A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer. Issue 149 (April 2022)

• Record Type: Journal Article
• Authors: Jimenez, Jorge E.; Abdelhafez, Abeer; Mittendorf, Elizabeth A.; Elshafeey, Nabil; Yung, Joshua P.; Litton, Jennifer K.; Adrada, Beatriz E.; Candelaria, Rosalind P.; White, Jason; Thompson, Alastair M.; Huo, Lei; Wei, Peng; Tripathy, Debu; Valero, Vicente; Yam, Clinton; Hazle, John D.; Moulder, Stacy L.; Yang, Wei T.; Rauch, Gaiane M.
• Is Part Of: European journal of radiology. Issue 149(2022)
• Publication Date: 2022-04
• Subjects: Breast Cancer -- Neoadjuvant systemic therapy -- Radiomics -- Breast MRI -- TIL; MRIRF MRI-based radiomic features -- pCR pathologic complete response -- RF radiomic feature -- TIL tumor-infiltrating lymphocytes -- TNBC triple-negative breast cancer; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110220
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Pretreatment MRI radiomic features can predict treatment response in breast cancer. Combined MRI radiomic and TIL model improves accuracy of response prediction. Proposed "bio-radiomic" model could help to optimize management of breast cancer. 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.