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Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial
PURPOSE: Image registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Neoplasia Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990167/ https://www.ncbi.nlm.nih.gov/pubmed/35395604 http://dx.doi.org/10.1016/j.tranon.2022.101411 |
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author | Thakran, Snekha Cohen, Eric Jahani, Nariman Weinstein, Susan P. Pantalone, Lauren Hylton, Nola Newitt, David DeMichele, Angela Davatzikos, Christos Kontos, Despina |
author_facet | Thakran, Snekha Cohen, Eric Jahani, Nariman Weinstein, Susan P. Pantalone, Lauren Hylton, Nola Newitt, David DeMichele, Angela Davatzikos, Christos Kontos, Despina |
author_sort | Thakran, Snekha |
collection | PubMed |
description | PURPOSE: Image registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer. METHODS: 130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots. RESULTS: A baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV(2))) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model. CONCLUSION: Incorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods. |
format | Online Article Text |
id | pubmed-8990167 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Neoplasia Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89901672022-04-15 Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial Thakran, Snekha Cohen, Eric Jahani, Nariman Weinstein, Susan P. Pantalone, Lauren Hylton, Nola Newitt, David DeMichele, Angela Davatzikos, Christos Kontos, Despina Transl Oncol Original Research PURPOSE: Image registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer. METHODS: 130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots. RESULTS: A baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV(2))) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model. CONCLUSION: Incorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods. Neoplasia Press 2022-04-05 /pmc/articles/PMC8990167/ /pubmed/35395604 http://dx.doi.org/10.1016/j.tranon.2022.101411 Text en © 2022 Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Thakran, Snekha Cohen, Eric Jahani, Nariman Weinstein, Susan P. Pantalone, Lauren Hylton, Nola Newitt, David DeMichele, Angela Davatzikos, Christos Kontos, Despina Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title | Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title_full | Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title_fullStr | Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title_full_unstemmed | Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title_short | Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial |
title_sort | impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: results from the ispy 1/acrin 6657 trial |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990167/ https://www.ncbi.nlm.nih.gov/pubmed/35395604 http://dx.doi.org/10.1016/j.tranon.2022.101411 |
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