Journal of Bone Oncology最新文献

{"title":"Using β-Elemene to reduce stemness and drug resistance in osteosarcoma: A focus on the AKT/FOXO1 signaling pathway and immune modulation","authors":"Shaochun Zhang ,&nbsp;Zhijie Xing ,&nbsp;Jing Ke","doi":"10.1016/j.jbo.2024.100655","DOIUrl":"10.1016/j.jbo.2024.100655","url":null,"abstract":"<div><h3>Objective</h3><div>Osteosarcoma, a highly malignant bone tumor, poses significant treatment challenges due to its propensity for stemness and drug resistance, particularly against doxorubicin (DOX). This study aims to investigate the mechanism by which β-elemene reduces the stemness of osteosarcoma stem cells and ultimately decreases DOX resistance by inhibiting the Akt/FoxO1 signaling pathway and activating a macrophage-mediated inflammatory microenvironment.</div></div><div><h3>Methods</h3><div>Osteosarcoma stem cells were isolated and induced for DOX resistance. <em>In vitro</em> and <em>in vivo</em> models were employed to assess β-elemene’s impact on cell viability, stemness, and drug resistance. Bioinformatics analysis, flow cytometry, and immunofluorescence staining were used to evaluate signaling pathway activity and macrophage polarization. Additionally, an osteosarcoma xenograft mouse model was established to confirm the therapeutic effects of β-elemene.</div></div><div><h3>Results</h3><div><em>In vivo</em> animal experiments demonstrated that β-elemene reduces osteosarcoma resistance. Bioinformatics analysis revealed that AKT1 is a key core gene in osteosarcoma progression, acting through the FOXO signaling pathway. Additionally, AKT inhibits immune cell infiltration in osteosarcoma and suppresses immune responses during osteosarcoma progression. β-elemene may influence osteosarcoma progression by mediating TP53 to regulate PTEN and subsequently AKT1. <em>In vitro</em> experiments showed that β-elemene promotes M1 macrophage activation by inhibiting the Akt/FoxO1 signaling axis, thereby reducing the stemness of osteosarcoma stem cells. Finally, <em>in vivo</em> animal experiments confirmed that β-elemene reduces osteosarcoma resistance by promoting M1 macrophage activation through inhibition of the Akt/FoxO1 signaling axis.</div></div><div><h3>Conclusion</h3><div>β-Elemene demonstrates promising potential in reducing osteosarcoma stemness and drug resistance via dual mechanisms: targeting the AKT/FOXO1 pathway and modulating the tumor immune microenvironment. These findings suggest β-elemene as a potential adjunct therapy for osteosarcoma, providing novel therapeutic strategies to overcome chemotherapy resistance and improve patient outcomes.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100655"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029469","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":"Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT","authors":"Hao-Nan Zhu ,&nbsp;Yi-Fan Guo ,&nbsp;YingMin Lin ,&nbsp;Zhi-Chao Sun ,&nbsp;Xi Zhu ,&nbsp;YuanZhe Li","doi":"10.1016/j.jbo.2024.100653","DOIUrl":"10.1016/j.jbo.2024.100653","url":null,"abstract":"<div><h3>Background</h3><div>Bone metastasis from breast cancer significantly elevates patient morbidity and mortality, making early detection crucial for improving outcomes. This study utilizes radiomics to analyze changes in the thoracic vertebral bone marrow microenvironment from chest computerized tomography (CT) images prior to bone metastasis in breast cancer, and constructs a model to predict metastasis. Methods: This study retrospectively gathered data from breast cancer patients who were diagnosed and continuously monitored for five years from January 2013 to September 2023. Radiomic features were extracted from the bone marrow of thoracic vertebrae on non-contrast chest CT scans. Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. The effectiveness of this combined model was assessed through receiver operating characteristic (ROC) analysis as well as decision curve analysis (DCA). Results: The study included a total of 106 patients diagnosed with breast cancer, among whom 37 developed bone metastases within five years. The radiomics model’s area under the curve (AUC) for the test set, calculated using logistic regression, is 0.929, demonstrating superior predictive performance compared to alternative machine learning models. Furthermore, DCA demonstrated the potential of radiomics models in clinical application, with a greater clinical benefit in predicting bone metastasis than clinical model and nomogram. Conclusion: CT-based radiomics can capture subtle changes in the thoracic vertebral bone marrow before breast cancer bone metastasis, offering a predictive tool for early detection of bone metastasis in breast cancer.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100653"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878439","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":"Out with the old (not so!) and in with the new","authors":"Rob Coleman (Editor in Chief)","doi":"10.1016/j.jbo.2024.100658","DOIUrl":"10.1016/j.jbo.2024.100658","url":null,"abstract":"","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100658"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081457","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":"A multicenter, randomized, double-blind trial comparing LY01011, a biosimilar, with denosumab (Xgeva®) in patients with bone metastasis from solid tumors","authors":"Mingchuan Zhao ,&nbsp;Xichun Hu ,&nbsp;Pengpeng Zhuang ,&nbsp;Aiping Zeng ,&nbsp;Yan Yu ,&nbsp;Zhendong Chen ,&nbsp;Hongmei Sun ,&nbsp;Weihua Yang ,&nbsp;Lili Sheng ,&nbsp;Peijian Peng ,&nbsp;Jingfen Wang ,&nbsp;Tienan Yi ,&nbsp;Minghong Bi ,&nbsp;Huaqiu Shi ,&nbsp;Mingli Ni ,&nbsp;Xiumei Dai ,&nbsp;Changlu Hu ,&nbsp;Hongjie Xu ,&nbsp;Dongqing Lv ,&nbsp;Qingshan Li ,&nbsp;Changlin Dou","doi":"10.1016/j.jbo.2025.100661","DOIUrl":"10.1016/j.jbo.2025.100661","url":null,"abstract":"<div><h3>Introduction</h3><div>Denosumab (Xgeva®) is a standard treatment for the prevention of skeletal-related events (SREs) in patients with bone metastases (BM). This trial was designed to assess the equivalence of LY01011 to denosumab in terms of efficacy and safety.</div></div><div><h3>Materials and methods</h3><div>Eligible patients with BM from solid tumors were randomized at a 1:1 ratio to receive 120 mg of LY01011 or 120 mg of denosumab subcutaneously every four weeks during a 12-week double-blind treatment period, and then all enrolled patients continued to receive LY01011 until week 53. The primary endpoint was the natural logarithm of change of the urinary N-terminal crosslinked telopeptide of type I collagen level normalized to the urine creatinine level (uNTX/uCr) at week 13 from baseline. Other endpoints included the uNTX/uCr ratio, serum bone-specific alkaline phosphatase level alteration, status of anti-drug antibodies and neutralizing antibodies, adverse events and SREs.</div></div><div><h3>Results</h3><div>850 eligible patients were randomized into the LY01011 group (n = 424) or the denosumab group (n = 426). The least-squares means (SEs) of the natural logarithms of the changes in the uNTX/uCr ratios at week 13 from baseline were −1.810 (0.0404) in the LY01011 group and −1.791 (0.0406) in the denosumab group. The LSM difference [90 % CI] between two arms was −0.019 [-0.110, 0.073] within the equivalence margins (−0.135, 0.135) and met the predetermined primary endpoint. The AEs, ADAs and the PK data showed no statistically significant difference.</div></div><div><h3>Conclusions</h3><div>This study demonstrated the equivalent efficacy and safety of LY01011 to denosumab in patients with BM.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100661"},"PeriodicalIF":3.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332852","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":"Lipid metabolic reprogramming and associated ferroptosis in osteosarcoma: From molecular mechanisms to potential targets","authors":"Zhiyang Yin ,&nbsp;Guanlu Shen ,&nbsp;Minjie Fan ,&nbsp;Pengfei Zheng","doi":"10.1016/j.jbo.2025.100660","DOIUrl":"10.1016/j.jbo.2025.100660","url":null,"abstract":"<div><div>Osteosarcoma is a common bone tumor in adolescents, which is characterized by lipid metabolism disorders and plays a key role in tumorigenesis and disease progression. Ferroptosis is an iron-dependent form of programmed cell death associated with lipid peroxidation. This review provides an in-depth analysis of the complex relationship between lipid metabolic reprogramming and associated ferroptosis in OS from the perspective of metabolic enzymes and metabolites. We discussed the molecular basis of lipid uptake, synthesis, storage, lipolysis, and the tumor microenvironment, as well as their significance in OS development. Key enzymes such as adenosine triphosphate-citrate lyase (ACLY), acetyl-CoA synthetase 2 (ACSS2), fatty acid synthase (FASN) and stearoyl-CoA desaturase-1 (SCD1) are overexpressed in OS and associated with poor prognosis.</div><div>Based on specific changes in metabolic processes, this review highlights potential therapeutic targets in the lipid metabolism and ferroptosis pathways, and in particular the HMG-CoA reductase inhibitor simvastatin has shown potential in inducing apoptosis and inhibiting OS metastasis. Targeting these pathways provides new strategies for the treatment of OS. However, challenges such as the complexity of drug development and metabolic interactions must be overcome. A comprehensive understanding of the interplay between dysregulation of lipid metabolism and ferroptosis is essential for the development of innovative and effective therapies for OS, with the ultimate goal of improving patient outcomes.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100660"},"PeriodicalIF":3.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103134","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":"Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model","authors":"Guanfeng Chen ,&nbsp;Wenxi Liu ,&nbsp;Yingmin Lin ,&nbsp;Jie Zhang ,&nbsp;Risheng Huang ,&nbsp;Deqiu Ye ,&nbsp;Jing Huang ,&nbsp;Jieyun Chen","doi":"10.1016/j.jbo.2024.100659","DOIUrl":"10.1016/j.jbo.2024.100659","url":null,"abstract":"<div><h3>Background</h3><div>Colorectal cancer is a prevalent malignancy with a significant risk of metastasis, including to bones, which severely impacts patient outcomes. Accurate prediction of bone metastasis risk is crucial for optimizing treatment strategies and improving prognosis.</div></div><div><h3>Purpose</h3><div>This study aims to develop a predictive model combining radiomics and Vision Transformer (ViT) deep learning techniques to assess the risk of bone metastasis in colorectal cancer patients using both plain and contrast-enhanced CT images.</div></div><div><h3>Materials and methods</h3><div>We conducted a retrospective analysis of 155 colorectal cancer patients, including 81 with bone metastasis and 74 without. Radiomic features were extracted from segmented tumors on both plain and contrast-enhanced CT images. LASSO regression was applied to select key features, which were then used to build traditional machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, LightGBM, and XGBoost. Additionally, a dual-modality ViT model was trained on the same CT images, with a late fusion strategy employed to combine outputs from the different modalities. Model performance was evaluated using AUC-ROC, accuracy, sensitivity, and specificity, and differences were statistically assessed using DeLong’s test.</div></div><div><h3>Results</h3><div>The ViT model demonstrated superior predictive performance, achieving an AUC of 0.918 on the test set, significantly outperforming all traditional radiomics-based models. The SVM model, while the best among traditional models, still underperformed compared to the ViT model. The ViT model’s strength lies in its ability to capture complex spatial relationships and long-range dependencies within the imaging data, which are often missed by traditional models. DeLong’s test confirmed the statistical significance of the ViT model’s enhanced performance, highlighting its potential as a powerful tool for predicting bone metastasis risk in colorectal cancer patients.</div></div><div><h3>Conclusion</h3><div>The integration of radiomics with ViT-based deep learning offers a robust and accurate method for predicting bone metastasis risk in colorectal cancer patients. The ViT model’s ability to analyze dual-modality CT imaging data provides greater precision in risk assessment, which can improve clinical decision-making and personalized treatment strategies. These findings underscore the promise of advanced deep learning models in enhancing the accuracy of metastasis prediction. Further validation in larger, multicenter studies is recommended to confirm the generalizability of these results.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100659"},"PeriodicalIF":3.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103136","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":"Determinants of tumor necrosis and its impact on outcome in patients with Localized osteosarcoma uniformly treated with a response adapted regimen without high dose Methotrexate– A retrospective institutional analysis","authors":"Prabhat Gautam Roy ,&nbsp;Shuvadeep Ganguly ,&nbsp;Archana Sasi ,&nbsp;Vivek Kumar ,&nbsp;Adarsh Barwad ,&nbsp;Asit Ranjan Mridha ,&nbsp;Shah Alam Khan ,&nbsp;Venkatesan Sampath Kumar ,&nbsp;Love Kapoor ,&nbsp;Deepam Pushpam ,&nbsp;Sameer Bakhshi","doi":"10.1016/j.jbo.2024.100651","DOIUrl":"10.1016/j.jbo.2024.100651","url":null,"abstract":"<div><h3>Purpose</h3><div>Response to neoadjuvant chemotherapy in form of tumor necrosis predicts outcome in osteosarcoma; although response-adapted treatment escalation failed to improve outcome among patients treated with high-dose methotrexate-based (HDMTx) chemotherapy. This study aimed to identify factors predicting tumor necrosis and its impact on survival among patients with non-metastatic osteosarcoma treated with a response-adapted non-HDMTx regimen.</div></div><div><h3>Methods</h3><div>A retrospective single-institutional study was conducted among non-metastatic osteosarcoma patients treated with neoadjuvant therapy between 2004–2019. Patients were treated uniformly with three cycles of neoadjuvant cisplatin/doxorubicin. Post-operatively, patients with favourable necrosis (≥90 %) received 3 cycles of cisplatin/doxorubicin, while patients with poor necrosis (&lt;90 %) received escalated treatment with alternating six cycles of cisplatin/doxorubicin and ifosfamide/etoposide. Propensity score matching (PSM) analyses were conducted to ascertain independent impact of necrosis on event-free survival (EFS) and overall survival (OS).</div></div><div><h3>Results</h3><div>Of 594 registered osteosarcoma patients, 280 patients (median age 17 years; male 67.1 %) were included for analysis. 73 patients (26.1 %) achieved favourable necrosis. Patients with smaller tumor size (≤10 cm) (aOR = 2.28; p = 0.030), lower serum alkaline phosphatase (≤450 IU/L) (aOR = 2.10; p = 0.035), and who had surgery earlier (&lt;115 days) (aOR = 2.28; p = 0.016) were more likely to have favourable necrosis. On 1:2 PSM analysis, patients not achieving favourable necrosis demonstrated inferior EFS (HR = 2.68; p = 0.003) and OS (HR = 3.42; p = 0.003).</div></div><div><h3>Conclusions</h3><div>Patients of osteosarcoma with smaller tumor, lower serum alkaline phosphatase and earlier surgery are more likely to achieve favourable necrosis. Tumor necrosis independently predicts outcome in osteosarcoma, and response-adapted treatment escalation fails to overcome the adverse impact of poor necrosis in non-HDMTx based regimen.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100651"},"PeriodicalIF":3.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748553","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":"Novel lipid metabolism factor HIBCH inhibitor synergizes with doxorubicin to suppress osteosarcoma growth and impacts clinical prognosis in osteosarcoma patients","authors":"Xuhui Yuan ,&nbsp;Bo Yu ,&nbsp;Haiqi Ding ,&nbsp;Hongyan Li ,&nbsp;Qijing Wang ,&nbsp;Lan Lin ,&nbsp;Wenming Zhang ,&nbsp;Xinyu Fang","doi":"10.1016/j.jbo.2024.100652","DOIUrl":"10.1016/j.jbo.2024.100652","url":null,"abstract":"<div><h3>Background</h3><div>Osteosarcoma (OS) is a highly malignant primary bone tumor primarily affecting children and adolescents. Despite advancements in therapeutic strategies, long-term survival rates for OS remain unfavorable, especially in advanced or recurrent cases. Emerging evidence has noted the involvement of lipid metabolism dysregulation in OS progression, but the specific mechanisms remain unclear.</div></div><div><h3>Methods</h3><div>A risk model incorporating lipid metabolism-related genes was established to stratify OS patients into high-risk and low-risk groups. Functional assays were conducted to assess the role of 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) in OS cell activities. Ultra-fast liquid chromatography-mass spectrometry was adopted to analyze the impact of HIBCH on OS cell metabolism. Moreover, the combined effect of HIBCH inhibitor SBF-1 with doxorubicin (DOX) was evaluated through <em>in vitro</em> studies and mouse xenograft models.</div></div><div><h3>Results</h3><div>HIBCH was identified as a key gene involved in the malignant behaviors of OS cells. HIBCH knockdown disrupted tricarboxylic acid (TCA) cycle activity and reduced oxidative phosphorylation in OS cells. SBF-1 showed synergistic effects with DOX in inhibiting malignant phenotypes of OS cells by modulating the Akt-mTOR pathway. <em>In vivo</em> experiments demonstrated that the combination of SBF-1 and DOX significantly suppressed tumor growth in mouse xenograft models.</div></div><div><h3>Conclusions</h3><div>This study reveals the critical role of lipid metabolism in OS progression and suggests a new therapeutic strategy against chemotherapy resistance in OS based on the synergistic combination of SBF-1 with DOX.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100652"},"PeriodicalIF":3.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748609","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":"Chinese expert consensus on the diagnosis and clinical management of medication-related osteonecrosis of the jaw","authors":"Han-Jin Ruan ,&nbsp;Heng Chen ,&nbsp;Jin-Song Hou ,&nbsp;Jin-Gang An ,&nbsp;Yu-Xing Guo ,&nbsp;Bing Liu ,&nbsp;Lei Tian ,&nbsp;Jian Pan ,&nbsp;Jin-Song Li ,&nbsp;Can-Hua Jiang ,&nbsp;Zhen Tian ,&nbsp;Jie Xu ,&nbsp;Ling Zhu ,&nbsp;Chang-Fu Sun ,&nbsp;Ke-Qian Zhi ,&nbsp;Qing Qu ,&nbsp;Chun-Lin Zong ,&nbsp;Meng-Yu Li ,&nbsp;Zhi-Yuan Zhang ,&nbsp;Yue He","doi":"10.1016/j.jbo.2024.100650","DOIUrl":"10.1016/j.jbo.2024.100650","url":null,"abstract":"<div><div>Medication-related osteonecrosis of the jaw (MRONJ) is a side effect that occurs after treatment for systemic diseases. However, most institutions currently rely on empirical methods to make diagnosis and treatment plans, and there is a lack of consensus or guidelines for the classification, staging and treatment of MRONJ in China. To address this gap and improve prognosis, an expert panel representing 11 renowned domestic medical colleges and affiliated hospitals in China was convened. The panel made a comprehensive literature review of previous treatment experiences and research findings to address issues of definitions, etiology and risk factors, diagnosis, treatment and prevention methods. The panel concluded that the diagnosis of MRONJ can be made on the basis of a history of related medications and typical clinical manifestations, with either typical radiographic manifestations or histopathological manifestations, after excluding jaw metastasis. Surgical treatment should be considered for symptomatic patients with sequestrum or bone abnormalities accompanied by recurrent infections, and He’s classification was considered a practical clinical MRONJ staging system. Multidisciplinary comprehensive treatment should be proposed to achieve optimal treatment outcomes.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100650"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701503","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":"A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor","authors":"Weimin Chen ,&nbsp;Yong Han ,&nbsp;Muhammad Awais Ashraf ,&nbsp;Junhan Liu ,&nbsp;Mu Zhang ,&nbsp;Feng Su ,&nbsp;Zhiguo Huang ,&nbsp;Kelvin K.L. Wong","doi":"10.1016/j.jbo.2024.100649","DOIUrl":"10.1016/j.jbo.2024.100649","url":null,"abstract":"<div><h3>Background and objective</h3><div>Magnetic resonance imaging (MRI) plays a vital role in diagnosing spinal diseases, including different types of spinal tumors. However, conventional segmentation techniques are often labor-intensive and susceptible to variability. This study aims to propose a full-automatic segmentation method for spine MRI images, utilizing a convolutional-deconvolution neural network and patch-based deep learning. The objective is to improve segmentation efficiency, meeting clinical needs for accurate diagnoses and treatment planning.</div></div><div><h3>Methods</h3><div>The methodology involved the utilization of a convolutional neural network to automatically extract deep learning features from spine data. This allowed for the effective representation of anatomical structures. The network was trained to learn discriminative features necessary for accurate segmentation of the spine MRI data. Furthermore, a patch extraction (PE) based deep neural network was developed using a convolutional neural network to restore the feature maps to their original image size. To improve training efficiency, a combination of pre-training and an enhanced stochastic gradient descent method was utilized.</div></div><div><h3>Results</h3><div>The experimental results highlight the effectiveness of the proposed method for spine image segmentation using Gadolinium-enhanced T1 MRI. This approach not only delivers high accuracy but also offers real-time performance. The innovative model attained impressive metrics, achieving 90.6% precision, 91.1% recall, 93.2% accuracy, 91.3% F1-score, 83.8% Intersection over Union (IoU), and 91.1% Dice Coefficient (DC). These results indicate that the proposed method can accurately segment spine tumors CT images, addressing the limitations of traditional segmentation algorithms.</div></div><div><h3>Conclusion</h3><div>In conclusion, this study introduces a fully automated segmentation method for spine MRI images utilizing a convolutional neural network, enhanced by the application of the PE-module. By utilizing a patch extraction based neural network (PENN) deep learning techniques, the proposed method effectively addresses the deficiencies of traditional algorithms and achieves accurate and real-time spine MRI image segmentation.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100649"},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701505","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}