Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: Initial results Journal Article


Authors: 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.
Article Title: Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: Initial results
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 resonance 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: magnetic resonance imaging; breast cancer; pd-l1; radiomics
Journal Title: Cancers
Volume: 13
Issue: 24
ISSN: 2072-6694
Publisher: MDPI  
Date Published: 2021-12-01
Start Page: 6273
Language: English
DOI: 10.3390/cancers13246273
PROVIDER: scopus
PMCID: PMC8699819
PUBMED: 34944898
DOI/URL:
Notes: Article -- Export Date: 3 January 2022 -- Source: Scopus
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MSK Authors
  1. Maxine Jochelson
    119 Jochelson
  2. Hannah Yong Wen
    265 Wen
  3. Sunitha Bai Thakur
    91 Thakur
  4. Peter Gibbs
    32 Gibbs
  5. Raza Syed Hoda
    10 Hoda
  6. Jeffrey S Reiner
    11 Reiner