Statistical analysis to assess automated level of suspicion scoring methods in breast ultrasound

Michael Galperin

doi:10.1117/12.479890

22 May 2003 Statistical analysis to assess automated level of suspicion scoring methods in breast ultrasound

Michael Galperin

Author Affiliations +

Proceedings Volume 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment; (2003) https://doi.org/10.1117/12.479890
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

A well-defined rule-based system has been developed for scoring 0-5 the Level of Suspicion (LOS) based on qualitative lexicon describing the ultrasound appearance of breast lesion. The purposes of the research are to asses and select one of the automated LOS scoring quantitative methods developed during preliminary studies in benign biopsies reduction. The study has used Computer Aided Imaging System (CAIS) to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses. The overall goal is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers (will require biopsy anyway). On complex cysts and fibroadenoma cases experienced radiologists were up to 50% less certain in true negatives than CAIS. Full correlation analysis was applied to determine which of the proposed LOS quantification methods serves CAIS accuracy the best. This paper presents current results of applying statistical analysis for automated LOS scoring quantification for breast masses with known biopsy results. It was found that First Order Ranking method yielded most the accurate results. The CAIS system (Image Companion, Data Companion software) is developed by Almen Laboratories and was used to achieve the results.

Citation Download Citation

Michael Galperin "Statistical analysis to assess automated level of suspicion scoring methods in breast ultrasound", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.479890

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