Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment
Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.
| Errataetall: |
CommentIn: Radiol Imaging Cancer. 2023 Jul;5(4):e230089. - PMID 37505103 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Radiology. Imaging cancer - 5(2023), 4 vom: 01. Juli, Seite e220126 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Onishi, Natsuko [VerfasserIn] |
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| Links: |
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| Themen: |
Breast |
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| Anmerkungen: |
Date Completed 31.07.2023 Date Revised 29.12.2023 published: Print ClinicalTrials.gov: NCT01042379 CommentIn: Radiol Imaging Cancer. 2023 Jul;5(4):e230089. - PMID 37505103 Citation Status MEDLINE |
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| doi: |
10.1148/rycan.220126 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36005014X |
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| 245 | 1 | 0 | |a Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment |
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| 500 | |a CommentIn: Radiol Imaging Cancer. 2023 Jul;5(4):e230089. - PMID 37505103 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Breast | |
| 650 | 4 | |a Cancer | |
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