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Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.
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 PDFTargeting AXL inhibits the growth and metastasis of prostate cancer in bone.
Clin 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.
Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
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.
Undesirable occupants of bone marrow creating a menace: A 4.5-year audit from a tertiary care centre in Eastern India.
Niger Med J
January 2025
Department Of Medical Oncology, Indira Gandhi Institute of Medical Sciences, Patna, India.
Background: Bone marrow (BM) in addition to being the origin of primary hematological malignancies is also commonly involved in metastatic solid tumors. Bone marrow examination includes aspiration and biopsy, and it is a well-known procedure not only to diagnose hematological malignancies but also for staging and prognosis of various solid tumors. The presence of metastasis in the bone marrow is of grave prognostic significance and it is imperative to rule out marrow involvement in any malignancy where curative treatment is considered.
View Article and Find Full Text PDFHead-to-Head Comparison Between [68Ga]Ga-DOTA.SA.FAPi And [18F]F-FDG PET/CT Imaging in Patients With Sarcoma.
Clin Nucl Med
January 2025
From the Departments of Nuclear Medicine.
Purpose: This study aimed to compare the diagnostic efficacy of [68Ga]Ga-DOTA.SA.FAPi and [18F]F-FDG PET/CT for detecting primary and metastatic lesions in sarcoma patients.
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