Paper
11 March 2014 The patterns of false positive lesions for chest radiography observer performance: insights into errors and locations
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Abstract
To examine the lobar distribution of false positives on a set of nodule-free and nodule-containing chest radiographs when radiologists are requested to perform an unframed task (oral report) compared to a framed task (nodule/s identification). A set of 40 chest images, 21 nodule-free (NF) and 19 nodule-containing (NC), was used. Ten radiologists participated in the study; first an oral clinical report was performed (unframed task, UFT) naming any abnormality seen, a confidence score and the location of reported abnormalities. The second (framed task, FT) had the same images randomly presented and radiologists were asked to locate any lung nodule/s and record their confidence and location of nodules. There was no statistical difference between the mean number of false positives (FPs) per lobe per case type (UFT or FT) with the exception of the right lower lobe (RLL) P=0.021. When the comparison of FPs for tasks and case types was carried out there were significant changes. For the NF cases there are significant differences for right upper lobe (RUL) P=0.0003, left upper lobe (LUL) P=0.0412; for NC cases there are significant differences for RUL P=0.009, RLL P=0.0112, LUL P=0.0337 and left lower lobe (LLL) P=0.0209. There was no significant correlation between the presence of a nodule in a given lobe and the occurrence of a FP in that lobe. The number and lobar location of FPs identified on a chest image by a radiologist is influenced by the task and case type.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John W. Robinson, Patrick C. Brennan, Claudia R. Mello-Thoms, Mariusz W. Pietrzyk, and Sarah J. Lewis "The patterns of false positive lesions for chest radiography observer performance: insights into errors and locations", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370E (11 March 2014); https://doi.org/10.1117/12.2044436
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KEYWORDS
Fourier transforms

Chest imaging

Chest

Lung cancer

Lung

Radiography

Cancer

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