The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images

PurposeThe purpose of this study was to evaluate the usefulness of a computer-aided diagnosis (CAD) scheme for improving the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images.MethodsThe database included 97 ultrasonographic images with hypoechoic areas: 48 benign cases [benign lesion with benign mammary tissue or fibrocystic disease (n = 20), fibroadenoma (n = 11), and intraductal papilloma (n = 17)] and 49 malignant cases [ductal carcinoma in situ (n = 17) and invasive ductal carcinoma (n = 32)]. Seven clinicians, three expert breast surgeons, and four general surgeons participated in the observer study. They were asked their confidence level concerning the possibility of malignancy in all 97 cases with and without the use of the CAD scheme. Receiver operating characteristic (ROC) analysis was performed to evaluate the usefulness of the CAD scheme.ResultsThe areas under the ROC curve (AUC) improved for all observers when they used the CAD scheme and increased from 0.649 to 0.783 (P = 0.0167). Notably, the AUC for the general surgeon group increased from 0.625 to 0.793 (P = 0.045).ConclusionsThis study showed that the performance of clinicians to diagnose non-mass lesions appearing as hypoechoic areas on breast ultrasonographic images was improved by the use of a CAD scheme.

[1]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[2]  P. Schlepckow.  Ultrasound screening of newborn's hip , 1992 .

[3]  K. Doi,et al.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs. , 1996, Radiology.

[4]  Hong Wang,et al.  Novel Computer-Aided Diagnosis Algorithms on Ultrasound Image: Effects on Solid Breast Masses Discrimination , 2010, Journal of Digital Imaging.

[5]  Pavel Crystal,et al.  Using sonography to screen women with mammographically dense breasts. , 2003, AJR. American journal of roentgenology.

[6]  Yoichi Tanaka,et al.  The role of contrast enhanced mri in the diagnosis of non-mass image-forming lesions on breast ultrasonograph , 2007, Breast cancer.

[7]  Hiroharu Kawanaka,et al.  Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images , 2013, Journal of Digital Imaging.

[8]  H Nakata,et al.  ROC analysis of detection of metastatic pulmonary nodules on digital chest radiographs with temporal subtraction. , 2001, Academic radiology.

[9]  Z. Wang,et al.  Non-mass-like lesions on breast ultrasound: classification and correlation with histology , 2015, La radiologia medica.

[10]  T. Endo,et al.  Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial , 2016, The Lancet.

[11]  Akiyoshi Hizukuri,et al.  Improved differential diagnosis of breast masses on ultrasonographic images with a computer-aided diagnosis scheme for determining histological classifications. , 2013, Academic radiology.

[12]  Ryohei Nakayama,et al.  Potential Usefulness of Presentation of Histological Classifications with Computer-Aided Diagnosis (CAD) Scheme in Differential Diagnosis of Clustered Microcalcifications on Mammograms , 2014, Digital Mammography / IWDM.

[13]  E. Ueno,et al.  Ultrasound screening of breast cancer , 2009, Breast Cancer.

[14]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..