Pub Date : 2025-02-19DOI: 10.1016/j.jbo.2025.100665
Yibing Yao , Victor Kwan Min Lee , Ee Sin Chen
Giant cell tumor of bone (GCTB) is a primary bone tumor that typically exhibits benign histological appearance and clinical behavior in most cases, with local aggressiveness and rare metastasis. It predominantly affects individuals in the young adult age group. It is characterized by the presence of multinucleated osteoclastic giant cells and a stromal population of neoplastic cells. A key hallmark for GCTB pathogenesis is the G34W genetic mutation in the histone H3.3 gene, which is restricted to the population of cancerous stromal cells and is absent in osteoclasts and their progenitor cells. This review presents a comprehensive overview of the pathology of GCTB, including its histopathological characteristics, cytological features, histopathological variants, and their clinical relevance. We also discuss recent insights into genetic alterations in relation to the molecular pathways implicated in GCTB. A summary of the current understanding of GCTB pathology will update the knowledge base to guide the diagnosis and management of this unique bone tumor.
{"title":"Molecular pathological insights into tumorigenesis and progression of giant cell tumor of bone","authors":"Yibing Yao , Victor Kwan Min Lee , Ee Sin Chen","doi":"10.1016/j.jbo.2025.100665","DOIUrl":"10.1016/j.jbo.2025.100665","url":null,"abstract":"<div><div>Giant cell tumor of bone (GCTB) is a primary bone tumor that typically exhibits benign histological appearance and clinical behavior in most cases, with local aggressiveness and rare metastasis. It predominantly affects individuals in the young adult age group. It is characterized by the presence of multinucleated osteoclastic giant cells and a stromal population of neoplastic cells. A key hallmark for GCTB pathogenesis is the G34W genetic mutation in the histone H3.3 gene, which is restricted to the population of cancerous stromal cells and is absent in osteoclasts and their progenitor cells. This review presents a comprehensive overview of the pathology of GCTB, including its histopathological characteristics, cytological features, histopathological variants, and their clinical relevance. We also discuss recent insights into genetic alterations in relation to the molecular pathways implicated in GCTB. A summary of the current understanding of GCTB pathology will update the knowledge base to guide the diagnosis and management of this unique bone tumor.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100665"},"PeriodicalIF":3.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487723","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}
Pub Date : 2025-02-11DOI: 10.1016/j.jbo.2025.100666
Qizheng Wang , Yongye Chen , Guangjin Zhou , Tongyu Wang , Jingchao Fang , Ke Liu , Siyuan Qin , Weili Zhao , Dapeng Hao , Ning Lang
Background
Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy.
Purpose
To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors.
Methods
119 aggressive spinal tumor patients (median age, 40; range, 13–74 years) confirmed by pathological findings with a mean follow-up of 36 months were enrolled and divided into the recurrence and non-recurrence group. The histogram metrics of whole-lesion, including the maximum, mean, kurtosis, skewness, entropy, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values, were evaluated and take the average. Fractal dimension (FD) was assessed in the three orthogonal directions and take maximum. Clinical and general imaging features were used to construct an alternative prognostic model for comparison. Variables with statistical differences would be included in stepwise logistic regression analysis.
Results
As for the clinical model, Enneking staging (odds ratio [OR]: 3.572; P = 0.04) and vertebral compression (OR: 4.302; P = 0.002) were independent predictors of recurrence. There was no statistical difference in FD between the two groups (P = 0.623). Among the ADC histogram parameters compared, skewness, maximum, and mean ADC values were independent risk factors and constructed ADC histogram prediction models. The ADC histogram model (AUC = 0.871) and the combined model (AUC = 0.884) performed better than the clinical prediction model (AUC = 0.704) with P-values of 0.004 and 0.001, respectively.
Conclusion
Prediction models based on the ADC histogram analysis might represent serviceable instruments for the aggressive spinal tumors.
{"title":"Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors","authors":"Qizheng Wang , Yongye Chen , Guangjin Zhou , Tongyu Wang , Jingchao Fang , Ke Liu , Siyuan Qin , Weili Zhao , Dapeng Hao , Ning Lang","doi":"10.1016/j.jbo.2025.100666","DOIUrl":"10.1016/j.jbo.2025.100666","url":null,"abstract":"<div><h3>Background</h3><div>Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy.</div></div><div><h3>Purpose</h3><div>To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors.</div></div><div><h3>Methods</h3><div>119 aggressive spinal tumor patients (median age, 40; range, 13–74 years) confirmed by pathological findings with a mean follow-up of 36 months were enrolled and divided into the recurrence and non-recurrence group. The histogram metrics of whole-lesion, including the maximum, mean, kurtosis, skewness, entropy, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values, were evaluated and take the average. Fractal dimension (FD) was assessed in the three orthogonal directions and take maximum. Clinical and general imaging features were used to construct an alternative prognostic model for comparison. Variables with statistical differences would be included in stepwise logistic regression analysis.</div></div><div><h3>Results</h3><div>As for the clinical model, Enneking staging (odds ratio [OR]: 3.572; <em>P</em> = 0.04) and vertebral compression (OR: 4.302; <em>P</em> = 0.002) were independent predictors of recurrence. There was no statistical difference in FD between the two groups (<em>P</em> = 0.623). Among the ADC histogram parameters compared, skewness, maximum, and mean ADC values were independent risk factors and constructed ADC histogram prediction models. The ADC histogram model (AUC = 0.871) and the combined model (AUC = 0.884) performed better than the clinical prediction model (AUC = 0.704) with <em>P</em>-values of 0.004 and 0.001, respectively.</div></div><div><h3>Conclusion</h3><div>Prediction models based on the ADC histogram analysis might represent serviceable instruments for the aggressive spinal tumors.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100666"},"PeriodicalIF":3.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394627","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}
Pub Date : 2025-02-10DOI: 10.1016/j.jbo.2025.100662
Jin Qi , Sihang Liu , Baomin Wu , Gang Xue
Osteosarcoma, a prevalent and aggressive skeletal malignancy, significantly impacts the prognosis of individuals, particularly young patients. Current treatments, including surgery and chemotherapy, often prove inadequate for advanced osteosarcoma with metastasis. This study investigates the role of the METTL3/TGF-β1 signaling axis in promoting osteosarcoma progression by inducing mesenchymal stem cells (MSCs) to differentiate into cancer-associated fibroblasts (CAFs). Utilizing co-culture technology, we demonstrated that osteosarcoma cells secrete TGF-β1, which is crucial for MSC differentiation into CAFs, as evidenced by the increased expression of CAF markers α-SMA, FSP-1, and FAP. Additionally, METTL3 was found to enhance the stability and expression of TGF-β1 mRNA through m6A modification, thereby facilitating the differentiation process of MSCs. In vivo xenograft experiments further confirmed that the METTL3/TGF-β1 axis significantly promotes tumor growth in osteosarcoma by mediating the differentiation of MSCs into CAFs. These findings provide new insights into the molecular mechanisms underlying osteosarcoma progression and highlight potential therapeutic targets for treating advanced stages of this malignancy.
{"title":"The METTL3/TGF-β1 signaling axis promotes osteosarcoma progression by inducing MSC differentiation into CAFs via m6A modification","authors":"Jin Qi , Sihang Liu , Baomin Wu , Gang Xue","doi":"10.1016/j.jbo.2025.100662","DOIUrl":"10.1016/j.jbo.2025.100662","url":null,"abstract":"<div><div>Osteosarcoma, a prevalent and aggressive skeletal malignancy, significantly impacts the prognosis of individuals, particularly young patients. Current treatments, including surgery and chemotherapy, often prove inadequate for advanced osteosarcoma with metastasis. This study investigates the role of the METTL3/TGF-β1 signaling axis in promoting osteosarcoma progression by inducing mesenchymal stem cells (MSCs) to differentiate into cancer-associated fibroblasts (CAFs). Utilizing co-culture technology, we demonstrated that osteosarcoma cells secrete TGF-β1, which is crucial for MSC differentiation into CAFs, as evidenced by the increased expression of CAF markers α-SMA, FSP-1, and FAP. Additionally, METTL3 was found to enhance the stability and expression of TGF-β1 mRNA through m<sup>6</sup>A modification, thereby facilitating the differentiation process of MSCs. <em>In vivo</em> xenograft experiments further confirmed that the METTL3/TGF-β1 axis significantly promotes tumor growth in osteosarcoma by mediating the differentiation of MSCs into CAFs. These findings provide new insights into the molecular mechanisms underlying osteosarcoma progression and highlight potential therapeutic targets for treating advanced stages of this malignancy.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100662"},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422459","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}
Giant cell tumor of bone (GCTB) is among the most prevalent benign primary bone tumors, characterized by its potential for aggressive local recurrence, soft tissue invasion, and, though rare, lung metastasis. Emerging evidence suggests unique behavioral patterns of GCTB in extremities. This study seeks to rigorously compare the aggressiveness of GCTB in the upper versus lower extremities, centering on recurrence rates.
Method
This systematic review and meta-analysis, conducted in accordance with PRISMA guidelines, sourced data from MEDLINE/PubMed, Cochrane, Scopus, CINAHL/EBSCO, and reference lists of pertinent studies. Two independent reviewers screened studies, with discrepancies resolved by discussion. Eligible studies included a minimum of 10 participants. Data extraction and analysis were performed by an additional team of two researchers.
Results
Out of 1,283 studies spanning from 1984 to 2023, 30 met eligibility, encompassing 2,672 participants. The mean age was 32.77 ± 12.99 years, with an average follow-up of 75.53 ± 65.88 months. GCTB predominantly affected the lower extremities, accounting for 1,937 cases. Notably, comparisons of aggressiveness between upper and lower extremity GCTB revealed no statistically significant difference (OR = 1.10, p = 0.56 for Surgery Group; OR = 1.16, p = 0.45 for Local Adjuvant Group; and OR = 1.71, p = 0.32 for Drug/Denosumab Group).
Conclusion
This analysis underscores the lower extremities as the primary site for GCTB but finds no significant difference in aggressiveness between upper and lower extremities. These findings challenge assumptions about GCTB behavior based on tumor location and highlight the need for further investigation to fully elucidate the complex biology of extremity GCTB.
{"title":"Comparative analysis of aggressiveness in giant cell tumor of bone between upper and lower extremities: A systematic review and meta-analysis","authors":"Rhyan Darma Saputra , Dita Anggara Kusuma , Fathih Kaldani , Khoirul Fahmi","doi":"10.1016/j.jbo.2025.100663","DOIUrl":"10.1016/j.jbo.2025.100663","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Giant cell tumor of bone (GCTB) is among the most prevalent benign primary bone tumors, characterized by its potential for aggressive local recurrence, soft tissue invasion, and, though rare, lung metastasis. Emerging evidence suggests unique behavioral patterns of GCTB in extremities. This study seeks to rigorously compare the aggressiveness of GCTB in the upper versus lower extremities, centering on recurrence rates.</div></div><div><h3>Method</h3><div>This systematic review and meta-analysis, conducted in accordance with PRISMA guidelines, sourced data from MEDLINE/PubMed, Cochrane, Scopus, CINAHL/EBSCO, and reference lists of pertinent studies. Two independent reviewers screened studies, with discrepancies resolved by discussion. Eligible studies included a minimum of 10 participants. Data extraction and analysis were performed by an additional team of two researchers.</div></div><div><h3>Results</h3><div>Out of 1,283 studies spanning from 1984 to 2023, 30 met eligibility, encompassing 2,672 participants. The mean age was 32.77 ± 12.99 years, with an average follow-up of 75.53 ± 65.88 months. GCTB predominantly affected the lower extremities, accounting for 1,937 cases. Notably, comparisons of aggressiveness between upper and lower extremity GCTB revealed no statistically significant difference (OR = 1.10, p = 0.56 for Surgery Group; OR = 1.16, p = 0.45 for Local Adjuvant Group; and OR = 1.71, p = 0.32 for Drug/Denosumab Group).</div></div><div><h3>Conclusion</h3><div>This analysis underscores the lower extremities as the primary site for GCTB but finds no significant difference in aggressiveness between upper and lower extremities. These findings challenge assumptions about GCTB behavior based on tumor location and highlight the need for further investigation to fully elucidate the complex biology of extremity GCTB.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"51 ","pages":"Article 100663"},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394628","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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100657
Yingang Li , Xizhuang Bai
Osteosarcoma is a common malignant tumor found in adolescents, characterized by a high metastatic potential and poor prognosis, but it is sensitive to radiotherapy and chemotherapy. Ferroptosis is a novel form of regulated cell death induced by excessive iron accumulation, leading to lipid peroxidation that results in cellular dysfunction and death. Naringenin is a flavonoid known for its anti-cancer properties, yet its role in osteosarcoma has not been thoroughly studied. In this study, we found that naringenin significantly reduced the viability of osteosarcoma cells while increasing the accumulation of reactive oxygen species (ROS), iron overload, and the excessive expression of malondialdehyde (MDA). Bioinformatics analysis revealed that microsomal glutathione S-transferase 2 (MGST2) is highly expressed in osteosarcoma cells. Silencing MGST2 decreased the proliferation, migration, and invasion of these cells and enhanced their sensitivity to ferroptosis. Mechanistically, signal transducer and activator of transcription 3 (STAT3) binds to the MGST2 promoter, promoting its transcription. Naringenin inhibits STAT3, blocking the expression of MGST2, while the STAT3 agonist Colivelin reverses this effect. In vivo experiments further confirmed that naringenin inhibited tumor growth in subcutaneous xenograft models and exhibited good biosafety. In summary, our study demonstrates that naringenin induces ferroptosis in osteosarcoma cells through the STAT3-MGST2 signaling pathway, providing a promising strategy for osteosarcoma treatment.
{"title":"Naringenin induces ferroptosis in osteosarcoma cells through the STAT3-MGST2 signaling pathway","authors":"Yingang Li , Xizhuang Bai","doi":"10.1016/j.jbo.2024.100657","DOIUrl":"10.1016/j.jbo.2024.100657","url":null,"abstract":"<div><div>Osteosarcoma is a common malignant tumor found in adolescents, characterized by a high metastatic potential and poor prognosis, but it is sensitive to radiotherapy and chemotherapy. Ferroptosis is a novel form of regulated cell death induced by excessive iron accumulation, leading to lipid peroxidation that results in cellular dysfunction and death. Naringenin is a flavonoid known for its anti-cancer properties, yet its role in osteosarcoma has not been thoroughly studied. In this study, we found that naringenin significantly reduced the viability of osteosarcoma cells while increasing the accumulation of reactive oxygen species (ROS), iron overload, and the excessive expression of malondialdehyde (MDA). Bioinformatics analysis revealed that microsomal glutathione S-transferase 2 (MGST2) is highly expressed in osteosarcoma cells. Silencing MGST2 decreased the proliferation, migration, and invasion of these cells and enhanced their sensitivity to ferroptosis. Mechanistically, signal transducer and activator of transcription 3 (STAT3) binds to the MGST2 promoter, promoting its transcription. Naringenin inhibits STAT3, blocking the expression of MGST2, while the STAT3 agonist Colivelin reverses this effect. In vivo experiments further confirmed that naringenin inhibited tumor growth in subcutaneous xenograft models and exhibited good biosafety. In summary, our study demonstrates that naringenin induces ferroptosis in osteosarcoma cells through the STAT3-MGST2 signaling pathway, providing a promising strategy for osteosarcoma treatment.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100657"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014503","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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100656
Francesco Bertoldo , Cristina Eller-Vainicher , Vittorio Fusco , Rodolfo Mauceri , Jessica Pepe , Alberto Bedogni , Andrea Palermo , Umberto Romeo , Giuseppe Guglielmi , Giuseppina Campisi
Background
Low-doses of bone modifying agents (LD-BMAs) compared to those used to treat bone metastases are used in breast or prostate cancer patients on adjuvant endocrine therapy to prevent Cancer Treatment Induced Bone Loss (CTIBL). Their use is associated with an increased risk of developing Medication-Related Osteonecrosis of the Jaw (MRONJ). However, there is not clarity about strategies aimed to minimize the MRONJ risk in cancer patients at different conditions as low- vs high-doses of BMA. This joint report from the Italian Societies of Oral Pathology and Medicine (SIPMO) and of Italian Society of Osteoporosis, Mineral Metabolism and Skeletal Diseases (SIOMMMS) aims to define the dental management of breast and prostate cancer patients with CTIBL under LD-BMAs, to reduce their risk to develop MRONJ.
Methods
This interdisciplinary SIPMO-SIOMMMS Expert Italian Panel reviewed the available international scientific literature and developed a set of recommendations to implement strategies of MRONJ prevention in breast (BC) and prostate cancer (PC) patients undertaking LD-BMAs to prevent CTIBL.
Results
The Expert Panel, after addressing some introductive topics (i.e., CTIBL and its management, pharmacology and pharmacodynamics of BMAs, definition and diagnosis of MRONJ), developed a joint report on the following five issues: a) prevention and dental management in cancer patients candidates to LD-BMAs, or under LD-BMAs; b) prophylactic drug holiday; c) MRONJ treatment; d) LD-BMAs therapeutic drug holiday; and e) restart of LD-BMA treatment after successful healing of MRONJ.
Finally, ten key questions with answers were prepared and placed at the end of the document.
Conclusions
Despite obvious weaknesses of the available international literature, the Expert Panel recognized the need to tailor separate MRONJ preventive approach for breast and prostate cancer patients on adjuvant endocrine therapy who begin low-dose BMA therapy to prevent CTIBL and provided this practical guidance for bone specialists and oral healthcare providers. In view of a MRONJ risk for BC and PC patients receiving low-dose BMAs, which approximates that of patients with osteoporosis and other non-malignant diseases undergoing similar treatment schedules, the SIPMO-SIOMMMS Expert Panel recognizes the need for less stringent preventive strategies than those already developed for BC or PC patients with bone metastases taking HD-BMAs.
{"title":"Medication related osteonecrosis (MRONJ) in the management of CTIBL in breast and prostate cancer patients. Joint report by SIPMO AND SIOMMMS","authors":"Francesco Bertoldo , Cristina Eller-Vainicher , Vittorio Fusco , Rodolfo Mauceri , Jessica Pepe , Alberto Bedogni , Andrea Palermo , Umberto Romeo , Giuseppe Guglielmi , Giuseppina Campisi","doi":"10.1016/j.jbo.2024.100656","DOIUrl":"10.1016/j.jbo.2024.100656","url":null,"abstract":"<div><h3>Background</h3><div>Low-doses of bone modifying agents (LD-BMAs) compared to those used to treat bone metastases are used in breast or prostate cancer patients on adjuvant endocrine therapy to prevent Cancer Treatment Induced Bone Loss (CTIBL). Their use is associated with an increased risk of developing Medication-Related Osteonecrosis of the Jaw (MRONJ). However, there is not clarity about strategies aimed to minimize the MRONJ risk in cancer patients at different conditions as low- vs high-doses of BMA. This joint report from the Italian Societies of Oral Pathology and Medicine (SIPMO) and of Italian Society of Osteoporosis, Mineral Metabolism and Skeletal Diseases (SIOMMMS) aims to define the dental management of breast and prostate cancer patients with CTIBL under LD-BMAs, to reduce their risk to develop MRONJ.</div></div><div><h3>Methods</h3><div>This interdisciplinary SIPMO-SIOMMMS Expert Italian Panel reviewed the available international scientific literature and developed a set of recommendations to implement strategies of MRONJ prevention in breast (BC) and prostate cancer (PC) patients undertaking LD-BMAs to prevent CTIBL.</div></div><div><h3>Results</h3><div>The Expert Panel, after addressing some introductive topics (i.e., CTIBL and its management, pharmacology and pharmacodynamics of BMAs, definition and diagnosis of MRONJ), developed a joint report on the following five issues: a) prevention and dental management in cancer patients candidates to LD-BMAs, or under LD-BMAs; b) prophylactic drug holiday; c) MRONJ treatment; d) LD-BMAs therapeutic drug holiday;<!--> <!-->and e) restart of LD-BMA treatment after successful healing of MRONJ.</div><div>Finally, ten key questions with answers were prepared and placed at the end of the document.</div></div><div><h3>Conclusions</h3><div>Despite obvious weaknesses of the available international literature, the Expert Panel recognized the need to tailor separate MRONJ preventive approach for breast and prostate cancer patients on adjuvant endocrine therapy who begin low-dose BMA therapy to prevent CTIBL and provided this practical guidance for bone specialists and oral healthcare providers. In view of a MRONJ risk for BC and PC patients receiving low-dose BMAs, which approximates that of patients with osteoporosis and other non-malignant diseases undergoing similar treatment schedules, the SIPMO-SIOMMMS Expert Panel recognizes the need for less stringent preventive strategies than those already developed for BC or PC patients with bone metastases taking HD-BMAs.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100656"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11728904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980197","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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100654
Kun Wang , Yong Han , Yuguang Ye , Yusi Chen , Daxin Zhu , Yifeng Huang , Ying Huang , Yijie Chen , Jianshe Shi , Bijiao Ding , Jianlong Huang
Objective
Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation. After realizing automatic segmentation and 3D reconstruction of osteosarcoma by optimizing feature extraction and improving target space clustering capabilities, we built a mixed reality (MR) infrastructure and explored the application prospects of the infrastructure combining deep learning-based medical image segmentation and mixed reality in the diagnosis and treatment of bone tumors.
Methods
We conducted experiments using a hospital dataset for bone tumor segmentation, used the optimized DCU-Net and 3D reconstruction technology to generate bone tumor models, and used set similarity (DSC), recall (R), precision (P), and 3D vertex distance error (VDE) to evaluate segmentation performance and 3D reconstruction effects. Then, two surgeons conducted clinical examination experiments on patients using two different methods, viewing 2D images and virtual reality infrastructure, and used the Likert scale (LS) to compare the effectiveness of surgical plans of the two methods.
Results
The DSC, R and P values of the model introduced in this paper all exceed 90%, which has significant advantages compared with methods such as U-Net and Attention-Uet. Furthermore, LS showed that clinicians in the DCU-Net-based MR group had better spatial awareness of tumor preoperative planning.
Conclusion
The deep learning DCU-Net algorithm model can improve the performance of tumor CT image segmentation, and the reconstructed fine model can better reflect the actual situation of individual tumors; the MR system constructed based on this model enhances clinicians’ understanding of tumor morphology and spatial relationships. The MR system based on deep learning and three-dimensional visualization technology has great potential in the diagnosis and treatment of bone tumors, and is expected to promote clinical practice and improve efficacy.
{"title":"Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net","authors":"Kun Wang , Yong Han , Yuguang Ye , Yusi Chen , Daxin Zhu , Yifeng Huang , Ying Huang , Yijie Chen , Jianshe Shi , Bijiao Ding , Jianlong Huang","doi":"10.1016/j.jbo.2024.100654","DOIUrl":"10.1016/j.jbo.2024.100654","url":null,"abstract":"<div><h3>Objective</h3><div>Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation. After realizing automatic segmentation and 3D reconstruction of osteosarcoma by optimizing feature extraction and improving target space clustering capabilities, we built a mixed reality (MR) infrastructure and explored the application prospects of the infrastructure combining deep learning-based medical image segmentation and mixed reality in the diagnosis and treatment of bone tumors.</div></div><div><h3>Methods</h3><div>We conducted experiments using a hospital dataset for bone tumor segmentation, used the optimized DCU-Net and 3D reconstruction technology to generate bone tumor models, and used set similarity (DSC), recall (R), precision (P), and 3D vertex distance error (VDE) to evaluate segmentation performance and 3D reconstruction effects. Then, two surgeons conducted clinical examination experiments on patients using two different methods, viewing 2D images and virtual reality infrastructure, and used the Likert scale (LS) to compare the effectiveness of surgical plans of the two methods.</div></div><div><h3>Results</h3><div>The DSC, R and P values of the model introduced in this paper all exceed 90%, which has significant advantages compared with methods such as U-Net and Attention-Uet. Furthermore, LS showed that clinicians in the DCU-Net-based MR group had better spatial awareness of tumor preoperative planning.</div></div><div><h3>Conclusion</h3><div>The deep learning DCU-Net algorithm model can improve the performance of tumor CT image segmentation, and the reconstructed fine model can better reflect the actual situation of individual tumors; the MR system constructed based on this model enhances clinicians’ understanding of tumor morphology and spatial relationships. The MR system based on deep learning and three-dimensional visualization technology has great potential in the diagnosis and treatment of bone tumors, and is expected to promote clinical practice and improve efficacy.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"50 ","pages":"Article 100654"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11745962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014498","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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100655
Shaochun Zhang , Zhijie Xing , Jing Ke
Objective
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.
Methods
Osteosarcoma stem cells were isolated and induced for DOX resistance. In vitro and in vivo 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.
Results
In vivo 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. In vitro 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, in vivo animal experiments confirmed that β-elemene reduces osteosarcoma resistance by promoting M1 macrophage activation through inhibition of the Akt/FoxO1 signaling axis.
Conclusion
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.
{"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 , Zhijie Xing , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100653
Hao-Nan Zhu , Yi-Fan Guo , YingMin Lin , Zhi-Chao Sun , Xi Zhu , YuanZhe Li
Background
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.
{"title":"Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT","authors":"Hao-Nan Zhu , Yi-Fan Guo , YingMin Lin , Zhi-Chao Sun , Xi Zhu , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.jbo.2024.100658
Rob Coleman (Editor in Chief)
{"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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号