Analysis of a CT patient dose database with an unsupervised clustering approach

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study

BACKGROUND

The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM.

METHODS

The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system.

RESULTS

From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system.

CONCLUSIONS

The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

Complex Relationship Between Artificial Intelligence and CT Radiation Dose

Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.

Classifying healthcare warehouses according to their performance. A Cluster Analysis-based approach

Purpose

The objective of this paper is to propose an approach to comparatively analyze the performance of drugs and consumable products warehouses belonging to different healthcare institutions.

Design/methodology/approach

A Cluster Analysis is completed in order to classify warehouses and identify common patterns based on similar organizational characteristics. The variables taken into account are associated with inventory levels, the number of SKUs, and incoming and outgoing flows.

Findings

The outcomes of the empirical analysis are confirmed by additional indicators reflecting the demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the performed Cluster Analysis can be considered as coherent.

Research limitations/implications

The study proposes an approach aimed at grouping healthcare warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the healthcare Supply Chain Management. The present work is associated with only one regional healthcare system.

Practical implications

The approach might support healthcare agencies in comparing the performance of their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the managerial similarities and differences that could be a first step toward warehouse aggregation in homogeneous logistics units.

Originality/value

This analysis puts forward an approach based on a consolidated statistical tool, to assess the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the factors determining them.

Comparison of univariate and multivariate anthropometric accommodation of the northwest Mexico population

ABSTRCTErgonomic workstation design is crucial to prevent work-related musculoskeletal disorders. Many researchers have proposed multivariate analysis for human accommodation. However, no multivariate anthropometric analysis exists for the Mexican population. This study compares common multivariate human accommodation approaches (e.g. principal component and archetypal analyses) and clustering techniques (e.g. k-means and Ward's algorithm) with the classical percentile-based univariate accommodation approach, using the Chi-squared goodness-of-fit test and the McNemar's test. The theoretical accommodation percentage obtained by multivariate approaches was higher than those obtained by the percentile univariate approach considering the central 98% data. k-means and archetypal analysis obtained similar and the highest accommodation values, followed by Ward's algorithm and principal component analysis. The study findings can be deployed to assess the design of workstations in Mexico, such as electronic components assembly and crew designs, and the effects of different anthropometric measurements in human accommodation. Practitioner summary: Products and workplaces design are commonly based on the classical univariate approach, using the extreme percentiles. In this study, multivariate approaches were tested on dimensions for sitting workstations, and results showed a bigger accommodation level in comparison to the univariate 1%-99% approaches. Abbreviations: RHM: representative human model; DHM: digital human model; PCA: principal component analysis; AA: archetypal analysis (AA); PCs: principal components; FA: factor analysis; RSS: residual sum of squares; SSE: sum of squared estimated errors; WA: Ward's algorithm; DBI: Davies-Bouldin index; CHI: Calinski-Harabaz index; SI: silhouette index; SH: sitting height; EHS: eye height, sitting; AHS: acromial height, sitting; PH: popliteal height; KHS: knee height, sitting; BPL: buttock-popliteal length; BKL: buttock-knee length; FGR: functional grip reach; AD: anthropometric dimension; E: expected; A: achieved.