Altmann, E.M., Trafton, J.G. (2004). Task interruption: resumption lag and the role of cues. Proc Ann Mtg Cog Sci Soc, 26. Available online at: https://escholarship.org/uc/item/18b4r66 (accessed November 16, 2017).Google Scholar

Andriole, K.P., Wolfe, J.M., Khorasani, R., Treves, S.T., Getty, D.J., Jacobson, F.L., Steigner, M.L., Pan, J.J., Sitek, A., Seltzer, S.E. (2011). Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day. Radiology, 259(2), 346–362.Google Scholar

Balint, B.J., Steenburg, S.D., Lin, H., Shen, C., Steele, J.L., Gunderman, R.B. (2014). Do telephone call interruptions have an impact on radiology resident diagnostic accuracy? Acad Radiol, 21(12), 1623–1628.Google Scholar

Ball, K.K., Beard, B.L., Roenker, D.L., Miller, R.L., Griggs, D.S. (1988). Age and visual search: expanding the useful field, J Opt Soc Am, 5, 1–10.Google Scholar

Bertram, R., Kaakinen, J., Bensch, F., Helle, L., Lantto, E., Niemi, P., Lundbom, N. (2016). Eye movements of radiologists reflect expertise in CT study interpretation: a potential tool to measure resident development. Radiology, 281(3), 805–815.CrossRefGoogle ScholarPubMed

Beyer, F., Zierott, L., Fallenberg, E.M., Juergens, K.U., Stoeckel, J., Heindel, W., Wormanns, D. (2007). Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol, 17(11), 2941–2947.CrossRefGoogle ScholarPubMed

Burling, D., Halligan, S., Altman, D.G., Atkin, W., Bartram, C., Fenlon, H., Laghi, A., Stoker, J., Taylor, S., Frost, R., Dessey, G., De Villiers, M., Florie, J., Foley, S., Honeyfield, L., Iannaccone, R., Gallo, T., Kay, C., Lefere, P., Lowe, A., Mangiapane, F., Marrannes, J., Neri, E., Nieddu, G., Nicholson, D., O Hare, A., Ori, S., Politi, B., Poulus, M., Regge, D., Renaut, L., Rudralingham, V., Signoretta, S., Vagli, P., Van der Hulst, V., Williams-Butt, J. (2006). CT colonography interpretation times: effect of reader experience, fatigue, and scan findings in a multi-centre setting. Eur Radiol, 16(8), 1745–1749.CrossRefGoogle Scholar

Cavanagh, P. (1987). Reconstructing the third dimension: interactions between color, texture, motion, binocular disparity, and shape. Comput Vision, Graph Image Process, 37(2), 171–195.CrossRefGoogle Scholar

Cooper, L., Gale, A., Darker, I., Toms, A., Saada, J. (2009). Radiology image perception and observer performance: how does expertise and clinical information alter interpretation? Stroke detection explored through eye-tracking. Proc SPIE Med Imag, 7263, 72630K.CrossRefGoogle Scholar

Coughlin, B.F., Seltzer, S.E., Swensson, R.G., Judy, P.F. (1992). Practices and attitudes about cathode-ray tube-based and film-based image interpretation. J Digit Imag, 5(1), 50–53.Google Scholar

Diaz, I., Schmidt, S., Verdun, F.R., Bochud, F.O. (2015). Eye-tracking of nodule detection in lung CT volumetric data. Med Phys, 42(6), 2925–2932.Google Scholar

Dreizin, D., Munera, F. (2012). Blunt polytrauma: evaluation with 64-section whole-body CT angiography. Radiographics, 32(3), 609–631.Google Scholar

Drew, T., Evans, K., Võ, M.L.-H., Jacobson, F.L., Wolfe, J.M. (2013a). Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images? Radiographics, 33(1), 263–274.CrossRefGoogle Scholar

Drew, T., Vo, M.L.-H., Olwal, A., Jacobson, F., Seltzer, S.E., Wolfe, J.M. (2013b). Scanners and drillers: characterizing expert visual search through volumetric images. J Vision, 13(10), 1–13.Google Scholar

Drew, T., Võ, M.L.-H., Wolfe, J.M. (2013c). The invisible gorilla strikes again: sustained inattentional blindness in expert observers. Psychol Sci, 24(9), 1848–1853.Google Scholar

Duncan, J., Humphreys, G.W. (1989). Visual-search and stimulus similarity. Psychol Rev, 96(3), 433–458.Google Scholar

Ebner, L., Tall, M., Choudhury, K.R., Ly, D.L., Roos, J.E., Napel, S., Rubin, G.D. (2017). Variations in the functional visual field for detection of lung nodules on chest computed tomography: impact of nodule size, distance, and local lung complexity. Med Phys, 44(7), 3483–3490.Google Scholar

Edelman, R.R. (2014). The history of MR imaging as seen through the pages of radiology. Radiology, 273(2 Suppl), S181–S200.CrossRefGoogle ScholarPubMed

Ellis, S.M., Hu, X., Dempere-Marco, L., Yang, G.Z., Wells, A.U., Hansell, D.M. (2006). Thin-section CT of the lungs: eye-tracking analysis of the visual approach to reading tiled and stacked display formats. Eur J Radiol, 59(2), 257–264.Google Scholar

Evans, K.K., Birdwell, R.L., Wolfe, J.M. (2013). If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PLoS One, 8(5), e64366.Google Scholar

Fishman, E.K., Ney, D.R., Heath, D.G., Corl, F.M., Horton, K.M., Johnson, P.T. (2006). Volume rendering versus maximum intensity projection in CT angiography: what works best, when, and why. RadioGraphics, 26(3), 905–922.Google Scholar

Ghekiere, O., Lesnik, A., Millet, I., Hoa, D., Guillon, F., Taourel, P. (2007). Direct visualization of perforation sites in patients with a non-traumatic free pneumoperitoneum: added diagnostic value of thin transverse slices and coronal and sagittal reformations for multi-detector CT. Eur Radiol, 17(9), 2302–2309.CrossRefGoogle ScholarPubMed

Godoy, M.C.B., Kim, T.J., White, C.S., Bogoni, L., de Groot, P., Florin, C., Obuchowski, N., Babb, J.S., Salganicoff, M., Naidich, D.P., Anand, V., Park, S., Vlahos, I., Ko, J.P. (2013). Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT. AJR, 200(1), 74–83.Google Scholar

Halligan, S., Mallett, S., Altman, D.G., McQuillan, J., Proud, M., Beddoe, G., Honeyfield, L., Taylor, S.A. (2011). Incremental benefit of computer-aided detection when used as a second and concurrent reader of CT colonographic data: multiobserver study. Radiology, 258(2), 469–476.Google Scholar

Helbren, E., Halligan, S., Phillips, P., Boone, D., Fanshawe, T.R., Taylor, S.A., Manning, D., Gale, A., Altman, D.G., Mallett, S. (2014). Towards a framework for analysis of eye-tracking studies in the three dimensional environment: a study of visual search by experienced readers of endoluminal CT colonography. Br J Radiol, 87(1037), 20130614.Google Scholar

Helbren, E., Fanshawe, T.R., Phillips, P., Mallett, S., Boone, D., Gale, A., Altman, D.G., Taylor, S.A., Manning, D., Halligan, S. (2015). The effect of computer-aided detection markers on visual search and reader performance during concurrent reading of CT colonography. Eur Radiol, 25(6), 1570–1578.Google Scholar

Horowitz, T.S. (2017). Prevalence in visual search: from the clinic to the lab and back again. Jpn Psychol Res, 59(2), 65–108.Google Scholar

Hu, C.H., Kundel, H.L., Nodine, C.F., Krupinski, E.A., Toto, L.C. (1994). Searching for bone fractures: a comparison with pulmonary nodule search. Acad Radiol, 1(1), 25–32.Google Scholar

Huang, H.K. (2011). Short history of PACS. Part I: USA. Eur J Radiol, 78(2), 163–176.Google Scholar

Krupinski, E.A. (1996). Influence of experience on scanning strategies in mammography. Proc SPIE, 2712, 1–8.Google Scholar

Krupinski, E.A., Berger, W.G., Dallas, W.J., Roehrig, H. (2003). Searching for nodules: what features attract attention and influence detection? Acad Radiol, 10(8), 861–868.Google Scholar

Krupinski, E.A., Tillack, A.A., Richter, L., Henderson, J.T., Bhattacharyya, A.K., Scott, K.M., Graham, A.R., Descour, M.R., Davis, J.R., Weinstein, R.S. (2006). Eye-movement study and human performance using telepathology virtual slides. Implications for medical education and differences with experience. Hum Pathol, 37(12), 1543–1556.Google Scholar

Kundel, H.L. (2015). Visual search and lung nodule detection on CT scans. Radiology, 274(1), 14–16.Google Scholar

Kundel, H.L., Nodine, C.F. (1975). Interpreting chest radiographs without visual search. Radiology, 116(3), 527–532.Google Scholar

Kundel, H.L., La Follette, P.S. (1972). Visual search patterns and experience with radiological images. Radiology, 103(3), 523–528.Google Scholar

Kundel, H.L., Nodine, C.F., Carmody, D. (1978). Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol, 13(3), 175–181.CrossRefGoogle ScholarPubMed

Kundel, H.L., Nodine, C.F., Thickman, D., Toto, L. (1987). Searching for lung nodules. A comparison of human performance with random and systematic scanning models. Invest Radiol, 22(5), 417–422.Google Scholar

Kundel, H.L., Nodine, C.F., Krupinski, E.A. (1989). Searching for lung nodules. Visual dwell indicates locations of false-positive and false-negative decisions. Invest Radiol, 24(6), 472–478.Google Scholar

Kundel, H.L., Nodine, C.F., Conant, E.F., Weinstein, S.P. (2007) Holistic component of image perception in mammogram interpretation: gaze-tracking study 1. Radiology, 242(2), 396–402.CrossRefGoogle Scholar

Larsson, L., Nystrom, M., Andersson, R., Stridh, M. (2015) Detection of fixations and smooth pursuit movements in high-speed eye-tracking data. Biomed Sig Proc Control, 18, 145–152.Google Scholar

Larsson, L., Nystrom, M., Ardo, H., Astrom, K., Stridh, M. (2016). Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation. J Vision, 16(15), 20.Google Scholar

Mallett, S., Phillips, P., Fanshawe, T.R., Helbren, E., Boone, D., Gale, A., Taylor, S.A., Manning, D., Altman, D.G., Halligan, S. (2014). Tracking eye gaze during interpretation of endoluminal three-dimensional CT colonography: visual perception of experienced and inexperienced readers. Radiology, 273(3), 783–792.CrossRefGoogle ScholarPubMed

Manning, D., Ethell, S., Donovan, T., Crawford, T. (2006). How do radiologists do it? The influence of experience and training on searching for chest nodules. Radiography, 12(2), 134–142.Google Scholar

Matsumoto, H., Terao, Y., Yugeta, A., Fukuda, H., Emoto, M., Furubayashi, T., Okano, T., Hanajima, R., Ugawa, Y. (2011). Where do neurologists look when viewing brain CT images? An eye-tracking study involving stroke cases. PLoS One, 6(12), e28928.Google Scholar

McConkie, G.W., Rayner, K. (1975). The span of the effective stimulus during a fixation in reading. Percep Psychophys, 17(6), 578–586.Google Scholar

McDonald, R.J., Schwartz, K.M., Eckel, L.J., Diehn, F.E., Hunt, C.H., Bartholmai, B.J., Erickson, B.J., Kallmes, D.F. (2015) The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol, 22(9), 1191–1198.CrossRefGoogle ScholarPubMed

Mital, P.K., Smith, T.J., Hill, R.L., Henderson, J.M. (2011) Clustering of gaze during dynamic scene viewing is predicted by motion. Cogn Comput, 3(1), 5–24.CrossRefGoogle Scholar

Napel, S., Rubin, G.D., Jeffrey, R.B. (1993). STS-MIP: a new reconstruction technique for CT of the chest. JCAT, 17(5), 832–838.Google Scholar

Nodine, C.F., Mello-Thoms, C., Kundel, H.L., Weinstein, S.P. (2002). Time course of perception and decision making during mammographic interpretation. AJR, 179(4), 917–923.CrossRefGoogle ScholarPubMed

Paik, D.S., Beaulieu, C.F., Jeffrey, R.B., Rubin, G.D., Napel, S. (1998). Automated flight path planning for virtual endoscopy. Med Phys, 25(5), 629–637.Google Scholar

Phillips, P., Boone, D., Mallett, S., Taylor, S.A., Altman, D.G., Manning, D., Gale, A., Halligan, S. (2013). Method for tracking eye gaze during interpretation of endoluminal 3D CT colonography: technical description and proposed metrics for analysis. Radiology, 267(3), 924–931.Google Scholar

Pickhardt, P.J. (2003). Three-dimensional endoluminal CT colonography (virtual colonoscopy): comparison of three commercially available systems. AJR, 181(6), 1599–1606.Google Scholar

Potter, M.C. (1975). Meaning in visual search. Science, 187(4180), 965–966.Google Scholar

Ratwani, R.M., Wang, E., Fong, A., Cooper, C.J. (2016). A human factors approach to understanding the types and sources of interruptions in radiology reading rooms. J Am Coll Radiol, 13(9), 1102–1105.Google Scholar

Rubin, G.D. (2000). Data explosion: the challenge of multidetector-row CT. Eur J Radiol, 36(2), 74–80.Google Scholar

Rubin, G.D. (2003). 3-D imaging with MDCT. Eur J Radiol, 45, S37–S41.Google Scholar

Rubin, G.D. (2014). Computed tomography: revolutionizing the practice of medicine for 40 years. Radiology, 273(2 Suppl), S45–S74.CrossRefGoogle Scholar

Rubin, G.D. (2015). Lung nodule and cancer detection in computed tomography screening. J Thorac Imag, 30(2), 130–138.Google Scholar

Rubin, G.D., Krupinski, E.A. (2017). Tracking eye movements during CT interpretation: inferences of reader performance and clinical competency require clinically realistic procedures for unconstrained search. Radiology, 283(3), 920.CrossRefGoogle ScholarPubMed

Rubin, G.D., Beaulieu, C.F., Argiro, V., Ringl, H., Norbash, A.M., Feller, J.F., Dake, M.D., Jeffrey, R.B., Napel, S. (1996). Perspective volume rendering of CT and MR images: applications for endoscopic imaging. Radiology, 199(2), 321–330.Google Scholar

Rubin, G.D., Shiau, M.C., Schmidt, A.J., Fleischmann, D., Logan, L., Leung, A.N., Jeffrey, R.B., Napel, S. (1999). Computed tomographic angiography: historical perspective and new state-of-the-art using multi detector-row helical computed tomography. JCAT, 23(Suppl 1), S83–S90.Google Scholar

Rubin, G.D., Sedati, P., Wei, J.L. (2009). Postprocessing and data analysis. In: Rubin, G.D., Rofsky, M.R. (eds.) CT and MR Angiography. Philadelphia, PA: Wolters Kluwer/ Lippincott Williams and Wilkins, pp. 197–251.Google Scholar

Rubin, G.D., Harrawood, B., Napel, S., Roos, J. E., Choudhury, K. R., Ebner, L. (2015a). The moment of recognition: method and analysis of gaze behavior in the search for lung nodules in CT scans. Available online at: archive.rsna.org/2015/15047526.html (accessed November 11, 2017).Google Scholar

Rubin, G.D., Roos, J.E., Tall, M., Harrawood, B., Bag, S., Ly, D.L., Seaman, D.M., Hurwitz, L.M., Napel, S., Roy Choudhury, K. (2015b). Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking. Radiology, 274(1), 276–286.Google Scholar

Seltzer, S.E., Judy, P.F., Adams, D.F., Jacobson, F.L., Stark, P., Kikinis, R., Swensson, R.G., Hooton, S., Head, B., Feldman, U. (1995). Spiral CT of the chest: comparison of cine and film-based viewing. Radiology, 197(1), 73–78.Google Scholar

Shen, Y.J., Jiang, Y.V. (2006). Interrupted visual searches reveal volatile search memory. J Exp Psychol Hum Percep Perform, 32(5), 1208–1220.Google Scholar

Simons, D.J., Chabris, C.F. (1999). Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception, 28(9), 1059–1074.Google Scholar

Solomon, J., Marin, D., Roy Choudhury, K., Patel, B., Samei, E. (2017). Effect of radiation dose reduction and reconstruction algorithm on image noise, contrast, resolution, and detectability of subtle hypoattenuating liver lesions at multidetector CT: filtered back projection versus a commercial model-based iterative reconstruction algorithm. Radiology, 284(3), 777–787.Google Scholar

Straub, W.H., Gur, D., Good, W.F., Campbell, W.L., Davis, P.L., Hecht, S.T., Skolnick, M.L., Thaete, F.L., Rosenthal, M.S., Sashin, D. (1991). Primary CT diagnosis of abdominal masses in a PACS environment. Radiology, 178(3), 739–743.Google Scholar

Suwa, K., Furukawa, A., Matsumoto, T., Yosue, T. (2001). Analyzing the eye movement of dentists during their reading of CT images. Odontology, 89(1), 54–61.CrossRefGoogle ScholarPubMed

Tall, M., Choudhury, K.R., Napel, S., Roos, J.E., Rubin, G.D. (2012). Accuracy of a remote eye tracker for radiologic observer studies: effects of calibration and recording environment. Acad Radiol, 19(2), 196–202.Google Scholar

Trafton, J.G., Altmann, E.M., Brock, D.P. (2005). Huh, what was I doing? How people use environmental cues after an interruption. Proc Hum Fact Ergon Soc Ann Meet, 49(3), 468–472.Google Scholar

Venjakob, A.C., Mello-Thoms, C.R. (2016). Review of prospects and challenges of eye tracking in volumetric imaging. J Med Imag Int Soc Optic Phot, 3(1), 011002.Google Scholar

Waite, S., Kolla, S., Jeudy, J., Legasto, A., Macknik, S.L., Martinez-Conde, S., Krupinski, E.A., Reede, D.L. (2017). Tired in the reading room: the influence of fatigue in radiology. J Am Coll Radiol, 14(2), 191–197.Google Scholar

Westbrook, J.I., Woods, A., Rob, M.I., Dunsmuir, W.T.M., Day, R.O. (2010). Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med, 170(8), 683–690.Google Scholar

Williams, L.H., Drew, T. (2017). Distraction in diagnostic radiology: how is search through volumetric medical images affected by interruptions? Cogn Res: Princ Implic, 2(1), 12.Google Scholar

Wolfe, J.M. (1994). Guided search 2.0: a revised model of visual search. Psychon Bull Rev, 1(2), 202–238.Google Scholar

Wolfe, J.M., Horowitz, T.S., Van Wert, M.J., Kenner, N.M., Place, S.S., Kibbi, N. (2007). Low target prevalence is a stubborn source of errors in visual search tasks. J Exp Psychol Gen, 136(4), 623–638.Google Scholar

Wolfe, J.M., Evans, K.K., Drew, T., Aizenman, A., Josephs, E. (2016). How do radiologists use the human search engine? Radiat Protect Dosim, 169(1–4), 24–31.CrossRefGoogle ScholarPubMed

Young, A.H., Hulleman, J. (2013). Eye movements reveal how task difficulty molds visual search. J Exp Psychol Hum Percep Perf, 39(1), 168–190.Google Scholar

Yu, J.-P.J., Kansagra, A.P., Mongan, J. (2014). The radiologist’s workflow environment: evaluation of its disruptors and potential implications. J Am Coll Radiol, 11(6), 589–593.CrossRefGoogle ScholarPubMed

American College of Radiology (2017). www.acraccreditation.org/~/media/ACRAccreditation/Documents/Mammography/Requirements.pdf?la=en (accessed November 2, 2017).Google Scholar

Boyd, N.F., Rommens, J.M., Vogt, K., Lee, V., Hopper, J.L., Yaffe, M.J., Paterson, A.D. (2005). Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol, 6, 798–780.Google Scholar

Burns, F.G. (2011). An Independent External Review of the Breast Screening Unit at East Lancashire NHS Trust. Burnley, UK: East Lancashire Hospitals NHS Trust.Google Scholar

Chen, Y. (2010). Intelligent computing applications based on eye gaze: their role in mammographic interpretation training. PhD thesis, Loughborough University, UK.Google Scholar

Chen, Y., Gale, A.G., Evanoff, M. (2013). Does routine breast screening practice over-ride display quality in reporting enriched test sets? Proc SPIE Med Imag, 8673, 86730v.Google Scholar

Chen, Y., James, J.J., Turnbull, A.E., Gale, A.G. (2015). The use of lower resolution viewing devices for mammographic interpretation: implications for education and training. Eur J Radiol, 25, 3003–3008.Google Scholar

Chen, Y., Dong, L., Nevisi, H., Gale, A.G. (2016). The international use of PERFORMS mammographic test sets. In: Tingberg, A., Lång, K., Timberg, P. (eds.) Breast Imaging: 13th International Workshop, IWDM 2016, Lecture Notes in Computer Science. Geneva, Switzerland: Springer, pp. 130–135.Google Scholar

Chen, Y., James, J., Dong, L., Gale, A.G. (2017). Measuring performance in the interpretation of chest radiographs – a pilot study. Clin Radiol, 72, 230–235.CrossRefGoogle ScholarPubMed

Cowley, H.C., Gale, A.G. (1997). Time of day effects on mammographic film reading performance. Proc SPIE Med Imag, 3036, 212–221.Google Scholar

Cowley, H.C., Gale, A.G. (1999). Breast cancer screening: comparison of radiologists’ performance in a self-assessment scheme and in actual breast screening. Proc SPIE Med Imag, 3663, 157–168.Google Scholar

Darker, I.T., Chen, Y., Gale, A.G. (2011). Health professionals’ agreement on density judgements and successful abnormality identification within the UK breast screening programme. Proc SPIE Med Imag, 7966, 796604.Google Scholar

Dong, L., Chen, Y., Gale, A.G., Chakraborty, D.P. (2012). A potential method to identify poor breast screening performance? Proc SPIE Med Imag, 8318, 831819.CrossRefGoogle Scholar

Esserman, L., Cowley, H., Eberle, C., Kirkpatrick, A., Chang, S., Berbaum, K., Gale, A.G. (2002). Improving the accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst, 94, 369–375.Google Scholar

Findlay, J.M., Gilchrist, I.D. (2003). Active Vision: The Psychology of Looking and Seeing. Oxford, England: Oxford University Press.Google Scholar

Forrest, A.P.M. (1986). Report to the Health Ministers of England, Wales, Scotland and Northern Ireland. London: HMSO.Google Scholar

Gale, A.G. (1997). Human response to visual stimuli. In: Hendee, W., Wells, P. (eds.) Perception of Visual Information. New York, NY: Springer Verlag, pp. 127–147.Google Scholar

Gale, A.G. (2003). PERFORMS – a self-assessment scheme for radiologists in breast screening. Semin Breast Dis, 6, 148–152.Google Scholar

Gale, A.G., Walker, G.E. (1991). Design for performance: quality assessment in a national breast screening programme. In: Lovesay, E. (ed.) Ergonomics – Design for Performance 1991. London, England: Taylor & Francis.Google Scholar

Gale, A.G., Roebuck, E.J., Riley, P., Worthington, B.S. (1987). Computer aids to mammography diagnosis. Br J Radiol, 60, 887–891.Google Scholar

Garland, L.H. (1949). On the scientific evaluation of diagnostic procedures. Radiology, 52, 309–328.Google Scholar

Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137–144.Google Scholar

Krupinski, E.A., Berbaum, K.S. (2010). Does reader visual fatigue impact interpretation accuracy? Proc SPIE Med Imag, 7627, 762701.Google Scholar

Kundel, H.L., Nodine, C.F., Carmody, D. (1978). Visual scanning, pattern recognition and decision making in pulmonary nodule detection. Invest Radiol, 13, 175–181.Google Scholar

National Health Service. (2017). https://digital.nhs.uk/catalogue/PUB23376 (accessed November 2, 2017).Google Scholar

Neisser, U. (1976). Cognition and Reality. San Francisco, CA: W.H. Freeman.Google Scholar

Nevisi, H., Dong, L., Chen, Y., Gale, A.G. (2017). How quickly do breast screeners learn their skills? Proc SPIE Med Imag, 10136, 101360D.Google Scholar

Royal College of Radiologists. (1990). Quality Assurance Guidelines for Radiologists. London, UK: Royal College of Radiologists.Google Scholar

Scott, H.J., Gale, A.G (2005). Breast screening: when is a difficult case truly difficult and for whom? Proc SPIE Med Imag, 5749, 557–565.Google Scholar

Scott, H.J., Gale, A.G., Wooding, D.S. (2004). Breast screening technologists: does real-life case volume affect performance? Proc SPIE Med Imag, 5372, 399–406.Google Scholar

Scott, H.J., Evans, A., Gale, A.G., Murphy, A., Reed, J. (2009). The relationship between real life breast screening and an annual self-assessment scheme. Proc SPIE Med Imag, 7263, E1–E9.Google Scholar

Sickles, E.A., D’Orsi, C.J., Bassett, L.W., et al. (2013). ACR BI-RADS mammography. In: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology.Google Scholar

Tang, Q., Dong, L., Chen, Y., Gale, A.G. (2017). The implementation of an AR (augmented reality) approach to support mammographic interpretation training – an initial feasibility study. Proc SPIE Med Imag, 10136, 1013604.Google Scholar

Yerushalmy, J. (1969). The statistical assessment of the variability in observer perception and description of roentgenographic pulmonary shadows. Radiol Clin N Am, 1, 381–390.Google Scholar

Al Mousa, D.S., Brennan, P.C., Ryan, E.A., Lee, W.B., Tan, J., Mello-Thoms, C. (2014a). How mammographic breast density affects radiologists’ visual search patterns. Acad Radiol, 21(11), 1386–1393.Google Scholar

Al Mousa, D.S., Mello-Thoms, C., Ryan, E.A., Lee, W.B., Pietrzyk, M.W., Reed, W.M., et al. (2014b). Mammographic density and cancer detection: does digital imaging challenge our current understanding? Acad Radiol, 21(11), 1377–1385.Google Scholar

Alakhras, M.M., Brennan, P.C., Rickard, M., Bourne, R., Mello-Thoms, C. (2015). Effect of radiologists’ experience on breast cancer detection and localization using digital breast tomosynthesis. Eur Radiol, 25(2), 402–409.CrossRefGoogle ScholarPubMed

Australian Government. (2009). BreastScreen Australia Evaluation. Screening Monograph No. 1/2009. Canberra, Australia: Australian Government.Google Scholar

Baker, J.A., Rosen, E.L., Lo, J.Y., Gimenez, E.I., Walsh, R., Soo, M.S. (2003). Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. Am J Roentgenol, 181(4), 1083–1088.Google Scholar

Bargallo, X., Velasco, M., Santamaria, G., Del Amo, M., Arguis, P., Sanchez Gomez, S. (2013). Role of computer-aided detection in very small screening detected invasive breast cancers. J Digit Imag, 26(3), 572–577.Google Scholar

Barlow, W.E., Chi, C., Carney, P.A., Taplin, S.H., D’Orsi, C., Cutter, G., et al. (2004). Accuracy of screening mammography interpretation by characteristics of radiologists. J Natl Cancer Inst, 96(24), 1840–1850.Google Scholar

Bassett, L.W. (1997). Diagnosis of Diseases of the Breast. Philadelphia, PA: W.B. Saunders.Google Scholar

Bird, R.E., Wallace, T.W., Yankaskas, B.C. (1992). Analysis of cancers missed at screening mammography. Radiology, 184(3), 613–617.Google Scholar

Boyd, N.F., Byng, J.W., Jong, R.A., Fishell, E.K., Little, L.E., Miller, A.B., et al. (1995). Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst, 87(9), 670–675.Google Scholar

Boyd, N.F., Lockwood, G.A., Martin, L.J., Knight, J.A., Byng, J.W., Yaffe, M.J., Tritchler, D.L. (1998). Mammographic densities and breast cancer risk. Breast Dis, 10, 113–126.Google Scholar

Brem, R.F., Baum, J., Lechner, M., Kaplan, S., Souders, S., Naul, L.G., Hoffmeister, J. (2003). Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial. Am J Roentgenol, 181(3), 687–693.Google Scholar

Broeders, M.J., Onland-Moret, N.C., Rijken, H.J., Hendriks, J.H., Verbeek, A.L., Holland, R. (2003). Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection. Eur J Cancer, 39(12), 1770–1775.CrossRefGoogle ScholarPubMed

Buist, D.S.M., Porter, P.L., Lehman, C., Taplin, S.H., White, E. (2004). Factors contributing to mammography failure in women aged 40–49 years. J Natl Cancer Inst, 96(19), 1432–1440.Google Scholar

Burrell, H.C., Sibbering, D.M., Wilson, A.R., Pinder, S.E., Evans, A.J., Yeoman, L.J., et al. (1996). Screening interval breast cancers: mammographic features and prognosis factors. Radiology, 199(3), 811–817.Google Scholar

Burrell, H.C., Evans, A.J., Wilson, A.R., Pinder, S.E. (2001). False-negative breast screening assessment: what lessons can we learn? Clin Radiol, 56(5), 385–388.Google Scholar

Carney, P.A., Miglioretti, D.L., Yankaskas, B.C., Kerlikowske, K., Rosenberg, R., Rutter, C. M., et al. (2003). Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med, 138(3), 168–175.Google Scholar

Cawson, J.N., Nickson, C., Amos, A., Hill, G., Whan, A.B., Kavanagh, A.M. (2009). Invasive breast cancers detected by screening mammography: a detailed comparison of computer-aided detection-assisted single reading and double reading. J Med Imag Radiat Oncol, 53(5), 442–449.Google Scholar

Chiarelli, A.M., Kirsh, V.A., Klar, N.S., Shumak, R., Jong, R., Fishell, E., et al. (2006). Influence of patterns of hormone replacement therapy use and mammographic density on breast cancer detection. Cancer Epidemiol Biomarkers Prev, 15(10), 1856–1862.Google Scholar

Ciatto, S., Visioli, C., Paci, E., Zappa, M. (2004). Breast density as a determinant of interval cancer at mammographic screening. Br J Cancer, 90(2), 393–396.Google Scholar

Ciatto, S., Houssami, N., Bernardi, D., Caumo, F., Pellegrini, M., Brunelli, S., et al. (2013). Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol, 14(7), 583–589.Google Scholar

Committee. (1997). Quality Assurance Guidelines for Radiologists. Sheffield, England: NHSBSP Publications.Google Scholar

Digabel-Chabay, C., Allioux, C., Labbe-Devilliers, C., Meingan, P., Ricaud Couprie, M. (2004). Architectural distortion and diagnostic difficulties. J Radiol, 85, 2099–2106.Google Scholar

Doi, K., MacMahon, H., Katsuragawa, S., Nishikawa, R.M., Jiang, Y. (1999). Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol, 31(2), 97–109.Google Scholar

D’Orsi, C.J., Kopans, D.B. (1993). Mammographic feature analysis. Semin Roentgenol, 28(3), 204–230.Google Scholar

D’Orsi, C.J.B., Feig, S.A., et al. (1998). Illustrated Breast Imaging Reporting and Data System, Illustrated BI-RADS, 3rd edn. Reston, VA: American College of Radiology.Google Scholar

Elmore, J.G., Jackson, S.L., Abraham, L., Miglioretti, D.L., Carney, P.A., Geller, B.M., et al. (2009). Variability in interpretive performance at screening mammography and radiologists’ characteristics associated with accuracy. Radiology, 253(3), 641–651.Google Scholar

Giger, M.L. (2000). Computer-aided diagnosis of breast lesions in medical images. Comput Sci Eng, 2(5), 39–45.Google Scholar

Giger, M.L., Karssemeijer, N., Armato, S.G. (2001). Computer-aided diagnosis in medical imaging. IEEE Trans Med Imag, 20(12), 1205–1208.Google Scholar

Gilbert, F.J., Tucker, L., Gillan, M.G., Willsher, P., Cooke, J., Duncan, K.A., et al. (2015). Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY trial). Radiology, 277(3), 697–706.Google Scholar

Goergen, S.K., Evans, J., Cohen, G.P., MacMillan, J.H. (1997). Characteristics of breast carcinomas missed by screening radiologists. Radiology, 204(1), 131–135.CrossRefGoogle ScholarPubMed

Homer, M.J.S. (1997). Mammographic Interpretation: A Practical Approach,. New York, NY: McGraw-Hill.Google Scholar

Huynh, P.T., Jarolimek, A.M., Daye, S. (1998). The false-negative mammogram. Radiographics, 18(5), 1137–1154; quiz 1243–1134.Google Scholar

Jain, A. (2000). Artificial intelligence techniques in breast cancer diagnosis and prognosis. Paper presented at the World Scientific, Singapore.Google Scholar

Jiang, Y.N., Nishikawa, R.M., Papaioannou, J. (1998). Requirement of microcalcifications detection for computerized classification of malignant and benign clustered microcalcifications. Proc SPIE Med Imag, 3338.Google Scholar

Kan, L., Olivotto, I.A., Warren Burhenne, L.J., Sickles, E.A., Coldman, A.J. (2000). Standardized abnormal interpretation and cancer detection ratios to assess reading volume and reader performance in a breast screening program. Radiology, 215(2), 563–567.Google Scholar

Knutzen, A.M., Gisvold, J.J. (1993). Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clin Proc, 68(5), 454–460.Google Scholar

Kolb, T.M., Lichy, J., Newhouse, J.H. (2002). Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology, 225(1), 165–175.Google Scholar

Kundel, H.L., Nodine, C.F., Carmody, D. (1978). Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol, 13(3), 175–181.Google Scholar

Lang, K., Andersson, I., Rosso, A., Tingberg, A., Timberg, P., Zackrisson, S. (2016). Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmo Breast Tomosynthesis Screening Trial, a population-based study. Eur Radiol, 26(1), 184–190.Google Scholar

Majid, A.S., de Paredes, E.S., Doherty, R.D., Sharma, N.R., Salvador, X. (2003). Missed breast carcinoma: pitfalls and pearls. Radiographics, 23(4), 881–895.Google Scholar

Mandelson, M.T., Oestreicher, N., Porter, P.L., White, D., Finder, C.A., Taplin, S.H., White, E. (2000). Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst, 92(13), 1081–1087.Google Scholar

Miglioretti, D.L., Smith-Bindman, R., Abraham, L., Brenner, R.J., Carney, P.A., Bowles, E.J., et al. (2007). Radiologist characteristics associated with interpretive performance of diagnostic mammography. J Natl Cancer Inst, 99(24), 1854–1863.Google Scholar

National Accreditation Committee. (1994). National Program for the Early Detection of Breast Cancer – National Accreditation Requirements. Canberra, Australia: National Accreditation Committee.Google Scholar

Pohlman, S., Powell, K.A., Obuchowski, N.A., Chilcote, W.A., Grundfest-Broniatowski, S. (1996). Quantitative classification of breast tumors in digitized mammograms. Med Phys, 23(8), 1337–1345.Google Scholar

Rangayyan, R.M., Mudigonda, N.R., Desautels, J.E. (2000). Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput, 38(5), 487–496.Google Scholar

Rangayyan, R.M., Banik, S., Desautels, J.E. (2010). Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imag, 23(5), 611–631.Google Scholar

Rawashdeh, M.A., Bourne, R.M., Ryan, E.A., Lee, W.B., Pietrzyk, M.W., Reed, W.M., et al. (2013a). Quantitative measures confirm the inverse relationship between lesion spiculation and detection of breast masses. Acad Radiol, 20(5), 576–580.Google Scholar

Rawashdeh, M.A., Lee, W.B., Bourne, R.M., Ryan, E.A., Pietrzyk, M.W., Reed, W.M., et al. (2013b). Markers of good performance in mammography depend on number of annual readings. Radiology, 269(1), 61–67.Google Scholar

Reed, W.M., Lee, W.B., Cawson, J.N., Brennan, P.C. (2010). Malignancy detection in digital mammograms: important reader characteristics and required case numbers. Acad Radiol, 17(11), 1409–1413.Google Scholar

Rosenberg, R.D., Hunt, W.C., Williamson, M.R., Gilliland, F.D., Wiest, P.W., Kelsey, C.A., et al. (1998). Effects of age, breast density, ethnicity, and estrogen replacement therapy on screening mammographic sensitivity and cancer stage at diagnosis: review of 183,134 screening mammograms in Albuquerque, New Mexico. Radiology, 209(2), 511–518.Google Scholar

Sampat, M.P., Whitman, G.J., Markey, M.K., Bovik, A.C. (2005). Evidence based detection of spiculated masses and architectural distortions. Proc SPIE Med Imag, 5747.Google Scholar

Scott, H.J., Gale, A.G. (2006). Breast screening: PERFORMS identifies key mammographic training needs. Br J Radiol, 79, S127–S133.Google Scholar

Shen, L., Rangayyan, R.M., Desautels, J.L. (1994). Application of shape analysis to mammographic calcifications. IEEE Trans Med Imag, 13(2), 263–274.Google Scholar

Shi, J., Sahiner, B., Chan, H.P., Ge, J., Hadjiiski, L., Helvie, M.A., et al. (2008). Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys, 35(1), 280–290.CrossRefGoogle ScholarPubMed

Sickles, E.A. (1989). Breast masses: mammographic evaluation. Radiology, 173(2), 297–303.Google Scholar

Soh, B.P., Lee, W., McEntee, M.F., Kench, P.L., Reed, W.M., Heard, R., et al. (2013). Screening mammography: test set data can reasonably describe actual clinical reporting. Radiology, 268(1), 46–53.Google Scholar

Soh, B.P., Lee, W.B., McEntee, M.F., Kench, P.L., Reed, W.M., Heard, R., et al. (2014). Mammography test sets: reading location and prior images do not affect group performance. Clin Radiol, 69(4), 397–402.Google Scholar

Soh, B.P., Lee, W.B., Mello-Thoms, C., Tapia, K., Ryan, J., Hung, W.T., et al. (2015). Certain performance values arising from mammographic test set readings correlate well with clinical audit. J Med Imag Radiat Oncol, 59(4), 403–410.Google Scholar

Soh, B.P.L., Lee, W.B., Wong, J., Sim, L., Hillis, S.L., Tapia, K.A., Brennan, P.C. (2016). Varying performance in mammographic interpretation across two countries: do results indicate reader or population variances? Proc SPIE Med Imag, 9787.Google Scholar

Strickland, R.N., Hahn, H. (1996). Wavelet transforms for detecting microcalcifications in mammograms. IEEE Trans Med Imag, 15(2), 218–229.Google Scholar

Suleiman, W.I., McEntee, M.F., Lewis, S.J., Rawashdeh, M.A., Georgian-Smith, D., Heard, R., et al. (2016a). In the digital era, architectural distortion remains a challenging radiological task. Clin Radiol, 71(1), e35–e40.Google Scholar

Suleiman, W.I., Rawashdeh, M.A., Lewis, S.J., McEntee, M.F., Lee, W., Tapia, K., Brennan, P.C. (2016b). Impact of breast reader assessment strategy on mammographic radiologists’ test reading performance. J Med Imag Radiat Oncol, 60(3), 352–358.Google Scholar

Troxel, D.B. (2006). Medicolegal aspects of error in pathology. Arch Pathol Lab Med, 130(5), 617–619.Google Scholar

US Department of Health and Human Services. (1997). An Overview of the Final Regulations Implementing the Mammography Quality Standards Act of 1992. Rockville, MD: US Department of Health and Human Services.Google Scholar

Vyborny, C.J., Doi, T., O’Shaughnessy, K.F., Romsdahl, H.M., Schneider, A.C., Stein, A.A. (2000a). Breast cancer: importance of spiculation in computer-aided detection. Radiology, 215(3), 703–707.CrossRefGoogle ScholarPubMed

Vyborny, C.J., Giger, M.L., Nishikawa, R.M. (2000b). Computer-aided detection and diagnosis of breast cancer. Radiol Clin N Am, 38(4), 725–740.Google Scholar

Yankaskas, B.C., Schell, M.J., Bird, R.E., Desrochers, D.A. (2001). Reassessment of breast cancers missed during routine screening mammography: a community-based study. Am J Roentgenol, 177(3), 535–541.Google Scholar