PubMed · 14989031
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
M Scimia. 2003. [Thinprep imaging system].. https://pubmed.ncbi.nlm.nih.gov/14989031/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
BACKGROUND: No research to date has been able to discriminate differences in lifting technique for healthy individuals who eventually develop low back pain compared to those that do not while employed in a manual materials handling industry. The purpose of this study was to demonstrate the ability of principal component analysis to identify differences in lifting technique. METHODS: Principal component analysis was applied to sixteen kinematic and kinetic waveforms describing the two-dimensional motion of the trunk and load. The principal component scores for each variable were used as the dependent measures in a one-way ANOVA to determine group differences. FINDINGS: Significant group differences (P<0.05) were found for five of the principal component scores capturing associated kinematic waveform patterns related to the control and placement of the box on the shelf, and associated kinetic waveform patterns related to the relative timing of extension moment generation in the sacral and thoracic regions. A related waveform pattern for trunk compression was also found. INTERPRETATION: Due to the coordinated movements involved in tasks such as lifting, differences among clinical populations have been difficult to demonstrate empirically. We were able to identify different characteristics in lifting kinematics and kinetics prior to the development of low back pain. Principal component analysis was able to identify important biomechanical differences where traditional analyses failed. This is the first study to identify such lifting differences prior to the development of low back pain.
Endometrial intraepithelial neoplasia (also known as 'EIN') is a precursor to endometrioid endometrial adenocarcinoma characterized by monoclonal growth of mutated cells, a distinctive histopathologic appearance, and 45-fold elevated cancer risk. We have applied diagnostic criteria for EIN to 97 successive endometrial biopsies classified as hyperplastic according to World Health Organization criteria and correlated results with computer-assisted morphometry (D-score) and clinical cancer outcomes. Three pathologists separately reviewed all cases for presence or absence of EIN using published criteria (gland area>stromal area, cytologic change in focus of altered architecture, lesion size>1 mm, and exclusion of cancer and mimics). Discordant cases were resolved by a consensus review at a multiheaded scope. Clinical outcomes were obtained in 84 patients from patient visit and pathology records. Diagnoses of presence or absence of EIN were unanimous among all three pathologists in 75% of cases, and intraobserver-reproducibility was very good (kappa 0.73-0.90). Cases rediagnosed as EIN encompassed hyperplasias previously diagnosed as atypical (n=18) or nonatypical (eight complex, two simple). Eight follow-up cancers were scattered between hyperplasia types (5/21 atypical, 3/63 nonatypical), but all classified as EIN (8/25) and D-score <or=1 (8/38). Subjective application of criteria for diagnosis of EIN correlates well with objective morphometry and successfully segregates patients into high and low cancer risk subgroups with better reproducibility than atypical hyperplasia diagnosis.
For computer-aided detection (CAD) and volumetry of small pulmonary nodules, a number of algorithms have been developed for multislice CT data sets in recent years, with the goal of improving the diagnostic work-up and the follow-up of findings. Recent data show that the detection of small lesions may improve with CAD, suggesting that especially experienced readers may benefit from using CAD systems. This has lead to the recommendation of CAD as a replacement of the second reader in clinical practice. Furthermore, computer-aided volumetry of pulmonary nodules allows a precise determination of nodular growth rates as a prerequisite for a better classification of nodules as benign or malignant. In this article, we review recent developments of CAD and volumetry tools for pulmonary nodules, and address open questions regarding the use of these software tools in clinical routine.