External beam radiotherapy (EBRT) has been reported to be effective in palliating painful bone metastases, but the optimal fractions and doses for treating bone metastases from hepatocelluar carcinoma (HCC) are not established. This study aimed to compare toxicity and efficacy for conventional fraction versus hypofraction schedules. From January 2009 through December 2014, 183 patients with HCC bone metastases were randomly assigned to conventional fraction EBRT (Group A) or hypofraction radiotherapy (Group B). Study outcomes were pain relief, response rate and duration, overall survival, and toxicity incidence. Median follow-up time was 9.3 months. Response times were 6.7 ± 3.3 fractions in Group A and 4.1 ± 1.2 fractions in Group B (p <0.001). Pain relief rates were 96.7% and 91.2% in Group A and B, respectively (p=0.116). Time to treatment failure for Group A was significantly longer than Group B (p=0.025). Median overall survival was similar between two groups (p=0.628). Toxicity incidence in both groups was minimal, with no significant differences observed. In conclusion, hypofractionated radiotherapy is safe for patients with HCC bone metastases and may achieve earlier pain relief compared to conventional radiotherapy. This protocol should be considered for patients with shorter predicted survival times.
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http://dx.doi.org/10.7150/jca.28674 | DOI Listing |
Breast Cancer (Auckl)
January 2025
Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
Background: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to better predict the outcome of the biopsy to better reflect the immunohistochemistry status of the tumor.
View Article and Find Full Text PDFNucl Med Mol Imaging
February 2025
National Cyclotron and PET Centre, Chulabhorn Hospital, 906 Kamphaeng Phet 6 Rd., Talat Bang Khen, Lak Si, Bangkok, 10210 Thailand.
Purpose: Prostate-specific membrane antigen (PSMA) Positron emission tomography/magnetic resonance imaging (PET/MRI) surpasses conventional MRI (cMRI) in prostate cancer (PCa) evaluation. Our objective is to evaluate correlation of quantitative parameters in PCa using Fluorine-18 (F-18) PSMA-1007 PET/MRI and their potential for predicting metastases.
Methods: This retrospective study included 51 PCa patients.
Sci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
View Article and Find Full Text PDFClin Cancer Res
January 2025
Stanford University, Palo Alto, CA, United States.
Purpose: After failing primary and secondary hormonal therapy, castration-resistant and neuroendocrine prostate cancer metastatic to the bone is invariably lethal, although treatment with docetaxel and carboplatin can modestly improve survival. Therefore, agents targeting biologically relevant pathways in PCa and potentially synergizing with docetaxel and carboplatin in inhibiting bone metastasis growth are urgently needed.
Experimental Design: Phosphorylated (activated) AXL expression in human prostate cancer bone metastases was assessed by immunohistochemical staining.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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