Journal of Bone Oncology最新文献

{"title":"Soft tissue recurrence in giant cell tumor of Bone: A comprehensive review of pathogenesis, imaging features, and clinical management","authors":"Khodamorad Jamshidi ,&nbsp;Hamed Naghizadeh ,&nbsp;Sadegh Saberi ,&nbsp;Farshad Zand Rahimi ,&nbsp;Aidin Arabzadeh ,&nbsp;Seyyed Saeed Khabiri","doi":"10.1016/j.jbo.2025.100725","DOIUrl":"10.1016/j.jbo.2025.100725","url":null,"abstract":"<div><h3>Background</h3><div>Giant cell tumor of bone (GCTB) is a benign but locally aggressive neoplasm with a high risk of recurrence. Among its patterns of relapse, soft-tissue recurrence (STR) is an uncommon but clinically significant entity, often presenting as ossified or non-ossified perilesional nodules. Despite its rarity, STR poses diagnostic and therapeutic challenges that require clarification.</div></div><div><h3>Methods</h3><div>A comprehensive literature review was performed across PubMed, Embase, and Google Scholar from 1980 through January 2025, focusing on the epidemiology, pathogenesis, imaging features, histopathology, management, and outcomes of STR in GCTB. Case reports, series, and retrospective studies explicitly distinguishing STR from intraosseous recurrence were included, and findings were synthesized narratively.</div></div><div><h3>Results</h3><div>STR occurs in approximately 2–3 % of GCTB cases, typically within 6–12 months after surgery. Major risk factors include curettage procedures, pathological fractures, cortical breaches, and unrecognized microscopic soft-tissue extension. Imaging reveals three distinct patterns: peripheral “eggshell” ossification, central nodular calcification, and purely soft-tissue lesions. Histology mirrors primary GCTB, often with osteogenic metaplasia, while molecular testing confirms retention of H3F3A mutations. Surgical excision with clear margins remains the mainstay of treatment, yielding excellent functional outcomes. However, up to 60 % of patients experience multiple recurrences, highlighting the need for vigilant surveillance. Systemic agents such as denosumab or bisphosphonates remain investigational, and radiotherapy is generally contraindicated due to malignant transformation risk.</div></div><div><h3>Conclusion</h3><div>STR represents a rare but distinct subset of GCTB recurrences. Awareness of risk factors, early imaging-based detection, and complete surgical excision are critical for optimal outcomes. Further multicenter studies are required to define surveillance protocols, validate molecular predictors, and clarify the role of systemic therapy in this challenging condition.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100725"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"hMSCs-derived exosomal MIR17HG promotes follicular helper T cell differentiation and osteosarcoma progression via the miR-372-3p/BCL6 axis","authors":"Jin Qi ,&nbsp;Gang Xue ,&nbsp;Baomin Wu ,&nbsp;Peng Zhu ,&nbsp;Yapeng Wang","doi":"10.1016/j.jbo.2025.100726","DOIUrl":"10.1016/j.jbo.2025.100726","url":null,"abstract":"<div><h3>Background</h3><div>Osteosarcoma (OS) is a life-threatening malignancy in children and adolescents, with limited treatment options for resistant or metastatic disease. Exosomes derived from hMSCs regulate tumor immunity by transporting molecules such as long non-coding RNA (lncRNA). The lncRNA MIR17HG is known to promote OS progression, yet its role in regulating T follicular helper (Tfh) cell differentiation in OS is unclear. This study is the first to systematically explore how MIR17HG influences Tfh cell differentiation, activation, and OS cell proliferation via the miR-372-3p/BCL6 signaling cascade.</div></div><div><h3>Methods</h3><div>Exosomes were extracted from hMSCs using differential centrifugation. Characterization of hMSCs-derived exosomes was conducted by TEM, NTA and western blotting. The expression of MIR17HG, BCL6 and PD-1 was determined by qRT‒PCR or western blotting. The differentiation and activation of Tfh cells, as well as OS apoptosis, were assessed through flow cytometry. OS cell viability was determined by a CCK-8 assay. The effect of hMSCs-Exo-MIR17HG on tumors was assessed in an MG63-derived xenograft mouse model. The expression of Ki67 was examined via IHC. RNA immunoprecipitation was performed to validate the interaction between MIR17HG and miR-372-3p. A dual-luciferase reporter assay was performed to explore the correlation between miR-372-3p and BCL6.</div></div><div><h3>Results</h3><div>The lncRNA MIR17HG was expressed in hMSCs-derived exosomes and upregulated by overexpression. hMSCs-Exo or hMSCs-Exo-MIR17HG promoted the differentiation and activation of Tfh cells, accompanied by increased PD-1 and BCL6 expression in CD4⁺ T cells, which enhanced OS cell proliferation. Furthermore, hMSCs-Exo-MIR17HG exerted a tumor-promoting effect in a CDX mouse model. Mechanistically, the effects driven by MIR17HG were abolished by miR-372-3p overexpression, and BCL6 was identified as a direct functional target of miR-372-3p.</div></div><div><h3>Conclusion</h3><div>These findings demonstrate that exosomal MIR17HG derived from hMSCs drives the differentiation of follicular helper T cells and the progression of OS via the miR-372-3p/BCL6 axis.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100726"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers","authors":"Hang Sang ,&nbsp;Tao Lin ,&nbsp;Lincong Luo ,&nbsp;Mingrui Liu ,&nbsp;Jiaying Li ,&nbsp;Xiang Luo ,&nbsp;Jianlin Shen ,&nbsp;Shizhen Zhong ,&nbsp;Lin Xu ,&nbsp;Wenhua Huang","doi":"10.1016/j.jbo.2025.100718","DOIUrl":"10.1016/j.jbo.2025.100718","url":null,"abstract":"<div><div>Accurate classification of bone tumors as benign, malignant, or intermediate is crucial for patient treatment decisions. Misclassification may result in overtreatment of benign cases or delayed intervention for aggressive tumors, significantly impacting patient prognosis. However, current methods rely heavily on single-modality imaging analysis, making it difficult to handle variable lesion locations and complex cancer types. To address these limitations, we propose a novel multimodal deep learning framework that integrates clinical images, pathological slices, and blood biomarkers for automated bone tumor detection and three-class classification. The framework operates in two stages: first, a YOLOv5-based detection model localizes tumor regions on clinical images. Next, a classification model utilizes ResNet to extract deep features from both the clinical images and pathological slices, while abnormal blood biomarkers are transformed into descriptive text by a large language model and subsequently encoded into semantic features using BioBERT. Finally, features from all three modalities are integrated via a fusion module to capture complementary information and enable accurate tumor classification. The evaluation was performed using two distinct datasets: a clinical imaging dataset for bone tumor detection, and a separate multi-modal cohort comprising clinical imaging, pathology, and blood biomarkers for tumor classification. The detection model demonstrated strong localization capabilities, achieving a test [email protected] of 0.7925. For the classification task, ablation studies validated the complementary contribution of each modality. Notably, our multimodal fusion approach outperformed unimodal baselines, attaining a macro-average precision of 0.9056, F1-score of 0.8736, and AUC of 0.9759 in tumor classification—outperforming existing models.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100718"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benign and malignant bone lesion diagnosis based on self-supervised and radiomics fusion using SPECT/CT images","authors":"Weiming Xie ,&nbsp;Xiaozhou Bai ,&nbsp;Miao Liu ,&nbsp;Haonan Shangguan ,&nbsp;Ying Zhan ,&nbsp;Xiaodan Wu ,&nbsp;Yingxin Dai ,&nbsp;Yusong Pei ,&nbsp;Guoxu Zhang ,&nbsp;Zhiguo Wang ,&nbsp;Zhaomin Yao","doi":"10.1016/j.jbo.2025.100723","DOIUrl":"10.1016/j.jbo.2025.100723","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to improve the diagnostic accuracy of SPECT/CT imaging in distinguishing benign from malignant bone lesions by integrating self-supervised deep learning and radiomics, reducing subjectivity in traditional image interpretation for more reliable clinical decision-making.</div></div><div><h3>Methods</h3><div>We developed a multi-scale, multi-modal framework combining radiomics with self-supervised learning. The novel SPECT-guided model, SPARC-Net, uses functional SPECT data as semantic priors to extract discriminative features from CT scans without manual annotations. Deep features from SPARC-Net were fused with radiomics to form a unified representation. The model was trained and validated on 741 confirmed bone lesion cases using five-fold cross-validation, with interpretability assessed via Grad-CAM.</div></div><div><h3>Results</h3><div>The fused model achieved 82.3 % accuracy, 0.890 AUC, 72.3 % F1 score, 79.3 % precision, 66.6 % sensitivity, and 90.7 % specificity, outperforming single-modality models. Grad-CAM confirmed the model focused on metabolically active regions identified by SPECT.</div></div><div><h3>Conclusion</h3><div>SPARC-Net, integrating SPECT-guided self-supervised learning with CT-based radiomics, improves classification of benign and malignant lesions, enhancing the accuracy, robustness, and interpretability of SPECT/CT imaging for bone tumor diagnosis.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100723"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bone disease burden does not impact overall survival in newly diagnosed patients with multiple myeloma − a single center, retrospective imaging analysis on 119 patients","authors":"Evangelos Terpos ,&nbsp;Vassilis Koutoulidis ,&nbsp;Ioannis Ntanasis-Stathopoulos ,&nbsp;Stylianos Mavropoulos-Papoudas ,&nbsp;Maria Douka ,&nbsp;Maria Gavriatopoulou ,&nbsp;Panagiotis Malandrakis ,&nbsp;Vasiliki Spiliopoulou ,&nbsp;Foteini Theodorakakou ,&nbsp;Despina Fotiou ,&nbsp;Magdalini Migkou ,&nbsp;Nikolaos Kanellias ,&nbsp;Evangelos Eleutherakis-Papaiakovou ,&nbsp;Efstathios Kastritis ,&nbsp;Lia-Angela Moulopoulos ,&nbsp;Meletios A Dimopoulos","doi":"10.1016/j.jbo.2025.100720","DOIUrl":"10.1016/j.jbo.2025.100720","url":null,"abstract":"<div><h3>Background</h3><div>Multiple myeloma (MM) frequently presents with myeloma bone disease (MBD), manifesting as osteolytic lesions and skeletal-related events (SREs), significantly impairing quality of life and increasing morbidity. Whole-body low-dose computed tomography (WBLDCT) has become the standard for assessing bone involvement at diagnosis, but its prognostic significance remains unclear. The aim of this study was to evaluate the burden of MBD in newly diagnosed MM patients using WBLDCT and examined associations between imaging characteristics and survival outcomes.</div></div><div><h3>Methods</h3><div>In this retrospective, single center, analysis of 119 MM patients, WBLDCT was performed at diagnosis prior to treatment initiation. Imaging findings, including vertebral compression fractures (VCFs), lesion number, cortical destruction, and appendicular skeleton medullary cavity (ASMC) patterns, were recorded. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier curves and Cox regression models.</div></div><div><h3>Results</h3><div>VCFs were significantly associated with inferior PFS (18.1 vs. 33.6 months; p = 0.013) and OS (51.5 months vs. not reached; p = 0.023) in univariate analyses. However, in multivariable models, no imaging parameter, including VCFs, retained independent prognostic significance. Other imaging variables (lesion count, ASMC subtype, cortical destruction) were not predictive of outcomes.</div></div><div><h3>Conclusions</h3><div>While VCFs identified on WBLDCT correlate with poor outcomes in univariate analysis, they do not serve as independent prognostic markers when adjusting for established clinical factors. These findings suggest that in the era of novel anti-myeloma therapeutics, the bone disease burden at diagnosis may not impact prognosis significantly.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100720"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment and comprehensive characterization of a subline with highly bone-metastatic propensity derived from the lung adenocarcinoma A549 cell line","authors":"Yujian Xu ,&nbsp;Yahan Qin ,&nbsp;Wenjun Chai ,&nbsp;Ke Xue ,&nbsp;Xiaoli Liu ,&nbsp;Jing Li ,&nbsp;Yue Cao ,&nbsp;Lei Sun ,&nbsp;Hongyu Pan ,&nbsp;Mingxia Yan","doi":"10.1016/j.jbo.2025.100719","DOIUrl":"10.1016/j.jbo.2025.100719","url":null,"abstract":"<div><div>Bone metastasis is a major cause of mortality in lung adenocarcinoma, but the mechanisms remain poorly understood. Here, we established a highly bone-metastatic subline, A549-BM5, from the A549 cell line through five rounds of <em>in vivo</em> selection. A549-BM5 cells exhibited enhanced migration and invasion, and preferentially colonized specific skeletal sites in mouse models. In the bone microenvironment, they promoted the recruitment and differentiation of osteoblasts and osteoclasts, disrupting bone homeostasis. Transcriptomic and proteomic profiling revealed dysregulation in pathways such as EMT, adhesion, and bone morphogenesis. We further applied a random forest model to identify potential therapeutic targets associated with bone metastasis. Compared to existing A549-based models, A549-BM5 offers earlier metastasis onset, stable bone tropism, and broader interaction with the bone niche. This model provides a valuable platform for mechanistic studies and therapeutic development targeting lung cancer bone metastasis.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100719"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malignant and metastatic giant cell tumors of bone; clinical course of primary or secondary malignant and pulmonary metastatic variants","authors":"Floortje G.M. Verspoor ,&nbsp;Gitte G.J. Krebbekx ,&nbsp;Mylene J.C. Duivenvoorden ,&nbsp;Vaiyapuri Sumathi ,&nbsp;Scott Evans","doi":"10.1016/j.jbo.2025.100728","DOIUrl":"10.1016/j.jbo.2025.100728","url":null,"abstract":"<div><h3>Aims</h3><div>Giant cell tumor of bone (GCTB) is a rare, locally aggressive, intermediate-grade neoplasm. While typically benign, GCTB can exhibit atypical behavior, including indolent pulmonary metastases and malignant transformation. This study characterizes malignant and metastatic GCTB, distinguishing primary malignant GCTB (PM-GCTB), secondary malignant GCTB (SM-GCTB), and secondary osteosarcoma, and evaluates their outcomes.</div></div><div><h3>Methods</h3><div>A retrospectively review of 520 patients with suspected GCTB treated between 1985–2021 was performed, with malignant cases followed through 2025. Diagnosis was confirmed histologically and, where available, by H3F3A/H3F3B mutation testing. PM-GCTB was defined as a de novo high-grade sarcoma. SM-GCTB and secondary osteosarcoma were defined as malignant transformations of previously benign, histologically proven GCTB. Benign pulmonary metastases were analyzed separately. Outcomes were descriptively assessed.</div></div><div><h3>Results</h3><div>Twelve patients (2.4 %) had malignant GCTB: five PM-GCTB (1.0 %), five SM-GCTB (1.0 %), and two secondary osteosarcoma (0.4 %). Three patients (0.6 %) developed histologically benign pulmonary metastases with indolent behavior. Three SM-GCTB patients transformed within one year despite benign baseline pathology, indicating true rapid malignant progression rather than diagnostic error. Median latency to malignant transformation was 7.5 years for SM-GCTB and up to 35 years for secondary osteosarcoma. SM-GCTB showed the highest disease-specific mortality (80 %), compared to PM-GCTB (20 %) and secondary osteosarcoma (0 %). None malignant transformations occurred following denosumab or radiotherapy.</div></div><div><h3>Conclusion</h3><div>Three aggressive GCTB variants were identified: benign pulmonary metastatic GCTB, PM-GCTB, and secondary malignant transformation. SM-GCTB and osteosarcoma arise from benign GCTB but differ in morphology and prognosis. Because malignant transformation is exceptionally rare and symptomatic, a patient-centered, symptom-driven follow-up strategy is preferred over routine lifelong radiologic surveillance.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100728"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the mechanism of osteosarcoma induced by long-term PET exposure: prediction from combined network toxicology, machine learning and molecular docking","authors":"Yu Qiao ,&nbsp;Fahu Yuan ,&nbsp;Anna Curto-Vilalta ,&nbsp;Rüdiger von Eisenhart-Rothe ,&nbsp;Florian Hinterwimmer","doi":"10.1016/j.jbo.2025.100714","DOIUrl":"10.1016/j.jbo.2025.100714","url":null,"abstract":"<div><h3>Objective</h3><div>Polyethylene terephthalate (PET) has emerged as a focal point in addressing global pollution and a critical environmental issue due to its potential health hazards. However, its role in the pathogenesis of osteosarcoma (OS) and the underlying molecular mechanisms remain largely unexplored, further highlighting the necessity of assessing its molecular toxicity.</div></div><div><h3>Methods</h3><div>This study integrated network toxicology, machine learning, molecular docking, and CIBERSORT-based immune infiltration analysis to systematically investigate the potential impact of PET exposure on contracting OS, elucidating its biological functions, signaling mechanisms, and immune microenvironment. Molecular docking was further applied to characterize the binding properties of PET with hub proteins, and potential therapeutic agents for OS were predicted.</div></div><div><h3>Results</h3><div>We identified 12 potential key targets of OS associated with PET exposure and, through machine learning models, selected six hub genes (i.e., BCAT1, CDK4, CSF1R, CXCR4, MYB, and PRTN3). Gene Ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were conducted to elucidate the roles of these genes in biological processes, cellular components, molecular functions, and signaling pathways. Molecular docking results revealed that PET exhibits high specificity in binding to these hub genes, particularly by interacting with CSF1R (−8.312 kcal/mol), potentially activating the PI3K-Akt signaling pathway and modulating the OS immune microenvironment to promote tumor progression through multiple mechanisms. Furthermore, drug prediction analysis identified <em>p</em>-Benzoquinone and JNK-9L as potential therapeutic candidates for OS.</div></div><div><h3>Conclusion</h3><div>This study reveals that PET may play a critical role in OS development by regulating hub genes and signaling pathways. Molecular docking analysis demonstrates that PET can tightly bind to specific target proteins, suggesting a potential molecular mechanism underlying OS progression. These findings provide a scientific basis for further evaluating PET-related health risks and offer theoretical support for the development of future prevention and treatment strategies.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100714"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of prognostic models for bone metastasis in Non-Small cell lung cancer based on Machine learning algorithms","authors":"Jiabin Fang ,&nbsp;Xiaojie Yang ,&nbsp;Lingfeng Chen ,&nbsp;Liuying Hong ,&nbsp;Yingqiu He ,&nbsp;Ji Huang ,&nbsp;Jie Lin ,&nbsp;Nengluan Xu ,&nbsp;Hongru Li","doi":"10.1016/j.jbo.2025.100716","DOIUrl":"10.1016/j.jbo.2025.100716","url":null,"abstract":"<div><h3>Background</h3><div>Bone is a common site of metastasis in non-small cell lung cancer (NSCLC), yet no validated prognostic model is currently available for patients presenting with bone metastases at diagnosis.</div></div><div><h3>Methods</h3><div>We retrospectively reviewed 1,299 NSCLC patients who underwent high-throughput sequencing between 2016 and 2023. Of these, 195 were diagnosed with bone metastases at presentation. Three machine learning algorithms were applied to identify prognostic variables. A nomogram constructed with Cox regression was used to predict overall survival (OS) and was internally validated with 1,000 bootstrap resamples.</div></div><div><h3>Results</h3><div>Four independent prognostic factors were identified, including age, serum calcium, monocyte-to-albumin ratio, and prognostic nutritional index. The nomogram demonstrated strong predictive performance, with areas under the curve (AUCs) of 86.53%, 78.32%, and 77.85% for 6-month, 1-year, and 2-year OS, respectively. Calibration plots showed excellent agreement between predicted and observed survival outcomes.</div></div><div><h3>Conclusion</h3><div>This validated nomogram provides a practical and individualized tool for predicting survival in NSCLC patients with bone metastases at diagnosis, supporting risk stratification and clinical practice.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100716"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified OPTIModel with oligometastatic disease for the prediction of overall survival of patients with renal cell cancer and symptomatic long bone metastases","authors":"E.W. Dootjes ,&nbsp;J.J. Willeumier ,&nbsp;C.W.P.G. van der Wal ,&nbsp;R.J.P. van der Wal ,&nbsp;P. van der Zwaal ,&nbsp;A. Leithner ,&nbsp;A.A.M. van der Veldt ,&nbsp;M. Fiocco ,&nbsp;D.L.M. van Broekhoven ,&nbsp;Y.M. van der Linden","doi":"10.1016/j.jbo.2025.100709","DOIUrl":"10.1016/j.jbo.2025.100709","url":null,"abstract":"<div><h3>Aims</h3><div>For patients with long bone metastasis (LBM), we have previously developed OPTIModel. In this study, we investigated whether the OPTIModel could be improved for patients with metastatic renal cell cancer (mRCC) by including oligometastatic bone metastases (OBM) as a risk factor.</div></div><div><h3>Methods</h3><div>Patients with mRCC and symptomatic LBMs were included in a retrospective and prospective multicenter cohort. Bone metastases (BMs) were categorized as: solitary (SBM), limited BMs (2–4 BMs) or diffuse BMs (DBM; &gt;4 BMs). OBM were defined as ≤ 4 BMs. Overall survival was estimated using Kaplan Meier method. Effect of risk factors on overall survival were assessed using multivariate Cox regression model. Based on these results, the OPTIModel was modified. To assess the discriminatory ability, Harrell’s C-statistic was used.</div></div><div><h3>Results</h3><div>178 patients were included. Overall, median overall survival was 12.1 months (95 % confidence interval (CI): 8.8–15.3). Median survival for SBM (n = 53, 29.8 %), limited BMs (n = 60, 33.7 %) and DBMs (n = 65, 36.5 %) was 19.6 months (95 %CI: 6.8–32.4), 14.8 months (95 %CI: 7.6–21.9) and 6.1 months (95 %CI: 2.7–9.5), respectively. Median survival was 16.3 months (95 %CI: 10.6–22.0) in patients with OBM (n = 113, 63.5 %), with a hazard ratio of 2.11 (95 %CI: 1.44–3.09) compared to patients with DBM. Including OBM in the OPTIModel for mRCC improved C-statistic from 0.585 (standard error (SE) = 0.027) to 0.618 (SE = 0.024).</div></div><div><h3>Conclusion</h3><div>Both SBM and limited BMs were associated with a longer overall survival in patients with mRCC and symptomatic LBMs. The modified OPTIModel for mRCC with inclusion of oligometastatic disease could guide decisions about local treatment of symptomatic LBMs.</div></div>","PeriodicalId":48806,"journal":{"name":"Journal of Bone Oncology","volume":"55 ","pages":"Article 100709"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}