Background And Purpose: Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.
Material And Methods: First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.
Results: A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.
Conclusions: The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.
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http://dx.doi.org/10.3174/ajnr.A2691 | DOI Listing |
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFSci Rep
December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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November 2024
Pathology and Lab Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Hepatic mesenchymal hamartoma (HMH) is an uncommon, benign liver tumor predominantly affecting children under three years of age. It is characterized histologically by disorganized mesenchymal stroma, abnormal bile ducts, blood vessels, and hepatocytes. HMH can present as a large cystic mass, a solid mass, or a combination of both.
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December 2024
Medical Oncology, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France.
Introduction: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by enhancing the antitumor immune response. This case describes an 80-year-old male with synchronous multiple primary malignancies (MPMs), including lung metastatic hepatocellular carcinoma (HCC), and non-small cell lung carcinoma (NSCLC), and brain metastatic urothelial carcinoma, who was treated with dual ICI therapy.
Case Presentation: The patient, with a history of diabetes, hypertension, dyslipidaemia, well-differentiated neuroendocrine duodenal tumors and micronodular exogenous cirrhosis (Child-Pugh class A), presented with a non-invasive bladder carcinoma (pT1N0M0) resected endoscopically in December 2022.
Cytojournal
November 2024
Department of Respiratory and Critical Care Medicine, Wuyi County First People's Hospital, Jinhua, Zhejiang, China.
Objective: Epithelial-mesenchymal transition (EMT) and metastasis are the primary causes of mortality in non-small-cell lung cancer (NSCLC). 5'-3' exoribonuclease 2 (XRN2) plays an important role in the process of tumor EMT. Thus, this investigation mainly aimed to clarify the precise molecular pathways through which XRN2 contributes to EMT and metastasis in NSCLC.
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