Journal of Bone Oncology最新文献

{"title":"Is surgery without curettage effective for periacetabular Metastasis? Insights from a survival study of 93 patients","authors":"Thomas Amouyel ,&nbsp;Marie-Hélène Vieillard ,&nbsp;Alain Duhamel ,&nbsp;Carlos Maynou ,&nbsp;Martine Duterque-Coquillaud ,&nbsp;Cyrielle Dumont","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":"49 ","pages":"Article 100643"},"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":"Zhi Wang ,&nbsp;Yiyun Tan ,&nbsp;Kaibin Zeng ,&nbsp;Hao Tan ,&nbsp;Pingsen Xiao ,&nbsp;Guanghui Su","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":"49 ","pages":"Article 100641"},"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":"Wei Hua,&nbsp;Bing Xu,&nbsp;Xianwen Zhang,&nbsp;Tingting Chen","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":"48 ","pages":"Article 100639"},"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":"Xiao Zhao ,&nbsp;Yue-han Dong ,&nbsp;Li-yu Xu ,&nbsp;Yan-yan Shen ,&nbsp;Gang Qin ,&nbsp;Zheng-bo Zhang","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":"48 ","pages":"Article 100638"},"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":"Ingunn Holen ,&nbsp;Claire Edwards","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":"48 ","pages":"Article 100633"},"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":"Ketong Zhao ,&nbsp;Ping Dai ,&nbsp;Ping Xiao ,&nbsp;Yuhang Pan ,&nbsp;Litao Liao ,&nbsp;Junru Liu ,&nbsp;Xuemei Yang ,&nbsp;Zhenxing Li ,&nbsp;Yanjun Ma ,&nbsp;Jianxi Liu ,&nbsp;Zhengbo Zhang ,&nbsp;Shupeng Li ,&nbsp;Hailong Zhang ,&nbsp;Sheng Chen ,&nbsp;Feiyue Cai ,&nbsp;Zhen Tan","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":"48 ","pages":"Article 100637"},"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":"Taisheng Zeng ,&nbsp;Yusi Chen ,&nbsp;Daxin Zhu ,&nbsp;Yifeng Huang ,&nbsp;Ying Huang ,&nbsp;Yijie Chen ,&nbsp;Jianshe Shi ,&nbsp;Bijiao Ding ,&nbsp;Jianlong Huang","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":"48 ","pages":"Article 100640"},"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":"Nan Jiang ,&nbsp;Yu Xia ,&nbsp;Mingcong Luo ,&nbsp;Jianhua Chen ,&nbsp;Zongjian Qiu ,&nbsp;Jianfang Liu","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":"48 ","pages":"Article 100636"},"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}

{"title":"The efficacy and applicability of chimeric antigen receptor (CAR) T cell-based regimens for primary bone tumors: A comprehensive review of current evidence","authors":"Atefeh Barzegari ,&nbsp;Fateme Salemi ,&nbsp;Amirhossein Kamyab ,&nbsp;Adarsh Aratikatla ,&nbsp;Negar Nejati ,&nbsp;Mojgan Valizade ,&nbsp;Ehab Eltouny ,&nbsp;Alireza Ebrahimi","doi":"10.1016/j.jbo.2024.100635","DOIUrl":"10.1016/j.jbo.2024.100635","url":null,"abstract":"<div><div>Primary bone tumors (PBT), although rare, could pose significant mortality and morbidity risks due to their high incidence of lung metastasis. Survival rates of patients with PBTs may vary based on the tumor type, therapeutic interventions, and the time of diagnosis. Despite advances in the management of patients with these tumors over the past four decades, the survival rates seem not to have improved significantly, implicating the need for novel therapeutic interventions. Surgical resection with wide margins, radiotherapy, and systemic chemotherapy are the main lines of treatment for PBTs. Neoadjuvant and adjuvant chemotherapy, along with emerging immunotherapeutic approaches such as chimeric antigen receptor (CAR)-T cell therapy, have the potential to improve the treatment outcomes for patients with PBTs. CAR-T cell therapy has been introduced as an option in hematologic malignancies, with FDA approval for several CD19-targeting CAR-T cell products. This review aims to highlight the potential of immunotherapeutic strategies, specifically CAR T cell therapy, in managing PBTs.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"48 ","pages":"Article 100635"},"PeriodicalIF":3.4,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319813","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":"Synergistic effect between denosumab and immune checkpoint inhibitors (ICI)? A retrospective study of 268 patients with ICI and bone metastases","authors":"E. Mabrut ,&nbsp;S. Mainbourg ,&nbsp;J. Peron ,&nbsp;D. Maillet ,&nbsp;S. Dalle ,&nbsp;C. Fontaine Delaruelle ,&nbsp;E. Grolleau ,&nbsp;P. Clezardin ,&nbsp;E. Bonnelye ,&nbsp;C.B. Confavreux ,&nbsp;E. Massy","doi":"10.1016/j.jbo.2024.100634","DOIUrl":"10.1016/j.jbo.2024.100634","url":null,"abstract":"<div><h3>Background</h3><div>Bone metastasis is a significant concern in advanced solid tumors, contributing to diminished patient survival and quality of life due to skeletal-related events (SREs). Denosumab (DMAB), a monoclonal antibody targeting the receptor activator of nuclear factor kappa-B ligand (RANKL), is used to prevent SREs in such cases. The RANK/RANKL axis, crucial in immunological processes, has garnered attention, especially with the expanding use of immune checkpoint inhibitors (ICI) in modern oncology.</div></div><div><h3>Objective</h3><div>Our study aims to explore the potential synergistic antitumor effects of combining immunotherapy with denosumab, as suggested by anecdotal evidence, small cohort studies, and preclinical research.</div></div><div><h3>Methods</h3><div>We conducted a retrospective analysis using the IMMUCARE database, encompassing patients receiving ICI treatment since 2014 and diagnosed with bone metastases. We examined overall survival (OS), progression-free survival (PFS) and switch of treatment line based on denosumab usage. Patients were stratified into groups: without denosumab, ICI followed by denosumab, and denosumab followed by ICI. Survival curves and multivariate Cox regression analyses were performed.</div></div><div><h3>Results</h3><div>Among the 268 patients with bone metastases, 154 received treatment with ICI alone, while 114 received ICI in combination with denosumab at some point during their oncological history. No significant differences were observed in overall survival (OS) or progression-free survival (PFS) between patients receiving ICI monotherapy and those receiving ICI with denosumab (p = 0.29 and p = 0.79, respectively). However, upon analyzing patients who received denosumab following ICI initiation (17 patients), a notable difference emerged. The group receiving ICI followed by denosumab exhibited a significant advantage compared to those without denosumab (154 patients) or those receiving denosumab before ICI initiation (72 patients) (p = 0.022).</div></div><div><h3>Conclusion</h3><div>This retrospective investigation supports the notion of potential benefits associated with sequential administration of ICI and denosumab, although statistical significance was not achieved. Future studies, including prospective trials or updated retrospective analyses, focusing on cancers treated with first-line immunotherapy, could provide further insights into this therapeutic approach.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"48 ","pages":"Article 100634"},"PeriodicalIF":3.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319812","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}