Ms. Hyoyi Kim Profile

Hyoyi Kim

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Proceedings Article | 29 March 2024 Poster + Paper

Risk prediction of stereotactic-body-radiotherapy-induced vertebral compression fracture using multi-modal deep learning network

Seoyoung Lee, Hyoyi Kim, Haeyoung Kim, Seungryong Cho

Proceedings Volume 12928, 1292824 (2024) https://doi.org/10.1117/12.3006647

KEYWORDS: Radiotherapy, Computed tomography, Deep learning, Network architectures, Spine, Neural networks, Cancer, Tumors

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Stereotactic body radiotherapy (SBRT) has been widely used to treat spinal bone metastases. However, it has been reported that individuals may suffer from vertebral compression fracture (VCF) after the treatment, hence it is necessary to identify possible risk groups prior to performing SBRT. Several studies have been made to identify the risk factors, including spinal instability neoplastic score (SINS), dose fractionation, and radiomics. However, no studies have attempted to predict VCF occurrence by direct usage of patients’ pretreatment CT images. In this study, we propose a multi-modal deep network for risk prediction of VCF after SBRT that uses clinical records, CT images, and radiotherapy factors altogether without explicit feature extraction. The retrospective study was conducted on a cohort of 131 patients who received SBRT for spinal bone metastasis. We classified the risk factors into three categories: clinical factors, anatomical imaging factors, and radiotherapy factors. 1-D vectors were generated from clinical factors after a proper standardization. We cropped 3-D patches of the lesion area from pretreatment CT images and treatment planning dose images. We used data augmentation with translation and rotation in the sagittal plane based on the characteristics of the S-shaped spine to supplement the limited size of our available dataset. Numerical variables from radiotherapy factors are standardized along with the clinical feature vector. We designed a three-branch deep learning network with the aforementioned three factors as inputs. From the k-fold validation and ablation study, our proposed network showed performance with an area under the curve (AUC) of 0.7605 and an average precision (AP) of 0.7273. The results show an improvement over other unimodal comparison models. The prediction model would play a valuable role not only in the treated patients’ welfare but also in the treatment planning for those patients.

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