Ability of Dual-Energy CT to Detect Silicone Gel Breast Implant Rupture and Nodal Silicone Spread.

OBJECTIVE: The purpose of this study is to compare the performance of dual-energy CT (DECT) with that of breast MRI for detection of silicone gel breast implant rupture and nodal spread of silicone. SUBJECTS AND METHODS: This prospective study enrolled consecutive patients with current or prior silicone gel implants and clinical suspicion of implant rupture or extra-capsular silicone. All patients underwent MRI followed by unenhanced DECT. A breast radiologist not participating in image evaluation established reference standards for implant rupture (intra- or extracapsular) and regional nodal silicone spread (to axillary nodes and internal mammary [IM] and mediastinal nodes) using MRI, surgical findings, and medical records. After undergoing reader training, two radiologists who were blinded to all medical records interpreted randomized images in two sessions, indicating confidence in diagnosis using a 100-point visual scale. RESULTS: A total of 46 patients who had a subpectoral silicone gel implant (n = 31), a subglandular silicone gel implant (n = 14), or a silicone gel implant that was removed (n = 1) underwent MRI and DECT (mean [± SD] volume CT dose index, 8.2 ± 2.0 mGy). Nineteen patients had implant rupture, and 13 of these patients had silicone within the IM or axillary nodes. Pooled data showed no significant difference between MRI and DECT interpretation of intra- or extracapsular rupture of implants (AUC value for intracapsular rupture, 0.958 [for MRI] vs 0.989 [for DECT]; p = 0.28; AUC value for extracapsular rupture, 0.864 [for MRI] vs 0.878 [for DECT]; p = 0.78). No difference was noted in the AUC value for silicone spread to regional lymph nodes: 0.823-0.866 [for MRI] vs 0.892-0.906 [for DECT]; p = 0.34-0.54). CONCLUSION: DECT performs similar to MRI for the detection of silicone gel implant rupture and the presence of silicone in regional lymph nodes, and it may be an alternative for patients who are unable or unwilling to undergo MRI.

Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 Low Dose CT Grand Challenge.

PURPOSE: Using common datasets, to estimate and compare the diagnostic performance of image-based denoising techniques or iterative reconstruction algorithms for the task of detecting hepatic metastases. METHODS: Datasets from contrast-enhanced CT scans of the liver were provided to participants in an NIH-, AAPM- and Mayo Clinic-sponsored Low Dose CT Grand Challenge. Training data included full-dose and quarter-dose scans of the ACR CT accreditation phantom and 10 patient examinations; both images and projections were provided in the training data. Projection data were supplied in a vendor-neutral standardized format (DICOM-CT-PD). Twenty quarter-dose patient datasets were provided to each participant for testing the performance of their technique. Images were provided to sites intending to perform denoising in the image domain. Fully preprocessed projection data and statistical noise maps were provided to sites intending to perform iterative reconstruction. Upon return of the denoised or iteratively reconstructed quarter-dose images, randomized, blinded evaluation of the cases was performed using a Latin Square study design by 11 senior radiology residents or fellows, who marked the locations of identified hepatic metastases. Markings were scored against reference locations of clinically or pathologically demonstrated metastases to determine a per-lesion normalized score and a per-case normalized score (a faculty abdominal radiologist established the reference location using clinical and pathological information). Scores increased for correct detections; scores decreased for missed or incorrect detections. The winner for the competition was the entry that produced the highest total score (mean of the per-lesion and per-case normalized score). Reader confidence was used to compute a Jackknife alternative free-response receiver operating characteristic (JAFROC) figure of merit, which was used for breaking ties. RESULTS: 103 participants from 90 sites and 26 countries registered to participate. Training data were shared with 77 sites that completed the data sharing agreements. Subsequently, 41 sites downloaded the 20 test cases, which included only the 25% dose data (CTDIvol = 3.0 ± 1.8 mGy, SSDE = 3.5 ± 1.3 mGy). 22 sites submitted results for evaluation. One site provided binary images and one site provided images with severe artifacts; cases from these sites were excluded from review and the participants removed from the challenge. The mean (range) per-lesion and per-case normalized scores were -24.2% (-75.8%, 3%) and 47% (10%, 70%), respectively. Compared to reader results for commercially reconstructed quarter-dose images with no noise reduction, 11 of the 20 sites showed a numeric improvement in the mean JAFROC figure of merit. Notably two sites performed comparably to the reader results for full-dose commercial images. The study was not designed for these comparisons, so wide confidence intervals surrounded these figures of merit and the results should be used only to motivate future testing. CONCLUSION: Infrastructure and methodology were developed to rapidly estimate observer performance for liver metastasis detection in low-dose CT examinations of the liver after either image-based denoising or iterative reconstruction. The results demonstrated large differences in detection and classification performance between noise reduction methods, although the majority of methods provided some improvement in performance relative to the commercial quarter-dose images with no noise reduction applied.