Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer
PurposeDynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DW...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1237720/full |
| _version_ | 1797682391686316032 |
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| author | Maren M. Sjaastad Andreassen Maren M. Sjaastad Andreassen Stephane Loubrie Michelle W. Tong Michelle W. Tong Lauren Fang Tyler M. Seibert Tyler M. Seibert Tyler M. Seibert Anne M. Wallace Somaye Zare Haydee Ojeda-Fournier Joshua Kuperman Michael Hahn Neil P. Jerome Neil P. Jerome Tone F. Bathen Tone F. Bathen Ana E. Rodríguez-Soto Anders M. Dale Anders M. Dale Rebecca Rakow-Penner Rebecca Rakow-Penner |
| author_facet | Maren M. Sjaastad Andreassen Maren M. Sjaastad Andreassen Stephane Loubrie Michelle W. Tong Michelle W. Tong Lauren Fang Tyler M. Seibert Tyler M. Seibert Tyler M. Seibert Anne M. Wallace Somaye Zare Haydee Ojeda-Fournier Joshua Kuperman Michael Hahn Neil P. Jerome Neil P. Jerome Tone F. Bathen Tone F. Bathen Ana E. Rodríguez-Soto Anders M. Dale Anders M. Dale Rebecca Rakow-Penner Rebecca Rakow-Penner |
| author_sort | Maren M. Sjaastad Andreassen |
| collection | DOAJ |
| description | PurposeDynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.Experimental designBreast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.ResultsTen patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.ConclusionThe automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer. |
| first_indexed | 2024-03-11T23:59:04Z |
| format | Article |
| id | doaj.art-51223c94fb14463c96416be583347c3c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-11T23:59:04Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-51223c94fb14463c96416be583347c3c2023-09-18T05:49:15ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-09-011310.3389/fonc.2023.12377201237720Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancerMaren M. Sjaastad Andreassen0Maren M. Sjaastad Andreassen1Stephane Loubrie2Michelle W. Tong3Michelle W. Tong4Lauren Fang5Tyler M. Seibert6Tyler M. Seibert7Tyler M. Seibert8Anne M. Wallace9Somaye Zare10Haydee Ojeda-Fournier11Joshua Kuperman12Michael Hahn13Neil P. Jerome14Neil P. Jerome15Tone F. Bathen16Tone F. Bathen17Ana E. Rodríguez-Soto18Anders M. Dale19Anders M. Dale20Rebecca Rakow-Penner21Rebecca Rakow-Penner22Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Oncology, Vestre Viken, Drammen, NorwayDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United StatesDepartment of Surgery, University of California, San Diego, La Jolla, CA, United StatesDepartment of Pathology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Physics, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Radiology and Nuclear Medicine, St. Olav’s University Hospital, Trondheim, NorwayDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United StatesDepartment of Radiology, University of California, San Diego, La Jolla, CA, United StatesDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United StatesPurposeDynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.Experimental designBreast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.ResultsTen patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.ConclusionThe automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.https://www.frontiersin.org/articles/10.3389/fonc.2023.1237720/fullbreast cancerlocally-advanced breast cancerneoadjuvant therapymagnetic resonance imagingbreast MRIdiffusion-weighted imaging |
| spellingShingle | Maren M. Sjaastad Andreassen Maren M. Sjaastad Andreassen Stephane Loubrie Michelle W. Tong Michelle W. Tong Lauren Fang Tyler M. Seibert Tyler M. Seibert Tyler M. Seibert Anne M. Wallace Somaye Zare Haydee Ojeda-Fournier Joshua Kuperman Michael Hahn Neil P. Jerome Neil P. Jerome Tone F. Bathen Tone F. Bathen Ana E. Rodríguez-Soto Anders M. Dale Anders M. Dale Rebecca Rakow-Penner Rebecca Rakow-Penner Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer Frontiers in Oncology breast cancer locally-advanced breast cancer neoadjuvant therapy magnetic resonance imaging breast MRI diffusion-weighted imaging |
| title | Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| title_full | Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| title_fullStr | Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| title_full_unstemmed | Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| title_short | Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| title_sort | restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer |
| topic | breast cancer locally-advanced breast cancer neoadjuvant therapy magnetic resonance imaging breast MRI diffusion-weighted imaging |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1237720/full |
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