False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Mahoney, Mary C.; Meganathan, Karthikeyan

doi:10.1007/s10278-011-9389-7

Cited by 6 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, there was an average of 0.78 marks per image for both iCAD and R2 datasets. Mahoney and Meganathan [20] suggested that most false-positive marks on normal mammograms are readily dismissed by radiologists and do not impact performance. In our study, there was a cancer prevalence rate of 50%, which is over one hundred times the prevalence rate that would be experienced in practice, which is approximately five cancers per 1000 women.…”

Section: Discussionmentioning

confidence: 99%

Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Cole

Zhang²,

Marques³

et al. 2014

American Journal of Roentgenology

View full text Add to dashboard Cite

OBJECTIVE The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic Imaging Screening Trial (DMIST). MATERIALS AND METHODS Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system. RESULTS The average AUC was 0.71 (95% CI, 0.66–0.76) without and 0.72 (95% CI, 0.67–0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66–0.76) without and 0.72 (95% CI 0.67–0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant. CONCLUSION Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.

show abstract

Section: Discussionmentioning

confidence: 99%

Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Cole

Zhang²,

Marques³

et al. 2014

American Journal of Roentgenology

View full text Add to dashboard Cite

show abstract

“…One limitation of this study is that all dense-breast images evaluated by our CAD system were taken from screen-film mammography. Although there is an increasing trend toward full-field digital mammography (FFDM), this study still reflects the majority of practices [9,50]. Another limitation is that we used a previously developed CAD system to evaluate our preprocessing technique, instead of using a commercial CAD system.…”

Section: Discussionmentioning

confidence: 99%

“…We observed an average increase of 14.1 % (p00.01) in overall CAD performance (Figs. 8,9,and 10) when the restored real breast image set was used.…”

Section: Real Breast Image Setmentioning

confidence: 99%

See 1 more Smart Citation

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

et al. 2012

View full text Add to dashboard Cite

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.

show abstract

“…The false positive rate of CAD causes radiologists to dismiss high numbers of CAD marks detected in a screening population [11]. With the false positive rate, a typical screening population of 1,000 women would generate 2,000 false positive marks, while detecting approximately five cancers [12].…”

Section: Discussionmentioning

confidence: 99%

False Positive Value of Computer-Aided Detection in Full Field Digital Mammography in Detection of Breast Malignancy

Awad¹

2015

J Med Diagn Meth

View full text Add to dashboard Cite

Objective: The aim of this study is to estimate the false-positive rate of computer-aided detection (CAD) with fullfield digital mammography (FFDM) in the detection of breast cancer. Materials and Methods:This study was conducted in the period from February, 2013 until September, 2014. It included 659 patients who presented to the radiology department for mammography. Full-field digital mammography (FFDM), three-dimensional breast tomosynthesis and computer-aided detection (CAD) were done for all patients. The patients had breast ultrasound +/-MR Mammography, with special attention to the sites of CAD suspicious lesions. Follow up protocol was done for the selected patients.Results: From the 659 patients included in the study, 56 patients, with 70 lesions, had false positive findings of malignancy on CAD, giving a false positive rate of 8%. These cases had suspicious lesions on CAD not detected on further investigations. The results of the follow up of all patients showed no newly developed suspicious lesions. Conclusion:The false positive rate of a CAD system in detection of breast malignancy was introduced in this study. As per the available data, example the effect of breast density on false positive CAD scores helped the radiologists in our hospital to dismiss most of the false CAD marks with more confidence with no need for further investigation. However, if CAD marks were hard to dismiss, further investigation by ultrasound +/-MR mammography was advisable.

show abstract

False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Cited by 6 publications

References 31 publications

Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

False Positive Value of Computer-Aided Detection in Full Field Digital Mammography in Detection of Breast Malignancy

Contact Info

Product

Resources

About