Abstract PS05-08: Restriction Spectrum Imaging MRI for automated evaluation of response to neoadjuvant therapy in breast cancer
Authors: Maren Marie Sjaastad Andreassen, Stephane Loubrie, Michelle Tong, Lauren Fang, Tyler Seibert, Anne Wallace, Somaye Zare, Haydee Ojeda-Fournier, Joshua Kuperman, Michael Hahn, Neil Jerome, Tone Bathen, Ana Rodríguez-Soto, Anders Dale, Rebecca Rakow-Penner
Journal: Cancer Research (2024)

Abstract Introduction: Dynamic contrast-enhanced MRI (DCE) is currently used to evaluate neoadjuvant therapy response of breast cancer (1). However, DCE requires expert radiologist readers to assess the change in longest tumor dimension during therapy, as well as administration of Gadolinium contrast agents. One MRI modality that does not require contrast agents is diffusion-weighted MRI (DWI), a method that detects the microscopic diffusion of water molecules. However, the commonly used DWI method apparent diffusion coefficient (ADC) is not fully optimised in the breast (2). The purpose of the current study was to evaluate the recent DWI method Restriction Spectrum Imaging (RSI) (2) to automatically monitor breast tumor size during neoadjuvant therapy. Methods: Twenty-seven women underwent 3T MRI at four time points during therapy at University of California San Diego; 17 received all four scans (see Table 1 for patient details). Inclusion criteria included biopsy-proven unilateral invasive breast cancer ≥2.5 cm (defined on clinical examination/imaging) with indication for neoadjuvant therapy. The therapy used was primarily paclitaxel (+/-experimental agent) followed by anthracycline. The MRI protocol included Gadolinium DCE and DWI (b-values 0, 500, 1500, 4000 s/mm2); TE/TR = 82/9000 ms. ADC was calculated using b-values &amp;lt; 1000 s/mm2 while signal from all available b-values were fitted to the previously-developed three-component RSI model (2). The tumor size by RSI was assessed against manual DCE tumor size and mean ADC values. Prediction of therapy response during therapy and residual tumor post-therapy were assessed using non-pathological complete response (non-pCR) as endpoint. pCR was defined as ypT0/is or ypN0. Results: Ten patients experienced pCR. Prediction of non-pCR by ROC AUC at the early-therapy time point was 0.65 for RSI, 0.64 for DCE and 0.45 for ADC (Table 2). Prediction of post-therapy residual tumor is given in Table 3. Discussion: The novel RSI cancer tissue classifier predicted response to neoadjuvant therapy after only 19 days. RSI could also identify 71% of cases with residual tumor at surgery with 90% specificity post-therapy. RSI performance was similar to performance by standard MRI by manual tumor measurement on DCE. In contrast to standard-of-care MRI by using DCE and ADC that requires manual user input, the RSI classifier is automatic. This suggests that RSI may aid to cost-efficiently evaluate neoadjuvant therapy of breast cancer, with the aim to help guide clinical decision-making and enable tailored therapy regimens. References: 1. Reig B et al: Breast MRI for Evaluation of Response to Neoadjuvant Therapy. Radiographics, 2021 2. Andreassen MMS et al: Discrimination of breast cancer from healthy breast tissue using a three-component diffusion-weighted MRI model. Clin Cancer Res, 2021 Table 1 Patient cohort details. Table 2 Receiver operating characteristics (ROC) area under the curve (AUC) for performance of ∆DCE, ∆RSI and ∆ADC for predicting non-pCR at each time point. There were no significant differences between modalities at any timepoint (p&amp;gt;0.025) as assessed by DeLongs test. pCR = pathological complete response, Tx = therapy, ∆DCE = change in size from pre-therapy time point for manual dynamic contrast-enhanced MRI, ∆RSI = change in size from pre-therapy time point for automatic Restriction Spectrum Imaging classifier, ∆ADC = change in mean value from pre-therapy time point for apparent diffusion coefficient. Table 3 Sensitivity and accuracy given specificity ≥ 90% and receiver operating characteristics (ROC) area under the curve (AUC) for predicting non-pCR for manual dynamic contrast-enhanced MRI (DCE), Restriction Spectrum Imaging (RSI) classifier and the mean apparent diffusion coefficient (ADC) after all neoadjuvant therapy prior to surgical intervention (post-Tx time point). There was no significant difference for comparison between DCE and RSI (p=0.56) as assessed by McNamar’s test, but they were significant for DCE vs. ADC (p &amp;lt; 0.001) and RSI vs. ADC (p &amp;lt; 0.001). *Specificity ≥ 90% is achieved by a threshold where all cases are classified as pCR (specificity = 100%). For reference, sensitivity was 0.18 and accuracy 0.41 when using a specificity ≥ 80%. pCR = pathological complete response, Sens90 = sensitivity given specificity ≥ 90%, Acc90 = accuracy given specificity ≥ 90%, Tx = therapy. Citation Format: Maren Marie Sjaastad Andreassen, Stephane Loubrie, Michelle Tong, Lauren Fang, Tyler Seibert, Anne Wallace, Somaye Zare, Haydee Ojeda-Fournier, Joshua Kuperman, Michael Hahn, Neil Jerome, Tone Bathen, Ana Rodríguez-Soto, Anders Dale, Rebecca Rakow-Penner. Restriction Spectrum Imaging MRI for automated evaluation of response to neoadjuvant therapy in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-08.