Abstract PD11-06: Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer (2022)

Abstract Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC &amp;gt;70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC. Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752&amp;lt;0.001Range of Sum entropy1060.73930.6446-0.834500.72120.5753-0.867&amp;lt;0.001Angular Variance of Sum entropy1060.75960.6662-0.853500.70190.5538-0.8501&amp;lt;0.001Average of Sum entropy1060.73470.6367-0.8327500.70990.5613-0.8585&amp;lt;0.001Angular Variance of Sum variance1060.70160.602-0.8011500.70190.5543-0.8495&amp;lt;0.001Range of Sum variance1060.70050.6001-0.8009500.700.5499-0.8476&amp;lt;0.001 Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579&amp;lt;0.001Range of Sum entropy1060.76530.6759-0.8547500.70350.5503-0.8567&amp;lt;0.001Average of Entropy1060.75250.6568-0.8482500.71630.572-0.8607&amp;lt;0.001Average of Sum entropy1060.750.6552-0.8448500.70190.555-0.8488&amp;lt;0.001Angular Variance of Energy1060.7450.6493-0.8407500.73080.59-0.8715&amp;lt;0.001Range of Energy1060.74290.6466-0.8392500.72920.5885-0.8699&amp;lt;0.001Average of Energy1060.74110.6438-0.8384500.7260.5852-0.8667&amp;lt;0.001Average of Entropy1060.73360.635-0.8322500.74040.602-0.8787&amp;lt;0.001Average of Maximum probability1060.70760.6054-0.8098500.71630.5704-0.8623&amp;lt;0.001Range of Maximum probability1060.70550.6018-0.8092500.75640.6195-0.8933&amp;lt;0.001T2-mapAngular Variance of Energy1060.74820.6531-0.8433500.70990.5644-0.8555&amp;lt;0.001Range of Energy1060.7450.6495-0.8405500.70350.5569-0.8501&amp;lt;0.001Average of Entropy1060.74070.6416-0.8399500.72920.585-0.8733&amp;lt;0.001Average of Sum entropy1060.73860.6405-0.8367500.72440.5797-0.869&amp;lt;0.001Average of Energy1060.73180.6309-0.8327500.72120.5743-0.86&amp;lt;0.001Angular Variance of Sum entropy1060.7290.631-0.827500.72760.5857-0.8695&amp;lt;0.001Range of Sum entropy1060.72760.6295-0.8257500.72280.5796-0.8659&amp;lt;0.001Average of Information measure of correlation 11060.71580.6147-0.8169500.70990.5638-0.8561&amp;lt;0.001Average of Entropy1060.700.5903-0.8028500.74360.6014-0.8858&amp;lt;0.001 Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-06.