Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph.

A test bank of verified chest radiographs was compiled for visual search experiments to investigate radiology performance in the detection of early lung cancer. A measure of the physical characteristics of the lesions was derived to determine the conspicuity (chi) of the nodules and to investigate possible causes of failed detection. Observer performance was measured by alternate free response operating characteristic (AFROC) methodology and was supplemented with visual search recording. Correlation of AFROC scores and the chi values was poor but inspection of the visual search recordings showed that most nodules were fixated. Fixations on missed lesions produced average dwell times greater than three times the minimum duration thought to be associated with detection. We conclude that the majority of errors were failures of decision rather than detection and comment on the implications of this for strategies to improve diagnostic effectiveness.

[1]  Dev P. Chakraborty,et al.  The FROC, AFROC and DROC Variants of the ROC Analysis , 2000 .

[2]  H. Kundel,et al.  Lesion conspicuity, structured noise, and film reader error. , 1976, AJR. American journal of roentgenology.

[3]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[4]  G. Seeley,et al.  Computer-simulated lung nodules in digital chest radiographs for detection studies. , 1990, Investigative radiology.

[5]  E. Krupinski,et al.  Searching for lung nodules. Visual dwell indicates locations of false-positive and false-negative decisions. , 1989, Investigative radiology.

[6]  H L Kundel,et al.  Mechanism of satisfaction of search: eye position recordings in the reading of chest radiographs. , 1995, Radiology.

[7]  渡部 俊太郎,et al.  SPIE(Society of Photo-Optical Instrumentation Engineers)報告 , 1986 .

[8]  M. Greenstone,et al.  Misinterpretation of the chest x ray as a factor in the delayed diagnosis of lung cancer , 2002, Postgraduate medical journal.

[9]  J G Goldin,et al.  Detection of simulated lung nodules with computed radiography: effects of nodule size, local optical density, global object thickness, and exposure. , 1996, Academic radiology.

[10]  H L Kundel,et al.  Searching for lung nodules. The guidance of visual scanning. , 1991, Investigative radiology.

[11]  Thomas G. Cooper,et al.  Assessing the value of diagnostic imaging: the role of perception , 2000, Medical Imaging.

[12]  Harold L. Kundel,et al.  A Visual Dwell Algorithm Can Aid Search and Recognition of Missed Lung Nodules in Chest Radiographs , 1990 .

[13]  D. Manning,et al.  A comparison of expert and novice performance in the detection of simulated pulmonary nodules , 2000 .