CAD-Aided Mammogram Training1

Rationale and Objectives Although computer-aided detection (CAD) improves the diagnosis rate of early breast cancer, it has not been well integrated into radiology residency and technician training program. Moreover, CAD performance studies ignore the reader’s training and experience with CAD. The purpose of this study was to investigate whether CAD training via a cognitive-perceptual based hypermedia program has effects on the performance studies of mammogram reading. Materials and Methods Three observers read a pretest set of 80 breast cancer cases (43 negative, 23 benign, and 14 malignant cancer cases). During 4 weeks’ training, the observers used a hypermedia instructional program in CAD-aided mammography interpretation. The program includes modules of CAD attention-focusing schemes, CAD procedural knowledge, and case-based simulations in mammography interpretation in consensus with CAD. By the end of the fourth week of the training, they reviewed a posttest set of cases. Data were analyzed with multireader, multicase receiver operating characteristic methods. Results Three readers performed better in mammogram reading after training in CAD knowledge than they did before CAD training. CAD training and experience improved the performance of CAD-aided mammography interpretation. Conclusion A statistically significant difference was found in each observer’s performance in CAD-aided mammogram reading before and after the training. CAD training will influence the perception, recognition, and interpretation of early breast cancer and CAD performance studies. Furthermore, the young generation of radiologic professionals can have more training in various attention-focusing features, declarative knowledge, procedural knowledge, and conditional knowledge of CAD and incorporate them into their knowledge base and strategic processing for the purpose of improving the accuracy of mammography interpretation performance.

[1]  A. L'Abbate,et al.  Digital Autoradiography: Film and Electronic Multitracer Techniques for Heart Imaging , 1984, IEEE Transactions on Medical Imaging.

[2]  Neil Charness,et al.  Cognitive and developmental factors in expert performance , 1997 .

[3]  R. Glaser.  Advances in Instructional Psychology , 1978 .

[4]  J. Moran,et al.  Sensation and perception , 1980 .

[5]  L P Clarke,et al.  Digital mammography: hybrid four-channel wavelet transform for microcalcification segmentation. , 1998, Academic radiology.

[6]  A. Mack.  Inattentional Blindness , 2003 .

[7]  S. Feig,et al.  Adverse effects of screening mammography. , 2004, Radiologic clinics of North America.

[8]  Vimla L. Patel,et al.  Causal Explanation of Complex Physiological Concepts by Medical Students. , 1991 .

[9]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[10]  L. Tabár,et al.  Potential contribution of computer-aided detection to the sensitivity of screening mammography. , 2000, Radiology.

[11]  H L Kundel,et al.  Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. , 1978, Investigative radiology.

[12]  Baoyu Zheng,et al.  Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications , 1996, IEEE Trans. Medical Imaging.

[13]  Dansheng Song,et al.  Digital mammography: hybrid M-channel wavelet transform for microcalcification segmentation , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  H Eichenbaum,et al.  How Does the Brain Organize Memories? , 1997, Science.

[15]  H. Rockette,et al.  Re: Computer-aided detection of breast cancer: has promise outstripped performance? , 2004, Journal of the National Cancer Institute.

[16]  Marios A. Gavrielides,et al.  Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists , 2003, SPIE Medical Imaging.

[17]  K L Lam,et al.  Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. , 1995, Medical physics.

[18]  G. Tourassi.  Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[19]  Alan M. Lesgold,et al.  Competence in the workplace: How cognitive performance models and situated instruction can accelerate skill acquisition. , 2000 .

[20]  Charles A. Perfetti,et al.  Effects of Long-Term Vocabulary Instruction on Lexical Access and Reading Comprehension. , 1982 .

[21]  A Beghdadi,et al.  Entropic contrast enhancement. , 1991, IEEE transactions on medical imaging.

[22]  David F. Salisbury.  Cognitive psychology and its implications for designing drill and practice programs for computers , 1990 .

[23]  K. Doi,et al.  Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. , 2001, Radiology.

[24]  J. Bransford.  How people learn , 2000 .

[25]  Dansheng Song,et al.  Fuzzy image segmentation for lung nodule detection , 2004, SPIE Optics + Photonics.

[26]  Stephen M. Alessi,et al.  Multimedia for Learning: Methods and Development , 2000 .

[27]  Maryellen L. Giger,et al.  Computer-Aided Diagnosis in Mammography , 2000 .

[28]  Wei Qian,et al.  Tree-structured nonlinear filters in digital mammography , 1994, IEEE Trans. Medical Imaging.

[29]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.

[30]  Heang-Ping Chan,et al.  Computer-aided detection of breast cancer. , 2004, Radiology.

[31]  Maria Kallergi,et al.  Using BIRADS categories in ROC experiments , 2002, SPIE Medical Imaging.

[32]  K Doi,et al.  Effect of case selection on the performance of computer-aided detection schemes. , 1994, Medical physics.

[33]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[34]  B. Bloom.  Automaticity: "The Hands and Feet of Genius.". , 1986 .

[35]  Wei Qian,et al.  Multiresolution/multiorientation based nonlinear filters for image enhancement and detection in digital mammography , 2002 .

[36]  M. Chi,et al.  The Nature of Expertise , 1988 .

[37]  John Seely Brown,et al.  Diagnostic Models for Procedural Bugs in Basic Mathematical Skills , 1978, Cogn. Sci..

[38]  M. Day,et al.  The road to excellence [human resource management] , 1999 .

[39]  L. Clarke,et al.  Fragmentary window filtering for multiscale lung nodule detection: preliminary study. , 1998, Academic radiology.

[40]  S. Duffy,et al.  High-risk mammographic parenchymal patterns, hormone replacement therapy and other risk factors: a case-control study. , 2000, International journal of epidemiology.

[41]  Wei Qian,et al.  Image feature extraction for mass detection in digital mammography: Influence of wavelet analysis , 1999 .

[42]  K. Marten,et al.  Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists , 2004, European Radiology.

[43]  Rangaraj M. Rangayyan,et al.  Automatic detection and classification system for calcifications in mammograms , 1993, Electronic Imaging.

[44]  Vimla L. Patel,et al.  Diagnostic Reasoning and Medical Expertise , 1994 .