Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer

Article type: Research Article

Authors: Haraguchi, Takafumi^{a; *} | Kobayashi, Yasuyuki^b | Hirahara, Daisuke^{b; c} | Kobayashi, Tatsuaki^b | Takaya, Eichi^{b; d; e} | Nagai, Mariko Takishita^f | Tomita, Hayato^g | Okamoto, Jun^g | Kanemaki, Yoshihide^h | Tsugawa, Koichiro^f

Affiliations: [a] Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan | [b] Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan | [c] Department of AI Research Lab, Harada Academy, Higashitaniyama, Kagoshima, Kagoshima, Japan | [d] AI Lab, Tohoku University Hospital, Seiryomachi, Aoba-ku, Sendai, Miyagi, Japan | [e] School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan | [f] Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan | [g] Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan | [h] Department of Radiology, Breast and Imaging Center, St. Marianna University School of Medicine, Manpukuji, Asao-ku, Kawasaki, Kanagawa, Japan

Correspondence: [*] Corresponding author: Takafumi Haraguchi, MD, Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, 2–16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan Tel.: +8144 977 8111; Fax: +8144 977 2931; E-mail: hartak@marianna-u.ac.jp.

Abstract: BACKGROUND:In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE:This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS:A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS:For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548–0.982), 0.801 (0.597–1.000), and 0.779 (0.567–0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548–0.982), 0.757 (0.538–0.977), and 0.779 (0.567–0.992), respectively. CONCLUSIONS:Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

Keywords: Diffusion-weighted whole-body imaging, background signal suppression, DWIBS, radiomics, axillary lymph node status, breast cancer, machine learning

DOI: 10.3233/XST-230009

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 627-640, 2023