Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of radiation research
Feasibility of nonspinal bony landmark-based image registration in CyberKnife ® robotic radiotherapy for pelvic and femoral bone tumors
In stereotactic body radiotherapy for bone metastases using the CyberKnife system, spinal anatomy is generally employed as the alignment reference. However, when treating pelvic tumors such as those in the pubic bone or proximal femur, the spinal reference may be anatomically distant from the target, potentially compromising visualization and localization accuracy. This study evaluated the feasibility of using nonspinal bony landmarks, specifically the pubic bone and femoral neck, as reference points for image registration. Intrafractional positional deviations in six degrees of freedom and the frequency of intrafractional corrections were analyzed according to treatment site, including cases treated with conventional spine-based alignment. Alignment accuracy was visually and numerically verified by the treatment operator before beam delivery. Across all treatment sites, the mean translational and rotational deviations during treatment remained within ±0.5 mm and ± 0.5°, respectively. Furthermore, no substantial differences were observed in the mean frequency of intrafractional corrections among the different alignment approaches. These findings demonstrate that nonspinal tumor-adjacent bony landmarks can provide clinically acceptable accuracy comparable to conventional spine-based registration. To our knowledge, this is the first study to systematically assess the clinical feasibility of this approach in CyberKnife-based pelvic radiotherapy. The proposed method may serve as a practical and reliable alternative to spine-based alignment, particularly when the spine is remote from the target.
27 May 2026
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Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics
OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant osteosarcoma using baseline clinical and magnetic resonance (MRI)-derived radiomics features, with histological response as the reference standard and external validation included. MATERIALS AND METHODS: This retrospective single-center study included 115 patients with osteosarcoma from an institutional registry as the internal cohort, divided into training and test sets, and 49 patients from another institution as an external validation cohort. Tumor and peritumoral regions were manually segmented on baseline fat-suppressed T2-weighted MRI. Radiomics features were extracted using PyRadiomics, followed by two feature selection methods to identify potential predictors. Six machine learning models with varying feature combinations were trained to classify histologic chemoresistance in the internal training set. Model performance was assessed in the internal test set, and the best model was externally validated. RESULTS: The support vector machine model combining eight tumor radiomics features and four clinical-imaging parameters (presence of tumor necrosis > 50% on contrast-enhanced MRI, age, body mass index, and presence of metastasis at presentation) demonstrated the best performance. In the internal test set, it achieved a sensitivity of 83.3%, a specificity of 72.7%, an area under the receiver operating characteristic curve (AUROC) of 0.84, and a positive likelihood ratio of 3.06. External validation yielded a sensitivity of 88.5%, a specificity of 47.8%, and an AUROC of 0.77. CONCLUSION: A model combining tumor radiomics and clinical parameters at diagnosis showed strong performance in predicting chemoresistant osteosarcoma, with results confirmed by external validation. This approach may support personalized treatment strategies in high-grade osteosarcoma. RELEVANCE STATEMENT: The validated model may support early, individualized osteosarcoma management. KEY POINTS: Baseline T2-weighted MRI radiomics and clinical data can predict chemoresistant osteosarcoma. Tumor radiomics combined with clinical features achieved strong predictive accuracy. The support vector machine model reached an AUROC of 0.84 for the internal testing and of 0.77 for the external validation. The validated model may support early, individualized osteosarcoma management.
21 May 2026
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