Background: The efficacy of helical computerized tomography (CT) for lung cancer screening is currently being evaluated, and it is likely that some form of such screening will become commonplace in the future. However, its efficacy is limited by the rather tedious and error prone process required to read these studies. While accuracy is the most important factor, efficiency is becoming increasingly important as the volume of three-dimensional (3D) data generated in radiology departments steadily rises. Contributing to the difficulty of the task is the need for radiologists to differentiate between localized nodules and slices through linear structures such as blood vessels in each of a large number of slices acquired for each subject. To increase efficiency and accuracy, thin slices can be combined to provide thicker slabs for presentation, but the resulting superposition of tissues can make it more difficult to detect and characterize smaller nodules. The stereo display of a stack of thin CT slices may be able to clarify 3D structures, while avoiding the loss of resolution and ambiguities due to tissue superposition. Additionally, several studies have suggested that better viewing methods may be able to increase the ability of radiologists to classify nodules as benign, indolent, or malignant.
The rationale for proposing this project at the present time is that (1) lung cancer is the leading cause of cancer deaths in the US; (2) the potential value of lung cancer screening is just being established, but there is a clear need to increase its efficacy; (3) the technology for true volumetric display (without special glasses) is becoming available; (4) there is a rapid increase in the amount of 3D data being generated in radiology departments, and interpreting this data is placing an ever-increasing burden on radiologists; and (5) because of the geometry of lungs and the sparseness of tissues of interest, lung cancer screening by helical CT is an ideal context in which to establish the viability of stereographic displays. The convergence of these factors has produced a compelling case for performing the proposed study at this time.
Objective/Hypothesis: Our hypothesis is that the efficacy of lung cancer screening, by helical CT can be increased by use of a suitably designed stereoscopic display. Specifically, the objective is to significantly increase both efficiency and performance for the detection of lung nodules over what can be achieved when reading cases with maximum-intensity projection or in a slice-by-slice mode. It is known that certain kinds of objects can be detected in a stereo 3D display of data, which cannot be detected when the data is viewed in a slice-by-slice manner. Stereo projection can improve the visibility of objects by enhancing features that are correlated between slices while reducing noise in a manner analogous to the signal-to-noise improvements obtained by averaging slices or MIPs; however, stereo projection does not introduce tissue superposition ambiguities that would be caused by these methods.
Specific Aims: The primary aim of this study is to improve the prognosis of lung cancer cases by increasing the efficacy of lung cancer screening. Our goal is to have a fully working and tested system at the conclusion of this project.
Study Design: A stereo 3D display incorporating unique compositing methods tailored to the specific requirements of chest CT interpretation will be developed. The display will be evaluated and compared to both maximum intensity projection and slice-by-slice displays, in a receiver operating characteristic study (500 cases × 6 readers × 3 modes) to measure its performance and efficiency for detecting lung nodules.
Relevance: In the US, lung cancer results in more deaths than breast, prostate, and colon cancers combined. If successful, this project will significantly improve the efficacy of lung cancer screening by helical CT, which should result in a corresponding reduction in lung cancer mortality. Secondarily, by demonstrating the viability of stereographic displays for radiographic images, this project may inspire the application of these techniques to other kinds of 3D data sets.