Xu Yong, Meng Chengjie, Chen Dan, Cao Yongsheng, Wang Xin, Ji Peng
Department of Neurosurgery, Hefei Third Clinical College of Anhui Medical University, The Third People's Hospital of Hefei 230000, China.
Department of Neurosurgery, Yancheng First Peoples' Hospital, Affiliated Hospital of Nanjing University Medical School, Yancheng 224006, China.
J Bone Oncol. 2024 Aug 23;48:100630. doi: 10.1016/j.jbo.2024.100630. eCollection 2024 Oct.
Variability exists in the subjective delineation of tumor areas in MRI scans of patients with spinal bone metastases. This research aims to investigate the efficacy of the nnUNet radiomics model for automatic segmentation and identification of spinal bone metastases.
A cohort of 118 patients diagnosed with spinal bone metastases at our institution between January 2020 and December 2023 was enrolled. They were randomly divided into a training set (n = 78) and a test set (n = 40). The nnUNet radiomics segmentation model was developed, employing manual delineations of tumor areas by physicians as the reference standard. Both methods were utilized to compute tumor area measurements, and the segmentation performance and consistency of the nnUNet model were assessed.
The nnUNet model demonstrated effective localization and segmentation of metastases, including smaller lesions. The Dice coefficients for the training and test sets were 0.926 and 0.824, respectively. Within the test set, the Dice coefficients for lumbar and thoracic vertebrae were 0.838 and 0.785, respectively. Strong linear correlation was observed between the nnUNet model segmentation and physician-delineated tumor areas in 40 patients ( = 0.998, < 0.001).
The nnUNet model exhibits efficacy in automatically localizing and segmenting spinal bone metastases in MRI scans.
在脊柱骨转移瘤患者的MRI扫描中，肿瘤区域的主观勾画存在差异。本研究旨在探讨nnUNet放射组学模型在自动分割和识别脊柱骨转移瘤方面的有效性。
纳入2020年1月至2023年12月在本机构诊断为脊柱骨转移瘤的118例患者。他们被随机分为训练集（n = 78）和测试集（n = 40）。开发了nnUNet放射组学分割模型，以医生手动勾画的肿瘤区域作为参考标准。两种方法均用于计算肿瘤面积测量值，并评估nnUNet模型的分割性能和一致性。
nnUNet模型在转移瘤的定位和分割方面表现有效，包括较小的病变。训练集和测试集的Dice系数分别为0.926和0.824。在测试集中，腰椎和胸椎的Dice系数分别为0.838和0.785。在40例患者中，观察到nnUNet模型分割与医生勾画的肿瘤区域之间存在强线性相关性（ = 0.998， < 0.001）。
nnUNet模型在MRI扫描中自动定位和分割脊柱骨转移瘤方面表现出有效性。
