Breast Cancer: Computer-aided Detection with Digital Breast Tomosynthesis.

PURPOSE: To evaluate a commercial tomosynthesis computer-aided detection (CAD) system in an independent, multicenter dataset. MATERIALS AND METHODS: Diagnostic and screening tomosynthesis mammographic examinations (n = 175; cranial caudal and mediolateral oblique) were randomly selected from a previous institutional review board-approved trial. All subjects gave informed consent. Examinations were performed in three centers and included 123 patients, with 132 biopsy-proven screening-detected cancers, and 52 examinations with negative results at 1-year follow-up. One hundred eleven lesions were masses and/or microcalcifications (72 masses, 22 microcalcifications, 17 masses with microcalcifications) and 21 were architectural distortions. Lesions were annotated by radiologists who were aware of all available reports. CAD performance was assessed as per-lesion sensitivity and false-positive results per volume in patients with negative results. RESULTS: Use of the CAD system showed per-lesion sensitivity of 89% (99 of 111; 95% confidence interval: 81%, 94%), with 2.7 ± 1.8 false-positive rate per view, 62 of 72 lesions detected were masses, 20 of 22 were microcalcification clusters, and 17 of 17 were masses with microcalcifications. Overall, 37 of 39 microcalcification clusters (95% sensitivity, 95% confidence interval: 81%, 99%) and 79 of 89 masses (89% sensitivity, 95% confidence interval: 80%, 94%) were detected with the CAD system. On average, 0.5 false-positive rate per view were microcalcification clusters, 2.1 were masses, and 0.1 were masses and microcalcifications. CONCLUSION: A digital breast tomosynthesis CAD system can allow detection of a large percentage (89%, 99 of 111) of breast cancers manifesting as masses and microcalcification clusters, with an acceptable false-positive rate (2.7 per breast view). Further studies with larger datasets acquired with equipment from multiple vendors are needed to replicate the findings and to study the interaction of radiologists and CAD systems.

Performance of a fully automatic lesion detection system for breast DCE-MRI.

PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

Validation of an elastic registration technique to estimate anatomical lung modification in non-small-cell lung cancer tomotherapy.

BACKGROUND: The study of lung parenchyma anatomical modification is useful to estimate dose discrepancies during the radiation treatment of Non-Small-Cell Lung Cancer (NSCLC) patients. We propose and validate a method, based on free-form deformation and mutual information, to elastically register planning kVCT with daily MVCT images, to estimate lung parenchyma modification during Tomotherapy. METHODS: We analyzed 15 registrations between the planning kVCT and 3 MVCT images for each of the 5 NSCLC patients. Image registration accuracy was evaluated by visual inspection and, quantitatively, by Correlation Coefficients (CC) and Target Registration Errors (TRE). Finally, a lung volume correspondence analysis was performed to specifically evaluate registration accuracy in lungs. RESULTS: Results showed that elastic registration was always satisfactory, both qualitatively and quantitatively: TRE after elastic registration (average value of 3.6 mm) remained comparable and often smaller than voxel resolution. Lung volume variations were well estimated by elastic registration (average volume and centroid errors of 1.78% and 0.87 mm, respectively). CONCLUSIONS: Our results demonstrate that this method is able to estimate lung deformations in thorax MVCT, with an accuracy within 3.6 mm comparable or smaller than the voxel dimension of the kVCT and MVCT images. It could be used to estimate lung parenchyma dose variations in thoracic Tomotherapy.

A fully automatic algorithm for segmentation of the breasts in DCE-MR images.

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).