Biomedical subjects

Ronald A Castellino

Publications and source records attributed to Ronald A Castellino.

5 recordsLinked to original sources

PubMed2006

Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.

RATIONALE AND OBJECTIVES: To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size. MATERIALS AND METHODS: A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics. RESULTS: Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors. CONCLUSION: CAD detection performance of solid lung nodules did not suffer until 48:1 compression.

Algorithms↗

PubMed2005

Computer aided detection (CAD): an overview.

Computer aided detection (CAD) is a technology designed to decrease observational oversights--and thus the false negative rates--of physicians interpreting medical images. Prospective clinical studies have demonstrated an increase in breast cancer detection with CAD assistance. This overview briefly describes the metrics that have been used to define CAD system performance.

Humans↗

PubMed2004

Computer-aided detection in diagnostic mammography: detection of clinically unsuspected cancers.

OBJECTIVE: We had two objectives: to determine the percentage of women presenting with clinical findings whose diagnostic mammogram led to detection of a breast cancer at a site distant from the original clinical complaint and to assess the performance of computer-aided detection (CAD) on diagnostic mammography. MATERIALS AND METHODS: Three institutions contributed consecutive cases in which a mammogram was obtained to evaluate a clinical finding, after which a histologic diagnosis of breast cancer was made. Clinical data and the mammograms were reviewed to determine the nature of the clinical findings and to document the location and characteristics of 212 biopsy-proven cancers in 197 patients who met the study criteria. Standard four-view breast mammograms were then analyzed by a CAD system. RESULTS: The most common clinical finding was a palpable mass (90%, 177/197), with nipple discharge (5%, 9/197), focal tenderness or pain (2%, 5/197), and miscellaneous complaints (3%, 6/197) also noted. Two separate cancers were found in 7.6% (15/197) of the cases. In another 7.6% (15/197) of the cases, the single diagnosed cancer was not at the location of the specific clinical finding. The CAD system correctly marked 87% (26/30) of those cancers that were clinically unsuspected (i.e., not at the location of the clinical finding). CONCLUSION: Breast cancers occurred at locations other than the site of the presenting clinical finding in 15% (30/197) of patients undergoing diagnostic mammography in whom a cancer was detected. CAD identified 87% of these incidentally detected cancers and may therefore be useful as a detection aid to the radiologist when interpreting diagnostic mammograms.

Breast Neoplasms↗

PubMed2002

Invasive lobular carcinoma of the breast: mammographic characteristics and computer-aided detection.

PURPOSE: To characterize the mammographic appearance of invasive lobular carcinoma in a large series of screening-detected consecutive breast cancers and to evaluate the ability of a computer-aided detection system to mark these carcinomas. MATERIALS AND METHODS: Investigators used the Breast Imaging Reporting and Data System lexicon to characterize lesions as part of a retrospective review of 90 screening mammographic examinations that led to biopsy-proved diagnosis of 94 invasive lobular carcinoma lesions. The 40 available prior mammographic examinations (obtained 9-24 months earlier) were also reviewed to characterize any visible findings. The results of a computer-aided detection analysis were compared with the images, and the sensitivity of the algorithm was calculated for correct detection of the lesions. RESULTS: Fifty-six (60%) of 94 lesions manifested as masses, of which 40 (71%) were described as irregular and spiculated; 20 (21%) of 94, as architectural distortions; and the remainder, 18 (20%), as either asymmetric densities or calcifications. On the screening mammograms showing biopsy-proved cancers, the sensitivity of the computer-aided detection system was 86 (91%) of 94 lesions. Thirty-one of the 40 prior mammograms showed retrospectively visible findings, and 24 (77%) of 31 were marked by the computer-aided detection system. CONCLUSION: Spiculated masses and architectural distortions are the predominant appearances of invasive lobular carcinoma, and a computer-aided detection system correctly marked a high percentage of invasive lobular carcinoma lesions.

Adult↗

PubMedDate not supplied

Computer-aided detection in oncologic imaging: screening mammography as a case study.

Accuracy of the imaging report is dependent on the observational and interpretive skills of the radiologist, which varies between observers. Over the past several decades, research programs have focused on the use of computer algorithms to address both the perception and the interpretation aspects of diagnostic imaging. Computer-based technology that analyzes images in order to detect features of disease is called computer-aided detection (CAD). This paper reviews the current status of CAD as used with screening mammography.

Biopsy↗