Biomedical subjects

Nicholas Petrick

Publications and source records attributed to Nicholas Petrick.

12 recordsLinked to original sources

PubMed2006

Breast masses: computer-aided diagnosis with serial mammograms.

PURPOSE: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign. MATERIALS AND METHODS: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study. Ninety temporal pairs of two-view serial mammograms (depicting 47 malignant and 43 benign biopsy-proved masses) were obtained from 68 patient files and were digitized. Biopsy was the reference standard. Eight Mammography Quality Standards Act of 1992-accredited radiologists and two breast imaging fellows assessed digitized two-view temporal pairs (in preselected regions of interest only) by estimating likelihood of malignancy and Breast Imaging Reporting and Data System (BI-RADS) category without and with CAD. Observers' rating data were analyzed with Dorfman-Berbaum-Metz (DBM) multireader multicase method. Statistical significance of differences was estimated with the DBM method and Student two-tailed paired t test. RESULTS: Average area under the receiver operating characteristic curve for likelihood of malignancy across the 10 observers was 0.83 (range, 0.74-0.88) without CAD and improved to 0.87 (range, 0.80-0.92) with CAD (P < .05). The average partial area index above a sensitivity of 0.90 for likelihood of malignancy was 0.35 (range, 0.13-0.54) without CAD and 0.49 (range, 0.18-0.73) with CAD--a nonsignificant improvement (P = .11). For BI-RADS assessment, it was estimated that with CAD, six radiologists would correctly recommend additional biopsies for malignant masses (range, 4.3%-10.6%) and five would correctly recommend reduction of biopsy (ie, fewer biopsies) for benign masses (range, 2.3%-9.3%). However, five radiologists would incorrectly recommend additional biopsy for benign masses (range, 2.3%-14.0%), and one would incorrectly recommend reduction of biopsy (4.3%). CONCLUSION: CAD involving interval change analysis of preselected regions of interest can significantly improve radiologists' accuracy in classifying masses on digitized screen-film mammograms as malignant or benign.

Adult↗

PubMed2006

Assessment of polyp and mass histopathology by intravenous contrast-enhanced CT colonography.

RATIONALE AND OBJECTIVES: We sought to demonstrate that intravenous contrast-enhanced CT colonography (CTC) can distinguish colonic adenomas from carcinomas. METHODS: Supine intravenous contrast-enhanced CTC with colonoscopic and/or surgical correlation was performed on 25 patients with colonic adenomas or carcinomas. Standard deviation of mean polyp CT attenuation was computed and assessed using ANOVA and receiver-operating characteristic analyses. RESULTS: Colonoscopy confirmed 32 polyps or masses 1 to 8 cm in size. The standard deviations of CT attenuation were carcinomas (n = 13; 36 +/- 6 HU; range 28-48 HU) and adenomas (n = 19; 49 +/- 14 HU; range 31-100 HU) (P = 0.005). At a standard deviation threshold of 42 HU, the sensitivity and specificity for classifying a polyp or mass as a carcinoma were 92% and 79%, respectively. The area under the receiver-operating characteristic curve was 0.89 +/- 0.06 (95% confidence interval 0.73-0.96). CONCLUSIONS: Measurement of the standard deviation of CT attenuation on intravenous contrast-enhanced CTC permits histopathologic classification of polyps 1 cm or larger as carcinomas versus adenomas. The presence of ulceration or absence of muscular invasion in carcinomas creates overlap with adenomas, reducing the specificity of carcinoma classification.

Adenoma↗

PubMed2005

Computer-aided detection of breast masses on full field digital mammograms.

We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.

Algorithms↗

PubMed2004

Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study.

PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' characterization of masses on serial mammograms. MATERIALS AND METHODS: Two hundred fifty-three temporal image pairs (138 malignant and 115 benign) obtained from 96 patients who had masses on serial mammograms were evaluated. The temporal pairs were formed by matching masses of the same view from two different examinations. Eight radiologists and two breast imaging fellows assessed the temporal pairs with and without computer aid. The classification of accuracy was quantified by using the area under receiver operating characteristic curve (A(z)). The statistical significance of the difference in A(z) between the different reading conditions was estimated with the Dorfman-Berbaum-Metz method for analysis of multireader multicase data and with the Student paired t test for analysis of observer-specific paired data. RESULTS: The average A(z) for radiologists' estimates of the likelihood of malignancy was 0.79 without CAD and improved to 0.84 with CAD. The improvement was statistically significant (P =.005). The corresponding average partial area index was 0.25 without CAD and improved to 0.37 with CAD. The improvement was also statistically significant (P =.005). On the basis of Breast Imaging Reporting and Data System assessments, it was estimated that with CAD, each radiologist, on average, reduced 0.7% (0.8 of 115) of unnecessary biopsies and correctly recommended 5.7% (7.8 of 138) of additional biopsies. CONCLUSION: CAD based on analysis of interval changes can significantly increase radiologists' accuracy in classification of masses and thereby may be useful in improving correct biopsy recommendations.

Adult↗

PubMed2004

Virtual colonoscopy.

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Colonography, Computed Tomographic↗

PubMed2004

Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

PURPOSE: To evaluate a noncommercial computer-aided detection (CAD) program for breast cancer detection with screening mammography. MATERIALS AND METHODS: A CAD program was developed for mammographic breast cancer detection. The program was applied to 2,389 patients' screening mammograms at two geographically remote academic institutions (institutions A and B). Thirteen radiologists who specialized in breast imaging participated in this pilot study. For each case, the individual radiologist performed a prospective Breast Imaging Reporting and Data System (BI-RADS) assessment after viewing of the screening mammogram. Subsequently, the radiologist was shown CAD results and rendered a second BI-RADS assessment by using knowledge of both mammographic appearance and CAD results. Outcome analysis of results of examination in patients recalled for a repeat examination, of biopsy, and of 1-year follow-up examination was recorded. Correct detection with CAD included a computer-generated mark indicating a possible malignancy on craniocaudal or mediolateral oblique views or both. RESULTS: Eleven (0.46%) of 2,389 patients had mammographically detected nonpalpable breast cancers. Ten (91%) of 11 (95% CI: 74%, 100%) cancers were correctly identified with CAD. Radiologist sensitivity without CAD was 91% (10 of 11; 95% CI: 74%, 100%). In 1,077 patients, follow-up findings were documented at 1 year. Five (0.46%) patients developed cancers, which were found on subsequent screening mammograms. The area where the cancers developed in two (40%) of these five patients was marked (true-positive finding) by the computer in the preceding year. Because of CAD results, a 9.7% increase in recall rate from 14.4% (344 of 2,389) to 15.8% (378 of 2,389) occurred. Radiologists' recall rate of study patients prior to use of CAD was 31% higher than the average rate for nonstudy cases (10.3%) during the same time period at institution A. CONCLUSION: Performance of the CAD program had a very high sensitivity of 91% (95% CI: 74%, 100%).

Adult↗

PubMed2004

Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.

Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.

Algorithms↗

PubMed2004

Computerized nipple identification for multiple image analysis in computer-aided diagnosis.

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations which were used as the gold standard. In the training data set, 301 nipples were positively identified and were referred to as visible nipples. Seventy six nipples could not be positively identified and were referred to as invisible nipples. The radiologists provided their estimation of the nipple locations in the latter group for comparison with the computer estimates. The computerized method could detect 89.37% (269/301) of the visible nipples and 69.74% (53/76) of the invisible nipples within 1 cm of the gold standard. In the test data set, 298 and 69 of the nipples were classified as visible and invisible, respectively. 92.28% (275/298) of the visible nipples and 53.62% (37/69) of the invisible nipples were identified within 1 cm of the gold standard. The results demonstrate that the nipple locations on digitized mammograms can be accurately detected if they are visible and can be reasonably estimated if they are invisible. Automated nipple detection will be an important step towards multiple image analysis for CAD.

Algorithms↗

PubMed2002

Optimal neural network architecture selection: improvement in computerized detection of microcalcifications.

RATIONALE AND OBJECTIVES: The authors evaluated the effect of optimal neural network architecture selection on the performance of a computer-aided diagnostic system designed to detect microcalcification clusters on digitized mammograms. MATERIALS AND METHODS: The authors developed a computer program to detect microcalcification clusters automatically on digitized mammograms. Previously, they found that a properly selected and trained convolution neural network (CNN) could reduce false-positive (FP) findings and therefore improve the accuracy of microcalcification detection. In the current study, they evaluated the effectiveness of the CNN optimized with an automated optimization technique in improving the accuracy of the microcalcification detection program, comparing it with the manually selected CNN. An independent test data set was used, which included 472 mammograms selected from the University of South Florida public database and contained 253 biopsy-proved malignant clusters. RESULTS: At an FP rate of 0.7 cluster per image, the film-based sensitivity was 84.6% for the optimized CNN, compared with 77.2% for the manually selected CNN. For clusters imaged on both craniocaudal and mediolateral oblique views, a cluster could be considered detected when it was detected on one or both views. For this case-based approach, at an FP rate of 0.7 per image, the sensitivity was 93.3% for the optimized and 87.0% for the manually selected CNN. CONCLUSION: The classification of true and false signals is an important step in the microcalcification detection program. An optimized CNN can effectively reduce FP findings and improve the accuracy of the computer-aided detection system.

Breast Neoplasms↗

PubMed2002

Improvement of computerized mass detection on mammograms: fusion of two-view information.

Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more useful if the false-positive rate is reduced. Current CAD systems generally detect and characterize suspicious abnormal structures in individual mammographic images. Clinical experiences by radiologists indicate that screening with two mammographic views improves the detection accuracy of abnormalities in the breast. It is expected that the fusion of information from different mammographic views will improve the performance of CAD systems. We are developing a two-view matching method that utilizes the geometric locations, and morphological and textural features to correlate objects detected in two different views using a prescreening program. First, a geometrical model is used to predict the search region for an object in a second view from its location in the first view. The distance between the object and the nipple is used to define the search area. After pairing the objects in two views, textural and morphological characteristics of the paired objects are merged and similarity measures are defined. Linear discriminant analysis is then employed to classify each object pair as a true or false mass pair. The resulting object correspondence score is combined with its one-view detection score using a fusion scheme. The fusion information was found to improve the lesion detectability and reduce the number of FPs. In a preliminary study, we used a data set of 169 pairs of cranio-caudal (CC) and mediolateral oblique (MLO) view mammograms. For the detection of malignant masses on current mammograms, the film-based detection sensitivity was found to improve from 62% with a one-view detection scheme to 73% with the new two-view scheme, at a false-positive rate of 1 FP/image. The corresponding cased-based detection sensitivity improved from 77% to 91%.

Algorithms↗

PubMed2002

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.

Adolescent↗

PubMed2002

Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients.

PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per mammogram, respectively. The difference between malignant mass-detection performance in subsets of cases collected at each institution was found to be less than 1%. The detection accuracy for benign masses was lower than that for malignant masses. CONCLUSION: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.

Algorithms↗