Journal of Bone Oncology最新文献

{"title":"Does liquid nitrogen recycled autograft for treatment of bone sarcoma impact local recurrence rate? A systematic review","authors":"Ana Cecilia Belzarena,&nbsp;James L. Cook","doi":"10.1016/j.jbo.2024.100628","DOIUrl":"10.1016/j.jbo.2024.100628","url":null,"abstract":"<div><p>The gold standard treatment for primary bone sarcomas has been surgical resection with wide margins. However, there is no consensus regarding an optimal method for limb salvage reconstruction. In 2005, a technique for recycling resected bone after intraoperative treatment with liquid nitrogen was described. This technique has been reported to have a spectrum of advantages; nonetheless, acceptance for routine use has been limited, primarily for fear of local recurrence. A systematic search of the literature using PubMed and Google Scholar was performed. Full-text articles published between 2008 and 2023 were included if the study presented sufficient information regarding patients with a diagnosis of a primary bone sarcoma of the limbs or pelvis who had undergone reconstruction with liquid nitrogen recycled autografts. Sixteen studies that included 286 patients met criteria for analyses. Local recurrence occurred in 25 patients (8.7 %) during the first 4 years following limb salvage reconstruction using recycled autografts for treatment of primary bone sarcomas, which compares favorably to the 15–30 % local recurrence rates reported for patients undergoing limb salvage reconstruction using artificial implants. Systematic synthesis of the current evidence regarding local recurrence rates following use of the liquid nitrogen recycled autograft technique for limb salvage reconstruction after bone sarcoma resection suggests a favorable comparison to other limb salvage reconstruction options. As such, this technique warrants further consideration as a viable option for indicated patients based on relative advantages regarding costs, availability, and biologic and surgical reconstruction benefits.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"48 ","pages":"Article 100628"},"PeriodicalIF":3.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001088/pdfft?md5=e6cd37f6e7eabd30d30e7d6ca28f408e&pid=1-s2.0-S2212137424001088-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020915","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":"An enhanced AlexNet-Based model for femoral bone tumor classification and diagnosis using magnetic resonance imaging","authors":"Xu Chen ,&nbsp;Hongkun Chen ,&nbsp;Junming Wan ,&nbsp;Jianjun Li ,&nbsp;Fuxin Wei","doi":"10.1016/j.jbo.2024.100626","DOIUrl":"10.1016/j.jbo.2024.100626","url":null,"abstract":"<div><h3>Objective</h3><p>Bone tumors, known for their infrequent occurrence and diverse imaging characteristics, require precise differentiation into benign and malignant categories. Existing diagnostic approaches heavily depend on the laborious and variable manual delineation of tumor regions. Deep learning methods, particularly convolutional neural networks (CNNs), have emerged as a promising solution to tackle these issues. This paper introduces an enhanced deep-learning model based on AlexNet to classify femoral bone tumors accurately.</p></div><div><h3>Methods</h3><p>This study involved 500 femoral tumor patients from July 2020 to January 2023, with 500 imaging cases (335 benign and 165 malignant). A CNN was employed for automated classification. The model framework encompassed training and testing stages, with 8 layers (5 Conv and 3 FC) and ReLU activation. Essential architectural modifications included Batch Normalization (BN) after the first and second convolutional filters. Comparative experiments with various existing methods were conducted to assess algorithm performance in tumor staging. Evaluation metrics encompassed accuracy, precision, sensitivity, specificity, F-measure, ROC curves, and AUC values.</p></div><div><h3>Results</h3><p>The analysis of precision, sensitivity, specificity, and F1 score from the results demonstrates that the method introduced in this paper offers several advantages, including a low feature dimension and robust generalization (with an accuracy of 98.34 %, sensitivity of 97.26 %, specificity of 95.74 %, and an F1 score of 96.37). These findings underscore its exceptional overall detection capabilities. Notably, when comparing various algorithms, they generally exhibit similar classification performance. However, the algorithm presented in this paper stands out with a higher AUC value (AUC=0.848), signifying enhanced sensitivity and more robust specificity.</p></div><div><h3>Conclusion</h3><p>This study presents an optimized AlexNet model for classifying femoral bone tumor images based on convolutional neural networks. This algorithm demonstrates higher accuracy, precision, sensitivity, specificity, and F1-score than other methods. Furthermore, the AUC value further confirms the outstanding performance of this algorithm in terms of sensitivity and specificity. This research makes a significant contribution to the field of medical image classification, offering an efficient automated classification solution, and holds the potential to advance the application of artificial intelligence in bone tumor classification.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"48 ","pages":"Article 100626"},"PeriodicalIF":3.4,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001064/pdfft?md5=2b4c672034139c0c68135debac089a03&pid=1-s2.0-S2212137424001064-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149373","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 niches in the regulation of tumour cell dormancy","authors":"James T. Smith ,&nbsp;Ryan C. Chai","doi":"10.1016/j.jbo.2024.100621","DOIUrl":"10.1016/j.jbo.2024.100621","url":null,"abstract":"<div><p>Secondary metastases, accounting for 90 % of cancer-related deaths, pose a formidable challenge in cancer treatment, with bone being a prevalent site. Importantly, tumours may relapse, often in the skeleton even after successful eradication of the primary tumour, indicating that tumour cells may lay dormant within bone for extended periods of time. This review summarises recent findings in the mechanisms underlying tumour cell dormancy and the role of bone cells in this process. Hematopoietic stem cell (HSC) niches in bone provide a model for understanding regulatory microenvironments. Dormant tumour cells have been shown to exploit similar niches, with evidence suggesting interactions with osteoblast-lineage cells and other stromal cells via CXCL12-CXCR4, integrins, and TAM receptor signalling, especially through GAS6-AXL, led to dormancy, with exit of dormancy potentially regulated by osteoclastic bone resorption and neuronal signalling. A comprehensive understanding of dormant tumour cell niches and their regulatory mechanisms is essential for developing targeted therapies, a critical step towards eradicating metastatic tumours and stopping disease relapse.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100621"},"PeriodicalIF":3.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001015/pdfft?md5=d6f9dc5ed2cb3efa095c8ec2ccfa9b0e&pid=1-s2.0-S2212137424001015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691648","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":"Pharmacologic Hedgehog inhibition modulates the cytokine profile of osteolytic breast cancer cells","authors":"Natalie E. Bennett ,&nbsp;Dominique V. Parker ,&nbsp;Rachel S. Mangano ,&nbsp;Jennifer E. Baum ,&nbsp;Logan A. Northcutt ,&nbsp;Jade S. Miller ,&nbsp;Erik P. Beadle ,&nbsp;Julie A. Rhoades","doi":"10.1016/j.jbo.2024.100625","DOIUrl":"10.1016/j.jbo.2024.100625","url":null,"abstract":"<div><p>The establishment and progression of bone metastatic breast cancer is supported by immunosuppressive myeloid populations that enable tumor growth by dampening the innate and adaptive immune response. Much work remains to understand how to target these tumor-myeloid interactions to improve treatment outcomes. Noncanonical Hedgehog signaling is an essential component of bone metastatic tumor progression, and prior literature suggests a potential role for Hedgehog signaling and its downstream effector Gli2 in modulating immune responses. In this work, we sought to identify if inhibition of noncanonical Hedgehog signaling alters the cytokine profile of osteolytic breast cancer cells and the subsequent communication between the tumor cells and myeloid cells. Examination of large patient databases revealed significant relationships between Gli2 expression and expression of markers of myeloid maturation and activation as well as cytokine expression. We found that treatment with HPI-1 reduced tumor cell expression of numerous cytokine genes, including <em>CSF1</em>, <em>CSF2</em>, and <em>CSF3</em>, as well as <em>CCL2</em> and <em>IL6</em>. Secreted CSF-1 (M−CSF) was also reduced by treatment. Changes in tumor-secreted factors resulted in polarization of THP-1 monocytes toward a proinflammatory phenotype, characterized by increased CD14 and CD40 surface marker expression. We therefore propose M−CSF as a novel target of Hedgehog inhibition with potential future applications in altering the immune microenvironment in addition to its known roles in reducing tumor-induced bone disease.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100625"},"PeriodicalIF":3.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001052/pdfft?md5=521b0e442616311f4a4fd691ed686f1d&pid=1-s2.0-S2212137424001052-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851035","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 measurement of lumbar pedicle screw parameters using deep learning algorithm on preoperative CT scans","authors":"Qian Zhang ,&nbsp;Fanfan Zhao ,&nbsp;Yu Zhang ,&nbsp;Man Huang ,&nbsp;Xiangyang Gong ,&nbsp;Xuefei Deng","doi":"10.1016/j.jbo.2024.100627","DOIUrl":"10.1016/j.jbo.2024.100627","url":null,"abstract":"<div><h3>Purpose</h3><p>This study aims to devise and assess an automated measurement framework for lumbar pedicle screw parameters leveraging preoperative computed tomography (CT) scans and a deep learning algorithm.</p></div><div><h3>Methods</h3><p>A deep learning model was constructed employing a dataset comprising 1410 axial preoperative CT images of lumbar pedicles sourced from 282 patients. The model was trained to predict several screw parameters, including the axial angle and width of pedicles, the length of pedicle screw paths, and the interpedicular distance. The mean values of these parameters, as determined by two radiologists and one spinal surgeon, served as the reference standard.</p></div><div><h3>Results</h3><p>The deep learning model achieved high agreement with the reference standard for the axial angle of the left pedicle (ICC = 0.92) and right pedicle (ICC = 0.93), as well as for the length of the left pedicle screw path (ICC = 0.82) and right pedicle (ICC = 0.87). Similarly, high agreement was observed for pedicle width (left ICC = 0.97, right ICC = 0.98) and interpedicular distance (ICC = 0.91). Overall, the model’s performance paralleled that of manual determination of lumbar pedicle screw parameters.</p></div><div><h3>Conclusion</h3><p>The developed deep learning-based model demonstrates proficiency in accurately identifying landmarks on preoperative CT scans and autonomously generating parameters relevant to lumbar pedicle screw placement. These findings suggest its potential to offer efficient and precise measurements for clinical applications.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100627"},"PeriodicalIF":3.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001076/pdfft?md5=56bd5c5907144c0f8db870081bf12a06&pid=1-s2.0-S2212137424001076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882539","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":"Urinary biomarkers in metastatic bone pain: Results from a multicentre randomized trial of ibandronate compared to single dose radiotherapy for localized metastatic bone pain in prostate cancer (RIB)","authors":"P.J. Hoskin ,&nbsp;Aman Malhi ,&nbsp;Krystyna Reczko ,&nbsp;Allan Hackshaw","doi":"10.1016/j.jbo.2024.100624","DOIUrl":"10.1016/j.jbo.2024.100624","url":null,"abstract":"<div><h3>Background</h3><p>The Radiotherapy IBandronate (RIB) trial compared single dose radiotherapy and a single infusion of ibandronate in 470 bisphosphonate naïve patients with metastatic bone pain from prostate cancer randomised into a non-inferiority two arm study. Results for the primary endpoint of pain score response at 4 weeks showed that the ibandronate arm was non-inferior to single dose radiotherapy.</p></div><div><h3>Patients and method</h3><p>In addition to pain assessments including analgesic use made at baseline, 4, 8, 12, 26 and 52 weeks, urine was collected at baseline, 4 and 12 weeks. It was subsequently analysed for urinary N-telopeptide (NTx) and cystatin C. Linear regression models were used to compare the continuous outcome measures for urinary markers within treatment arms and baseline measurements were included as covariates. Interaction terms were fitted to allow for cross-treatment group comparisons.</p></div><div><h3>Results</h3><p>The primary endpoint of the RIB trial was worst pain response at 4 weeks and there was no treatment difference seen. Urine samples and paired pain scores at 4 weeks were available for 273 patients (radiotherapy 168; ibandronate 159)</p><p>The baseline samples measured for the RIB trial had an average concentration of 193 nM BCE/mM creatinine (range of 7.3–1871) compared to the quoted normal range of 33 nM BCE/mM creatinine (3 to 63). In contrast the average value of Cystatin C was 66 ng/ml (ranges ND – 1120 ng/ml) compared to the quoted normal range of 62.9 ng/ml (ranges 12.6–188 ng/ml). A statistically significant reduction in NTx concentrations between baseline and 4 weeks was seen in the ibandronate arm but not in the radiotherapy arm. No correlation between pain response and urinary marker concentration was seen in either the ibandronate or radiotherapy cohort at any time point.</p></div><div><h3>Conclusion</h3><p>NTx was significantly raised compared to the normal range consistent with a role as a biomarker for bone metastases from prostate cancer. A significant reduction in NTx 4 weeks after ibandronate is consistent with its action in osteoclast inhibition which was not seen after radiotherapy implying a different mode of action for radiation. There was no correlation between bone biomarker levels and pain response.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100624"},"PeriodicalIF":3.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001040/pdfft?md5=674fc5276d011467eed3b2f1a24c1371&pid=1-s2.0-S2212137424001040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732282","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 survival and complication profiles of the Compress® Endoprosthesis: A systematic review and meta-analysis","authors":"Haolong Li ,&nbsp;Xinxin Zhang ,&nbsp;Xinyu Li ,&nbsp;Jingnan Shen ,&nbsp;Junqiang Yin ,&nbsp;Changye Zou ,&nbsp;Xianbiao Xie ,&nbsp;Gang Huang ,&nbsp;Tiao Lin","doi":"10.1016/j.jbo.2024.100623","DOIUrl":"10.1016/j.jbo.2024.100623","url":null,"abstract":"<div><h3>Background/purpose</h3><p>This study aimed to summarize the survival and complication profiles of the compress® endoprosthesis (CPS) through a systematic review and <em>meta</em>-analysis.</p></div><div><h3>Methods</h3><p>Online databases (PubMed, EMBASE and Web of Science) were searched from inception to November 2023. Trials were included that involved the use of CPS for endoprosthetic replacement in patients with massive segmental bone defects. Patients’ clinical characteristics<!--> <!-->and demographic data were extracted using a standardized form. The methodological quality of included 13 non-comparative studies was assessed on basis of the Methodological Index for Non-Randomized Studies (MINORS). All the available Kaplan-Meier curves in the included studies were digitized and combined using Engauge-Digitizer software and the R Project for Statistical Computing.</p></div><div><h3>Results</h3><p>The <em>meta</em>-analysis of thirteen included studies indicated: the all-cause failure rates of CPS were 26.3 % after surgery, in which the occurrence rates of aseptic loosening were 5.8 %. And the incidences of other complications were as follows: soft tissue failure (1.8 %), structure failure (8.2 %), infection (9.5 %), tumor progression (1.1 %). The 1-, 4-, and 8-year overall survival rates for all-cause failure with 95 % CI were 89 % (86 %-92 %), 75 % (71 %-79 %) and 65 % (60 %-70 %), respectively. The estimated mean survival time of all-cause failure was 145 months (95 % CI, 127–148 months), and the estimated median survival time of all-cause failure was 187 months (95 % CI, 135–198 months). The 1-, 4-, and 8-year overall survival rates of aseptic loosening with 95 % CI were 96 % (94 %-98 %), 91 % (87 %-95 %) and 88 % (83 %-93 %), respectively. The estimated mean survival time of aseptic loosening was 148 months (95 % CI, 137–153 months).</p></div><div><h3>Conclusion</h3><p>CPS’s innovative spring system promotes bone ingrowth by providing immediate and high-compression fixation, thereby reducing the risk of aseptic loosening caused by stress shielding and particle-induced osteolysis. CPS requires less residual bone mass for reconstructing massive segmental bone defects and facilitates easier revision due to its non-cemented fixation. In addition, the survival rate, estimated mean survival time, and complication rates of CPS are not inferior to those of common endoprosthesis.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100623"},"PeriodicalIF":3.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001039/pdfft?md5=fa1880d0e1d725a2c57807cb9e74369c&pid=1-s2.0-S2212137424001039-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711140","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":"Comprehensive diagnostic model for osteosarcoma classification using CT imaging features","authors":"Yiran Wang ,&nbsp;Zhixiang Wang ,&nbsp;Bin Zhang ,&nbsp;Fan Yang","doi":"10.1016/j.jbo.2024.100622","DOIUrl":"https://doi.org/10.1016/j.jbo.2024.100622","url":null,"abstract":"<div><h3>Objective</h3><p>The main objective of this study was to create and assess a detailed diagnostic model with an optimizing feature selection algorithm that combines computed tomography (CT) imaging characteristics, demographic information, and genetic markers to enhance the accuracy of benign and malignant classification of osteosarcoma. This research seeks to enhance the early identification and categorization of benign and malignant of osteosarcoma, ultimately enabling more personalized and efficient treatment approaches.</p></div><div><h3>Methods</h3><p>Data from 225 patients diagnosed with osteosarcoma at two different medical institutions between June 2018 and June 2021 were gathered for this research study. A novel feature selection approach that combined Principal Component Analysis (PCA) with Improved Particle Swarm Optimization (IPSO) was utilized to analyze 1743 image-derived features. The performance of the resulting model was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), and compared to models developed using conventional feature selection methods.</p></div><div><h3>Results</h3><p>The proposed model showed promising predictive performance with an AUC of 0.87, accuracy of 0.80, sensitivity of 0.75, and specificity of 0.85. These results suggest improved predictive ability compared to models built using traditional feature selection techniques, particularly in terms of accuracy and specificity. However, there is room for improvement in enhancing sensitivity.</p></div><div><h3>Conclusion</h3><p>Our study introduces a novel predictive model for distinguishing between benign and malignant osteosarcoma., emphasizing its potential significance in clinical practice. Through the utilization of CT imaging features, our model shows improved accuracy and specificity, marking progress in the early detection and classification of osteosarcoma as either benign or malignant. Future investigations will concentrate on enhancing the model’s sensitivity and validating its effectiveness on a larger dataset, aiming to boost its clinical relevance and support personalized treatment approaches for osteosarcoma.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100622"},"PeriodicalIF":3.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001027/pdfft?md5=e6268d6b2ac90627843a1516f401e11a&pid=1-s2.0-S2212137424001027-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607329","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":"Real-world experiences in patients with Ewing sarcoma treated at a specialist centre in Turkey","authors":"Selma Çakmakcı ,&nbsp;Neriman Sarı ,&nbsp;Ebru Atasever Akkaş ,&nbsp;Fatih Yıldız ,&nbsp;Ebru Karakaya ,&nbsp;Bektaş Kaya ,&nbsp;Bedii Şafak Güngör ,&nbsp;Ömür Berna Çakmak Öksüzoğlu ,&nbsp;İnci Ergürhan İlhan","doi":"10.1016/j.jbo.2024.100619","DOIUrl":"https://doi.org/10.1016/j.jbo.2024.100619","url":null,"abstract":"<div><h3>Objectives</h3><p>The present study evaluates the clinical outcomes of children, adolescents and adults with Ewing sarcoma and identifies the prognostic factors.</p></div><div><h3>Methods</h3><p>Included in the study were 222 pediatric and adult patients diagnosed with Ewing sarcoma (EwS) who were followed up between 1992 and 2019, and whose data were analyzed retrospectively.</p></div><div><h3>Results</h3><p>The median age of 131 male and 91 female patients included in the study was 13 (1–64). The median follow-up duration of the survivors was 79 months (range, 11–182 months). The 3-year EFS rate of the 222 patients was 34 % (Confidence Interval (CI) (0.158–0.242 %) and the OS rate was 54 % (CI, 0.289–0.590 %). For the non-metastatic patients, the 3-year EFS rate was 47 % and the OS was 68 %, while for the metastatic patients the 3-year EFS rate was 13 % and the OS was 30 %. Of the patient sample, 81 (36, 5 %) survived, of whom 72 were continuously free of disease while the disease persisted in nine, and three developed a secondary neoplasm (2 of whom subsequently died while one survived disease-free). Of the 129 patients who relapsed with metastases and/or local recurrence, eight survived and are disease-free, nine are alive with uncontrolled disease; five were lost to follow-up and 107 died.</p></div><div><h3>Conclusion</h3><p>The findings of the present study suggest metastatic disease at presentation and positive margins after surgery to be of prognostic significance in EwS. Disruptions in aggressive local treatments may reduce the chances of cure in EwS.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100619"},"PeriodicalIF":3.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221213742400099X/pdfft?md5=483985fe57369390067afb74737965d1&pid=1-s2.0-S221213742400099X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607330","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":"Senescence in the bone marrow microenvironment: A driver in development of therapy-related myeloid neoplasms","authors":"Angelo Jose Guilatco ,&nbsp;Mithun Vinod Shah ,&nbsp;Megan Moore Weivoda","doi":"10.1016/j.jbo.2024.100620","DOIUrl":"https://doi.org/10.1016/j.jbo.2024.100620","url":null,"abstract":"<div><p>Therapy-related myeloid neoplasms (t-MN) are a growing concern due to the continued use of cytotoxic therapies to treat malignancies. Cytotoxic therapies have been shown to drive therapy-induced senescence in normal tissues, including in the bone marrow microenvironment (BMME), which plays a crucial role in supporting normal hematopoiesis. This review examines recent work that focuses on the contribution of BMME senescence to t-MN pathogenesis, as well as offers a perspective on potential opportunities for therapeutic intervention.</p></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"47 ","pages":"Article 100620"},"PeriodicalIF":3.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212137424001003/pdfft?md5=9b286f878ffa9d784caa6b6c9fac5f32&pid=1-s2.0-S2212137424001003-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593628","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}