A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

BMC Medical Imaging

Research

Niamh O’Halloran¹  Leon Walsh¹  Brian M. Moloney¹  Declan Sheppard¹  Sinead Walsh¹  Robert Browne²  Karl J. Sweeney²  Peter McAnena²  Michael J. Kerin³  Aoife J. Lowery³ 

[1] Department of Radiology, University Hospital Galway, Galway, Ireland;Department of Surgery, Clinical Sciences Institute, University Hospital Galway, Galway, Ireland;Department of Surgery, Clinical Sciences Institute, University Hospital Galway, Galway, Ireland;Discipline of Surgery, Lambe Institute for Translational Research, National University of Ireland, Galway, Ireland;

关键词: Breast cancer;    Radiomics;    Neoadjuvant chemotherapy;    MRI;    Biomarker;   

DOI  :  10.1186/s12880-022-00956-6

 received in 2021-10-13, accepted in 2022-12-19,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundMedical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.MethodsData on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller–Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.Results74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.ConclusionThis study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

【 授权许可】

CC BY   
© The Author(s) 2022

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