TY - JOUR
T1 - Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer
T2 - A Retrospective, Single-Center Study
AU - Cai, Lie
AU - Sidey-Gibbons, Chris
AU - Nees, Juliane
AU - Riedel, Fabian
AU - Schaefgen, Benedikt
AU - Togawa, Riku
AU - Killinger, Kristina
AU - Heil, Joerg
AU - Pfob, André
AU - Golatta, Michael
N1 - Publisher Copyright:
© 2023 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.
PY - 2024/3
Y1 - 2024/3
N2 - Objectives: Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis. Methods: We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC). Results: We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65–0.76 versus 0.64, 95% CI: 0.57–0.71, P =.004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P =.002). Conclusion: A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.
AB - Objectives: Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis. Methods: We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC). Results: We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65–0.76 versus 0.64, 95% CI: 0.57–0.71, P =.004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P =.002). Conclusion: A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.
KW - breast cancer
KW - diagnostic imaging
KW - machine learning
KW - neoadjuvant systemic treatment
KW - triple-negative breast cancer
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U2 - 10.1002/jum.16377
DO - 10.1002/jum.16377
M3 - Article
C2 - 38069582
AN - SCOPUS:85179356245
SN - 0278-4297
VL - 43
SP - 467
EP - 478
JO - Journal of Ultrasound in Medicine
JF - Journal of Ultrasound in Medicine
IS - 3
ER -