Evaluation of Monoexponential, Stretched-Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study

Abstract

Background: There has been a growing interest in exploring the applications of stretched-exponential (SEM) and intravoxel incoherent motion (IVIM) models of diffusion-weighted imaging (DWI) in breast imaging, with the focus on differentiation of breast lesions. However, the use of SEM and IVIM models to predict early response to neoadjuvant chemotherapy (NACT) has received less attention.

Purpose: To investigate the value of monoexponential, SEM, and IVIM models to predict early response to NACT in patients with primary breast cancer.

Study type: Prospective.

Population: Thirty-seven patients with primary breast cancer (aged 46 ± 11 years) due to undergo NACT.

Field strength/sequences: A 1.5-T MR scanner, T₁ -weighted three-dimensional spoiled gradient-echo, two-dimensional single-shot spin-echo echo-planar imaging sequence (DWI) at six b-values (0-800 s mm^-2 ).

Assessment: Tumor volume, apparent diffusion coefficient, tissue diffusion (D_t ), pseudo-diffusion coefficient (D_p ), perfusion fraction (f), distributed diffusion coefficient, and alpha (α) were extracted, following volumetric sampling of the tumors, at three time-points: pretreatment, post one and three cycles of NACT.

Statistical tests: Mann-Whitney test, receiver operating characteristic (ROC) curve. Statistical significance level was P < 0.05.

Results: Following NACT, 17 patients were determined to be pathological responders and 20 nonresponders. Tumor volume was significantly larger in nonresponders at each MRI time-point and demonstrated reasonable performance in predicting response (area under the ROC curve [AUC] = 0.83-0.87). No significant differences between groups were found in the diffusion coefficients at each time-point (P = 0.09-1). The parameters α (SEM), f, and f × D_p (IVIM) were able to differentiate between response groups after one cycle of NACT (AUC = 0.73, 0.72, and 0.74, respectively).

Conclusion: Diffusion coefficients derived from the monoexponential, SEM, and IVIM models did not predict pathological response. However, the IVIM-derived parameters f and f × D_p and the SEM-derived parameter α were able to predict response to NACT in breast cancer patients following one cycle of NACT.

Level of evidence: 2 TECHNICAL EFFICACY STAGE: 2.

Keywords: breast cancer; diffusion-weighted MRI; imaging biomarkers; neoadjuvant chemotherapy; quantitative evaluation.

FIGURE 1

MRI scans of a 39‐year‐old woman with invasive ductal carcinoma who was a nonresponder (residual cancer burden [RCB]‐II). Each row includes images acquired pretreatment, after one cycle of neoadjuvant chemotherapy (NACT), and at mid‐treatment. The seeded region of interest (ROI) for the given slice is shown in blue. The tables represent the parameter estimates of monoexponential, stretched‐exponential model (SEM) and intravoxel incoherent motion (IVIM) model at each time‐point.

FIGURE 2

The measured diffusion‐weighted imaging (DWI) signals and best‐fit curves of the tumor of the nonresponder patient in Fig. 1 at mid‐treatment.

FIGURE 3

MRI scans of a 45‐year‐old woman with invasive ductal carcinoma in the left breast who showed a complete pathological response after surgery (residual cancer burden [RCB]‐0). Each row includes images acquired at pretreatment, after one cycle of neoadjuvant chemotherapy (NACT), and at mid‐treatment. The seeded region of interest (ROI) for the given slice is shown in blue. The tables represent the parameter estimates of monoexponential, stretched‐exponential model (SEM) and intravoxel incoherent motion (IVIM) model at each time‐point. At mid‐treatment, no tumor was visible on the dynamic contrast‐enhanced (DCE) and diffusion‐weighted (DW) images obtained.