Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer - Andreassen, Maren M. Sjaastad; Loubrie, Stephane; Tong, Michelle W.; Fang, Lauren; Seibert, Tyler M.; Wallace, Anne M.; Zare, Somaye; Ojeda-Fournier, Haydee; Kuperman, Joshua; Hahn, Michael; Jerome, Neil P.; Bathen, Tone F.; et al. (Frontiers in Oncology, 2023)
Neoadjuvant therapy of breast cancer is used to downstage tumors to allow for breastconserving surgery and to provide important information regarding drug efficacy prognosis. The current gold standard for monitoring treatment response is manual assessment of change in tumor size on dynamic contrast-enhanced MRI (DCE). However, DCE requires expert radiologist readers, administration of exogenous Gadolinium contrast with unknown sequela, and relatively high time and cost. Here we present an automatic cancer tissue classifier to predict response to neoadjuvant treatment of breast cancer. We here employ an advanced MRI model, three-component Restriction Spectrum Imaging (RSI3C), to generate an automatic quantification procedure and find that the derived tissue classifier indicated the ability to assess treatment response after only three weeks of therapy. The RSI3C classifier was also able to identify cases that correspond to residual tumor at surgery at the later phase of therapy. The results were similar to manual tumor size measurements by standard-of-care MRI by DCE. The classifier eliminates the need for manual user input as well as exogenous contrast agents that conventional MRI workflow methods require. Our results suggest that RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.