reast cancer is the most commonly diagnosed type of cancer and the leading cause of cancer-related deaths among women worldwide, accounting for 23% of total patients with cancer and 14% of cancer deaths (1). Breast cancer-related mortality is primarily due to metastatic disease rather than the primary tumor itself, with survival rates of 90% for localized breast cancer and 20% for metastatic breast cancer (2). Nevertheless, the precise mechanism leading to distant metastasis is not yet fully understood, and identification of patients at high risk for developing distant metastasis is still a challenge. Research efforts have focused on developing relevant imaging markers that can help predict patient prognosis and allow therapy to be tailored based on individual risk. Several MRI features are correlated with poor clinical outcomes in patients with breast cancer. Li et al (3) reported that higher volume transfer constant, or K trans , values obtained from dynamic contrast material-enhanced MRI after neoadjuvant chemotherapy are associated with higher rates of recurrence and lower survival rates. At preoperative MRI, higher values of peak enhancement and washout component (4) and

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B reast MRI has been commonly used for cancer staging in women with newly diagnosed breast cancer, although controversy remains regarding its generalized use (1,2). In this preoperative setting, breast MRI has been reported to depict additional occult cancer in 10%-30% of ipsilateral breasts (3-5) and in 3%-5% of contralateral breasts in women with breast cancer (3,6,7). Furthermore, preoperative MRI features have been suggested as prognostic imaging markers that can be used to predict outcomes (8)(9)(10)(11)(12). Research is ongoing to identify reliable MRI biomarkers that can guide clinicians in decision making and potentially enable personalized treatment for breast cancer.Breast dynamic contrast material-enhanced (DCE) MRI depicts detailed morphologic features of breast tumors and reveals enhancement kinetics, which may reflect angiogenesis. Commercially available computer-aided diagnosis (CAD) systems provide clinicians with quantitative kinetic information regarding breast tumors on a pixel-by-pixel basis. These CAD systems have been shown to increase the specificity of DCE MRI diagnoses compared with assessments by radiologists through the exclusion of lesions with low threshold enhancement; moreover, they can reduce interpretation time by performing automatic analyses (13)(14)(15). Recent studies have suggested that survival outcomes and CAD-measured kinetic features of breast cancer as observed at preoperative MRI are associated. Kim et al showed that higher values of peak enhancement and washout component were associated with worse disease-free survival (10). Nam et al also found a relationship between higher peak enhancement and poor diseasefree survival (11). However, to our knowledge, there have been no studies of the associations between CAD-extracted kinetic features and distant metastasis outcomes in women with breast cancer. Because angiogenesis plays an important role in tumor growth, tumor progression, and metastasis (16,17), there is a potential for tumor enhancement kinetics to be associated with distant metastasis outcomes.

Background The associations between diffusion kurtosis imaging (DKI)‐derived parameters and clinical prognostic factors of breast cancer have not been fully evaluated; this knowledge may have implications for outcome prediction and treatment strategies. Purpose To determine associations between quantitative diffusion parameters derived from DKI and diffusion‐weighted imaging (DWI) and the prognostic factors and molecular subtypes of breast cancer. Study Type Retrospective. Population A total of 383 women (mean age, 53.8 years; range, 31–82 years) with breast cancer who underwent preoperative breast MRI including DKI and DWI. Field Strength/Sequence A 3.0 T; DKI using a spin‐echo echo‐planar imaging (EPI) sequence (b values: 200, 500, 1000, 1500, and 2000 sec/mm2), DWI using a readout‐segmented EPI sequence (b values: 0 and 1000 sec/mm2) and dynamic contrast‐enhanced breast MRI. Assessment Two radiologists (J.Y.K. and H.S.K. with 9 years and 1 year of experience in MRI, respectively) independently measured kurtosis, diffusivity, and apparent diffusion coefficient (ADC) values of breast cancer by manually placing a regions of interest within the lesion. Diffusion measures were compared according to nodal status, grade, and molecular subtypes. Statistical Tests Kruskal–Wallis test, Mann–Whitney U test with Bonferroni correction, receiver operating characteristic (ROC) analysis, and multivariate logistic regression analysis. (Statistical significance level of P < 0.05). Results All diffusion measures showed significant differences according to axillary nodal status and histological grade. Kurtosis showed significant differences among molecular subtypes. The luminal subtype (median 1.163) showed a higher kurtosis value compared to the HER2‐positive (median 0.962) or triple‐negative subtypes (median 1.072). ROC analysis for differentiating HER2‐positive from luminal subtypes revealed that kurtosis yielded the highest area under the curve of 0.781. In multivariate analyses, kurtosis remained a significant factor associated with differentiation between HER2‐positive and luminal (odds ratio [OR] = 0.993), triple‐negative and luminal (OR = 0.995), and HER2‐positive and triple‐negative subtypes (OR = 0.994). Data conclusion Quantitative diffusion parameters derived from DKI and DWI are associated with prognostic factors for breast cancer. Moreover, DKI‐derived kurtosis can help distinguish between the molecular subtypes of breast cancer. Evidence Level 4 Technical Efficacy 3

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