Computer-aided Detection Performance in Mammographic Examination of Masses: Assessment

Gur, David; Stalder, Jennifer S.; Hardesty, Lara A.; Zheng, Bin; Sumkin, Jules H.; Chough, Denise M.; Shindel, Betty E.; Rockette, Howard E.

doi:10.1148/radiol.2332040277

Cited by 81 publications

(87 citation statements)

References 32 publications

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“…For the queried region in MLO view, the CAD-generated detection and classification scores are 0.52 and 0.40, respectively. Based on CAD analysis, this is an uncued mass-like area (because the detection scores of two regions depicted on CC and MLO view images are smaller than CAD operating cueing threshold of 0.56 [31]) and the probability of this area associated with malignancy is also low based on the comparison of the CBIR-selected similar reference regions. Figure 4 shows and compares two ROC-type performance curves of our interactive CAD system evaluated using two KNN algorithms with the same reference database of 3,600 ROIs and a leave-one-out testing method.…”

Section: Resultsmentioning

confidence: 99%

“…A conventional CAD scheme [31] is pre-installed in the system, which can preprocess the images of interest stored in the system. For each initially detected suspicious region, this CAD scheme uses a multi-feature-based artificial neural network (ANN) to generate a detection score indicating the likelihood of the region being associated with a truepositive mass.…”

Section: Methodsmentioning

confidence: 99%

“…CAD scheme extracts and computes a set of 14 image features from the segmented region and its surrounding background. A pre-optimized artificial neural network based classifier [31] implemented in the conventional CAD scheme is called again to process this region and compute a detection score. This score is not provided and recorded in current CAD-cueing.…”

mentioning

confidence: 99%

“…Since conventional CAD could miss or be unable to cue a high fraction of subtle masses [31], we added a function in our interactive CAD system, which allows an observer to query any suspicious regions (no matter whether it is cued or not cued by CAD). Thus, the new CAD integrated with a region segmentation algorithm, a global feature-based classifier (ANN), and a local region-based learning method (CBIR) will process and analyze the queried region and provide the observer feedback or evidence of why CAD does or does not detect (or cue) this region.…”

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confidence: 99%

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An Interactive System for Computer-Aided Diagnosis of Breast Masses

Wang

Liu

et al. 2012

J Digit Imaging

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Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists "a visual aid" in interpreting mammograms, we developed and tested an interactive system for computeraided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting "abnormalities" similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ±0.006 to 0.897±0.005 (p<0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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An Interactive System for Computer-Aided Diagnosis of Breast Masses

Wang

Liu

et al. 2012

J Digit Imaging

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“…However, the lower CAD sensitivity for mass detection and the higher falsepositive rates reduce radiologists' confidence in CAD-cued masses [4]. As a result, although CAD schemes could potentially detect a larger fraction of false-negative cancers depicted as subtle masses [7,8], radiologists frequently discard CAD-cued subtle masses in the clinical practice [9,10]. A recent study reported that using current commercialized CAD systems did not increase cancer detection rate but significantly increase recall (false-positive) rate in the clinical practice [11].…”

Section: Introductionmentioning

confidence: 99%

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

et al. 2007

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Rationale and Objectives: The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions. Materials and Methods:A large and diverse reference library involving 3000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion. Results:In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (p = 0.128). Conclusion:This study suggests that using the new automated ICAD scheme, the inter-observer variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (p < 0.05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

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Breast Cancer: Computer-Aided Detection

Zheng

Methods of Cancer Diagnosis, Therapy and Prognosis

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Computer-aided Detection Performance in Mammographic Examination of Masses: Assessment

Abstract: Performance of CAD systems for mass detection at mammography varies significantly, depending on examination and system used. Actual performance of all systems in clinical environment can be improved.

Cited by 81 publications

References 32 publications

An Interactive System for Computer-Aided Diagnosis of Breast Masses

An Interactive System for Computer-Aided Diagnosis of Breast Masses

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

Breast Cancer: Computer-Aided Detection

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