Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.

As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object-based region-growing technique to improve mass segmentation. This segmentation method utilizes the density-weighted contrast enhancement (DWCE) filter as a pre-processing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object-based region growing was then applied to each of the identified structures. The region-growing technique uses gray-scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy-proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy-proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.     

Bilateral analysis based false positive reduction for computer-aided mass detection

We have developed a false positive (FP) reduction method based on analysis of bilateral mammograms for computerized mass detection systems. The mass candidates on each view were first detected by our unilateral computer-aided detection (CAD) system. For each detected object, a regional registration technique was used to define a region of interest (ROI) that is "symmetrical" to the object location on the contralateral mammogram. Texture features derived from the spatial gray level dependence matrices and morphological features were extracted from the ROI containing the detected object on a mammogram and its corresponding ROI on the contralateral mammogram. Bilateral features were then generated from corresponding pairs of unilateral features for each object. Two linear discriminant analysis (LDA) classifiers were trained from the unilateral and the bilateral feature spaces, respectively. Finally, the scores from the unilateral LDA classifier and the bilateral LDA asymmetry classifier were fused with a third LDA whose output score was used to distinguish true mass from FPs. A data set of 341 cases of bilateral two-view mammograms was used in this study, of which 276 cases with 552 bilateral pairs contained 110 malignant and 166 benign biopsy-proven masses and 65 cases with 130 bilateral pairs were normal. The mass data set was divided into two subsets for twofold cross-validation training and testing. The normal data set was used for estimation of FP rates. It was found that our bilateral CAD system achieved a case-based sensitivity of 70%, 80%, and 85% at average FP rates of 0.35, 0.75, and 0.95 FPs/image, respectively, on the test data sets with malignant masses. In comparison to the average FP rates for the unilateral CAD system of 0.58, 1.33, and 1.63, respectively, at the corresponding sensitivities, the FP rates were reduced by 40%, 44%, and 42% with the bilateral symmetry information. The improvement was statistically significance (p < 0.05) as estimated by JAFROC analysis.     

Impact of scanning direction on heart rate at certain levels of heart in electrocardiogram-gated 16-multidetector computed tomography angiography of coronary artery bypass grafts

OBJECTIVE: To assess impact of scanning direction on heart rates at certain levels of heart in electrocardiogram-gated 16-multidetector computed tomography (MDCT) detection of coronary artery bypass grafts and native coronary arteries. METHODS: Ninety patients with 219 grafts were studied by 16-MDCT. Forty-five patients were scanned craniocaudally. The remaining 45 patients were scanned caudocranially. Heart rates at each level were noted and compared between the 2 groups. RESULTS: The difference between mean heart rates of craniocaudal and caudocranial groups was found to be statistically significant at midcardiac, internal mammary artery origin, and cardiac base levels (P < 0.05). For the remaining levels, there was no statistically significant difference in mean heart rates. CONCLUSIONS: Performing electrocardiogram-gated 16-MDCT angiography for the evaluation of coronary arteries and bypass grafts in caudocranial direction provides lower heart rate at midcardiac and cardiac base levels of the heart, which are more prone to motion artifacts.     

Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy

PURPOSE: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. MATERIALS AND METHODS: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25-86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve. RESULTS: Without CAD, the five radiologists had an average area under the ROC curve (A(z)) of 0.83 (range, 0.81-0.87). With CAD, the average A(z) increased significantly (P = .006) to 0.90 (range, 0.86-0.93). When a 2% likelihood of malignancy was used as the threshold for biopsy recommendation, the average sensitivity of radiologists increased from 96% to 98% with CAD, while the average specificity for this data set decreased from 22% to 19%. If a biopsy recommendation threshold could be chosen such that sensitivity would be maintained at 96%, specificity would increase to 45% with CAD. CONCLUSION: Use of a computer algorithm may improve radiologists' accuracy in distinguishing malignant from benign breast masses on 3D US volumetric images.     

The number of negative pelvic lymph nodes removed does not affect the risk of biochemical failure after radical prostatectomy

Study Type – Therapy (case series)
Level of Evidence 4

OBJECTIVES

To assess patients who had radical prostatectomy (RP) and pelvic lymph node dissection (PLND) for pT2–4 N0M0 prostate cancer, to determine if LN yield affects the risk of biochemical failure (BCF), as the extent of PLND at the time of RP has become increasingly uncertain with the decreasing trend in tumour stage.

PATIENTS AND METHODS

We reviewed the Columbia University Urologic Oncology Database for patients with pT2–4 N0M0 prostate cancer treated with RP from 1990 to 2005. Exclusion criteria included <12 months of follow‐up, incomplete clinical and pathological data, and neoadjuvant androgen‐deprivation therapy (ADT) or immediate adjuvant ADT or external beam radiotherapy. Unadjusted and adjusted models were used to determine the ability of clinical and pathological variables to predict BCF.

RESULTS

The final dataset included 964 patients, with a mean age of 60.5 years and median preoperative prostate‐specific antigen (PSA) level of 6.2 ng/mL. The median (range) LN yield was 7 (1–42) and the median follow‐up 59 (12–190) months. In the unadjusted and adjusted models, preoperative PSA, pathological Gleason score, pathological stage, surgical margin status and year of surgery were significant predictors of BCF. The LN group was not a significant predictor of BCF in both the unadjusted and adjusted model (P = 0.759 and 0.408, respectively). When patients were stratified into high‐ and low‐risk groups, LN yield remained an insignificant predictor of BCF.

CONCLUSION

A higher LN yield at the time of RP does not increase the chance of cure for patients with pT2–4N0M0 prostate cancer. This lack of a survival advantage holds true for patients with high‐risk disease.