPA enhanced MRI for predicting the Ki-67 expression in solitary HCC.
MATERIALS AND METHODS: This retrospective study analyzed 258 patients with solitary HCC. Significant clinicoradiological factors were identified through univariate and multivariate analyses for distinguishing HCC with high (>20%) and low (≤20%) Ki-67 expression. Radiomic features were extracted at Gd-EOB-DTPA enhanced MRI. The recursive feature elimination (RFE) strategy was employed to screen robust radiomic features, and the Random Forest (RF) algorithm was utilized to rank radiomic features and construct prediction models. The AUC, accuracy, precision, recall, and f1-score were used to evaluate the performance of RF models.
RESULTS: Multivariate analysis identified serum AFP level, tumor size, growth type, and peritumoral enhancement as independent predictors for HCC with high Ki-67 expression. The clinicoradiological-radiomic model that incorporated the clinicoradiological predictors and the top ten radiomic features outperformed the clinicoradiological model in the training set (AUCs 0.876 vs. 0.780; p < 0.001), though the test set did not have a statistical significance (AUCs 0.809 vs. 0.723; p = 0.123). The addition of clinicoradiological predictors did not yield a significant improvement in the performance of radiomic features in both sets (training, p = 0.692; test, p = 0.229). Decision curve analysis further confirmed the clinical utility of the RF models.
CONCLUSION: The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
|---|---|
| Enthalten in: |
Academic radiology - 31(2024), 3 vom: 25. März, Seite 859-869 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yan, Yang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Contrast Media |
|---|
| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.acra.2023.07.019 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM361870450 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM361870450 | ||
| 003 | DE-627 | ||
| 005 | 20240325234254.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.acra.2023.07.019 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1346.xml |
| 035 | |a (DE-627)NLM361870450 | ||
| 035 | |a (NLM)37689559 | ||
| 035 | |a (PII)S1076-6332(23)00387-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yan, Yang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomic Analysis Based on Gd-EOB-DTPA Enhanced MRI for the Preoperative Prediction of Ki-67 Expression in Hepatocellular Carcinoma |
| 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 Completed 25.03.2024 | ||
| 500 | |a Date Revised 25.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a RATIONALE AND OBJECTIVES: To develop and validate a random forest model based on radiomic features in Gd-EOB-DTPA enhanced MRI for predicting the Ki-67 expression in solitary HCC | ||
| 520 | |a MATERIALS AND METHODS: This retrospective study analyzed 258 patients with solitary HCC. Significant clinicoradiological factors were identified through univariate and multivariate analyses for distinguishing HCC with high (>20%) and low (≤20%) Ki-67 expression. Radiomic features were extracted at Gd-EOB-DTPA enhanced MRI. The recursive feature elimination (RFE) strategy was employed to screen robust radiomic features, and the Random Forest (RF) algorithm was utilized to rank radiomic features and construct prediction models. The AUC, accuracy, precision, recall, and f1-score were used to evaluate the performance of RF models | ||
| 520 | |a RESULTS: Multivariate analysis identified serum AFP level, tumor size, growth type, and peritumoral enhancement as independent predictors for HCC with high Ki-67 expression. The clinicoradiological-radiomic model that incorporated the clinicoradiological predictors and the top ten radiomic features outperformed the clinicoradiological model in the training set (AUCs 0.876 vs. 0.780; p < 0.001), though the test set did not have a statistical significance (AUCs 0.809 vs. 0.723; p = 0.123). The addition of clinicoradiological predictors did not yield a significant improvement in the performance of radiomic features in both sets (training, p = 0.692; test, p = 0.229). Decision curve analysis further confirmed the clinical utility of the RF models | ||
| 520 | |a CONCLUSION: The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a HCC | |
| 650 | 4 | |a Ki-67 expression | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Radiomic | |
| 650 | 4 | |a Random Forest | |
| 650 | 7 | |a gadolinium ethoxybenzyl DTPA |2 NLM | |
| 650 | 7 | |a Ki-67 Antigen |2 NLM | |
| 650 | 7 | |a Contrast Media |2 NLM | |
| 650 | 7 | |a Gadolinium DTPA |2 NLM | |
| 650 | 7 | |a K2I13DR72L |2 NLM | |
| 700 | 1 | |a Lin, Xiao Shi |e verfasserin |4 aut | |
| 700 | 1 | |a Ming, Wang Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chuan, Zhang Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Hui, Gan |e verfasserin |4 aut | |
| 700 | 1 | |a Juan, Sun Ya |e verfasserin |4 aut | |
| 700 | 1 | |a Shuang, Wang |e verfasserin |4 aut | |
| 700 | 1 | |a Yang Fan, L V |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Zhang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Academic radiology |d 1995 |g 31(2024), 3 vom: 25. März, Seite 859-869 |w (DE-627)NLM087676818 |x 1878-4046 |7 nnns |
| 773 | 1 | 8 | |g volume:31 |g year:2024 |g number:3 |g day:25 |g month:03 |g pages:859-869 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.acra.2023.07.019 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 31 |j 2024 |e 3 |b 25 |c 03 |h 859-869 | ||