Journal of Bone Oncology最新文献

{"title":"Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography","authors":"","doi":"10.1016/j.jbo.2024.100646","DOIUrl":"10.1016/j.jbo.2024.100646","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to evaluate the role of computed tomography (CT) imaging in the diagnosis and management of bone cancer during periods of limited access to histopathological testing. We aimed to determine the correlation between CT severity levels and subsequent patient management and care decisions, adhering to established oncological CT reporting guidelines.</div></div><div><h3>Methodology</h3><div>A retrospective analysis was conducted on 60 symptomatic patients from January 2021 to January 2024. The cohort included patients aged between 50 and 86 years, with a mean age of 68 years, and 75 % were male. All patients had their bone cancer diagnosis confirmed through histopathological examination, and CT imaging was used as the reference method. The analysis involved assessing the correlation between CT severity scores and patient management, including ICU admissions.</div></div><div><h3>Results</h3><div>The study found that CT imaging demonstrated a sensitivity of 92.6% in diagnosing bone cancer, with accuracy increasing to 97.6% in cases with high-probability CT characteristics. CT specificity also showed a consistent rise. Osteolytic lesions were the predominant finding, detected in 85.9% of cases. Among these, 88% exhibited engagement across multiple skeletal regions, 92.8% showed bilateral distribution, and 92.8% presented with peripheral involvement. In ICU patients, bone consolidation was observed in 81.5% of cases and was predominant in 66.7% of the ICU cohort. Additionally, ICU patients had significantly higher CT severity scores, with scores exceeding 14 being notably prevalent.</div></div><div><h3>Conclusions</h3><div>During the management period of bone cancer at our hospital, characteristic features on CT imaging facilitated swift and sensitive investigation. Two distinct CT phenotypes, associated with the primary osteolytic phenotype and severity score, emerged as valuable indicators for assessing the severity of the disease, particularly during ICU care. These findings highlight the diverse manifestations and severity levels encountered in bone cancer patients and underscore the importance of CT imaging in their diagnosis and management.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progression of vertebral fractures in metastatic melanoma and non-small cell lung cancer patients given immune checkpoint inhibitors","authors":"","doi":"10.1016/j.jbo.2024.100642","DOIUrl":"10.1016/j.jbo.2024.100642","url":null,"abstract":"<div><h3>Introduction</h3><div>The immune system mediates important effects on bone metabolism, but little has been done to understand immunotherapy’s role in this interaction. This study aims to describe and identify risk factors for the occurrence and/or exacerbation of vertebral fractures (vertebral fracture progression) during immune checkpoint inhibitors (ICIs).</div></div><div><h3>Methods</h3><div>We conducted an observational, retrospective, monocentric study. We collected data on melanoma and NSCLC patients, treated with first-line ICIs at the Medical Oncology Department ASST Spedali Civili of Brescia, between January 2015 and November 2021, and with a median follow-up of 20.1 (6–36) months. We collected data on patients, diseases, immune-related adverse events, and cortico-steroid therapy initiated on concomitant ICIs.</div></div><div><h3>Results</h3><div>We identified 135 patients, 65 (48.2 %) with locally advanced/metastatic melanoma and 70 (51.8 %) with locally advanced/metastatic non-small cell lung cancer (NSCLC). Twenty-one (15.6 %) patients already had an asymptomatic vertebral fracture at baseline before starting ICIs in monotherapy. A total of ten patients, or 7.4 %, had a vertebra fracture progression defined as a new vertebral fracture or a worsening of a previous fracture. There was a strong relation between the steroid therapy and irAEs with vertebra fracture progression [OR (95 % CI) 8.1 (3.7–17.8) p-value &lt; 0.001] in univariable analysis. However, only steroid therapy resulted to be an independent risk factor [8.260 (95 % CI 0.909–75.095); p-value 0.061] at the multivariable analysis.</div></div><div><h3>Conclusion</h3><div>Concurrent steroid therapy in patients receiving immunotherapy exposes them to a high risk of fractures due to skeletal fragility. The use of bone resorption inhibitors should be considered in these patients to prevent these adverse events.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is surgery without curettage effective for periacetabular Metastasis? Insights from a survival study of 93 patients","authors":"","doi":"10.1016/j.jbo.2024.100643","DOIUrl":"10.1016/j.jbo.2024.100643","url":null,"abstract":"<div><h3>Background</h3><div>The main aim of this study was to analyse the 6-month survival rates in <em>peri</em>-acetabular metastasis patients undergoing total hip arthroplasty (THA) with an acetabular cage and without curettage. The secondary objectives were to analyse the global survival rates, the factors influencing patient survival and to evaluate mechanical complication rates after THA.</div></div><div><h3>Methods</h3><div>This study was carried out on a cohort of 93 consecutive patients who underwent THA with an acetabular cage without curettage for acetabular metastasis or multiple myeloma lesions between 2010 and 2020. The National Death Registry was consulted to obtain the exact date of death of the patients; the minimum follow-up time was 2 years.</div></div><div><h3>Results</h3><div>The 6-month survival rate for all types of cancer was 78 % [68 – 85], the 1-year survival rate was 66 % [55 – 74], and the 5-year survival rate was 26 % [17 – 36]. The median overall survival for the cohort was 24.37 months [16.10 – 32.63]. The mean overall survival was 46.02 months [32.89 – 59.16]. At last contact, 86 % of the operated patients were walking again.</div><div>No patient died from surgery. The ECOG performance status score, the number of bone metastatic sites, the presence of visceral metastases and the number of lines of systemic therapy undertaken prior to surgery were negative survival factors. Three patients (3.2 %) had early prosthetic dislocation, 2 patients (2.2 %) showed aseptic loosening of her partial hip implant after 10 and 11 years respectively and 4 patients (4.3 %) had an early infection treated by debridement, antibiotics and implant retention to control the infection. During the follow-up period, no new femoral metastases were detected in any patient.</div></div><div><h3>Conclusion</h3><div>Surgery without curettage is an effective treatment for periacetabular metastasis. It gives reliable results, regardless of the type of acetabular lesion, allowing most patients to walk again and does not modify the patient’s survival.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bone density measurement in patients with spinal metastatic tumors using chest quantitative CT deep learning model","authors":"","doi":"10.1016/j.jbo.2024.100641","DOIUrl":"10.1016/j.jbo.2024.100641","url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to develop a deep learning model using the 3DResUNet architecture to predict vertebral volumetric bone mineral density (vBMD) from Quantitative Computed Tomography (QCT) scans in patients with spinal metastatic tumors, enhancing osteoporosis screening capabilities.</div></div><div><h3>Methods</h3><div>749 patients with spinal metastatic tumors underwent QCT vertebral vBMD measurements. The dataset was randomly split into training (599 cases) and test sets (150 cases). The 3DResUNet model was trained for vBMD classification and prediction using QCT images processed with automated bone segmentation and ROI extraction.</div></div><div><h3>Results</h3><div>The deep learning model demonstrated strong performance with Spearman correlation coefficients of 0.923 (training set) and 0.918 (test set) between predicted and QCT-measured vBMD values. Bland-Altman analysis showed a slight bias of −1.42 mg/cm³ (training set) and −1.14 mg/cm³ (test set) between the model predictions and QCT measurements. The model achieved an area under the curve (AUC) of 0.977 (training set) and 0.966 (test set) for diagnosing Osteoporosis based on vBMD.</div></div><div><h3>Conclusion</h3><div>The developed deep learning model using 3DResUNet effectively predicts vertebral vBMD from QCT scans in patients with spinal metastatic tumors. It provides accurate and automated vBMD measurements, potentially facilitating widespread osteoporosis screening in clinical practice, mainly where DXA availability is limited.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"-AI-assisted diagnostic potential of CT in bone oncology and its impact on clinical decision-making for intensive care","authors":"","doi":"10.1016/j.jbo.2024.100639","DOIUrl":"10.1016/j.jbo.2024.100639","url":null,"abstract":"<div><h3>Objective</h3><div>This study evaluates the AI-assisted diagnostic potential of computed tomography (CT) for bone cancer and its influence on patient care during the pre- and post-treatment phases. It compares patient management approaches based on CT severity levels and identifies distinct CT phenotypes linked to disease severity.</div></div><div><h3>Methodology</h3><div>We retrospectively examined 50 patients diagnosed with bone cancer between December 2022 and June 2023. The CT scans were analyzed according to the Radiological Society of North America (RSNA) guidelines. This study was performed using the deep convolutional neutral network (DCNN) model to assist doctors in diagnosing bone tumors through CT scanning. Patients’ management approaches were compared based on the severity levels indicated by CT scans.</div></div><div><h3>Results</h3><div>Fifty patients participated in this study, with a median age of 67.2 years, ranging from 32 to 89 years. Of them, 38 % were female and 62 % were male. In 2022, 19 individuals (13 males and 6 females, ages 32 to 84) were assessed, with a mean age of 59.9 years. In 2023, 31 individuals, aged 54 to 89 with a mean age of 71.6 years, were assessed; among them were 18 men and 13 women. SPECT scans revealed the following key diagnostic features: 85.9 % of patients exhibited bone lesions with ground-glass opacities, 88 % had multipolar involvement, 92.8 % had bilateral involvement, and 92.8 % showed peripheral involvement. The severity scores based on CT scans were significantly higher in patients requiring intensive care, with scores above 14 being more common in this group.</div></div><div><h3>Conclusion</h3><div>Distinct CT findings during the AI-assisted diagnosis and treatment of bone cancer provided prompt and sensitive examination capabilities. Notably, two CT phenotypes emerged, associated with large consolidation patterns and high severity scores, offering crucial insights into disease severity and aiding in clinical decision-making for intensive care requirements. The study underscores the importance of CT in the effective monitoring and management of bone cancer pre- and post-treatment.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis","authors":"","doi":"10.1016/j.jbo.2024.100638","DOIUrl":"10.1016/j.jbo.2024.100638","url":null,"abstract":"<div><h3>Purpose</h3><div>The objective of this study is to develop a novel diagnostic tool using deep learning and radiomics to distinguish bone tumors on CT images as metastases from breast cancer. By providing a more accurate and reliable method for identifying metastatic bone tumors, this approach aims to significantly improve clinical decision-making and patient management in the context of breast cancer.</div></div><div><h3>Methods</h3><div>This study utilized CT images of bone tumors from 178 patients, including 78 cases of breast cancer bone metastases and 100 cases of non-breast cancer bone metastases. The dataset was processed using the Medical Image Segmentation via Self-distilling TransUNet (MISSU) model for automated segmentation. Radiomics features were extracted from the segmented tumor regions using the Pyradiomics library, capturing various aspects of tumor phenotype. Feature selection was conducted using LASSO regression to identify the most predictive features. The model’s performance was evaluated using ten-fold cross-validation, with metrics including accuracy, sensitivity, specificity, and the Dice similarity coefficient.</div></div><div><h3>Results</h3><div>The developed radiomics model using the SVM algorithm achieved high discriminatory power, with an AUC of 0.936 on the training set and 0.953 on the test set. The model’s performance metrics demonstrated strong accuracy, sensitivity, and specificity. Specifically, the accuracy was 0.864 for the training set and 0.853 for the test set. Sensitivity values were 0.838 and 0.789 for the training and test sets, respectively, while specificity values were 0.896 and 0.933 for the training and test sets, respectively. These results indicate that the SVM model effectively distinguishes between bone metastases originating from breast cancer and other origins. Additionally, the average Dice similarity coefficient for the automated segmentation was 0.915, demonstrating a high level of agreement with manual segmentations.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of combining CT-based radiomics and deep learning for the accurate detection of bone metastases from breast cancer. The high-performance metrics indicate that this approach can significantly enhance diagnostic accuracy, aiding in early detection and improving patient outcomes. Future research should focus on validating these findings on larger datasets, integrating the model into clinical workflows, and exploring its use in personalized treatment planning.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors' introduction: The microenvironment in bone metastasis – New dimensions","authors":"","doi":"10.1016/j.jbo.2024.100633","DOIUrl":"10.1016/j.jbo.2024.100633","url":null,"abstract":"","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated segmentation and source prediction of bone tumors using ConvNeXtv2 Fusion based Mask R-CNN to identify lung cancer metastasis","authors":"","doi":"10.1016/j.jbo.2024.100637","DOIUrl":"10.1016/j.jbo.2024.100637","url":null,"abstract":"<div><div>Lung cancer, which is a leading cause of cancer-related deaths worldwide, frequently metastasizes to the bones, significantly diminishing patients’ quality of life and complicating treatment strategies. This study aims to develop an advanced 3D Mask R-CNN model, enhanced with the ConvNeXt-V2 backbone, for the automatic segmentation of bone tumors and identification of lung cancer metastasis to support personalized treatment planning. Data were collected from two hospitals: Center A (106 patients) and Center B (265 patients). The data from Center B were used for training, while Center A’s dataset served as an independent external validation set. High-resolution CT scans with 1 mm slice thickness and no inter-slice gaps were utilized, and the regions of interest (ROIs) were manually segmented and validated by two experienced radiologists. The 3D Mask R-CNN model achieved a Dice Similarity Coefficient (DSC) of 0.856, a sensitivity of 0.921, and a specificity of 0.961 on the training set. On the test set, it achieved a DSC of 0.849, a sensitivity of 0.911, and a specificity of 0.931. For the classification task, the model attained an AUC of 0.865, an accuracy of 0.866, a sensitivity of 0.875, and a specificity of 0.835 on the training set, while achieving an AUC of 0.842, an accuracy of 0.836, a sensitivity of 0.847, and a specificity of 0.819 on the test set. These results highlight the model’s potential in improving the accuracy of bone tumor segmentation and lung cancer metastasis detection, paving the way for enhanced diagnostic workflows and personalized treatment strategies in clinical oncology.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics","authors":"","doi":"10.1016/j.jbo.2024.100640","DOIUrl":"10.1016/j.jbo.2024.100640","url":null,"abstract":"<div><div>This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making.</div></div><div><h3>Methods</h3><div>We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test.</div></div><div><h3>Results</h3><div>The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>The DenseNet-264 model significantly outperforms the radiomics model in predicting bone metastasis in lung cancer patients. The early and accurate prediction provided by the deep learning model can facilitate timely interventions and personalized treatment planning, potentially improving patient outcomes. Future studies should focus on validating these findings in larger, multi-center cohorts and integrating clinical data to further enhance predictive accuracy.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of newly developed multiple myeloma without bone disease detectable on conventional computed tomography (CT) scan by using dual-energy CT","authors":"","doi":"10.1016/j.jbo.2024.100636","DOIUrl":"10.1016/j.jbo.2024.100636","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the diagnostic utility of fat (hydroxyapatite) density [D_{Fat (HAP)}] on dual-energy computed tomography (DECT) for identifying clinical diagnosed multiple myeloma without bone disease (MNBD) that is not visible on conventional CT scans.</div></div><div><h3>Material and Methods</h3><div>In this age-gender-examination sites matched case control prospective study, Chest and/or abdominal images on Revolution CT of MNBDs and control subjects were consecutive enrolled in a 1:2 ratio from October 2022 to November 2023. Multiple myeloma was clinical diagnosed according to criteria of the International Myeloma Working Group. Regions of interest (ROIs) were drawn separately for all thoracolumbar vertebrae in the scanning range by two radiologists. Additionally, a radiologist specializing in musculoskeletal imaging supervised the process. D_{Fat (HAP)} was extracted from each ROI. The spine was divided into upper thoracic (UPT), middle and lower thoracic (MLT), thoracolumbar (TL), and middle and lower lumbar (MLL) vertebrae. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of D_{Fat (HAP)} in diagnosing multiple myeloma, and the sensitivity, specificity, and accuracy under the optimal cut-off were determined by Youden index (sensitivity + specificity −1).</div></div><div><h3>Results</h3><div>A total of 32 and MNBD patients and 64 control patients were included. The total number of ROIs outlined included MNBD group (n = 493) and control group (n = 986). For all vertebrae, D_Fat(HAP) got average performance in the diagnosis of MNBD (AUC = 0.733, <em>p</em> &lt; 0.001) with a cut-off value of 958 (mg/cm³); the sensitivity, specificity, and accuracy were 58.8 %, 77.8 %, and 71.7 %, respectively. Regarding segment analysis, the diagnostic performance was good for all (AUC, 0.803–0.837; <em>p</em> &lt; 0.001) but the UPT segment (AUC = 0.692, <em>p</em> = 0.002). The optimal diagnostic cut-off values for the MLT, TL, and MLL vertebrae were 955 mg/cm³, 947 mg/cm³, and 947 mg/cm³, respectively; the sensitivity, specificity, and accuracy were 80.0 %-87.5 %, 71.9 %-82.6 %, and 77.1 %-81.6 %, respectively.</div></div><div><h3>Conclusion</h3><div>DECT was effective for detecting MNBD, and better diagnostic results can be obtained by grouping different spine segments.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}