Characterization of Cystic Lesions by Spectral Mammography: Results of a Clinical Pilot Study. Issue 5 (May 2016)

• Record Type: Journal Article
• Authors: Erhard, Klaus; Kilburn-Toppin, Fleur; Willsher, Paula; Moa, Elin; Fredenberg, Erik; Wieberneit, Nataly; Buelow, Thomas; Wallis, Matthew G.
• Is Part Of: Investigative radiology. Volume 51:Issue 5(2016:May)
• Publication Date: 2016-05
• Subjects: lesion characterization -- digital mammography -- spectral imaging -- cyst -- solid -- lesion -- screening -- recall rate -- photon counting; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000246
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: Round lesions are a common mammographic finding, which can contribute more than 20% of overall recalls at screening. Discrimination of cystic fluid from solid tissue by spectral x-ray imaging has been demonstrated in specimen experiments. This work translates these results into a clinical pilot study to investigate the feasibility of discriminating cystic from solid lesions using spectral mammography. Materials and Methods: Women undergoing mammography as part of their routine diagnostic workup were consented for analysis of spectral information obtained from a photon-counting mammography system. Images were analyzed retrospectively after diagnosis was confirmed with ultrasound and pathology. Well-defined solitary lesions were delineated independently by 3 expert radiologists. A breast lesion model is generated from the spectral mammography data using the energy-dependent x-ray attenuation of cyst fluid, carcinoma, and adipose and glandular tissue. From the breast lesion model, 2 spectral features are computed and combined in a 2-feature discrimination algorithm, which is evaluated in an analysis of the receiver operating characteristic curve for the task of identifying solid lesions ("positive result"). Expected outcomes on a screening population are extrapolated from this pilot study by cross-validation with bootstrapping using a 95% confidence interval (CI). Results: The 2-feature discrimination algorithm was evaluated on the set of 119 eligible lesions (62 solids, 57 cysts) of diameter greater than 10 mm. The area under the receiver operating characteristic curve (AUC) was 0.88 with a specificity of 61% at the 99% sensitivity level on average over all expert radiologists. Cross-validation with bootstrapping of the clinical data revealed an AUC of 0.89 (95% CI, 0.79–0.96) and a specificity of 56% (95% CI, 33%–78%) when operating the algorithm at the 99% sensitivity level. Conclusions: Discriminating cystic from solid lesions with spectral mammography demonstrates promising results with the potential to reduce mammographic recalls. It is estimated that for each missed cancer at least 625 cystic lesions would have been correctly identified and hence would not have been needed to be recalled. Our results justify undertaking a larger reader study to refine the algorithm and determine clinically relevant thresholds to allow safe classification of cystic lesions by spectral mammography.