Jianzhong Qian

Interobserver variations on interpretation of multislice CT lung cancer screening studies, and the implications for computer-aided diagnosis

With low dose multi-slice CT for screening of lung cancer, physicians are now finding and examining increasingly smaller nodules. However as the size of detectable nodules becomes smaller, there may be greater differences among physicians as to what is detected and what constitutes a nodule. In this study, 10 CT screening studies of smokers were individually evaluated by three thoracic radiologists. After consensus to determine a gold standard, the number of nodules detected by individual radiologists ranged from 1.4 to 2.1 detections per patient. Each radiologist detected nodules missed by the other two. Although a total of 26 true nodules were detected by one or more radiologists, only 8 (31%) were detected by all three radiologists. The number of true nodules detected by an integrated automatic detection algorithm was 3.2 per patient after radiologist validation. Including these nodules in the gold standard set reduced the sensitivity of nodule detection by each radiologist to less than half. The sensitivity of nodule detection by the computer was better at 64%, proving especially efficacious for detecting smaller and more central nodules. Use of the automatic detection module would allow individual radiologists to increase the number of detected nodules by 114% to 207%.

Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system

Multi-slice CT (MSCT) scanners allow nodules as small as 3mm to be identified during screening. However the associated large data sets make it challenging for radiologists to identify all small nodules in a reasonable amount of time. Computer-aided detection may play a critical role in identifying missed nodules. 13 MSCT screening studies, initially interpreted as non-actionable by a radiologist, were selected from participants in a lung cancer screening study. The study protocol defines actionable studies as those containing at least 1 solid non-calcified nodule larger than 3mm, for which follow-up studies are recommended to exclude interval growth. An automatic detection algorithm was applied to the 13 studies to determine whether it might detect missed nodules, and whether any of these were of sufficient size to be considered actionable. There were a total of 138 automatically detected candidate nodules, an average of 10.6 per patient. 83 candidates were characterized as true positives, yielding a positive predictive value of 60.1%. 10 automatically detected candidates were judged to be actionable nodules greater than 3mm in diameter. 6 of 13 (46%) patients had at least one actionable finding detected by the computer that had been overlooked in the initial exam.

Automatic collimation in peripheral X-ray imaging

A method has been developed using which the foreground (body) in a fluoroscopy image of the peripherals can be segmented from the background (existing collimation and direct exposure). The extracted information about where the body is in an image is used to suggest settings for the collimator hardware during image acquisition. Segmentation is achieved through global feature extraction and supervised classification.