Comparing Multi-b-Value Diffusion MRI Models for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy...

Area of Science:

• Radiology and Imaging Science
• Oncology and Cancer Research
• Medical Physics

Background:

• Multi-b-value diffusion MRI models are utilized for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer.
• A gap exists in longitudinal comparative research evaluating these diffusion MRI parameters during NAC.
• Understanding early treatment response is crucial for optimizing breast cancer management.

Purpose of the Study:

• To compare the predictive performance of various longitudinal diffusion MRI-derived parameters during NAC for pCR in breast cancer.
• To evaluate the efficacy of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in predicting treatment response.
• To identify key imaging biomarkers that correlate with treatment outcomes.

Main Methods:

• A prospective study involving 160 women with breast cancer undergoing NAC.
• Intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) were performed at multiple time points before and during NAC.
• Statistical analyses included repeated-measures ANOVA, multivariable logistic regression, and receiver operating characteristic (ROC) curve analysis (AUC) to assess model performance.

Main Results:

• Apparent diffusion coefficient (ADC), tissue diffusion coefficient, and non-Gaussian ADC (Dapp) showed significant differences between pCR and non-pCR groups.
• Early in treatment (T2), progesterone receptor negativity, HER2 positivity, higher Dapp, and higher apparent kurtosis coefficient were associated with higher odds of pCR.
• A combined clinicopathologic-DKI model achieved an AUC of 0.90, outperforming models using only clinicopathologic or IVIM parameters.

Conclusions:

• A model integrating clinicopathologic variables and diffusion kurtosis imaging (DKI) parameters early during neoadjuvant chemotherapy (NAC) demonstrates excellent performance in predicting pCR for breast cancer.
• Longitudinal DKI parameters, particularly Dapp and apparent kurtosis, are valuable biomarkers for early prediction of treatment response.
• This approach offers a promising non-invasive method for assessing treatment efficacy and guiding clinical decisions in breast cancer management.