J Mol Diagn
Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden. Electronic address:
Published: December 2024
Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non-muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capture characteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.
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http://dx.doi.org/10.1016/j.jmoldx.2024.08.005 | DOI Listing |
Biomed Phys Eng Express
January 2025
Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
View Article and Find Full Text PDFOtol Neurotol
February 2025
Department of Otolaryngology-Head and Neck Surgery.
Objective: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
Study Design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.
Setting: Tertiary referral center.
PLoS One
January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Technology, Politeknik Negeri Padang, Padang, Sumatera Barat, Indonesia.
Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier.
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