Purpose: This study was designed to evaluate the ability of a previously published nuclear morphometry discriminant function to predict disease-free survival in patients with Wilms' tumor.
Patients And Methods: We identified 218 patients with stage I-IV Wilms' tumor of favorable histology who were entered onto the National Wilms' Tumor Study (NWTS) between January 1, 1990 and April 15, 1994. The nuclear morphometry score was calculated for each patient as follows: MV(f) = (0.02 x AGE) + (1.17 x SNRF) + (90.6 x LEFD) - 94, with AGE denoting age at diagnosis in months, SNRF the skewness of the nuclear roundness factor, and LEFD the lowest value of nuclear ellipticity as measured by the feret diameter method. Relative risks of relapse were estimated for the total score and for each of its components. Sensitivity and specificity were determined for the criterion of "MV(f) is greater than -0.35" as a predictor of relapse.
Results: By contrast with previously published results, neither the SNRF nor the LEFD made any contribution to the prediction of disease-free survival. Sensitivity and specificity of the criterion of "MV(f) is greater than -0.35" were 71% and 56%, respectively.
Conclusion: Re-evaluation of a published nuclear morphometry score showed that it did not predict disease-free survival in patients with Wilms' tumor. The earlier study very likely overestimated the predictive power of nuclear morphometry by using the same data set both to develop the score and to evaluate its properties. Because of the huge number of combinations of nuclear morphometry measurements that may enter into the multivariate discriminant function, use of appropriate statistical methods is essential to estimate accurately the sensitivity and specificity.
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http://dx.doi.org/10.1200/JCO.1999.17.7.2123 | DOI Listing |
Plants (Basel)
January 2025
Faculty of Forestry, University of Sarajevo, Zmaja od Bosne 8, 71 000 Sarajevo, Bosnia and Herzegovina.
Polyploidy is a powerful mechanism driving genetic, physiological, and phenotypic changes among cytotypes of the same species across both large and small geographic scales. These changes can significantly shape population structure and increase the evolutionary and adaptation potential of cytotypes. , an edaphic steno-endemic species with a narrow distribution in the Balkan Peninsula, serves as an intriguing case study.
View Article and Find Full Text PDFKidney Res Clin Pract
January 2025
Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Background: We aimed to explore changes in decision-related brain microstructure, brain functional activities, and functional connectivity, and their correlations with cognitive function in end-stage kidney disease (ESKD) patients undergoing peritoneal dialysis (PD). Furthermore, the impact of dialysis on these changes was examined.
Methods: Thirty ESKD patients undergoing PD, 20 chronic kidney disease (CKD) stage 5 patients without dialysis (predialysis CKD stage 5), and 30 healthy controls (HC) were recruited for the study.
J Alzheimers Dis
January 2025
Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Background: Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative diseases in individual patients. However, VBM is sensitive to the MRI scanner platform and details of the acquisition sequence. To mitigate this limitation, we recently proposed and validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database.
View Article and Find Full Text PDFNeuroimage
February 2025
Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China. Electronic address:
Hum Brain Mapp
January 2025
Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.
Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.
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