Biomedical subjects

Mark A Helvie

Publications and source records attributed to Mark A Helvie.

At least 19 recordsLinked to original sources

PubMed2007

Quasi-continuous and discrete confidence rating scales for observer performance studies: Effects on ROC analysis.

RATIONALE AND OBJECTIVES: To examine the effects of the number of categories in the rating scale used in an observer experiment on the results of ROC analysis by a simulation study. MATERIALS AND METHODS: We have previously evaluated the effects of computer-aided diagnosis on radiologists' characterization of malignant and benign breast masses in serial mammograms. The evaluation of the likelihood of malignancy was performed on a quasi-continuous (0-100 points) confidence rating scale. In this study, we simulated the use of discrete confidence rating scales with fewer number of categories and analyzed the results with receiver operating characteristic (ROC) methodology. The observers' estimates of the likelihood of malignancy were also mapped to BI-RADS assessments with five and seven categories and ROC analysis was performed. The area under the ROC curve and the partial area index obtained from ROC analysis of the different confidence rating scales were compared. RESULTS: The fitted ROC curves and the performance indices do not change significantly when the confidence rating scales were varied from 6 to 101 points if the estimated operating points obtained directly from the data are distributed relatively evenly over the entire range of true-positive fraction (TPF) and false-positive fraction (FPF). The mapping of the likelihood of malignancy observer data to the seven-category BI-RADS assessment scale allowed reliable ROC analysis, whereas mapping to the five-category BI-RADS scale could cause erratic ROC curve fitting because of the lack of operating points in the mid-range or failure in ROC curve fitting because of data degeneration for some observers. CONCLUSION: ROC analysis of discrete confidence rating scales with few but relatively evenly distributed data points over the entire FPF and TPF range is comparable to that of a quasi-continuous rating scale. However, ROC analysis of discrete confidence rating scales with few and unevenly distributed data points may cause unreliable estimations.

Breast Neoplasms↗

PubMed2006

Changes in surgical management resulting from case review at a breast cancer multidisciplinary tumor board.

BACKGROUND: The treatment of breast cancer requires a multidisciplinary approach, and patients are often referred to a multidisciplinary cancer clinic. The purpose of the current study was to evaluate the impact of this approach on the surgical management of breast cancer. METHODS: The medical records of 149 consecutive patients referred to a multidisciplinary breast cancer clinic over a 1-year period with a diagnosis of breast cancer were reviewed retrospectively for alterations in radiologic, pathologic, surgical, and medical interpretations and the effect that these alterations had on recommendations for surgical management. RESULTS: A review of the imaging studies resulted in changes in interpretations in 67 of the 149 patients studied (45%). This resulted in a change in surgical management in 11% of patients. Review of the pathology resulted in changes in the interpretation for 43 of the 149 patients (29%). Thirteen patients (9%) had surgical management changes made solely as a result of pathologic reinterpretation. In 51 patients (34%), a change in surgical management was recommended after discussion with the surgeons, medical oncologists, and radiation oncologists that was not based on reinterpretation of the radiologic or pathologic findings. Overall, a second evaluation of patients referred to a multidisciplinary tumor board led to changes in the recommendations for surgical management in 77 of 149 of those patients studied (52%). CONCLUSIONS: The changes in management stemmed from differences in mammographic interpretation, pathologic interpretation, and evaluation by medical and radiation oncologists and surgical breast specialists. Multidisciplinary review can provide patients with useful additional information when making difficult treatment decisions.

Breast Neoplasms↗

PubMed2006

Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories.

PURPOSE: To retrospectively compare computer-aided mammographic density estimation (MDEST) with radiologist estimates of percentage density and Breast Imaging Reporting and Data System (BI-RADS) density classification. MATERIALS AND METHODS: Institutional Review Board approval was obtained for this HIPAA-compliant study; patient informed consent requirements were waived. A fully automated MDEST computer program was used to measure breast density on digitized mammograms in 65 women (mean age, 53 years; range, 24-89 years). Pixel gray levels in detected breast borders were analyzed, and dense areas were segmented. Percentage density was calculated by dividing the number of dense pixels by the total number of pixels within the borders. Seven breast radiologists (five trained with MDEST, two not trained) prospectively assigned qualitative BI-RADS density categories and visually estimated percentage density on 260 mammograms. Qualitative BI-RADS assessments were compared with new quantitative BI-RADS standards. The reference standard density for this study was established by allowing the five trained radiologists to manipulate the MDEST gray-level thresholds, which segmented mammograms into dense and nondense areas. Statistical tests performed include Pearson correlation coefficients, Bland-Altman agreement method, kappa statistics, and unpaired t tests. RESULTS: There was a close correlation between the reference standard and radiologist-estimated density (R = 0.90-0.95) and MDEST density (R = 0.89). Untrained radiologists overestimated percentage density by an average of 37%, versus 6% for trained radiologists (P < .001). MDEST showed better agreement with the reference standard (average overestimate, 1%; range, -15% to +18%). MDEST correlated better with percentage density than with qualitative BI-RADS categories. There were large overlaps and ranges of percentage density in qualitative BI-RADS categories 2-4. Qualitative BI-RADS categories correlated poorly with new quantitative BI-RADS categories, and 16 (6%) of 260 views were erroneously classified by MDEST. CONCLUSION: MDEST compared favorably with radiologist estimates of percentage density and is more reproducible than radiologist estimates when qualitative BI-RADS density categories are used. Qualitative and quantitative BI-RADS density assessments differed markedly.

Adult↗

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

Joint two-view information for computerized detection of microcalcifications on mammograms.

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Candidate pairs were classified with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 116 pairs of mammograms containing microcalcification clusters and 203 pairs of normal images from the University of South Florida (USF) public database was used for training the two-view detection algorithm. The trained method was tested on an independent test set of 167 pairs of mammograms, which contained 71 normal pairs and 96 pairs with microcalcification clusters collected at the University of Michigan (UM). The similarity classifier had a very low FP rate for the test set at low and medium levels of sensitivity. However, the highest mammogram-based sensitivity that could be reached by the similarity classifier was 69%. The single-view classifier had a higher FP rate compared to the similarity classifier, but it could reach a maximum mammogram-based sensitivity of 93%. The fusion method combined the scores of these two classifiers so that the number of FPs was substantially reduced at relatively low and medium sensitivities, and a relatively high maximum sensitivity was maintained. For the malignant microcalcification clusters, at a mammogram-based sensitivity of 80%, the FP rates were 0.18 and 0.35 with the two-view fusion and single-view detection methods, respectively. When the training and test sets were switched, a similar improvement was obtained, except that both the fusion and single-view detection methods had superior test performances on the USF data set than those on the UM data set. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.

Breast↗

PubMed2006

Computer aided detection of clusters of microcalcifications on full field digital mammograms.

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from the artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.

Algorithms↗

PubMed2006

Dual system approach to computer-aided detection of breast masses on mammograms.

In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a computer-added detection (CAD) system optimized with "average" masses with another CAD system optimized with "subtle" masses. The two single CAD systems have similar image processing steps, which include prescreening, object segmentation, morphological and texture feature extraction, and false positive (FP) reduction by rule-based and linear discriminant analysis (LDA) classifiers. A feed-forward backpropagation artificial neural network was trained to merge the scores from the LDA classifiers in the two single CAD systems and differentiate true masses from normal tissue. For an unknown test mammogram, the two single CAD systems are applied to the image in parallel to detect suspicious objects. A total of three data sets were used for training and testing the systems. The first data set of 230 current mammograms, referred to as the average mass set, was collected from 115 patients. We also collected 264 mammograms, referred to as the subtle mass set, which were one to two years prior to the current exam from these patients. Both the average and the subtle mass sets were partitioned into two independent data sets in a cross validation training and testing scheme. A third data set containing 65 cases with 260 normal mammograms was used to estimate the FP marker rates during testing. When the single CAD system trained on the average mass set was applied to the test set with average masses, the FP marker rates were 2.2, 1.8, and 1.5 per image at the case-based sensitivities of 90%, 85%, and 80%, respectively. With the dual CAD system, the FP marker rates were reduced to 1.2, 0.9, and 0.7 per image, respectively, at the same case-based sensitivities. Statistically significant (p < 0.05) improvements on the free response receiver operating characteristic curves were observed when the dual system and the single system were compared using the test sets with either average masses or subtle masses.

Algorithms↗

PubMed2005

Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience.

The purpose of the study was to design a computer-aided detection (CAD) system for breast mass detection on digital breast tomosynthesis (DBT) mammograms and to perform a preliminary evaluation of the performance of this system. Twenty-six patients were imaged with a prototype DBT system. Institutional review board approval and written informed patient consent were obtained. Use of the data set in this study was HIPAA compliant. The CAD system first screened the three-dimensional volume of the mass candidates by means of gradient-field analysis. Each mass candidate was segmented from the structured background, and its image features were extracted. A feature classifier was designed to differentiate true masses from normal tissues. The CAD system was trained and tested by using a leave-one-case-out method. The classifier calculated a mean area under the test receiver operating characteristic curve of 0.91 +/- 0.03 (standard error of mean). The CAD system achieved a sensitivity of 85%, with 2.2 false-positive objects per case. The results demonstrate the feasibility of the authors' approach to the development of a CAD system for DBT mammography.

Adult↗

PubMed2005

Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy.

PURPOSE: To prospectively compare the ability of clinical examination, mammography, vascularity-sensitive ultrasound, and magnetic resonance imaging (MRI) to determine pathologic complete response (CR) in breast cancer patients undergoing neoadjuvant chemotherapy. PATIENTS AND METHODS: Participants were women with primary measurable, operable invasive breast cancer (Stages I-III) who presented to the University of Michigan Breast Care Center. Eligibility criteria were based on clinical need for chemotherapy as part of the overall treatment plan. The chemotherapy consisted of doxorubicin and docetaxel administered every 3 weeks for four cycles. Tumor size measurements by physical examination and by the three imaging modalities were performed before chemotherapy was initiated and after its completion, prior to definitive surgery. Response criteria were pre-specified in this prospective design, and study radiologists analyzed the mammographic, sonographic and MRI image sets blinded to information from the other modalities and blinded to final histological diagnosis. The pathologic CR rate obtained by the clinical and imaging modalities was compared to pathologic CR as determined pathologically. RESULTS: 41 of 43 enrolled patients had a determination of pathologic response, and 4 patients had a pathologic CR to this chemotherapy (9.8%). The accuracy of physical examination, mammography, ultrasound, and MRI in determining pathologic CR was 75, 89, 82, and 89% respectively (NS). CONCLUSION: Biopsy after neoadjuvant chemotherapy remains absolutely necessary to determine pathologic CR to neoadjuvant chemotherapy, as the accuracy of current imaging modalities is insufficient to make this determination. The accuracy of mammography, vascularity-sensitive ultrasound, and MRI were not observed to be significantly different.

Adult↗

PubMed2005

Comparison of similarity measures for the task of template matching of masses on serial mammograms.

We conducted a study to evaluate the effectiveness of twelve different similarity measures in matching the corresponding masses on temporal pairs of current and prior mammograms. To perform this comparison we implemented each of the twelve similarity measures in the final stage of our multistage registration technique for automated registration of breast lesions in serial mammograms. The multistage technique consists of three stages. In the first stage an initial fan-shape search region was estimated on the prior mammogram based on the geometrical position of the mass on the current mammogram. In the second stage, the location of the fan-shape region was refined by warping, based on an affine transformation and simplex optimization. A new refined search region was defined on the prior mammogram. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. Our data set consisted of 318 temporal pairs. We performed three experiments, using a different subset of the 318 temporal pairs for each experiment. In each experiment we further tested how the performance of the similarity measures varied as the size of the search region increased or decreased. We evaluated the twelve similarity measures based on four criteria. The first criterion was the mean Euclidean distance, which was the average distance of the true location of the mass to the location detected by the similarity measure. The second criterion was the percentage of temporal pairs that were aligned so that 50% or more of the lesion area overlapped. The third criterion was the percentage of pairs that were aligned so that 75% or more of the lesion area overlapped. The fourth and final criterion was the robustness of the similarity measure. Our results showed that three of the similarity measures, Pearson's correlation, the cosine coefficient, and Goodman and Kruskal's Gamma coefficient, provide significantly higher accuracy (p < 0.05) in the task of matching the corresponding masses on serial mammograms than the other nine similarity measures.

Algorithms↗

PubMed2005

ROC study of the effect of stereoscopic imaging on assessment of breast lesions.

An observer performance study was conducted to evaluate the usefulness of assessing breast lesion characteristics with stereomammography. Stereoscopic image pairs of 158 breast biopsy tissue specimens were acquired with a GE Senographe 2000D full field digital mammography system using a 1.8x magnification geometry. A phantom-shift method equivalent to a stereo shift angle of +/- 3 degrees relative to a central axis perpendicular to the detector was used. For each specimen, two pairs of stereo images were taken at approximately orthogonal orientations. The specimens contained either a mass, microcalcifications, both, or normal tissue. Based on pathological analysis, 39.9% of the specimens were found to contain malignancy. The digital specimen radiographs were displayed on a high resolution MegaScan CRT monitor driven by a DOME stereo display board using in-house developed software. Five MQSA radiologists participated as observers. Each observer read the 316 specimen stereo image pairs in a randomized order. For each case, the observer first read the monoscopic image and entered his/her confidence ratings on the presence of microcalcifications and/or masses, margin status, BI-RADS assessment, and the likelihood of malignancy. The corresponding stereoscopic images were then displayed on the same monitor and were viewed through stereoscopic LCD glasses. The observer was free to change the ratings in every category after stereoscopic reading. The ratings of the observers were analyzed by ROC methodology. For the 5 MQSA radiologists, the average Az value for estimation of the likelihood of malignancy of the lesions improved from 0.70 for monoscopic reading to 0.72 (p=0.04) after stereoscopic reading, and the average Az value for the presence of microcalcifications improved from 0.95 to 0.96 (p=0.02). The Az value for the presence of masses improved from 0.80 to 0.82 after stereoscopic reading, but the difference fell short of statistical significance (p=0.08). The visual assessment of margin clearance was found to have very low correlation with microscopic analysis with or without stereoscopic reading. This study demonstrates the potential of using stereomammography to improve the detection and characterization of mammographic lesions.

Biopsy↗

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

An academic health center cost analysis of screening mammography: creating a financially viable service.

BACKGROUND: The current study sought to determine the institutional financial impact of a screening mammography (SM) program in the context of an integrated cancer center. METHODS: Using administrative databases, 10,048 women were identified as receiving screening mammograms in fiscal year 1999 and the first one-half of fiscal year 2000. The utilization of breast care resources was followed for an average of 1208 days. The University of Michigan cost accounting system was then used to determine overall margin (revenues-total costs) and contribution margin (revenues-actual costs) of the SM program, as well as other breast care services. RESULTS: The percentage of variable costs to total costs for the SM program was 24%. The overall facility losses in the breast care line were $1.7 million with a positive contribution margin of $2.0 million. The annual yield of nonscreening/diagnostic mammographic procedures was 0.9%. All types of radiologic activity failed to cover their total costs, but did provide a positive contribution margin. Overall margins for surgery procedures were approximately even, and adjuvant medical and radiotherapy services were net positive. Modeling helped to identify overhead limits necessary to achieve margin targets associated with increased activity. CONCLUSIONS: The current study showed that SM programs are unlikely to succeed financially without careful selection of those screened to increase the yield of diagnostic and therapeutic procedures. Based on favorable contribution margins, SM programs were viable when viewed as incremental business.

Adult↗

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

Lobular carcinoma in situ or atypical lobular hyperplasia at core-needle biopsy: is excisional biopsy necessary?

PURPOSE: To retrospectively determine frequency of invasive cancer or ductal carcinoma in situ (DCIS) at excisional biopsy in women with atypical lobular hyperplasia (ALH) or lobular carcinoma in situ (LCIS) at percutaneous core-needle biopsy (CNB). MATERIALS AND METHODS: Review of results in 6,081 consecutive patients who underwent CNB at two institutions revealed that in 35 (0.58%), LCIS (n = 15) or ALH (n = 20) was the pathologic finding with highest risk. Patient age range was 41-84 years (mean, 59 years). Of 35 patients, 26 (74%) underwent excisional biopsy and nine (26%) underwent mammographic follow-up for longer than 2 years. Lesions with a pathologic upgrade were noted when invasive cancer or DCIS occurred at the CNB site. CNB results in patients with a diagnosis of atypical ductal hyperplasia (ADH) (75 of 6,081 [1.2%]) were reviewed; these patients underwent subsequent excisional biopsy. Statistical comparison of frequency of upgrading of lesions in patients with a diagnosis of LCIS or ALH at CNB and in those with a diagnosis of ADH at CNB was performed (Pearson chi(2) test). RESULTS: In six (17%) of 35 (95% CI: 4.7%, 29.6%) patients, lesions were upgraded to DCIS (n = 4) or invasive cancer (n = 2). In 15 patients with LCIS diagnosed at CNB, lesions in four (27%) were upgraded to either DCIS or invasive cancer. In 20 patients with ALH diagnosed at CNB, lesions were upgraded to DCIS in two (10%). Lesions in nine patients who underwent mammographic follow-up were stable. No mammographic or technical findings distinguished patients with upgraded lesions from those whose lesions were not upgraded. In 12 (16%) of 75 (95% CI: 7.7%, 24.3%) patients with ADH, lesions were upgraded. Difference between the upgrade rate in patients with LCIS or ALH and that in those with ADH was not significant (P =.88). CONCLUSION: Lesions in 17% of patients with LCIS or ALH at CNB were upgraded to invasive cancer or DCIS; this rate was similar to the upgrade rate in patients with ADH. Excisional biopsy is supported when LCIS, ALH, or ADH is diagnosed at CNB.

Adult↗

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

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Algorithms↗

PubMed2004

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images.

Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.

Absorptiometry, Photon↗