Faculty Bibliography

IMPORTANCE:Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. OBJECTIVES:To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. DESIGN, SETTINGS, AND PARTICIPANTS:This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. EXPOSURE:Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. MAIN OUTCOMES AND MEASURES:Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. RESULTS:In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). CONCLUSIONS AND RELEVANCE:In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.

BACKGROUND:Intradiverticular bladder tumors (IDBT) are rare but clinically important, as they are difficult to assess endoscopically due to limited anatomic access and risk of perforation. MRI may be helpful in assessing IDBT and providing relevant staging and prognostic information. PURPOSE:To assess MRI findings of IDBT and their relationship with overall survival. METHODS:This retrospective study included 31 consecutive patients with IDBT undergoing MRI from 2008 to 2018 identified through electronic medical records and PACS database search. Two radiologists independently assessed the following MRI features: size (>3 vs ≤3 cm), diverticular neck involvement, Vesical Imaging-Reporting and Data System (VI-RADS) score (>3 vs ≤3), perivesical fat infiltration, additional tumors and suspicious pelvic lymph nodes. Overall survival was estimated using Kaplan-Meier analysis; and the relationship with clinicopathological and MRI features was determined using the Cox proportional-hazards regression model. Inter-reader agreement was assessed using intraclass correlation coefficients (ICC) and Cohen's kappa (K). RESULTS:Median follow-up was 1044 days (interquartile range, 474-1952 days). Twenty-six (83.9%) patients underwent surgical treatment with or without neoadjuvant chemotherapy. On MRI, greater tumor size (>3 cm), diverticular neck involvement, perivesical extension, and suspicious lymph nodes were associated with lower overall survival (HR = 3.6-8.1 and 4.3-6.3 for the 2 radiologists, p ≤ 0.03). Other clinicopathological or MRI findings were not associated with survival (p = 0.27-0.65). Inter-reader agreement was excellent for tumor size (ICC = 0.991; 95% CI 0.982-0.996), fair for VI-RADS (K = 0.52, 95% CI, 0.22-0.82), and moderate for others (K = 0.61-0.79). CONCLUSION:In patients with IDBT, several MRI features were significantly associated with overall survival. Utilizing all available clinicopathological and imaging information may improve estimation of prognosis.

The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.

INTRODUCTION/BACKGROUND:Radiographic progression-free survival (rPFS) based on Prostate Cancer Working Group 2 (PCWG2) has been increasingly used as a meaningful imaging-based intermediate endpoint (IBIE) for overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC). In randomized phase III trials, rPFS showed good correlation with OS at the individual trial level. We aimed to assess the correlation between the hazard ratios (HR) of IBIE and OS among PCWG2-based randomized trials. MATERIALS AND METHODS:PubMed and EMBASE databases were systematically searched for randomized trials evaluating systemic treatments on mCRPC using PCWG2 up to April 15, 2020. Hazard ratios for OS and IBIEs were extracted and their correlation was assessed using weighted linear regression. Subgroup analyses were performed according to various clinical settings: prior chemotherapy, drug category, type of IBIE (rPFS vs. composite IBIE, latter defined as progression by imaging and one or a combination of PSA, pain, skeletal-related events, and performance status), and publication year. RESULTS: = 0.32-0.91). CONCLUSION:IBIEs in the era of PCWG2 correlate well with OS in randomized trials for systemic drugs in patients with mCRPC. PCWG2-based rPFS should be used instead of a composite IBIE that includes PSA and other clinical variables.

Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice. Imaging data can be digitally post-processed for quantitative assessment. The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. Beyond streamlining certain clinical tasks, the power of AI lies in its ability to reveal previously undetected or even imperceptible radiographic patterns that may be difficult to ascertain by the human sensory system. Here, we provide a narrative review of the emerging AI applications relevant to the oncological imaging spectrum and elaborate on emerging paradigms and opportunities. We envision that these technical advances will change radiology in the coming years, leading to the optimization of imaging acquisition and discovery of clinically relevant biomarkers for cancer diagnosis, staging, and treatment monitoring. Together, they pave the road for future clinical translation in precision oncology.

OBJECTIVE:While single sugar tests are controversially discussed, combination tests with meals are gaining more attention. The aim of this study was to analyze the impact of adding a test meal to lactulose hydrogen breath tests (LHBT) on hydrogen values and abdominal symptoms in patients with functional gastrointestinal disorders (FGIDs). METHODS:Data of 81 FGID patients between 2014-2018 were analyzed. Patients underwent LHBT with 30 g lactulose + 300 mL water and a nutrient challenge test (NCT) including 400 mL liquid test meal + 30 g lactulose. To statistically assess the effect of a test meal on abdominal symptoms and H2, mixed-effect models were used. RESULTS:Adding a test meal to LHBT showed a significant increase in nausea [odds ratio (OR) 1.4; 95% confidence interval (CI), 1.1-1.7], decrease in abdominal pain (OR 0.7; 95% CI, 0.6-0.9), borborygmi (OR 0.5; 95% CI, 0.4-0.6), diarrhea (OR 0.4; 95% CI, 0.3-0.6), and H2 production (estimate -5.3, SE 0.7, P < 0.001). The effect on bloating was only significant in functional dyspepsia, irritable bowel syndrome-functional dyspepsia mixed type and functional abdominal pain/bloating (OR 0.1; 95% CI, 0.0-0.2; OR 1.7; 95% CI, 1.2-2.3 resp OR 4.4; 95% CI, 1.8-10.6). CONCLUSIONS:Significant effects on abdominal symptoms and H2 production by adding a test meal to LHBT in FGID patients are shown. Increased occurrence of nausea may be caused by gastric/duodenal hypersensitivity; decreased H2, diarrhea and borborygmi by slower and more physiologic gastric emptying resulting in later arrival of the test substance in the bowel. We recommend NCTs instead of LHBT to more physiologically represent FGID patients' meal-induced burden.

BACKGROUND:F-fluorodeoxyglucose (FDG) PET or PET/CT has shown the capacity to better identify the primary tumour site and detect additional sites of metastasis. However, its clinical impact is not well established. We performed a systematic review and meta-analysis of prior studies to assess the impact of FDG-PET or PET/CT on the management of patients with CUP. MATERIALS AND METHODS:Pubmed and EMBASE databases were searched up to 4th February 2021. Studies that reported the proportion of patients with CUP who experienced a management change after FDG-PET or PET/ computed tomography (CT) were included and the proportions were pooled using the random-effects model. Study quality was assessed using QUADAS-2. Subgroup analysis was conducted to explore heterogeneity. RESULTS: = 82%). The specific reason for management change was more commonly detection of the primary site (22% [95% CI 18-28%]) than detection of additional metastatic sites (14% [95% CI 10-19%]). The pooled proportions of patients with management changes were similar among numerous subgroups (range, 32.8%-38.2%). CONCLUSION:FDG-PET or PET/CT had a meaningful impact on the management of patients with CUP. Approximately, a third of patients had their management changed because of FDG-PET or PET/CT results, and this finding was consistent across numerous subgroups.

RATIONALE AND OBJECTIVES:Prostate gland volume (PGV) should be routinely included in MRI reports of the prostate. The recently updated Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 includes a change in the recommended measurement method for PGV compared to version 2.0. The purpose of this study was to evaluate the agreement of MRI-based PGV calculations with the volumetric manual slice-by-slice prostate segmentation as a reference standard using the linear measurements per PI-RADS versions 2.0 and 2.1. Furthermore, to assess inter-reader agreement for the different measurement approaches, determine the influence of an enlarged transition zone on measurement accuracy and to assess the value of the bullet formula for PGV calculation. MATERIALS AND METHODS:Ninety-five consecutive treatment-naive patients undergoing prostate MRI were retrospectively analyzed. Prostates were manually contoured and segmented on axial T2-weighted images. Four different radiologists independently measured the prostate in three dimensions according to PI-RADS v2.0 and v2.1, respectively. MRI-based PGV was calculated using the ellipsoid and bullet formulas. Calculated volumes were compared to the reference manual segmentations using Wilcoxon signed-rank test. Inter-reader agreement was calculated using intraclass correlation coefficient (ICC). RESULTS:Inter-reader agreement was excellent for the ellipsoid and bullet formulas using PI-RADS v2.0 (ICC 0.985 and 0.987) and v2.1 (ICC 0.990 and 0.994), respectively. The median difference from the reference standard using the ellipsoid formula derived PGV was 0.4 mL (interquartile range, -3.9 to 5.1 mL) for PI-RADS v2.0 (p = 0.393) and 2.6 mL (interquartile range, -1.6 to 7.3 mL) for v2.1 (p < 0.001) with a median difference of 2.2 mL. The bullet formula overestimated PGV by a median of 13.3 mL using PI-RADS v2.0 (p < 0.001) and 16.0 mL using v2.1 (p < 0.001). In the presence of an enlarged transition zone the PGV tended to be higher than the reference standard for PI-RADS v2.0 (median difference of 4.7 mL; p = 0.018) and for v2.1 (median difference of 5.7 mL, p < 0.001) using the ellipsoid formula. CONCLUSION:Inter-reader agreement was excellent for the calculated PGV for both methods. PI-RADS v2.0 measurements with the ellipsoid formula yielded the most accurate volume estimates. The differences between PI-RADS v2.0 and v2.1 were statistically significant although small in absolute numbers but may be of relevance in specific clinical scenarios like prostate-specific antigen density calculation. These findings validate the use of the ellipsoid formula and highlight that the bullet formula should not be used for prostate volume estimation due to systematic overestimation.

PURPOSE:Ga-labeled prostate-specific membrane antigen (PSMA-PET/MRI) to detect and localize primary sigPCa (ISUP grade group 3 and/or cancer core length ≥ 6 mm) and guide biopsy. METHODS:Prospective, open-label, single-center, non-randomized, diagnostic accuracy study including patients with suspected PCa by elevation of prostate-specific antigen (PSA) level and a suspicious lesion (PIRADS ≥3) on multiparametric MRI (mpMRI). Forty-two patients underwent PSMA-PET/MRI followed by both PSMA-PET/MRI-guided and section-based saturation template biopsy between May 2017 and February 2019. Primary outcome was the accuracy of PSMA-PET/MRI for biopsy guidance using section-based saturation template biopsy as the reference standard. RESULTS:SigPCa was found in 62% of the patients. Patient-based sensitivity, specificity, negative and positive predictive value, and accuracy for sigPCa were 96%, 81%, 93%, 89%, and 90%, respectively. One patient had PSMA-negative sigPCa. Eight of nine false-positive lesions corresponded to cancer on prostatectomy and one in six false-negative lesions was negative on prostatectomy. CONCLUSION:PSMA-PET/MRI has a high accuracy for detecting sigPCa and is a promising tool to select patients with suspicion of PCa for biopsy. TRIAL REGISTRATION:This trial was retrospectively registered under the name "Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI) Guided Biopsy in Men with Elevated PSA" (NCT03187990) on 06/15/2017 ( https://clinicaltrials.gov/ct2/show/NCT03187990 ).

INTRODUCTION/BACKGROUND:The role of positron-emission tomography/computed-tomography (PET/CT) in the management of sarcomas and as a prognostic tool has been studied. However, it remains unclear which metric is the most useful. We aimed to investigate if volume-based PET metrics (Tumor volume (TV) and total lesions glycolysis (TLG)) are superior to maximal standardized uptake value (SUVmax) and other metrics in predicting survival of patients with soft tissue and bone sarcomas. MATERIALS AND METHODS/METHODS:In this retrospective cohort study, we screened over 52'000 PET/CT scans to identify patients diagnosed with either soft tissue, bone or Ewing sarcoma and had a staging scan at our institution before initial therapy. We used a Wilcoxon signed-rank to assess which PET/CT metric was associated with survival in different patient subgroups. Receiver-Operating-Characteristic curve analysis was used to calculate cutoff values. RESULTS:= 0.03). DISCUSSION/CONCLUSIONS:Our analysis shows that the outcome of soft tissue, bone and Ewing sarcomas is associated with different PET/CT metrics. We could not confirm the previously suggested superiority of volume-based metrics in soft tissue sarcomas, for which we found SUVmax to remain the best prognostic factor. However, bone sarcomas should probably be evaluated with tumor volume rather than FDG PET activity.