The Value of Radiomic Features of Primary Breast Tumor in STIR Sequences in Predicting Axilla Metastasis STIR sequences in predicting axilla metastasis

Günay Rona (1), Meral Arifoğlu (2), Tekin Ahmet Serel (3), Tamer Baysal (4)
(1) a:1:{s:5:"en_US";s:3:"Dr.";}, Turkey,
(2) Department of Radiology, Kartal Doktor Lütfi Kırdar Training and Research Hospital, University of Health Sciences, Istanbul, Turkey, Turkey,
(3) Department of Urology, Suleyman Demirel University School of Medicine, Istanbul, Turkey , Turkey,
(4) Department of Radiology, Kartal Doktor Lütfi Kırdar Training and Research Hospital, University of Health Sciences, Istanbul, Turkey , Turkey


Background: Detection of axillary metastases in breast cancer is critical for treatment options and prognosis. The aim of this study is to investigate the value of radiomic features obtained from short tau inversion recovery (STIR) sequences in magnetic resonance imaging (MRI) of primary tumor in breast cancer in predicting axillary lymph node metastasis (ALNM).

Methods: Lesions of 165 patients with a mean age of 51.12 ±11 (range 28-82) with newly diagnosed invasive breast cancer who underwent breast MRI before treatment were manually segmented from STIR sequences in the 3D Slicer program in all sections. Machine learning (ML) analysis was performed using the extracted 851 features Python 3.11, Pycaret library program. Datasets were randomly divided into train (123, 80%) and independent test (63, 20%) datasets.  The performances of ML algorithms were compared with area under curve (AUC), accuracy, recall, presicion and F1 scores.

Results: Accuracy and AUC in the training set were in the range of 57 %-86 % and 0.50-0.95, respectively. The best model in the training set was the catBoost classifier with an AUC of 0.95 and 84% accuracy. The AUC, accuracy, recall, precision values and F1 score of the CatBoost classifier on the test set were  0.92, 84 %, 89 %, 85 %, 86 %, respectively. 

Conclusion: Radiomic features obtained from primary tumors on STIR sequences have the potential to predict ALNM in invasive breast cancer.

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Beenken SW, Urist MM, Zhang Y, Desmond R, Krontiras H, Medina H, et al. Axillary lymph node status, but not tumor size, predicts locoregional recurrence and overall survival after mastectomy for breast cancer. Ann Surg 2003;237:732-739.

Rao R, Euhus D, Mayo HG, Balch C. Axillary node interventions in breast cancer: a systematic review. JAMA 2013;310:1385-1394.

Diessner J, Anders L, Herbert S, Kiesel M, Bley T, Schlaiss T, et al. Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm. J Cancer Res Clin Oncol 2022;10.1007/s00432-022-04221-9.

Chua B, Ung O, Taylor R, Boyages J. Frequency and predictors of axillary lymph node metastases in invasive breast cancer. ANZ J Surg 2001;71:723-728.

Van Zee KJ, Manasseh DM, Bevilacqua JL, Boolbol SK, Fey JV, Tan LK, et al. A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy. Ann Surg Oncol 2003;10:1140-1151.

Zhang J, Li X, Huang R, Feng WL, Kong YN, Xu F, et al. A nomogram to predict the probability of axillary lymph node metastasis in female patients with breast cancer in China: A nationwide, multicenter, 10-year epidemiological study. Oncotarget 2017;8:35311-35325.

Cutuli B, Velten M, Martin C. Assessment of axillary lymph node involvement in small breast cancer: analysis of 893 cases. Clin Breast Cancer 2001;2:59-66.

Andea AA, Bouwman D, Wallis T, Visscher DW. Correlation of tumor volume and surface area with lymph node status in patients with multifocal/multicentric breast carcinoma. Cancer 2004;100:20-27.

Chen W, Wang C, Fu F, Yang B, Chen C, Sun Y. A Model to Predict the Risk of Lymph Node Metastasis in Breast Cancer Based on Clinicopathological Characteristics. Cancer Manag Res 2020;12:10439-10447.

Van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020;11:91.

Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol 2021;72:238-250.

Parekh VS, Jacobs MA. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric. MRI NPJ Breast Cancer 2017;3:43.

Whitney HM, Taylor NS, Drukker K, Edwards AV, Papaioannou J, Schacht D,et al. Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset. Acad Radiol. 2019;26:202-209.

Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H, et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017;46:604-616.

Bickelhaupt S, Jaeger PF, Laun FB, Lederer W, Daniel H, Kuder TA, et al. Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. Radiology 2018;287:761-770.

Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 2017;44:5162-5171.

Samiei S, Granzier RWY, Ibrahim A, Primakov S, Lobbes MBI, Beets-Tan RGH, et al. Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Cancers (Basel) 2021 12;13:757.

Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 2018;28:582-591.

Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine 2021;69:103460.

Qiu Y, Zhang X, Wu Z, Wu S, Yang Z, Wang D, et al. MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer. Front Oncol 2022;12:811347.

Wang D, Hu Y, Zhan C, Zhang Q, Wu Y, Ai T. A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer. Front Oncol 2022;12:940655.

Chen Y, Wang L, Dong X, Luo R, Ge Y, Liu H, Zhang Y, et al. Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer. J Digit Imaging 2023;1-9.

Liu Y, Li X, Zhu L, Zhao Z, Wang T, Zhang X, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram. Contrast Media Mol Imaging 2022 18;2022:6729473.

Cui X, Wang N, Zhao Y, Chen S, Li S, Xu M, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI. Sci Rep 2019;9(1):2240.

Morrow M. Management of the Node-Positive Axilla in Breast Cancer in 2017: Selecting the Right Option. JAMA Oncol 2018;4:250-251.

Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 2020;295:500-515.

Cooper KL, Meng Y, Harnan S, Ward SE, Fitzgerald P, Papaioannou D, et al. Positron emission tomography (PET) and magnetic resonance imaging (MRI) for the assessment of axillary lymph node metastases in early breast cancer: systematic review and economic evaluation. Health Technol Assess 2011;15:iii-134


Günay Rona (Primary Contact)
Meral Arifoğlu
Tekin Ahmet Serel
Tamer Baysal
Rona G, Arifoğlu M, Serel TA, Baysal T. The Value of Radiomic Features of Primary Breast Tumor in STIR Sequences in Predicting Axilla Metastasis: STIR sequences in predicting axilla metastasis. Arch Breast Cancer [Internet]. [cited 2024 Jun. 16];11(3). Available from:

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