Journal of Bone Oncology最新文献

{"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}

{"title":"Auxiliary diagnosis of primary bone tumors based on Machine learning model","authors":"Sandong Deng ,&nbsp;Yugang Huang ,&nbsp;Cong Li ,&nbsp;Jun Qian ,&nbsp;Xiangdong Wang","doi":"10.1016/j.jbo.2024.100648","DOIUrl":"10.1016/j.jbo.2024.100648","url":null,"abstract":"<div><h3>Objective</h3><div>Research on auxiliary diagnosis of primary bone tumors can enhance diagnostic accuracy, facilitate early detection, and enable personalized treatment, thereby reducing misdiagnosis and missed cases, ultimately leading to improved patient prognosis and survival rates. In this study, we established a whole slide imaging (WSI) database comprising histopathological samples from all categories of bone tumors and integrated multiple neural network architectures for machine learning models. We then evaluated the accuracy of these models in diagnosing primary bone tumors.</div></div><div><h3>Methods</h3><div>In this paper, the machine learning model based on the deep convolutional neural network (DC-NN) method was combined with imaging omics analysis to analyze and discuss its clinical value in diagnosing primary bone tumors. In addition, this paper proposed a screening method for differentially expressed genes. Based on the paired T-test method, the process first estimated the tumor purity in the experimental data of each sample case, then assessed the actual gene expression value of the experimental data of each sample case, and finally calculated the optimized paired T-test statistics, and screened differentially expressed genes according to the threshold value.</div></div><div><h3>Results</h3><div>The selected model demonstrated excellent diagnostic accuracy in distinguishing between normal and tumor images, with overall accuracy of (99.8 ± 0.4) % for five rounds of testing using the DCNN model and positive and negative predictive values of (100.0 ± 0.0) % and (99.6 ± 0.8) %, respectively. The mean area under each dataset’s curve (AUC) was (0.998 ± 0.004). Further, ten rounds of testing using the DCNN model showed an overall accuracy of (71.2 ± 1.6) % and a substantial positive predictive value of (91.9 ± 8.5) % in distinguishing benign from malignant bone tumors, with an average AUC of (0.62 ± 0.06) across datasets.</div></div><div><h3>Conclusion</h3><div>The deep learning model accurately classifies bone tumor histopathology based on the degree of infiltration, achieving diagnostic performance comparable to that of senior pathologists. These findings affirm the feasibility and effectiveness of histopathological diagnosis in bone tumors, providing a theoretical foundation for the application and advancement of machine learning-assisted histopathological diagnosis in this field.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100648"},"PeriodicalIF":3.4,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer","authors":"Taisheng Zeng ,&nbsp;Yuguang Ye ,&nbsp;Yusi Chen ,&nbsp;Daxin Zhu ,&nbsp;Yifeng Huang ,&nbsp;Ying Huang ,&nbsp;Yijie Chen ,&nbsp;Jianshe Shi ,&nbsp;Bijiao Ding ,&nbsp;Jianlong Huang ,&nbsp;Mengde Ling","doi":"10.1016/j.jbo.2024.100645","DOIUrl":"10.1016/j.jbo.2024.100645","url":null,"abstract":"<div><h3>Background</h3><div>Due to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively.</div></div><div><h3>Method</h3><div>The proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images.</div></div><div><h3>Results</h3><div>The study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably.</div></div><div><h3>Conclusions</h3><div>This innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100645"},"PeriodicalIF":3.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3","authors":"Qian Liu ,&nbsp;Xing She ,&nbsp;Qian Xia","doi":"10.1016/j.jbo.2024.100644","DOIUrl":"10.1016/j.jbo.2024.100644","url":null,"abstract":"<div><h3>Objective</h3><div>The incidence of osteosarcoma (OS) is low, but primary malignant bone tumors rank third among the causes of death in cancer patients under the age of 20. Currently, analysis of cellular structure and tumor morphology through microscopic images remains one of the main diagnostic methods for osteosarcoma. However, this completely manual approach is tedious, time-consuming, and difficult to diagnose accurately due to the similarities in certain characteristics of malignant and benign tumors.</div></div><div><h3>Methods</h3><div>Leveraging the potential of artificial intelligence (AI) in assessing and classifying images, this study explored a modified CA-MobileNet V3 model that was embedded into innovative microscope products to enhance the microscope’s feature extraction capabilities and help reduce misclassification during diagnosis.</div></div><div><h3>Results</h3><div>The intelligent recognition model method introduced in this paper has significant advantages in retrieval and classification of osteosarcoma cells and other cell types. Compared with models such as ShuffleNet V2, EfficientNet V2, Mobilenet V3 (without transfer learning), TL-MobileNet V3 (with transfer learning), etc., the model size is only 5.33 MB, is a lightweight model, and the accuracy of the improved model reached 98.69 %. In addition, the artificial intelligence microscope (AIM) with integrated design based on this model can also help improve diagnostic efficiency.</div></div><div><h3>Conclusion</h3><div>The innovative method of the CA-MobileNet V3 automatic classification model based on deep learning provides an efficient and reliable solution for the pathological diagnosis of osteosarcoma. This study contributes to medical image analysis and provides doctors with an accurate and valuable tool for microscopic diagnosis. It also promotes the advancement of artificial intelligence in medical imaging technology.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100644"},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656666","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":"Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography","authors":"Huan Xu ,&nbsp;Qunfang Zhao ,&nbsp;Xiaoyan Miao ,&nbsp;Lijun Zhu ,&nbsp;Junping Wang","doi":"10.1016/j.jbo.2024.100646","DOIUrl":"10.1016/j.jbo.2024.100646","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to evaluate the role of computed tomography (CT) imaging in the diagnosis and management of bone cancer during periods of limited access to histopathological testing. We aimed to determine the correlation between CT severity levels and subsequent patient management and care decisions, adhering to established oncological CT reporting guidelines.</div></div><div><h3>Methodology</h3><div>A retrospective analysis was conducted on 60 symptomatic patients from January 2021 to January 2024. The cohort included patients aged between 50 and 86 years, with a mean age of 68 years, and 75 % were male. All patients had their bone cancer diagnosis confirmed through histopathological examination, and CT imaging was used as the reference method. The analysis involved assessing the correlation between CT severity scores and patient management, including ICU admissions.</div></div><div><h3>Results</h3><div>The study found that CT imaging demonstrated a sensitivity of 92.6% in diagnosing bone cancer, with accuracy increasing to 97.6% in cases with high-probability CT characteristics. CT specificity also showed a consistent rise. Osteolytic lesions were the predominant finding, detected in 85.9% of cases. Among these, 88% exhibited engagement across multiple skeletal regions, 92.8% showed bilateral distribution, and 92.8% presented with peripheral involvement. In ICU patients, bone consolidation was observed in 81.5% of cases and was predominant in 66.7% of the ICU cohort. Additionally, ICU patients had significantly higher CT severity scores, with scores exceeding 14 being notably prevalent.</div></div><div><h3>Conclusions</h3><div>During the management period of bone cancer at our hospital, characteristic features on CT imaging facilitated swift and sensitive investigation. Two distinct CT phenotypes, associated with the primary osteolytic phenotype and severity score, emerged as valuable indicators for assessing the severity of the disease, particularly during ICU care. These findings highlight the diverse manifestations and severity levels encountered in bone cancer patients and underscore the importance of CT imaging in their diagnosis and management.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100646"},"PeriodicalIF":3.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel adjunctive diagnostic method for bone cancer: Osteosarcoma cell segmentation based on Twin Swin Transformer with multi-scale feature fusion","authors":"Tingxi Wen,&nbsp;Binbin Tong,&nbsp;Yuqing Fu,&nbsp;Yunfeng Li,&nbsp;Mengde Ling,&nbsp;Xinwen Chen","doi":"10.1016/j.jbo.2024.100647","DOIUrl":"10.1016/j.jbo.2024.100647","url":null,"abstract":"<div><h3>Background</h3><div>Osteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma.</div></div><div><h3>Methods</h3><div>In this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion.</div></div><div><h3>Results</h3><div>The experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings.</div></div><div><h3>Conclusion</h3><div>The development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100647"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progression of vertebral fractures in metastatic melanoma and non-small cell lung cancer patients given immune checkpoint inhibitors","authors":"Marco Meazza Prina ,&nbsp;Andrea Alberti ,&nbsp;Valeria Tovazzi ,&nbsp;Marco Ravanelli ,&nbsp;Greta Schivardi ,&nbsp;Alice Baggi ,&nbsp;Luca Ammoni ,&nbsp;Lucilla Guarneri ,&nbsp;Francesca Salvotti ,&nbsp;Manuel Zamparini ,&nbsp;Davide Farina ,&nbsp;Margherita Parolise ,&nbsp;Salvatore Grisanti ,&nbsp;Alfredo Berruti","doi":"10.1016/j.jbo.2024.100642","DOIUrl":"10.1016/j.jbo.2024.100642","url":null,"abstract":"<div><h3>Introduction</h3><div>The immune system mediates important effects on bone metabolism, but little has been done to understand immunotherapy’s role in this interaction. This study aims to describe and identify risk factors for the occurrence and/or exacerbation of vertebral fractures (vertebral fracture progression) during immune checkpoint inhibitors (ICIs).</div></div><div><h3>Methods</h3><div>We conducted an observational, retrospective, monocentric study. We collected data on melanoma and NSCLC patients, treated with first-line ICIs at the Medical Oncology Department ASST Spedali Civili of Brescia, between January 2015 and November 2021, and with a median follow-up of 20.1 (6–36) months. We collected data on patients, diseases, immune-related adverse events, and cortico-steroid therapy initiated on concomitant ICIs.</div></div><div><h3>Results</h3><div>We identified 135 patients, 65 (48.2 %) with locally advanced/metastatic melanoma and 70 (51.8 %) with locally advanced/metastatic non-small cell lung cancer (NSCLC). Twenty-one (15.6 %) patients already had an asymptomatic vertebral fracture at baseline before starting ICIs in monotherapy. A total of ten patients, or 7.4 %, had a vertebra fracture progression defined as a new vertebral fracture or a worsening of a previous fracture. There was a strong relation between the steroid therapy and irAEs with vertebra fracture progression [OR (95 % CI) 8.1 (3.7–17.8) p-value &lt; 0.001] in univariable analysis. However, only steroid therapy resulted to be an independent risk factor [8.260 (95 % CI 0.909–75.095); p-value 0.061] at the multivariable analysis.</div></div><div><h3>Conclusion</h3><div>Concurrent steroid therapy in patients receiving immunotherapy exposes them to a high risk of fractures due to skeletal fragility. The use of bone resorption inhibitors should be considered in these patients to prevent these adverse events.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"49 ","pages":"Article 100642"},"PeriodicalIF":3.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}