cancers Article Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results Roberto Lo Gullo 1 , Hannah Wen 2 , Jeffrey S. Reiner 1 , Raza Hoda 2 , Varadan Sevilimedu 3 , Danny F. Martinez 1 , Sunitha B. Thakur 1,4 , Maxine S. Jochelson 1 , Peter Gibbs 1,4,† and Katja Pinker 1, * ,†   Citation: Lo Gullo, R.; Wen, H.; Reiner, J.S.; Hoda, R.; Sevilimedu, V.; Martinez, D.F.; Thakur, S.B.; Jochelson, M.S.; Gibbs, P.; Pinker, K. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers 2021, 13, 6273. https:// doi.org/10.3390/cancers13246273 Academic Editor: Brigitta G. Baumert Received: 28 October 2021 Accepted: 9 December 2021 Published: 14 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; logullor@mskcc.org (R.L.G.); reinerj@mskcc.org (J.S.R.); martind4@mskcc.org (D.F.M.); thakurs@mskcc.org (S.B.T.); jochelsm@mskcc.org (M.S.J.); gibbsp@mskcc.org (P.G.) 2 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; weny@mskcc.org (H.W.); hodar@ccf.org (R.H.) 3 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA; SevilimS@mskcc.org 4 Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA * Correspondence: pinkerdk@mskcc.org; Tel.: +1-646-888-5200 † Both authors contributed equally. Simple Summary: To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract: The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic res- onance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1- patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1- tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. Keywords: radiomics; PD-L1; breast cancer; magnetic resonance imaging Cancers 2021, 13, 6273. https://doi.org/10.3390/cancers13246273 https://www.mdpi.com/journal/cancers