Binghui Zhao1, Xiaohua Zhang2, Weixing Cai3, David Conover4, Ruola Ning5. 1. Department of Radiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China. 2. Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA. 3. Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY 14642, USA. 4. Koning Corporation, West Henrietta, NY 14586, USA. 5. Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY 14642, USA. Electronic address: ruola_ning@urmc.rochester.edu.
Abstract
OBJECTIVE: This pilot study was to evaluate cone beam breast computed tomography (CBBCT) with multiplanar and three dimensional (3D) visualization in differentiating breast masses in comparison with two-view mammograms. METHODS: Sixty-five consecutive female patients (67 breasts) were scanned by CBBCT after conventional two-view mammography (Hologic, Motarget, compression factor 0.8). For CBBCT imaging, three hundred (1024 × 768 × 16b) two-dimensional (2D) projection images were acquired by rotating the x-ray tube and a flat panel detector (FPD) 360 degree around one breast. Three-dimensional CBBCT images were reconstructed from the 2D projections. Visage CS 3.0 and Amira 5.2.2 were used to visualize reconstructed CBBCT images. RESULTS: Eighty-five breast masses in this study were evaluated and categorized under the breast imaging reporting and data system (BI-RADS) according to plain CBBCT images and two-view mammograms, respectively, prior to biopsy. BI-RADS category of each breast was compared with biopsy histopathology. The results showed that CBBCT with multiplanar and 3D visualization would be helpful to identify the margin and characteristics of breast masses. The category variance ratios for CBBCT under the BI-RADS were 23.5% for malignant tumors (MTs) and 27.3% for benign lesions in comparison with pathology, which were evidently closer to the histopathology results than those of two-view mammograms, p value <0.01. With the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) of CBBCT was 0.911, larger than that (AUC 0.827) of two-view mammograms, p value <0.01. CONCLUSION: CBBCT will be a distinctive noninvasive technology in differentiating and categorizing breast masses under BI-RADS. CBBCT may be considerably more effective to identify breast masses, especially some small, uncertain or multifocal masses than conventional two-view mammography.
OBJECTIVE: This pilot study was to evaluate cone beam breast computed tomography (CBBCT) with multiplanar and three dimensional (3D) visualization in differentiating breast masses in comparison with two-view mammograms. METHODS: Sixty-five consecutive female patients (67 breasts) were scanned by CBBCT after conventional two-view mammography (Hologic, Motarget, compression factor 0.8). For CBBCT imaging, three hundred (1024 × 768 × 16b) two-dimensional (2D) projection images were acquired by rotating the x-ray tube and a flat panel detector (FPD) 360 degree around one breast. Three-dimensional CBBCT images were reconstructed from the 2D projections. Visage CS 3.0 and Amira 5.2.2 were used to visualize reconstructed CBBCT images. RESULTS: Eighty-five breast masses in this study were evaluated and categorized under the breast imaging reporting and data system (BI-RADS) according to plain CBBCT images and two-view mammograms, respectively, prior to biopsy. BI-RADS category of each breast was compared with biopsy histopathology. The results showed that CBBCT with multiplanar and 3D visualization would be helpful to identify the margin and characteristics of breast masses. The category variance ratios for CBBCT under the BI-RADS were 23.5% for malignant tumors (MTs) and 27.3% for benign lesions in comparison with pathology, which were evidently closer to the histopathology results than those of two-view mammograms, p value <0.01. With the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) of CBBCT was 0.911, larger than that (AUC 0.827) of two-view mammograms, p value <0.01. CONCLUSION:CBBCT will be a distinctive noninvasive technology in differentiating and categorizing breast masses under BI-RADS. CBBCT may be considerably more effective to identify breast masses, especially some small, uncertain or multifocal masses than conventional two-view mammography.
Authors: S Pacilè; F Brun; C Dullin; Y I Nesterest; D Dreossi; S Mohammadi; M Tonutti; F Stacul; D Lockie; F Zanconati; A Accardo; G Tromba; T E Gureyev Journal: Biomed Opt Express Date: 2015-07-29 Impact factor: 3.732
Authors: Muhammad U Ghani; Liqiang Ren; Molly Wong; Yuhua Li; Bin Zheng; Xiujiang John Rong; Kai Yang; Hong Liu Journal: J Comput Assist Tomogr Date: 2017-01 Impact factor: 1.826
Authors: Sarina Wan; Benedicta D Arhatari; Yakov I Nesterets; Sheridan C Mayo; Darren Thompson; Jane Fox; Beena Kumar; Zdenka Prodanovic; Daniel Hausermann; Anton Maksimenko; Christopher Hall; Matthew Dimmock; Konstantin M Pavlov; Darren Lockie; Mary Rickard; Ziba Gadomkar; Alaleh Aminzadeh; Elham Vafa; Andrew Peele; Harry M Quiney; Sarah Lewis; Timur E Gureyev; Patrick C Brennan; Seyedamir Tavakoli Taba Journal: J Med Imaging (Bellingham) Date: 2021-07-12