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

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 (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.

Keywords: Breast; Cancer; Dynamic Contrast-enhanced; MRI; Tumor Response.

Graphical abstract

Figure 1:

Schematic shows study protocol. Participants were randomly assigned to one of 10 neoadjuvant drug arms (nine experimental drug arms and one standard-of-care control arm). Each participant underwent MRI examination at four treatment time points (T0, T1, T2, T3) during neoadjuvant chemotherapy. FTV0, FTV1, FTV2 = functional tumor volume at T0, T1, and T2, respectively; ΔFTV1, ΔFTV2 = percentage change of functional tumor volume relative to T0 at T1 and T2, respectively.

Figure 2:

Flowchart shows study inclusion. I-SPY 2 TRIAL = Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, T0 = pretreatment time point, T1 = early treatment time point, T2 = inter-regimen time point.

Figure 3:

Within bounding boxes (yellow line), tumors on early postcontrast-phase axial MR images (top) and corresponding functional tumor volume (FTV) estimation on signal enhancement ratio (SER) maps (bottom) show the variation in FTV estimation as visually assessed using five categories, with representative cases of each category shown. The SER maps are colored as follows: red, SER >1.1 (washout); green, 0.9 ≤ SER ≤ 1.1 (plateau); blue, SER <0.9 (persistent).

Figure 4:

Pairwise interreader agreement matrices for functional tumor volume (FTV) show estimation categorization at three MRI time points as follows: (A–C) pretreatment (T0), (D–F) early treatment (T1), and (G–I) inter-regimen (T2). Data within the matrices are numbers of participants, and the gray scale represents the proportion of participants. Agreements are indicated in red text and disagreements are indicated in blue text, per category (CTG).