bims-rebome 2026-07-26 papers

Expert Rev Hematol. 2026 Jul 24. 1-9

Effective treatment with preventing skeletal-related event development in multiple myeloma patients.

Nicola Giuliani, Matteo Scita, Anna Benedetta Dalla Palma, Laura Notarfranchi, Nicolas Thomas Iannozzi.

INTRODUCTION: Osteolytic bone disease is the hallmark of multiple myeloma (MM) patients caused by an excessive osteoclast activation and impaired osteoblast function. It occurs in up to 80-90% of patients at the diagnosis or during the disease. Osteolytic lesions lead to skeletal-related events (SREs) such as pathological fractures, severe bone pain or spinal cord compression. Consequently, SREs are associated with reduced quality of life and potentially decreased survival.
AREAS COVERED: Bone-targeted therapy with bisphosphonates (BPs) including zoledronic acid or pamidronate has long been the standard of care, showing a significant effect on the prevention of the SREs; however, renal toxicity may limit their use. Denosumab, a monoclonal antibody anti-RANKL has been approved for the treatment of bone disease also in patients with renal insufficiency. The efficacy and the possible toxic effects of these drugs are discussed in this review balancing effective treatment with preventing SREs development in MM patients.
EXPERT OPINION: Bone modifying agents (BMAs) including BPs and denosumab are effective in the prevention of SREs in MM patients. Overall, these drugs are manageable with a low incidence of side effects. The effect of BMAs on MM patient survival in the era of the new drugs is still debated.

Keywords: Multiple myeloma; RANKL inhibitor; bisphosphonates; bone disease; bone fracture; osteoclast

DOI: https://doi.org/10.1080/17474086.2026.2708711

Spine J. 2026 Jul 20. pii: S1529-9430(26)00609-1. [Epub ahead of print]

Postoperative Pulmonary Complications After Surgery for Spinal Metastases From Lung Cancer: A Six-Center Risk Analysis and External Validation of Three Established Risk Scores.

Yuan Yan, Xin Wang, Shuang Li, Jiazheng Zhao, Guochao Zhou, Guoqing Zhong, Jianjun Xun, Yongcheng Hu, Jiatao Zhang, Meng Xu, Yu Zhang.

BACKGROUND CONTEXT: Early identification of postoperative pulmonary complications (PPC) is crucial in patients undergoing surgery for spinal metastases. Patients with lung cancer-derived spinal metastases (LC-SM) appear to be particularly vulnerable to PPC. However, the incidence, risk factors, and prognostic significance of PPC in this population remain poorly defined, and commonly used PPC risk scores have not been adequately validated in this setting.
PURPOSE: This study aimed to: (1) characterize the incidence of PPC in patients with LC-SM after surgery and determine its association with early postoperative mortality; (2) identify risk factors for PPC in this population, explore the value of a series of inflammatory and nutritional indices, and develop a multivariable model; and (3) externally validate three established PPC risk scores (the ARISCAT score, the Arozullah Postoperative Pneumonia Risk Index, and the Arozullah Respiratory Failure Risk Index) and assess their clinical utility.
STUDY DESIGN/SETTING: Retrospective multicenter cohort study.
PATIENT SAMPLE: A total of 419 patients with spinal metastases from lung cancer who underwent open spinal surgery at six tertiary centers between January 2018 and March 2025 were included.
OUTCOME MEASURES: Outcome measures included the occurrence of PPC within 14 days after surgery and postoperative survival time.
METHODS: The association between PPC and early postoperative mortality was assessed, and overall survival was analyzed using Kaplan-Meier methods. Multivariable logistic regression with minimum Akaike information criterion was performed to identify perioperative factors associated with PPC. Discrimination (receiver operating curve), calibration (calibration plot, intercept, and slope), the overall performance (Brier score), and decision curve analysis were used to assess the overall performance of the final multivariable model and three conventional PPC risk scores.
RESULTS: PPC occurred in 50 of 419 patients (11.9%) and was associated with worse survival (median overall survival, 4.43 vs 14.39 months, p<0.01), as well as higher 30-day (26.0% vs 2.4%) and 90-day (42.0% vs 11.1%) mortality. Independent risk factors for PPC were longer ventilation time (OR 1.16, 95% CI 1.09-1.24), blood transfusion (OR 1.12, 95% CI 1.02-1.23), liver metastasis (OR 2.64, 95% CI 1.26-5.54), prior systemic chemotherapy and/or thoracic radiotherapy (OR 2.52, 95% CI 1.26-5.05), pre-existing pulmonary abnormalities (OR 2.07, 95% CI 1.01-4.24), and male sex (OR 2.26, 95% CI 1.03-4.93). The final model showed good discrimination and precision-recall performance (AUC 0.825; 5-fold cross-validated AUC 0.803; bootstrap-corrected AUC 0.781; PR-AUC 0.533), with good calibration and clinical net benefit. Conventional PPC risk scores showed limited predictive value (ARISCAT: AUC 0.659, PR-AUC 0.234; Arozullah pneumonia: AUC 0.541, PR-AUC 0.140; Arozullah respiratory failure: AUC 0.643, PR-AUC 0.063).
CONCLUSIONS: Patients with LC-SM represent a distinct high-risk population for PPC after spinal surgery, which is associated with early postoperative mortality. Conventional PPC risk scores and inflammation and nutrition indices showed limited applicability in this setting. Longer ventilation time, blood transfusion, liver metastasis, prior systemic chemotherapy and/or thoracic radiotherapy, pre-existing pulmonary abnormalities, and male sex were identified as independent risk factors for PPC. These findings suggest that spine surgeons should pay attention to perioperative pulmonary vulnerability in this population, especially when evaluating surgical indications, interpreting preoperative chest CT findings, and planning perioperative respiratory management.

Keywords: external validation; lung cancer; postoperative pulmonary complications; risk factors; spinal metastases; spine surgery

DOI: https://doi.org/10.1016/j.spinee.2026.07.016

Neurosurgery. 2026 Jul 22.

A Tale of 2 Institutions: Differences in Sociodemographic Factors, Presenting Features, Treatment Characteristics, and Outcomes in Patients Undergoing Surgery for Spinal Metastases.

Hammad A Khan, Adhith Palla, Kimberly Ashayeri, Lily McLaughlin, David B Kurland, Anthony Frempong-Boadu, Darryl Lau, Ilya Laufer, Donato Pacione.

BACKGROUND AND OBJECTIVES: The objective of this study was to compare sociodemographic factors, presenting characteristics, and outcomes between cohorts of patients receiving surgery for spinal metastases at 2 neighboring institutions, 1 private and 1 public, affiliated with a single major academic medical center in a large metropolitan area.
METHODS: This analysis included all patients who underwent decompressive surgery for extradural spinal metastases. Sociodemographic factors, treatment characteristics, and outcomes were compared between those treated at a private hospital and a neighboring public hospital using Rao-Scott χ 2 tests for categorical variables, Student t tests for continuous variables, and the Kaplan-Meier product-limit method for overall survival and progression-free survival.
RESULTS: Compared with those treated at our private hospital, patients treated at our public hospital were more often younger ( P = .005), of Black or Hispanic race (72.6% vs 19%, P < .001), and uninsured (16% vs 5.6%, P = .005). They more frequently presented with epidural spinal cord compression grade 3 (76% vs 56.8%, P = .027), were nonambulatory before surgery (56.9% vs 13.5%, P < .001), and had increased neurological impairment as denoted by American Spinal Injury Association Impairment Scale grades of A, B, or C (39.2% vs 7.5%). Patients treated at our public hospital had shorter median follow-up time (92 vs 302.5 days, P = .004). Multivariate analysis did not reveal a significant difference in overall survival or progression-free survival between hospitals, instead demonstrating associations with primary tumor histology and number of spinal metastases ( P < .05).
CONCLUSION: There were substantial disparities in sociodemographic factors, presenting local disease burden, and postoperative neurological outcome but no difference in survival outcome, between patients treated at our public and private hospitals. These findings underscore the need for more equitable screening, surveillance, and referral structures.

Keywords: Epidural spinal cord compression; Healthcare disparities; Spinal metastases; Spine tumors

DOI: https://doi.org/10.1227/neu.0000000000004165

Musculoskelet Surg. 2026 Jul 20.

Photodynamic bone stabilization: a novel approach to elderly metastatic patients.

R Totti, F Cavola, R M Comodo, A El Motassime, G Bocchino, O El Ezzo, A Ziranu, G Maccauro, R Vitiello.

Treatment of metastatic bone fractures could be challenging, especially in elderly oncologic patients. Recent treatment options like photodynamic nailing are a mini-invasive procedure that allows fractures stabilization and early recovery. We reviewed five studies following the PRISMA guidelines obtaining 148 patients treated with Photodynamic Bone Stabilization (PBS). Mean follow-up period after surgery was 10.5 months as reported in 4 of 5 studies (range 1.5-24 months). All patients evaluated have been treated due to metastasis from other primary tumors. IlluminOss® System demonstrated positive outcomes in the reviewed studies, decreasing mean VAS score from pre-surgery values (7.71) to post-surgery values (4.19). As reported in 3 of 5 studies evaluated, mean preoperatively MSTS score was 30.38% (27-40%), earliest postoperative MSTS was 61.59% (52-87.8%). Complications were reported in 27 patients (18.24%). Several complications were described: device rupture, thromboembolic events, aseptic dehiscence, and malunion, respectively, in 14 patients (9.45%), 3 patients (2.02%), 2 patients (1.35%), 2 patients (1.35%). Our analysis offers a preliminary overview of the outcomes and complications related to PDNs (IlluminOss® System). While classical intramedullary nailing is often linked to favorable functional outcomes, the risk of complications, such as: hardware failure, and the necessity for future hardware removal, should not be ignored. The results obtained from this analysis are promising; however, further randomized studies will be necessary to determine the superiority of the method over current treatments.

Keywords: Bone metastasis; Bone tumor; IlluminOss; Impending fracture; Pathologic fracture; Photodynamic bone stabilization system

DOI: https://doi.org/10.1007/s12306-026-00964-7

J Med Imaging (Bellingham). 2026 Jul;13(4): 044503

Radiomics-based machine learning to evaluate immunotherapy efficacy in non-small cell lung cancer patients with bone metastases.

Reza Kakavand, Nils D Forkert, Annalise Abbott, Joseph Kendal, Peter Connell, Hila Yashar, Michael J Monument, W Brent Edwards.

Purpose: Assessing treatment response in bone metastases from non-small cell lung cancer (NSCLC) remains a major clinical challenge, particularly for patients receiving immune checkpoint inhibitors (ICIs). The existing response criteria are not optimized for osseous disease, leading to inconsistent evaluation. We aimed to develop and validate a radiomics-based machine learning (ML) framework to non-invasively distinguish immunotherapy response categories-progression, stable disease, and partial response-in NSCLC patients with bone metastases.
Approach: Chest computed tomography (CT) scans from 99 NSCLC patients were analyzed before and during ICI therapy. Bone structures were automatically segmented using TotalSegmentator, and 1051 radiomic features were extracted per time point. Clinical variables were incorporated as optional features. Three ML classifiers-random forest, XGBoost, and support vector machine-were trained using fivefold cross-validation. A multistep feature selection pipeline (correlation filtering, mutual information, recursive feature elimination, and ReliefF ranking) was applied. Model performance was evaluated using area under the curve (AUC), F1 -score, accuracy, sensitivity, and specificity, with additional statistical testing using Kruskal-Wallis, Mann-Whitney U , bootstrapping, and permutation analysis.
Results: Inter-rater agreement for radiological response categories was high (Cohen's kappa = 0.91). Post-treatment radiomic features yielded the best performance. The random forest model achieved an AUC of 0.94, an F1 -score of 0.79, an accuracy of 0.79, a sensitivity of 0.80, and a specificity of 0.83. Clinical features did not meaningfully improve performance. Models based on the largest lesion showed lower accuracy than those using the overall response.
Conclusions: Post-treatment CT radiomics captured therapy-induced skeletal changes and enabled differentiation of immunotherapy response categories in NSCLC bone metastases. These findings highlight radiomics as a non-invasive tool for response assessment and guiding personalized treatment strategies.

Keywords: bone metastases; computed tomography; immunotherapy response; machine learning; non-small cell lung cancer; radiomics

DOI: https://doi.org/10.1117/1.JMI.13.4.044503

Front Oncol. 2026 ;16 1692909

Development and validation of an interpretable machine learning-based predictive model for breast cancer bone metastasis.

Caiyun Fan, Ming Tian, Zhendong Ding, Jun Peng, Gulisitan Yiliyiming, Mingjiang Fan, Abuduaini Tuerxun, Binxu Qiu, Xiaojuan Zhu.

Background: Breast cancer is one of the most common malignancies worldwide, with bone metastasis representing its most frequent distant metastatic form, significantly worsening patient prognosis. This study aims to develop a machine learning-based predictive model for accurately assessing the risk of bone metastasis in breast cancer patients, thereby enabling personalized risk stratification, early clinical intervention, and optimized treatment strategies.
Methods: This study utilized the Surveillance, Epidemiology, and End Results database as the primary data source to develop machine learning models for predicting bone metastasis risk in breast cancer patients. Initially, univariate and multivariate logistic regression analyses were conducted to screen key predictive variables; subsequently, eight machine learning algorithms were constructed based on the screening results.10-fold cross-validation employed for hyperparameter optimization. Following training, model performance was evaluated on an internal test cohort and externally validated on 342 real-world cases from an independent hospital cohort. Model assessment incorporated multiple metrics, including area under the curve (AUC), area under the precision-recall curve (AUPRC), decision curve analysis, and calibration curves. Additionally, SHAP analysis was applied to enhance model interpretability, and a web-based calculator was developed based on the optimal model to facilitate clinical application and decision support.
Results: Baseline characteristics across cohorts indicated that the majority of patients were aged over 50 years, female, predominantly with the HR+/HER2- molecular subtype, and exhibited a low incidence of bone metastasis. Univariate and multivariate logistic regression analyses identified key independent risk factors, including age >50 years, higher tumor grade, advanced T stage, N stage, clinical stage and HR-/HER2- subtype factors included radiotherapy, surgery, and married status. The LGB model demonstrated superior performance, achieving an AUC of 0.98 in the training set and 10-fold cross-validation (standard deviation=0.00), 0.98 in the internal validation set, and 0.91 in the external validation set; AUPRC values across the three cohorts were 0.96, 0.79, and 0.87, respectively; decision curve analysis showed excellent net clinical benefit within the 0.1-0.8 threshold range; calibration curves further confirmed high concordance between predicted probabilities and actual event rates. SHAP analysis highlighted surgery as the primary protective factor, followed by N stage, T stage, and radiotherapy as risk enhancers; for example, advanced N stage was associated with positive SHAP values, indicating a significant increase in bone metastasis risk.
Conclusions: This study developed an interpretable LGB model accompanied by a web-based calculator, thereby advancing personalized risk stratification, early detection of bone metastasis and optimized treatment strategies.

Keywords: bone; breast cancer; interpretable; machine learning; metastasis

DOI: https://doi.org/10.3389/fonc.2026.1692909