A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer

Jorge E. Jimenez, Abeer Abdelhafez, Elizabeth A. Mittendorf, Nabil Elshafeey, Joshua P. Yung, Jennifer K. Litton, Beatriz E. Adrada, Rosalind P. Candelaria, Jason White, Alastair M. Thompson, Lei Huo, Peng Wei, Debu Tripathy, Vicente Valero, Clinton Yam, John D. Hazle, Stacy L. Moulder, Wei T. Yang, Gaiane M. Rauch

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Purpose: We aimed to develop a predictive model based on pretreatment MRI radiomic features (MRIRF) and tumor-infiltrating lymphocyte (TIL) levels, an established prognostic marker, to improve the accuracy of predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) patients. Methods: This Institutional Review Board (IRB) approved retrospective study included a preliminary set of 80 women with biopsy-proven TNBC who underwent NAST, pretreatment dynamic contrast enhanced MRI, and biopsy-based pathologic assessment of TIL. A threshold of ≥ 20% was used to define high TIL. Patients were classified into pCR and non-pCR based on pathologic evaluation of post-NAST surgical specimens. pCR was defined as the absence of invasive carcinoma in the surgical specimen. Segmentation and MRIRF extraction were done using a Food and Drug Administration (FDA) approved software QuantX. The top five features were combined into a single MRIRF signature value. Results: Of 145 extracted MRIRF, 38 were significantly correlated with pCR. Five nonredundant imaging features were identified: volume, uniformity, peak timepoint variance, homogeneity, and variance. The accuracy of the MRIRF model, P = .001, 72.7% positive predictive value (PPV), 72.0% negative predictive value (NPV), was similar to the TIL model (P = .038, 65.5% PPV, 72.6% NPV). When MRIRF and TIL models were combined, we observed improved prognostic accuracy (P < .001, 90.9% PPV, 81.4% NPV). The models area under the receiver operating characteristic curve (AUC) was 0.632 (TIL), 0.712 (MRIRF) and 0.752 (TIL + MRIRF). Conclusion: A predictive model combining pretreatment MRI radiomic features with TIL level on pretreatment core biopsy improved accuracy in predicting pCR to NAST in TNBC patients.

Original languageEnglish (US)
Article number110220
JournalEuropean Journal of Radiology
Volume149
DOIs
StatePublished - Apr 2022

Keywords

  • Breast Cancer
  • Breast MRI
  • Neoadjuvant systemic therapy
  • Radiomics
  • TIL

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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