Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms.

Background Artificial intelligence (AI) algorithms have shown high accuracy for detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) studies in academic studies. Purpose To determine whether use of an AI triage system to detect PE on CTPA studies improves radiologist performance or examination and report turnaround times in a clinical setting. Materials and Methods This prospective single-center study included adult participants who underwent CTPA for suspected PE in a clinical practice setting. Consecutive CTPA studies were evaluated in two phases, first by radiologists alone (n = 31) (May 2021 to June 2021) and then by radiologists aided by a commercially available AI triage system (n = 37) (September 2021 to December 2021). Sixty-two percent of radiologists (26 of 42 radiologists) interpreted studies in both phases. The reference standard was determined by an independent re-review of studies by thoracic radiologists and was used to calculate performance metrics. Diagnostic accuracy and turnaround times were compared using Pearson χ2 and Wilcoxon rank sum tests. Results Phases 1 and 2 included 503 studies (participant mean age, 54.0 years ± 17.8 [SD]; 275 female, 228 male) and 1023 studies (participant mean age, 55.1 years ± 17.5; 583 female, 440 male), respectively. In phases 1 and 2, 14.5% (73 of 503) and 15.9% (163 of 1023) of CTPA studies were positive for PE (P = .47). Mean wait time for positive PE studies decreased from 21.5 minutes without AI to 11.3 minutes with AI (P < .001). The accuracy and miss rate, respectively, for radiologist detection of any PE on CTPA studies was 97.6% and 12.3% without AI and 98.6% and 6.1% with AI, which was not significantly different (P = .15 and P = .11, respectively). Conclusion The use of an AI triage system to detect any PE on CTPA studies improved wait times but did not improve radiologist accuracy, miss rate, or examination and report turnaround times. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Murphy and Tee in this issue.

Role of Imaging in Renal Cell Carcinoma: A Multidisciplinary Perspective.

With the expansion in cross-sectional imaging over the past few decades, there has been an increase in the number of incidentally detected renal masses and an increase in the incidence of renal cell carcinomas (RCCs). The complete characterization of an indeterminate renal mass on CT or MR images is challenging, and the authors provide a critical review of the best imaging methods and essential, important, and optional reporting elements used to describe the indeterminate renal mass. While surgical staging remains the standard of care for RCC, the role of renal mass CT or MRI in staging RCC is reviewed, specifically with reference to areas that may be overlooked at imaging such as detection of invasion through the renal capsule or perirenal (Gerota) fascia. Treatment options for localized RCC are expanding, and a multidisciplinary group of experts presents an overview of the role of advanced medical imaging in surgery, percutaneous ablation, transarterial embolization, active surveillance, and stereotactic body radiation therapy. Finally, the arsenal of treatments for advanced renal cancer continues to grow to improve response to therapy while limiting treatment side effects. Imaging findings are important in deciding the best treatment options and to monitor response to therapy. However, evaluating response has increased in complexity. The unique imaging findings associated with antiangiogenic targeted therapy and immunotherapy are discussed. An invited commentary by Remer is available online. Online supplemental material is available for this article. ©RSNA, 2021.

Prospective validation of a rapid CT-based bone mineral density screening method using colored spinal images.

PURPOSE: To prospectively validate a method to accurately and rapidly differentiate normal from abnormal spinal bone mineral density (BMD) using colored abdominal CT images. METHODS: For this prospective observational study, 196 asymptomatic women ≥ 50 years of age presenting for screening mammograms underwent routine nonenhanced CT imaging of the abdomen. The CT images were processed with software designed to generate sagittal colored images with green vertebral trabecular bone indicating normal BMD and red indicating abnormal BMD (low BMD or osteoporosis). Four radiologists evaluated L1/L2 BMD on sagittal images using visual assessment of grayscale images, quantitative measurements of mean vertebral attenuation, and visual assessment of colored images. Mean BMD values at L1/L2 using quantitative CT with a phantom served as the reference standard. The average accuracy and time of interpretation were calculated. Inter-observer agreement was assessed using intraclass correlation coefficient (ICC). RESULTS: Mean attenuation at L1/L2 was highly correlated with mean BMD (r = 0.96/0.91, p < 0.001 for both). The average accuracy and mean time to assess BMD among four readers for differentiating normal from abnormal BMD was 66% and 6.0 s using visual assessment of grayscale images, 88% and 15.2 s using quantitative measurements of mean vertebral attenuation, and 92% and 2.1 s using visual assessment of colored images (p < 0.001 and p < 0.001, respectively). Inter-observer agreement was poor using visual assessment of grayscale images (ICC:0.31), good using quantitative measurements of mean vertebral attenuation (ICC:0.73), and excellent using visual assessment of colored images (ICC:0.90). CONCLUSION: Detection of abnormal BMD using colored abdominal CT images was highly accurate, rapid, and had excellent inter-observer agreement.

Approach to Renal Cystic Masses and the Role of Radiology.

Most renal masses are benign cysts; a subset are malignant. Most renal masses are incidental findings. Evaluation of renal cysts has evolved with updates to the Bosniak classification system and other guidelines. The Bosniak classification provides detailed definitions and extends the system from computed tomography to MR imaging. This article provides a simple approach to the evaluation of cystic or potentially cystic renal masses. The radiologist is central to this process. Key elements include confirming that a renal lesion is cystic and not solid, determining the need for further characterization by imaging, and judicious application of the Bosniak classification system.

MR Imaging Texture Analysis in the Abdomen and Pelvis.

Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.

Precision analysis of a quantitative CT liver surface nodularity score.

PURPOSE: To evaluate precision of a software-based liver surface nodularity (LSN) score derived from CT images. METHODS: An anthropomorphic CT phantom was constructed with simulated liver containing smooth and nodular segments at the surface and simulated visceral and subcutaneous fat components. The phantom was scanned multiple times on a single CT scanner with adjustment of image acquisition and reconstruction parameters (N = 34) and on 22 different CT scanners from 4 manufacturers at 12 imaging centers. LSN scores were obtained using a software-based method. Repeatability and reproducibility were evaluated by intraclass correlation (ICC) and coefficient of variation. Using abdominal CT images from 68 patients with various stages of chronic liver disease, inter-observer agreement and test-retest repeatability among 12 readers assessing LSN by software- vs. visual-based scoring methods were evaluated by ICC. RESULTS: There was excellent repeatability of LSN scores (ICC:0.79-0.99) using the CT phantom and routine image acquisition and reconstruction parameters (kVp 100-140, mA 200-400, and auto-mA, section thickness 1.25-5.0 mm, field of view 35-50 cm, and smooth or standard kernels). There was excellent reproducibility (smooth ICC: 0.97; 95% CI 0.95, 0.99; CV: 7%; nodular ICC: 0.94; 95% CI 0.89, 0.97; CV: 8%) for LSN scores derived from CT images from 22 different scanners. Inter-observer agreement for the software-based LSN scoring method was excellent (ICC: 0.84; 95% CI 0.79, 0.88; CV: 28%) vs. good for the visual-based method (ICC: 0.61; 95% CI 0.51, 0.69; CV: 43%). Test-retest repeatability for the software-based LSN scoring method was excellent (ICC: 0.82; 95% CI 0.79, 0.84; CV: 12%). CONCLUSION: The software-based LSN score is a quantitative CT imaging biomarker with excellent repeatability, reproducibility, inter-observer agreement, and test-retest repeatability.