Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment

Authors

Natsuko Onishi
Teffany Joy Bareng
Jessica Gibbs
Wen Li
Elissa R Price
Bonnie N Joe
John Kornak
Laura J Esserman
David C Newitt
Nola M Hylton
I-SPY 2 Investigator Network

Publication Date

7-1-2023

Journal

Radiology Imaging Center

Abstract

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 (

Keywords

Female, Humans, Middle Aged, Breast Neoplasms, Neoadjuvant Therapy, Tumor Burden, Retrospective Studies, Prospective Studies, Treatment Outcome, Magnetic Resonance Imaging

Comments

See commentary "One Size Fits All?—Not Anymore: Personalizing Breast Cancer Treatment with Use of a Semiautomated Functional Tumor Volume–based Predictive Model in the Assessment of Neoadjuvant Therapy Response" in volume 5, e230089.

PMID: 37505107