Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model
©2020 American Association for Cancer Research..
PURPOSE: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.
EXPERIMENTAL DESIGN: Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.
RESULTS: Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).
CONCLUSIONS: The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
Clinical cancer research : an official journal of the American Association for Cancer Research - 27(2021), 4 vom: 15. Feb., Seite 1094-1104 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Andreassen, Maren M Sjaastad [VerfasserIn] |
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| Links: |
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| Themen: |
Comparative Study |
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| Anmerkungen: |
Date Completed 20.01.2022 Date Revised 02.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1158/1078-0432.CCR-20-2017 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM317168843 |
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| 100 | 1 | |a Andreassen, Maren M Sjaastad |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model |
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| 500 | |a Date Revised 02.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©2020 American Association for Cancer Research. | ||
| 520 | |a PURPOSE: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model | ||
| 520 | |a EXPERIMENTAL DESIGN: Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC | ||
| 520 | |a RESULTS: Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991) | ||
| 520 | |a CONCLUSIONS: The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI | ||
| 650 | 4 | |a Comparative Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Rodríguez-Soto, Ana E |e verfasserin |4 aut | |
| 700 | 1 | |a Conlin, Christopher C |e verfasserin |4 aut | |
| 700 | 1 | |a Vidić, Igor |e verfasserin |4 aut | |
| 700 | 1 | |a Seibert, Tyler M |e verfasserin |4 aut | |
| 700 | 1 | |a Wallace, Anne M |e verfasserin |4 aut | |
| 700 | 1 | |a Zare, Somaye |e verfasserin |4 aut | |
| 700 | 1 | |a Kuperman, Joshua |e verfasserin |4 aut | |
| 700 | 1 | |a Abudu, Boya |e verfasserin |4 aut | |
| 700 | 1 | |a Ahn, Grace S |e verfasserin |4 aut | |
| 700 | 1 | |a Hahn, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Jerome, Neil P |e verfasserin |4 aut | |
| 700 | 1 | |a Østlie, Agnes |e verfasserin |4 aut | |
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| 700 | 1 | |a Goa, Pål Erik |e verfasserin |4 aut | |
| 700 | 1 | |a Rakow-Penner, Rebecca |e verfasserin |4 aut | |
| 700 | 1 | |a Dale, Anders M |e verfasserin |4 aut | |
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