The goal of this project is to use computer analysis to classify small lung nodules, identified on CT, into likely benign
and likely malignant categories. We compared discrete wavelet transforms (DWT) based features and a modification of
classical features used and reported by others. To determine the best combination of features for classification, several
intensities of white noise were added to the original images to determine the effect of such noise on classification
accuracy. Two different approaches were used to determine the effect of noise: in the first method the best features for
classification of nodules on the original image were retained as noise was added. In the second approach, we
recalculated the results to reselect the best classification features for each particular level of added noise. The CT images
are from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI). For this study, nodules were
extracted in window frames of three sizes. Malignant nodules were cytologically or histogically diagnosed, while benign
had two-year follow-up. A linear discriminant analysis with Fisher criterion (FLDA) approach was used for feature
selection and classification, and decision matrix for matched sample to compare the classification accuracy. The initial
features mode revealed sensitivity to both the amount of noise and the size of window frame. The recalculated feature
mode proved more robust to noise with no change in terms of classification accuracy. This indicates that the best
features for computer classification of lung nodules will differ with noise, and, therefore, with exposure.
Using CT images from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI), interpreted by radiologists at the Georgetown University, our goal was to investigate the feature extraction method using discrete wavelet transform (DWT) and to demonstrate their potential in distinguishing between benign and malignant nodule status. We analyzed multiple 2 mm thick slices of 40 subjects with benign nodules and 7 subjects with malignant
nodules for a total of 112 and 78 slices, respectively. Data was analyzed in the region-of-interest (ROI) that included nodule and surrounding areas in three different-sized windows. A linear discriminant analysis (LDA) of wavelets coefficients was used for data analysis. In particular we examined discriminative power of the wavelet based features using Fisher LDA, and evaluated the classification results using decision matrix (DM) for matched sample (MS). For visualization we used 3-D Heat Maps, originally developed in MATLAB(R) (MathWorks, Natick, MA) for gene expression array analysis, modified to display the magnitude of similarities between cases under analysis. The use of DWT in the image pre-processing modules resulted in a significant improvement in discrimination between benign and malignant nodules. The results show better classification accuracy with the DWT based features, as compared to
previously proposed classification features (p-values: 0.008, 0.022, and 0.039, depending on window size). The Heat Maps provide useful data visualization for further investigation as they have the ability to identify cases that should be further explored to understand why some of the benign nodules look similar to malignant in the wavelet domain.
In this study, a segmentation algorithm based on the steepest changes of a probabilistic cost function was tested on non-processed and pre-processed dense breast images in an attempt to determine the efficacy of pre-processing for dense breast masses. Also, the inter-observer variability between expert radiologists was studied. Background trend correction was used as the pre-processing method. The algorithm, based on searching the steepest changes on a probabilistic cost function, was tested on 107 cancerous masses and 98 benign masses with density ratings of 3 or 4 according to the American College of Radiology's density rating scale. The computer-segmented results were validated using the following statistics: overlap, accuracy, sensitivity, specificity, Dice similarity index, and kappa. The mean accuracy statistic value ranged from 0.71 to 0.84 for cancer cases and 0.81 to 0.86 for benign cases. For nearly all statistics there were statistically significant differences between the expert radiologists.
Using data from a clinical trial of a commercial CAD system for lung cancer detection we separately analyzed the location, if any, selected on each film by 15 radiologists as they interpreted chest radiographs, 160 of which did not contain cancers. On the cancer-free cases, the radiologists showed statistically significant difference in decisions while using the CAD (p-value 0.002). Average specificity without computer assistance was 78%, and with computer assistance 73%. In a clinical trial with CAD for lung cancer detection there are multiple machine false positives. On chest radiographs of older current or former smokers, there are many scars that can appear like cancer to the interpreting radiologists. We are reporting on the radiologists' false positives and on the effect of machine false positive detections on observer performance on cancer-free cases. The only difference between radiologists occurred when they changed their initial true negative decision to false positive (p-value less than 0.0001), average confidence level increased, on the scale from 0.0 to 100.0, from 16.9 (high confidence of non-cancer) to 53.5 (moderate confidence cancer was present). We are reporting on the consistency of misinterpretation by multiple radiologists when they interpret cancer-free radiographs of smokers in the absence of CAD prompts. When multiple radiologists selected the same false positive location, there was usually a definite abnormality that triggered this response. The CAD identifies areas that are of sufficient concern for cancer that the radiologists will switch from a correct decision of no cancer to mark a false positive, previously overlooked, but suspicious appearing cancer-free area; one that has often been marked by another radiologist without the use of the CAD prompt. This work has implications on what should be accepted as ground truth in ROC studies: One might ask, "What a false positive response means?" when the finding, clinically, looks like cancer-it just isn’t cancer, based on long-term follow-up or histology.
This paper evaluates the effect of Computer-Aided Detection prompts on the confidence and detection of cancer on chest radiographs. Expected findings included an increase in confidence rating and a decrease in variance in confidence when radiologists interacted with a computer prompt that confirmed their initial decision or induced them to switch from an incorrect to a correct decision. Their confidence rating decreased and the variance of confidence rating increased when the computer failed to confirm a correct or incorrect decision. A population of cases was identified that changed among reading modalities. This unstable group of cases differed between the Independent and Sequential without CAD modalities in cancer detection by radiologists and cancer detection by machine. CAD prompts induced the radiologists to make two types of changes in cases: changes on the sequential modality with CAD that restored an initial diagnosis made in the Independent read and new changes that were not present in the Independent or Sequential reads without CAD. This has implications for double reading of cases. The effects of intra-observer variability and inter-observer variability are suggested as potential causes for differences in statistical significance of the Independent and Sequential Design approaches to ROC studies.
Using data from a clinical trial of a commercial CAD system for lung cancer detection, we are comparing the time used for interpreting chest radiographs between the radiologists showing improvement in detecting lung cancer with computer assistance to those not showing improvement. While measurement showed that the 15 radiologists as a group showed improvement (the Az was 0.8288 in independent reading, and 0.8654 in sequential reading with CAD, improvement has a P-value of 0.0058), there were 9 radiologists who showed improvement and 6 who did not. The behavior of the radiologists differed between the cases that contained cancer and those that were cancer-free. For the cases that contained a cancer, there was no statistically significant difference in time between the two groups (P-value 0.26). For the cancer-free cases, we found a statistically significant greater interpretation time for the radiologists whose performance in cancer detection was better with computer assistance compared to those without improvement (P-value 0.02). This work shows that radiologists who increased their detection of lung cancer using CAD, compared to those who showed no improvement, significantly increased their reading time when they determined that true negative cases for cancer were indeed true negative cases, but did not increase reading time for true positive decision on cancer cases.
This paper describes the effect of a computer-aided detection (CAD) system's false positive marks on observer performance when interpreting films containing lung cancer. We compared the location/no location chosen initially by the radiologists and the stability or change in location that followed the provision of the CAD information. We found a difference in radiologists' behavior that depended on whether the radiologists' initial interpretation was a true positive or a false positive detection. When the radiologist made an incorrect initial decision, that decision was less stable than when the initial decision was correct.
In this paper, we look at a different potentially useful method of behavior analysis, a method that may allow one to derive from the ROC confidence ratings of individual radiologists, a behavioral operating point that closely reflects the point where the radiologist would have decided to act or take no action on a case. This behavioral operating point appears appropriate for the calculation of cost benefit relationships and for studying how a radiologist shifts within ROC space when provided with Computer Aided Diagnosis (CADx) information.
Our goal was to perform a pre-clinical test of the performance of a new pre-commercial system for detection of primary early-stage lung cancer on chest radiographs developed by Deus Technologies, LLC. The RapidScreenTM RS 2000 System integrates state of the art technical development in this field.
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