L M Warren1, M D Halling-Brown2, P T Looney3, D R Dance4, M G Wallis5, R M Given-Wilson6, L Wilkinson6, R McAvinchey7, K C Young4. 1. National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK. Electronic address: lucy.warren@nhs.net. 2. Scientific Computing, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK. 3. National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK. 4. National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK; Department of Physics, University of Surrey, Guildford, Surrey, GU2 7JP, UK. 5. Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK; NIHR Cambridge Biomedical Research Centre, Cambridge, CB2 0QQ, UK. 6. Department of Radiology, St George's University Hospitals NHS Foundation Trust, Tooting, London, SW17 0QT, UK. 7. Jarvis Breast Screening and Diagnostic Centre, Guildford, GU1 1LJ, UK.
Abstract
AIM: To investigate the effect of image processing on cancer detection in mammography. METHODS AND MATERIALS: An observer study was performed using 349 digital mammography images of women with normal breasts, calcification clusters, or soft-tissue lesions including 191 subtle cancers. Images underwent two types of processing: FlavourA (standard) and FlavourB (added enhancement). Six observers located features in the breast they suspected to be cancerous (4,188 observations). Data were analysed using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis. Characteristics of the cancers detected with each image processing type were investigated. RESULTS: For calcifications, the JAFROC figure of merit (FOM) was equal to 0.86 for both types of image processing. For soft-tissue lesions, the JAFROC FOM were better for FlavourA (0.81) than FlavourB (0.78); this difference was significant (p=0.001). Using FlavourA a greater number of cancers of all grades and sizes were detected than with FlavourB. FlavourA improved soft-tissue lesion detection in denser breasts (p=0.04 when volumetric density was over 7.5%) CONCLUSIONS: The detection of malignant soft-tissue lesions (which were primarily invasive) was significantly better with FlavourA than FlavourB image processing. This is despite FlavourB having a higher contrast appearance often preferred by radiologists. It is important that clinical choice of image processing is based on objective measures.
AIM: To investigate the effect of image processing on cancer detection in mammography. METHODS AND MATERIALS: An observer study was performed using 349 digital mammography images of women with normal breasts, calcification clusters, or soft-tissue lesions including 191 subtle cancers. Images underwent two types of processing: FlavourA (standard) and FlavourB (added enhancement). Six observers located features in the breast they suspected to be cancerous (4,188 observations). Data were analysed using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis. Characteristics of the cancers detected with each image processing type were investigated. RESULTS: For calcifications, the JAFROC figure of merit (FOM) was equal to 0.86 for both types of image processing. For soft-tissue lesions, the JAFROC FOM were better for FlavourA (0.81) than FlavourB (0.78); this difference was significant (p=0.001). Using FlavourA a greater number of cancers of all grades and sizes were detected than with FlavourB. FlavourA improved soft-tissue lesion detection in denser breasts (p=0.04 when volumetric density was over 7.5%) CONCLUSIONS: The detection of malignant soft-tissue lesions (which were primarily invasive) was significantly better with FlavourA than FlavourB image processing. This is despite FlavourB having a higher contrast appearance often preferred by radiologists. It is important that clinical choice of image processing is based on objective measures.
Authors: Mark D Halling-Brown; Lucy M Warren; Dominic Ward; Emma Lewis; Alistair Mackenzie; Matthew G Wallis; Louise S Wilkinson; Rosalind M Given-Wilson; Rita McAvinchey; Kenneth C Young Journal: Radiol Artif Intell Date: 2020-11-25