bims-rebome 2026-06-28 papers

bims-rebome

Biomed News

on Management of bone metastases

Issue of 2026–06–28
eight papers selected by
Alberto Selvanetti, Azienda Ospedaliera San Giovanni Addolorata

Beyond SINS: A Critical Review of Biomechanical, Microstructural, and Radiomic Biomarkers for Predicting Fracture Risk in Spinal Metastases.
PET-MR Examination in Diagnosing Bone Metastases in Prostate Cancer.
A Fully Automated Pipeline for Vertebral Structural Assessment From Medical Images. Application Under Metastatic Conditions.
Individualized radiotherapy of bone metastases from prostate cancer: time trends and 5-year survival results.
Predicting Postoperative Complications in Lung Cancer Spinal Metastases: A Nomogram Based on Nutritional, Low Psoas Muscle Index, and Functional Status.
A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.
FAST-02: Results from the second in-human prospective evaluation of single-fraction proton FLASH for symptomatic thoracic bone metastases.
Intramedullary Nailing Versus Modular Megaprosthesis in Pathological Humeral Fractures: A 10-Year Retrospective Comparative Study.

Diagnostics (Basel). 2026 Jun 13. pii: 1835. [Epub ahead of print]16(12):

Beyond SINS: A Critical Review of Biomechanical, Microstructural, and Radiomic Biomarkers for Predicting Fracture Risk in Spinal Metastases.

An Sen Tan, Calvin Kai En Tjio, Jonathan Jiong Hao Tan, Naresh Kumar, Wilson Ong, Shuliang Ge, Yi Liang Tan, Eric Fang, Balamurugan A Vellayappan, James Thomas Patrick Decourcy Hallinan.

  Background/Objectives: Although the Spinal Instability Neoplastic Score (SINS) is widely used to estimate spinal metastases fracture risk and guide decisions on stabilisation procedures, prior studies have demonstrated mixed results. Patients with the same score exhibit clinically heterogeneous outcomes, with some SINS criteria correlating less well with the estimated fracture risk than others. There are also barriers to implementation such as the time burden required for manual calculation and interobserver variability associated with qualitative morphological criteria. SINS also lacks sensitivity for detecting latent structural compromise in treatment-naive patients and those susceptible to the iatrogenic effects of stereotactic body radiation therapy. This review aims to evaluate emerging imaging, biomechanical, and microstructural markers with the potential to improve fracture risk stratification and prognostication for spinal oncology patients. Methods: We synthesise evidence across three innovative frontiers: (1) biomechanical modelling, including CT-derived finite element analysis and failure-load pattern models; (2) radiomics, utilizing radiomics features from radiological imaging to develop a predictive model; and (3) microstructural MRI biomarkers, exploring the translatability of the Vertebral Bone Quality score, fat fraction, and paraspinal muscle atrophy from osteoporosis to the metastatic spine. Results: Emerging biomechanical, radiomic and microstructural imaging markers show potential in addressing some limitations of traditional SINS criteria for fracture risk stratification across the spinal oncology treatment continuum, from initial diagnosis to post-radiation surveillance, thereby facilitating more precise risk assessment. However, current evidence remains largely retrospective and heterogeneous, and further validation is required before clinical adoption. Conclusions: We propose a framework that shifts the paradigm from conventional morphological scoring toward a multiparametric assessment of spinal stability.

Keywords:  biomechanical modelling; machine learning; pathologic fracture; radiation therapy; radiomics; spinal instability neoplastic score; spinal metastases; spinal stability

DOI:  https://doi.org/10.3390/diagnostics16121835
Cancer Manag Res. 2026 ;18 572865

PET-MR Examination in Diagnosing Bone Metastases in Prostate Cancer.

Xiang Su, Kehan Li, Jianan Wang, Zhaoqiang Jiang, Lei Shan.

   Background: Positron Emission Tomography-magnetic resonance (PET-MR) has the advantage of high contrast and has a high potential for diagnosing bone metastases in prostate cancer (PC). This study assesses the effectiveness of PET-MR imaging for detecting bone metastasis in PC.
Methods: A total of 212 prostate cancer patients admitted to our hospital from June 2021 to June 2024 were selected, all of whom were diagnosed with prostate cancer through pathological puncture. According to the most valuable comparative method (BVC), patients were divided into a bone metastasis group (n=40) and a non-bone metastasis group (n=172). General information such as age was collected and compared between the two groups of patients. PET-MR examination was performed on all patients, and the number of lesions was recorded (a total of 95 bone metastases were detected). Among them, BVC confirmed 58 metastatic lesions (due to the presence of multiple site metastases in some patients). The Gleason score, SUVmax, and ADCmin levels were compared between groups. ROC curve analysis determined the AUC, sensitivity, and specificity. Multivariate Logistic regression identified influencing factors.
Results: Among patients with bone metastases, 38 patients with positive PET-MR (95.00%) and 169 patients with non-bone metastases (79.72%) were PET-MR negative. The sensitivity of PET-MR in diagnosing prostate cancer bone metastasis was 95.00% (95% CI: 83.5% - 99.4%), the specificity was 79.72% (95% CI: 73.1% - 85.3%), the positive predictive value was 92.68% (95% CI: 80.1% - 98.4%), and the negative predictive value was 98.83% (95% CI: 95.8% - 99.9%). The proportion of PET-MR diagnosed prostate cancer bone metastases with pelvic, spinal, extremity, rib, and scapular lesions were 31.03%, 44.83%, 8.62%, 3.45%, and 87.93%, respectively. Compared with the non-metastatic group, the proportion of patients with Gleason score > 9, the proportion of tPSA > 100 ng/mL, ALP level and SUVmax and SUVmax/ADCmin levels were significantly higher in the metastatic group. ADCmin levels were significantly lower, and the differences were statistically significant (P < 0.05). Factors such as PET-MR positivity, Gleason score >9, tPSA >100 ng/mL, ALP level, SUVmax, ADCmin and SUVmax/ADCmin were significant risk factors for bone metastasis (P<0.05). The AUC of SUVmax, ADCmin and SUVmax/ADCmin in the diagnosis of bone metastasis of prostate cancer were 0.756, 0.777 and 0.864, respectively. SUVmax, ADCmin and SUVmax/ADCmin were abnormally expressed in bone metastases of PC by PET-MR examination.
Conclusion: SUVmax, ADCmin, and SUVmax/ADCmin are significantly abnormally expressed in patients with prostate cancer bone metastases during PET-MR examination. The sensitivity and specificity of PET-MR in diagnosing prostate cancer bone metastasis are 95.00% and 79.72%, respectively. Among them, the SUVmax/ADCmin ratio has the best diagnostic efficacy (AUC = 0.864, sensitivity 76.74%, specificity 82.50%). PET-MR has certain value in the diagnosis of prostate cancer bone metastasis and can be used as a reference indicator for clinical auxiliary evaluation.

Keywords:  PET-MR; bone metastasis; clinical value; prostate cancer

DOI:  https://doi.org/10.2147/CMAR.S572865
Int J Numer Method Biomed Eng. 2026 Jul;42(7): e70193

A Fully Automated Pipeline for Vertebral Structural Assessment From Medical Images. Application Under Metastatic Conditions.

B Gandia-Vañó, J M Navarro-Jiménez, J J Ródenas, E Arana, E Nadal.

  Spinal bone metastases often lead to vertebral fractures and other skeletal events that severely affect patients' quality of life. Predicting structural failure is essential for guiding treatment and preventing complications. However, conventional assessment tools have limited predictive power, highlighting the need for computational methods capable of simulating disease progression and its mechanical consequences. This study aimed to develop a fully automated, patient-specific methodology to predict vertebral structural behavior from computed tomography (CT) data, supporting clinical decision-making and treatment planning. The proposed pipeline integrates three main components: a deep neural network for semantic segmentation of vertebrae and metastatic regions from CT scans, the Coherent Point Drift (CPD) algorithm to ensure consistent alignment and automated definition of boundary conditions across datasets, and the Cartesian Grid Finite Element Method (cgFEM) to simulate the vertebral mechanical response under metastatic involvement. Model performance was evaluated by comparing the predicted outcomes with reference data, using precision, sensitivity, and specificity to assess reliability. The proposed workflow achieved full automation from CT imaging to fracture risk estimation. The segmentation module showed high accuracy across multiple metrics, enabling robust model generation. Geometric normalization and CPD-based boundary condition assignment standardized vertebral geometries across studies, while cgFEM simulations provided clinically interpretable metrics such as safety factors and stability variations associated with tumor size, location, and density. These analyses enabled the identification of scenarios linked to a higher fracture risk. The main limitations include the use of fixed boundary conditions and a predefined voxel threshold, which may reduce physiological realism and generate false positives. Overall, this work presents an end-to-end, patient-specific framework for automated fracture risk evaluation in metastatic vertebrae. By combining deep-learning-based segmentation, geometric normalization, CPD alignment, and cgFEM simulations, the method produces clinically relevant outputs that can guide therapeutic strategies. Future developments will focus on integrating patient-specific loading data and predictive modeling to support incorporation into clinical decision support systems.

Keywords:  CPD; CT scan; cgFEM; semantic segmentation; vertebra structural analysis

DOI:  https://doi.org/10.1002/cnm.70193
Strahlenther Onkol. 2026 Jun 25.

Individualized radiotherapy of bone metastases from prostate cancer: time trends and 5-year survival results.

Carsten Nieder, Luka Stanisavljevic, Ellinor C Haukland, Bård Mannsåker, Bence Fajth, Astrid Dalhaug.

   PURPOSE: This article describes a regional study performed in patients with bone metastases from prostate cancer hypothesizing that intensified radiotherapy (metastases directed and prostate directed) may result in longer overall survival.
METHODS: A retrospective study of 246 consecutive male patients who received 347 courses of radiotherapy in the time period 2011-2025 was performed. Patients were stratified into three cohorts spanning 5‑year intervals (2011-2015, 2016-2020, 2021-2025). Survival was analyzed in allcomers and separately in patients without visceral metastases treated within the first year after diagnosis (n = 119).
RESULTS: Median overall survival was shortest in the earliest phase of the study (2011-15). The two recent cohorts had similar survival. Outpatients with high performance status irradiated early after diagnosis and to all known sites of disease experienced superior survival. In the subgroup of 119 patients, simultaneous prostate radiotherapy prolonged survival (numerically better than no prostate RT, median 40 vs. 26 months, 5‑year rate 47 vs. 18%, p = 0.11). The impact of bone radiotherapy dose (dichotomized < 3 Gy × 13 or its biological equivalent vs. higher doses) was significant (if synchronous metastases, 5‑year rate 64 vs. 17%, p = 0.026; if metachronous metastases, 5‑year rate 48 vs. 8%, p < 0.001). Bone dose remained significant in multivariate analyses, together with clinical baseline parameters.
CONCLUSION: In our healthcare region, outcomes largely resemble those of prospective trials. Comprehensive radiotherapy to the prostate and bone oligometastases plus guideline-based systemic therapy resulted in favorable 5‑year survival.

Keywords:  Bone metastases; Prostate cancer; Radiation treatment; Stereotactic radiotherapy; Systemic treatment

DOI:  https://doi.org/10.1007/s00066-026-02560-x
Curr Oncol. 2026 Jun 05. pii: 336. [Epub ahead of print]33(6):

Predicting Postoperative Complications in Lung Cancer Spinal Metastases: A Nomogram Based on Nutritional, Low Psoas Muscle Index, and Functional Status.

Xinyao Lv, Ruizhao Zhao, Yuyu Fan, Zijian Wang, Xiutong Fang, Junjie Qiao.

  Objective: This study seeks to investigate the association between nutritional, muscular, and functional status and moderate-to-severe postoperative complications (Clavien-Dindo ≥ grade II) in patients with lung cancer spinal metastases and to construct an individualized risk prediction nomogram. Methods: A total of 162 patients with histologically confirmed lung cancer spinal metastases who underwent surgery were retrospectively enrolled. Preoperative clinical data were collected. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for postoperative complications, with variable selection based on a combination of statistical significance and clinical judgment. A nomogram model was constructed and evaluated using receiver operating characteristic curve, calibration curve, and decision curve analysis. Internal validation was performed using the bootstrap method. For exploratory assessment of the model's clinical stratification capability, patients were classified into low-, medium-, and high-risk groups based on tertiles of the predicted probability. Results: The incidence of Clavien-Dindo ≥ grade II complications during postoperative hospitalization or within 14 days was 57.4%. Multivariate analysis suggested that lower psoas muscle index (low PMI) (OR = 4.131, p = 0.034), lower body mass index (BMI) (continuous: OR = 0.539 per 1 kg/m2 increase, p = 0.001, indicating that lower BMI was associated with higher risk), lower prognostic nutritional index (PNI) (OR = 0.456, p < 0.001), and lower Karnofsky Performance Status (KPS) score (OR = 0.890, p = 0.009) were identified as potential independent factors associated with postoperative complications. The nomogram achieved an Area Under the Curve of 0.907, showed acceptable calibration (Hosmer-Lemeshow test, p = 0.735), and demonstrated a favorable net clinical benefit in the decision curve analysis. In the exploratory risk stratification analysis, complication rates in the low-, medium-, and high-risk groups were 28.8%, 63.6%, and 78.2%, respectively (p < 0.001). Patients with complications had significantly longer hospital stays (median 20 vs. 13 days). Conclusions: In this cohort, low PMI, low BMI, low PNI, and low KPS were identified as potential independent factors associated with short-term moderate-to-severe postoperative complications. The nomogram may preliminarily predict the risk and might serve as a quantitative reference for individualized perioperative management, but its clinical utility requires further confirmation in external validation. The exploratory risk stratification suggests that the model has preliminary potential for clinical discrimination.

Keywords:  low psoas muscle index; lung cancer spinal metastases; nomogram; postoperative complications; prognostic nutritional index

DOI:  https://doi.org/10.3390/curroncol33060336
Clin Transl Oncol. 2026 Jun 22.

A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.

Zi-Feng Jiang, Zhang-Yan Ke, Min Wang, Jin-Bao Fu, Yan-Bei Zhang.

   BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face limitations in early detection and cost-effectiveness. This study aimed to develop and validate an interpretable machine learning (ML) model using routine, cost-effective biochemical markers for the early, non-invasive prediction of BM.
METHODS: This retrospective study included 566 lung cancer patients. Clinicopathological and laboratory features such as alkaline phosphatase (ALP), D-dimer, and lactate dehydrogenase (LDH) were collected. The dataset was partitioned into training and independent test sets. Six ML algorithms were evaluated using cross-validation, with the gradient boosting decision tree (GBDT) identified as the optimal model. Robustness and transparency were rigorously assessed via SHAP analysis, 1000 bootstrap resamples, and multi-dimensional subgroup analyses.
RESULTS: ALP, D-dimer, and LDH were significantly elevated in BM( +) patients (P < 0.001). In the test set, GBDT (gradient boosting decision tree) achieved an overall AUC of 0.774 (95% CI: 0.721-0.827) and an F1-score of 0.762. After subgroup integration, predictive performance improved to an AUC of 0.811 (95% CI 0.752-0.870), significantly outperforming traditional logistic regression (AUC = 0.755). Peak performance was observed in lung adenocarcinoma (AUC = 0.864). SHAP analysis quantitatively revealed a synergistic, non-linear interaction between ALP and D-dimer as a primary, quantifiable driver of BM risk.
CONCLUSION: Our routine-marker-based ML model demonstrates high diagnostic accuracy and robust generalizability. By precisely identifying high-risk populations with high transparency, this cost-effective tool provides scientific decision support for implementing personalized bone scan screening strategies and optimizing resource allocation in clinical practice.

Keywords:  Adenocarcinoma; Biomarkers; Bone metastasis; Lung cancer; Machine learning

DOI:  https://doi.org/10.1007/s12094-026-04476-5
Radiother Oncol. 2026 Jun 24. pii: S0167-8140(26)00510-4. [Epub ahead of print] 111671

FAST-02: Results from the second in-human prospective evaluation of single-fraction proton FLASH for symptomatic thoracic bone metastases.

Emily C Daugherty, Yongbin Zhang, Zhiyan Xiao, Lori Backus, Julie M McDonald, Beth Stockman, Jennifer Woo, Claire McCann, Ken Russell, Ricky Sharma, Dee Khuntia, James McGee, Jeffrey D Bradley, Charles B Simone, John Breneman, John P Perentesis, Anthony E Mascia.

BACKGROUND AND PURPOSE: FAST-01 demonstrated that proton FLASH radiotherapy (FLASH-RT) is clinically feasible and has an acceptable safety profile in patients with painful bone metastases of the extremities. FAST-02 expanded the investigation of FLASH-RT to patients whose treatment sites lie closer to critical normal structures.
MATERIALS AND METHODS: FAST-02 was a prospective, single-arm clinical trial (FDA IDE G220086) in which adult patients with 1-3 painful non-vertebral thoracic bone metastases were treated with FLASH-RT (8 Gy, ≥40 Gy/second). The co-primary objectives of FAST-02 were patient-reported toxicity and efficacy of pain relief. The trial's secondary objective was to evaluate FLASH-RT workflow metrics. Patients reported adverse events as well as overall pain, treatment site pain, and pain flare. Time-on-table and device-related treatment malfunctions were recorded.
RESULTS: Twelve patients were enrolled, and 10 patients were treated with FLASH-RT. Histologic diagnoses spanned lung, prostate, and kidney cancers. One site was treated per patient in the rib or scapula. All patients completed treatment per protocol. Average time-on-table was 14.6 min. At a median follow-up of 189 days, there were no ≥ grade 2 acute or late adverse events at least possibly related to FLASH-RT. Three-month pain was available in 8 patients; 1-month pain was available in 2 patients. A complete response was observed in 6 patients, a partial response in 3 patients, and stable pain in 1 patient. Three patients reported pain flares by day 11 of follow-up.
CONCLUSION: FAST-02, the first prospective clinical study of FLASH adjacent to thoracic critical organs, demonstrated that FLASH-RT was effective and had an acceptable safety profile in study patients with painful thoracic bony metastases.

DOI: https://doi.org/10.1016/j.radonc.2026.111671
Diseases. 2026 Jun 18. pii: 218. [Epub ahead of print]14(6):

Intramedullary Nailing Versus Modular Megaprosthesis in Pathological Humeral Fractures: A 10-Year Retrospective Comparative Study.

Giuseppe Rovere, Federica Messina, Cesare Meschini, Maria Serena Oliva, Matteo Caredda, Fernando De Maio, Pasquale Farsetti, Giulio Maccauro, Antonio Ziranu.

   BACKGROUND: Pathological fractures of the humerus secondary to metastatic disease represent a significant cause of pain, disability, and reduced quality of life in oncologic patients. Surgical management aims to restore stability, reduce pain, and allow early mobilization. However, the optimal strategy between intramedullary nailing and modular megaprosthesis remains debated, particularly in relation to functional outcomes and long-term results.
METHODS: A retrospective observational study was conducted on 48 patients treated for pathological or impending humeral fractures between January 2015 and January 2025. Twenty-six patients underwent intramedullary nailing (IMN group), while twenty-two were treated with tumor resection and modular megaprosthesis reconstruction (MP group). Functional outcomes were assessed using the Musculoskeletal Tumor Society (MSTS) score, Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH), and Western Ontario Shoulder Instability Index (WOSI) at 1, 12, 24, 36, and 60 months, and at 10 years. Complications and overall survival were also analyzed.
RESULTS: Intramedullary nailing demonstrated significantly superior early functional outcomes, with higher MSTS scores at 1 month (78% vs. 63%, p < 0.001) and lower QuickDASH scores in the first 24 months (p = 0.002). WOSI scores also favored IMN in the early postoperative period (p = 0.004). Megaprosthesis showed a slower initial recovery but a progressive improvement over time, reaching comparable functional outcomes at long-term follow-up (p > 0.05). The overall complication rate was similar between groups (p = 0.28), although periprosthetic infections occurred only in the MP group. Survival analysis did not show significant differences between groups (p = 0.74).
CONCLUSIONS: Both intramedullary nailing and modular megaprosthesis represent effective surgical options for pathological and impending humeral fractures. Intramedullary nailing provides faster early functional recovery, whereas megaprosthetic reconstruction offers a durable reconstructive solution for extensive proximal lesions. Functional outcomes progressively converged between the two techniques approximately 2-3 years after surgery. Mid-term outcomes up to five years appeared comparable, suggesting that surgical decision-making should be individualized according to lesion characteristics, tumor biology, expected survival, and functional demands.

Keywords:  MSTS; QuickDASH; WOSI; functional outcomes; humeral metastases; intramedullary nailing; megaprosthesis; orthopedic oncology; pathological fractures; survival analysis

DOI:  https://doi.org/10.3390/diseases14060218