Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer
Copyright © 2024 Elsevier B.V. All rights reserved..
PURPOSE: To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC).
METHODS: From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated.
RESULTS: The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722.
CONCLUSIONS: Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:172 |
|---|---|
| Enthalten in: |
European journal of radiology - 172(2024) vom: 19. Feb., Seite 111325 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zeng, Fang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Axillary lymph node |
|---|
| Anmerkungen: |
Date Completed 19.02.2024 Date Revised 19.02.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.ejrad.2024.111325 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM367525739 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM367525739 | ||
| 003 | DE-627 | ||
| 005 | 20240219232013.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240124s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ejrad.2024.111325 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1299.xml |
| 035 | |a (DE-627)NLM367525739 | ||
| 035 | |a (NLM)38262156 | ||
| 035 | |a (PII)S0720-048X(24)00041-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zeng, Fang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer |
| 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 19.02.2024 | ||
| 500 | |a Date Revised 19.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a PURPOSE: To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC) | ||
| 520 | |a METHODS: From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated | ||
| 520 | |a RESULTS: The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722 | ||
| 520 | |a CONCLUSIONS: Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Axillary lymph node | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Histogram | |
| 650 | 4 | |a Synthetic MRI | |
| 700 | 1 | |a Yang, Zheting |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Xiaoxue |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Hailong |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yue |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zongmeng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Minyan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lili |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lihong |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Pu-Yeh |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chuang |e verfasserin |4 aut | |
| 700 | 1 | |a Xue, Yunjing |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t European journal of radiology |d 1993 |g 172(2024) vom: 19. Feb., Seite 111325 |w (DE-627)NLM012952397 |x 1872-7727 |7 nnns |
| 773 | 1 | 8 | |g volume:172 |g year:2024 |g day:19 |g month:02 |g pages:111325 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.ejrad.2024.111325 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 172 |j 2024 |b 19 |c 02 |h 111325 | ||