Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer

Abstract

To investigate whether intravoxel incoherent motion (IVIM) and/or non-Gaussian diffusion parameters are associated with distant disease-free survival (DDFS) in patients with invasive breast cancer.Methods: From May 2013 to March 2015, 101 patients (mean age 60.0, range 28-88) with invasive breast cancer were evaluated prospectively. IVIM parameters (flowing blood volume fraction [f IVIM ] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at a b value of 0 s/mm 2 [ADC 0 ] and kurtosis [K]) were estimated using a diffusionweighted imaging series of 16 b values up to 2500 s/mm 2 . Shifted ADC values (sADC 200-1500 ) and standard ADC values (ADC 0-800 ) were also calculated. The Kaplan-Meier method was used to generate survival analyses for DDFS, which were compared using the log-rank test. Univariable Cox proportional hazards models were used to assess any associations between each parameter and distant metastasis-free survival.Results: The median observation period was 80 months (range, 35-92 months). Among the 101 patients, 12 (11.9%) developed distant metastasis, with a median time to metastasis of 79 months (range, 10-92 months). Kaplan-Meier analysis showed that DDFS was significantly shorter in patients with K > 0.98 than in those with K ≤ 0.98 (P = 0.04). Cox regression analysis showed a marginal statistical association between K and distant metastasis-free survival (P = 0.05). Conclusion:Non-Gaussian diffusion may be associated with prognosis in invasive breast cancer. A higher K may be a marker to help identify patients at an elevated risk of distant metastasis, which could guide subsequent treatment.

Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study

“…For instance, extending routine DWI measurements to include low b-values enables the quantification of the microvasculaturespecific IVIM effect. The IVIM-MRI-related parameters D* and fIVIM have previously been explored in breast cancer xenografts as well as in clinical studies [35,36,[51][52][53][54]. Our study indicates that fIVIM can be used to differentiate triple-negative from Her2+, as well as luminal A, breast cancers.…”

Diffusion kurtosis imaging as a biomarker of breast cancer

“…Other DKI- or IVM-derived parameters, pseudodiffusion (D * ) and flowing blood volume fraction (fIVIM), did not affect outcome in their patient cohort. 27 …”

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

“…Li et al [11] 2018 retro 3.0T ROI 62 (no data) 58 (no data) 7 3000 0, 1000 pathology 106 Huang et al [12] 2018 pro 3.0T ROI 44 IDC, 3 ILC, 5 others 19 FA, 7 others 6 2500 0, 800 pathology 71 Liu et al [13] 2019 retro 3.0T VOI 42 (no data) 30 (no data) 4 2000 0, 800 pathology 71 Palm et al [5] 2019 retro Li et al [15] 2019 retro organizational structure. MK value was proportional to the complexity of the organizational process, and the more complex the structure, the greater the MK value.…”

“…Diffusion-weighted imaging (DWI) has been widely used for diagnosis, prognosis, efficacy monitoring and evaluation of recurrence and metastasis. [4–6]…”

“…Diffusionweighted imaging (DWI) has been widely used for diagnosis, prognosis, efficacy monitoring and evaluation of recurrence and metastasis. [4][5][6] However, more and more authors indicted that the movement of water molecules in breast tissue was affected by many factors and did not follow the Gaussian distribution in heterogeneous state, and DWI imaging model cannot full reveal the motion of molecules. [7,8] Jensen et al [9] first introduced a non-Gaussian diffusion model called diffusion kurtosis imaging (DKI) in 2005.…”