Faculty Bibliography

PURPOSE:The aim of this study was to systematically evaluate the accuracy of quantitative intravoxel incoherent motion (IVIM) analysis of the upper abdomen applying simultaneous multislice (SMS) diffusion-weighted imaging (DWI) to reduce acquisition time. MATERIALS AND METHODS:Diffusion-weighted imaging of parenchymal abdominal organs was performed in 8 healthy volunteers at 3 T using a standard DWI sequence (acceleration factor 1 [AF1]) and an SMS-accelerated echo planar imaging sequence with acceleration factors 2 and 3 (AF2/AF3). Intravoxel incoherent motion analysis was performed with a multistep algorithm for true diffusion coefficient (Dt), pseudodiffusion coefficient (D*), and fraction of perfusion (Fp) measured for the liver, kidney cortex and medulla, pancreas, spleen, and erector spinae muscle. Qualitative and quantitative parameters were compared using a repeated measurement 1-way analysis of variance test and the Bonferroni post hoc method. RESULTS:Simultaneous multislice DWI provided diagnostic image quality in all volunteers with a reduction of scan time of 50% for AF2 (67% for AF3) compared with the standard sequence. Decent IVIM analysis for Dt, D*, and Fp can be calculated on the images of both the SMS sequences AF2 and AF3 with typical organ characteristics of IVIM; however, systematical deviations from AF1 were observed: Dt values increased and Fp decreased significantly with higher acceleration factor for liver, kidney, pancreas, and muscle (P < 0.05). Fitting curves of higher acceleration factors tend to be more monoexponentially shaped. CONCLUSIONS:Simultaneous multislice acceleration provides considerable scan time reduction for upper abdomen DWI with equivalent quality of IVIM analysis compared with the standard nonaccelerated technique. Systematic discrepancies of the true Dt, D*, and Fp for SMS acquisitions need to be considered when comparing to standard DWI sequences.

OBJECTIVE:To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience. METHODS:In this retrospective study, all breast ultrasound examinations from January 1, 2014 to December 31, 2014 at our institution were reviewed. Patients with post-surgical scars, initially indeterminate, or malignant lesions with histological diagnoses or 2-year follow-up were included. The DLS was trained with 70% of the images, and the remaining 30% were used to validate the performance. Three readers with variable expertise also evaluated the validation set (radiologist, resident, medical student). Diagnostic accuracy was assessed with a receiver operating characteristic analysis. RESULTS:82 patients with malignant and 550 with benign lesions were included. Time needed for training was 7 min (DLS). Evaluation time for the test data set were 3.7 s (DLS) and 28, 22 and 25 min for human readers (decreasing experience). Receiver operating characteristic analysis revealed non-significant differences (p-values 0.45-0.47) in the area under the curve of 0.84 (DLS), 0.88 (experienced and intermediate readers) and 0.79 (inexperienced reader). CONCLUSION/CONCLUSIONS:DLS may aid diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists, and learns better and faster than a human reader with no prior experience. Further clinical trials with dedicated algorithms are warranted. Advances in knowledge: DLS can be trained classify cancer on breast ultrasound images high accuracy even with comparably few training cases. The fast evaluation speed makes real-time image analysis feasible.

PURPOSE/OBJECTIVE:Ga-PSMA-11 PET/MRI might further improve the detection rate in patients with EBR and low PSA values over PET/CT. METHODS:Ga-PSMA-11 PET/MRI for biochemical recurrence in our institution between April and December 2016 with three readers. Median PSA level was 0.99 ng/mL (interquartile range: 3.1 ng/mL). Detection of PSMA-positive lesions within the prostate fossa, local and distant lymph nodes, bones, or visceral organs was recorded. Agreement among observers was evaluated with Fleiss's kappa (k). RESULTS:Overall, in 44 of 56 patients (78.6%) PSMA-positive lesions were detected. In four of nine patients (44.4%) with a PSA < 0.2 ng/mL, suspicious lesions were detected (two pelvic and one paraaortic lymph nodes, and two bone metastases). In eight of 11 patients (72.7%) with a PSA between 0.2 and < 0.5 ng/mL, suspicious lesions were detected (two local recurrences, six lymph nodes, and one bone metastasis). Five out of 20 patients with a PSA < 0.5 ng/mL had extrapelvic disease. In 12 of 15 patients (80.0%) with a PSA between 0.5 and < 2.0 ng/mL, suspicious lesions were detected (four local recurrences, nine lymph nodes, and four bone metastases). In 20 of 21 patients (95.2%) with a PSA >2.0 ng/mL, suspicious lesions were detected. The overall interreader agreement for cancer detection was excellent (κ = 0.796, CI 0.645-0.947). CONCLUSIONS:Ga-PSMA-11 PET/MRI has a high detection rate for recurrent prostate cancer even at very low PSA levels <0.5 ng/mL. Furthermore, even at those low levels extrapelvic disease can be localized in 25% of the cases and local recurrence alone is seen only in 10%.

BACKGROUND:The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. METHODS:Eight male C57BL6 mice (age 8-10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected. RESULTS:Tumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity. CONCLUSIONS:Texture features may quantitatively detect liver metastases before they become visually detectable by the radiologist.

OBJECTIVE:The dual-energy CT (DECT) algorithm for urate detection is feasible only if hyperdense deposits are present. Based on our experience, around half of the performed DECT examinations show no such deposits and thus were useless for this indication. Our diagnostic accuracy study investigates whether conventional radiographs can serve as gatekeeper test prior to DECT for reliable exclusion of such radiopaque deposits. MATERIALS AND METHODS:In this retrospective study, 77 clinically indicated DECT examinations of the hand (n = 29), foot (n = 36) and ankle (n = 12) of 55 patients (13 female, mean age 62±15 years) with suspected gouty arthritis were included. Two blinded readers independently evaluated DECT, gray-scale CT images (reference standard) and corresponding standardized radiographs for the presence/location of dense soft tissue deposits. RESULTS:Interreader agreement for detection of soft tissue deposits with DECT and radiographs was excellent (DECT: both readers, κ = 1; radiographs: both readers, κ = 0.94). DECT showed soft tissue deposits in 54/77 DECT (70%) scans. 30/54 scans (56%) showed deposits on the corresponding radiographs, while in 24 scans (44%) no deposits were seen on radiographs. Test performance of radiographs for soft tissue deposit detection: sensitivity 56%, specificity 100%, PPV 100%, NPV 48.9%, and accuracy 69%. Low density of the deposits was the main reasons for false-negative radiographs (19 cases, 79%), followed by superimposition of deposits by osseous structures (5 cases, 21%). CONCLUSION:Conventional radiographs of the hand, foot and ankle cannot serve as a gatekeeper test for reliable exclusion of radiopaque soft tissue deposits prior to DECT.

PURPOSE/OBJECTIVE:To evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA). METHODS:In this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index). RESULTS:The DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60-0.77) at lower NPV (64-80%). When performing the readout together with the DLS recommendation, the radiologist's accuracy showed a non-significant tendency to improve (AUC 0.75-0.87, p = 0.07). CONCLUSION/CONCLUSIONS:Deep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies.

OBJECTIVES/OBJECTIVE:To develop age-, gender-, and regional-specific normative values for texture analysis (TA) of spinal computed tomography (CT) in subjects with normal bone mineral density (BMD), as defined by dual X-ray absorptiometry (DXA), and to determine age-, gender-, and regional-specific differences. MATERIALS AND METHODS/METHODS:In this retrospective, IRB-approved study, TA was performed on sagittal CT bone images of the thoracic and lumbar spine using dedicated software (MaZda) in 141 individuals with normal DXA BMD findings. Numbers of female and male subjects were balanced in each of six age decades. Three hundred and five TA features were analyzed in thoracic and lumbar vertebrae using free-hand regions-of-interest. Intraclass correlation (ICC) coefficients were calculated for determining intra- and inter-observer agreement of each feature. Further dimension reduction was performed with correlation analyses. RESULTS:The TA features with an ICC < 0.81 indicating compromised intra- and inter-observer agreement and with Pearson correlation scores r > 0.8 with other features were excluded from further analysis for dimension reduction. From the remaining 31 texture features, a significant correlation with age was found for the features mean (r = -0.489, p < 0.001), variance (r = -0.681, p < 0.001), kurtosis (r = 0.273, p < 0.001), and WavEnLL_s4 (r = 0.273, p < 0.001). Significant differences were found between genders for various higher-level texture features (p < 0.001). Regional differences among the thoracic spine, thoracic-lumbar junction, and lumbar spine were found for most TA features (p < 0.021). CONCLUSION/CONCLUSIONS:This study established normative values of TA features on CT images of the spine and showed age-, gender-, and regional-specific differences in individuals with normal BMD as defined by DXA.