Quantitative Assessment of Myocardial Ischemia With Positron Emission Tomography.

Recent advances in positron emission tomography (PET) technology and reconstruction techniques have now made quantitative assessment using cardiac PET readily available in most cardiac PET imaging centers. Multiple PET myocardial perfusion imaging (MPI) radiopharmaceuticals are available for quantitative examination of myocardial ischemia, with each having distinct convenience and accuracy profile. Important properties of these radiopharmaceuticals ( 15 O-water, 13 N-ammonia, 82 Rb, 11 C-acetate, and 18 F-flurpiridaz) including radionuclide half-life, mean positron range in tissue, and the relationship between kinetic parameters and myocardial blood flow (MBF) are presented. Absolute quantification of MBF requires PET MPI to be performed with protocols that allow the generation of dynamic multiframes of reconstructed data. Using a tissue compartment model, the rate constant that governs the rate of PET MPI radiopharmaceutical extraction from the blood plasma to myocardial tissue is calculated. Then, this rate constant ( K1 ) is converted to MBF using an established extraction formula for each radiopharmaceutical. As most of the modern PET scanners acquire the data only in list mode, techniques of processing the list-mode data into dynamic multiframes are also reviewed. Finally, the impact of modern PET technologies such as PET/CT, PET/MR, total-body PET, machine learning/deep learning on comprehensive and quantitative assessment of myocardial ischemia is briefly described in this review.

Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology Reports.

Purpose: To develop radiology domain-specific bidirectional encoder representations from transformers (BERT) models that can identify speech recognition (SR) errors and suggest corrections in radiology reports. Materials and Methods: A pretrained BERT model, Clinical BioBERT, was further pretrained on a corpus of 114 008 radiology reports between April 2016 and August 2019 that were retrospectively collected from two hospitals. Next, the model was fine-tuned on a training dataset of generated insertion, deletion, and substitution errors, creating Radiology BERT. This model was retrospectively evaluated on an independent dataset of radiology reports with generated errors (n = 18 885) and on unaltered report sentences (n = 2000) and prospectively evaluated on true clinical SR errors (n = 92). Correction Radiology BERT was separately trained to suggest corrections for detected deletion and substitution errors. Area under the receiver operating characteristic curve (AUC) and bootstrapped 95% CIs were calculated for each evaluation dataset. Results: Radiology-specific BERT had AUC values of >.99 (95% CI: >0.99, >0.99), 0.94 (95% CI: 0.93, 0.94), 0.98 (95% CI: 0.98, 0.98), and 0.97 (95% CI: 0.97, 0.97) for detecting insertion, deletion, substitution, and all errors, respectively, on the independently generated test set. Testing on unaltered report impressions revealed a sensitivity of 82% (28 of 34; 95% CI: 70%, 93%) and specificity of 88% (1521 of 1728; 95% CI: 87%, 90%). Testing on prospective SR errors showed an accuracy of 75% (69 of 92; 95% CI: 65%, 83%). Finally, the correct word was the top suggestion for 45.6% (475 of 1041; 95% CI: 42.5%, 49.3%) of errors. Conclusion: Radiology-specific BERT models fine-tuned on generated errors were able to identify SR errors in radiology reports and suggest corrections.Keywords: Computer Applications, Technology Assessment Supplemental material is available for this article. © RSNA, 2022See also the commentary by Abajian and Cheung in this issue.

Prediction of future healthcare expenses of patients from chest radiographs using deep learning: a pilot study.

Our objective was to develop deep learning models with chest radiograph data to predict healthcare costs and classify top-50% spenders. 21,872 frontal chest radiographs were retrospectively collected from 19,524 patients with at least 1-year spending data. Among the patients, 11,003 patients had 3 years of cost data, and 1678 patients had 5 years of cost data. Model performances were measured with area under the receiver operating characteristic curve (ROC-AUC) for classification of top-50% spenders and Spearman ρ for prediction of healthcare cost. The best model predicting 1-year (N = 21,872) expenditure achieved ROC-AUC of 0.806 [95% CI 0.793-0.819] for top-50% spender classification and ρ of 0.561 [0.536-0.586] for regression. Similarly, for predicting 3-year (N = 12,395) expenditure, ROC-AUC of 0.771 [0.750-0.794] and ρ of 0.524 [0.489-0.559]; for predicting 5-year (N = 1779) expenditure ROC-AUC of 0.729 [0.667-0.729] and ρ of 0.424 [0.324-0.529]. Our deep learning model demonstrated the feasibility of predicting health care expenditure as well as classifying top 50% healthcare spenders at 1, 3, and 5 year(s), implying the feasibility of combining deep learning with information-rich imaging data to uncover hidden associations that may allude to physicians. Such a model can be a starting point of making an accurate budget in reimbursement models in healthcare industries.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.

Prospective Comparison of Diagnostic Accuracy Between Point-of-Care and Conventional Ultrasound in a General Diagnostic Department: Implications for Resource-Limited Settings.

OBJECTIVES: To compare the diagnostic accuracy of hand-held point-of-care (POC) versus conventional sonography in a general diagnostic setting with the intention to inform medical providers or clinicians on the rational use of POC ultrasound in resource limited settings. METHODS: Over 3 months in 2010, 47 patients were prospectively enrolled at a single academic center to obtain 54 clinical conventional ultrasound examinations and 54 study-only POC ultrasound examinations. Indications were 48% abdominal, 26% retroperitoneal, and 24% obstetrical. Nine blinded readers (sonographers, residents, and attending radiologists) sequentially assigned diagnoses to POC and then conventional studies, yielding 476 interpreted study pairs. Diagnostic accuracy was obtained by comparing POC and conventional diagnoses to a reference diagnosis established by the unblinded, senior author. Analysis was stratified by study type, body mass index (BMI), diagnostic confidence, and image quality. RESULTS: The mean diagnostic accuracy of conventional sonography was 84% compared with 74% for POC (P < .001). This difference was constant regardless of reader, exam type, or BMI. The sensitivity and specificity to detect abnormalities with conventional was 85 and 83%, compared with 75 and 68% for POC. The POC sonography demonstrated greater variability in image quality and diagnostic confidence, and this accounted for lower diagnostic accuracy. When image quality and diagnostic confidence were similar between POC and conventional examinations, there was no difference in accuracy. CONCLUSIONS: Point-of-care was nearly as accurate as conventional sonography for basic, focused examinations. Observed differences in accuracy were attributed to greater variation in POC image quality.

Renal Cell Carcinoma Metastatic to the Liver: Early Response Assessment after Intraarterial Therapy Using 3D Quantitative Tumor Enhancement Analysis.

PURPOSE: Liver metastases from renal cell carcinoma (RCC) are not uncommon in the course of disease. However, data about tumor response to intraarterial therapy (IAT) are scarce. This study assessed whether changes of enhancing tumor volume using quantitative European Association for the Study of the Liver (qEASL) on magnetic resonance imaging (MRI) and computed tomography (CT) can evaluate tumor response and predict overall survival (OS) early after therapy. METHODS AND MATERIALS: Fourteen patients with liver metastatic RCC treated with IAT (transarterial chemoembolization: n= 9 and yttrium-90: n= 5) were retrospectively included. All patients underwent contrast-enhanced imaging (MRI: n= 10 and CT: n= 4) 3 to 4 weeks pre- and posttreatment. Response to treatment was evaluated on the arterial phase using Response Evaluation Criteria in Solid Tumors (RECIST), World Health Organization, modified RECIST, EASL, tumor volume, and qEASL. Paired t test was used to compare measurements pre- and post-IAT. Patients were stratified into responders (≥65% decrease in qEASL) and nonresponders (<65% decrease in qEASL). OS was evaluated using Kaplan-Meier curves with log-rank test and the Cox proportional hazard model. RESULTS: Mean qEASL (cm3) decreased from 93.5 to 67.2 cm3 (P= .004) and mean qEASL (%) from 63.1% to 35.6% (P= .001). No significant changes were observed using other response criteria. qEASL was the only significant predictor of OS when used to stratify patients into responders and nonresponders with median OS of 31.9 versus 11.1 months (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.19-0.97; P= .042) for qEASL (cm3) and 29.9 versus 10.2 months (HR, 0.09; 95% CI, 0.01-0.74; P= .025) for qEASL (%). CONCLUSION: Three-dimensional (3D) quantitative tumor analysis is a reliable predictor of OS when assessing treatment response after IAT in patients with RCC metastatic to the liver. qEASL outperforms conventional non-3D methods and can be used as a surrogate marker for OS early after therapy.