Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer

Abstract

ObjectiveTo explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer.MethodsIn this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.ResultsIn the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126–0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001–0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016–0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts.ConclusionsDSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

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“…In their study, the cancers scanned before neo-adjuvant chemotherapy in the three directional diffusion coefficients, λ 1 , λ 2 , and λ 3 , and the mean diffusivity. They found that the maximal anisotropy and l1–l3 had lower levels in cancerous locations than normal tissue, which were reported in other studies, as well [ 80 , 81 ]. They also reported an increase in the eigenvalues and mean diffusivity in response to neoadjuvant chemotherapy.…”

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“…The DKI parameters MD and MK, on the other hand, have shown particular signi cance in HR status and Luminal A, corroborating the literature that suggests DKI-derived parameters could distinguish between benign and malignant breast tumors and in grading BC 28,29 . ADC Kurtosis, derived from ADC 0-800 histogram analysis, demonstrates its merit in HER2 status with moderate AUC and OR values, underscoring its specialized utility in capturing the peakedness of the ADC distribution within that speci c subtype, which is in line with the emerging body of research that suggests ADC Kurtosis and other diffusion spectrum imaging parameters could be predictive of HER2 status 30,31 .…”

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“…In the case of breast cancer, the ability to predict tumor molecular subtypes with imaging may provide an important contribution to clinical practice of early treatment planning and understanding of prognosis. Until now, very little is known about the diffusionweighted MRI (DWI) characteristics of different subtypes of breast cancer (8). By studying the underlying biological and functional characteristics, DWI is expected to eventually improve our understanding of the subtypes of breast cancer, especially prognosis and treatment plans (9)(10)(11)(12)(13).…”