Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. RESULTS: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA CONCLUSION: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.

OBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.

Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management.

The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care for treating many localized renal masses. Although uncommon, complications resulting from PN can range from asymptomatic and mild to symptomatic and life-threatening. These complications include vascular injuries such as hematoma, pseudoaneurysm, arteriovenous fistula, and/or renal ischemia; injury to the collecting system causing urinary leak; infection; and tumor recurrence. The incidence of complications after any nephron-sparing surgery depends on many factors, such as the proximity of the tumor to blood vessels or the collecting system, the skill or experience of the surgeon, and patient-specific factors. More recently, image-guided percutaneous renal ablation has emerged as a safe and effective treatment option for small renal tumors, with comparable oncologic outcomes to those of PN and a low incidence of major complications. Radiologists must be familiar with the imaging findings encountered after these surgical and image-guided procedures, especially those indicative of complications. The authors review cross-sectional imaging characteristics of complications after PN and image-guided thermal ablation of kidney tumors and highlight the respective management strategies, ranging from clinical observation to interventions such as angioembolization or repeat surgery. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available in the Online Learning Center. See the invited commentary by Chung and Raman in this issue.

Patient and Provider Attitudes, Beliefs, and Biases That Contribute to a Marginalized Process of Care and Outcomes in Chronic Musculoskeletal Pain: A Systematic Review-Part I: Clinical Care.

OBJECTIVE: Chronic musculoskeletal pain (CMP) outcomes are affected by numerous variables, including the clinical conversation. When good therapeutic/working alliances are formed, congruent clinical conversations can lead to improved CMP outcomes. Identifying patient/provider attitudes, beliefs, and biases in CMP that can influence the clinical conversation, and thus clinical management decisions, is foundationally important. DESIGN: The aims of this systematic review were to 1) summarize the evidence of the attitudes and beliefs of patients and health care providers (HCPs) involved in the clinical conversation about CMP, and 2) examine whether and how these perceptions impacted the process of care. METHODS: A systematic search of CINAHL, PubMed, Scopus, Sociology Database in ProQuest, and Web of Science used the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Included studies were those investigating vulnerable adult populations with chronic pain. Study bias was examined with the Downs and Black tool. RESULTS: Seven retrospective studies were included. When making pharmaceutical management decisions, HCPs demonstrated negative implicit biases toward minorities and women. When making referrals to multidisciplinary care, HCPs demonstrated negative implicit biases toward women with lower educational attainment. Unmet patient expectations resulted in higher dropout rates at multidisciplinary pain management programs. Patients' trust was influenced by the health care setting, and patients often had limited options secondary to health insurance type/status. CONCLUSION: These findings suggest that patients with CMP may experience a marginalized process of care due to HCPs' negative implicit biases, unmet patient expectations, and the health care setting. Results suggest several factors may contribute to inequitable care and the recalcitrant nature of CMP, particularly in vulnerable populations with limited health care choices.

The role and contribution of treatment and imaging modalities in global cervical cancer management: survival estimates from a simulation-based analysis.

BACKGROUND: Cervical cancer is the fourth most common cancer among women worldwide, causing more than 300 000 deaths globally each year. In addition to screening and prevention, effective cancer treatment is needed to reduce cervical cancer mortality. We discuss the role of imaging in cervical cancer management and estimate the potential survival effect of scaling up imaging in several different contexts. METHODS: Using a previously developed microsimulation model of global cancer survival, we estimated stage-specific cervical cancer 5-year net survival in 200 countries and territories. We evaluated the potential survival effect of scaling up treatment (chemotherapy, surgery, radiotherapy, and targeted therapy), and imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single photon emission CT [SPECT]) to the mean level of high-income countries, both individually and in combination. FINDINGS: We estimate global cervical cancer 5-year net survival as 42·1% (95% uncertainty interval [UI] 33·8-48·5). Among individual imaging modalities, expanding MRI would yield the largest 5-year survival gains globally (data are absolute percentage point increase in survival 0·6, 95% UI 0·1-2·1), scaling up ultrasound would yield the largest gains in low-income countries (0·5, 0·0-3·7), expanding CT and x-ray would have the greatest effect in Latin America (0·8, 0·0-3·4) and Oceania (0·4, 0·0-3·2), and expanding PET would yield the largest gains in high-income countries (0·2, 0·0-0·8). Scaling up SPECT did not show major changes in any region. Among individual treatment modalities, scaling up radiotherapy would yield the largest absolute percentage point gains in low-income countries (5·2, 0·3-13·5), and expanding surgery would have the largest effect in lower-middle-income countries (7·4, 0·3-21·1) and upper-middle-income countries (0·8, 0·0-2·9). Estimated survival gains in high-income countries were very modest. However, the gains from expanding any single treatment or imaging modality individually were small across all income levels and geographical settings. Scaling up all treatment modalities could improve global 5-year net survival to 52·4% (95% UI 44·6-62·0). In addition to expanding treatment, improving quality of care could raise survival to 57·5% (51·2-63·5), and the cumulative effect of scaling up all imaging modalities together with expanded treatment and quality of care could improve 5-year net survival for cervical cancer to 62·5% (57·7-67·8). INTERPRETATION: Comprehensive scale-up of treatment, imaging, and quality of care could substantially improve global cervical cancer 5-year net survival, with quality of care and imaging improvements each contributing about 25% of the total potential gains. These findings suggest that a narrow focus on the availability of treatment modalities could forgo substantial survival gains. Investments in imaging equipment, personnel, and quality of care efforts will also be needed to successfully scale up cervical cancer treatment worldwide. FUNDING: Harvard T H Chan School of Public Health and National Cancer Institute.

Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease.

Background Identification of markers to aid in understanding the growth kinetics of Von Hippel-Lindau (VHL)-associated clear cell renal cell carcinoma (ccRCC) has the potential to allow individualization of patient care, thereby helping prevent unnecessary screening and optimizing intervention. Purpose To determine whether the degree of restricted diffusion at baseline MRI holds predictive potential for the growth rate of VHL-associated ccRCC. Materials and Methods Patients with VHL disease who underwent surgical resection of tumors between November 2014 and October 2017 were analyzed retrospectively in this HIPAA-compliant study. The change in ccRCC volume between two time points and apparent diffusion coefficient (ADC) at baseline was calculated by using segmentations by two readers at nephrographic-phase CT and diffusion-weighted MRI, respectively. Intraclass correlation coefficient was used to assess agreement between readers. Repeated-measures correlation was used to investigate relationships between ADC (histogram parameters) and tumor size at baseline with growth rate and volume doubling time (VDT). Predictive performance of the ADC parameter with highest correlation and tumor size at baseline was reviewed to differentiate tumors based on their VDT (≤1 year or >1 year). Results Forty-six patients (mean age, 46 years ± 7 [standard deviation]; 25 women) with 100 ccRCCs were evaluated. Interreader agreement resulted in mean κ scores of 0.89, 0.82, and 0.93 for mean ADC, baseline tumor volume, and follow-up tumor volume, respectively. ADC percentiles correlated negatively with tumor growth rate but correlated positively with VDT. Lower ADC values demonstrated stronger correlations. The 25th percentile ADC had the strongest correlation with growth rate (ρ = -0.52, P < .001) and VDT (ρ = 0.60, P < .001) and enabled prediction of VDT (≤1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P < .001). Conclusion Apparent diffusion coefficient at baseline was negatively correlated with tumor growth rate. Diffusion-weighted MRI may be useful to identify clear cell renal cell carcinomas with higher growth rates. © RSNA, 2020See also the editorial by Goh and Prezzi in this issue.

Transarterial Chemoembolization in a Woodchuck Model of Hepatocellular Carcinoma.

PURPOSE: To assess the feasibility of transarterial chemoembolization with drug-eluting embolic (DEE) microspheres in a woodchuck model of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Nine woodchucks were studied: 4 normal animals and 5 animals infected with woodchuck hepatitis virus in which HCC had developed. Three animals with HCC underwent multidetector CT. A 3-F sheath was introduced into the femoral artery, and the hepatic arteries were selectively catheterized with 2.0-2.4-F microcatheters. Normal animals underwent diagnostic angiography and bland embolization. Animals with HCC underwent DEE transarterial chemoembolization with 70-150-µm radiopaque microspheres loaded with 37.5 mg doxorubicin per milliliter. Cone-beam CT and multidetector CT were performed. Following euthanasia, explanted livers underwent micro-CT, histopathologic examination, and fluorescence imaging of doxorubicin. RESULTS: The tumors were hypervascular and supplied by large-caliber tortuous vessels, with arteriovenous shunts present in 2 animals. There was heterogeneous enhancement on multidetector CT with areas of necrosis. Six tumors were identified. The most common location was the right medial lobe (n = 3). Mean tumor volume was 30.7 cm3 ± 12.3. DEE chemoembolization of tumors was achieved. Excluding the 2 animals with arteriovenous shunts, the mean volume of DEE microspheres injected was 0.49 mL ± 0.17. Fluorescence imaging showed diffusion of doxorubicin from the DEE microspheres into the tumor. CONCLUSIONS: Woodchuck HCC shares imaging appearances and biologic characteristics with human HCC. Selective catheterization and DEE chemoembolization may similarly be performed. Woodchucks may be used to model interventional therapies and possibly characterize radiologic-pathologic correlations.

Opportunities in Interventional and Diagnostic Imaging by Using High-Performance Low-Field-Strength MRI.

Background Commercial low-field-strength MRI systems are generally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging techniques. An MRI system was developed that combines low field strength (0.55 T) with high-performance imaging technology. Purpose To evaluate applications of a high-performance low-field-strength MRI system, specifically MRI-guided cardiovascular catheterizations with metallic devices, diagnostic imaging in high-susceptibility regions, and efficient image acquisition strategies. Materials and Methods A commercial 1.5-T MRI system was modified to operate at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and advanced imaging methods. MRI was performed between January 2018 and April 2019. T1, T2, and T2* were measured at 0.55 T; relaxivity of exogenous contrast agents was measured; and clinical applications advantageous at low field were evaluated. Results There were 83 0.55-T MRI examinations performed in study participants (45 women; mean age, 34 years ± 13). On average, T1 was 32% shorter, T2 was 26% longer, and T2* was 40% longer at 0.55 T compared with 1.5 T. Nine metallic interventional devices were found to be intrinsically safe at 0.55 T (<1°C heating) and MRI-guided right heart catheterization was performed in seven study participants with commercial metallic guidewires. Compared with 1.5 T, reduced image distortion was shown in lungs, upper airway, cranial sinuses, and intestines because of improved field homogeneity. Oxygen inhalation generated lung signal enhancement of 19% ± 11 (standard deviation) at 0.55 T compared with 7.6% ± 6.3 at 1.5 T (P = .02; five participants) because of the increased T1 relaxivity of oxygen (4.7e-4 mmHg-1sec-1). Efficient spiral image acquisitions were amenable to low field strength and generated increased signal-to-noise ratio compared with Cartesian acquisitions (P < .02). Representative imaging of the brain, spine, abdomen, and heart generated good image quality with this system. Conclusion This initial study suggests that high-performance low-field-strength MRI offers advantages for MRI-guided catheterizations with metal devices, MRI in high-susceptibility regions, and efficient imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Grist in this issue.

Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Results in Humans.

Purpose To investigate whether photon-counting detector (PCD) technology can improve dose-reduced chest computed tomography (CT) image quality compared with that attained with conventional energy-integrating detector (EID) technology in vivo. Materials and Methods This was a HIPAA-compliant institutional review board-approved study, with informed consent from patients. Dose-reduced spiral unenhanced lung EID and PCD CT examinations were performed in 30 asymptomatic volunteers in accordance with manufacturer-recommended guidelines for CT lung cancer screening (120-kVp tube voltage, 20-mAs reference tube current-time product for both detectors). Quantitative analysis of images included measurement of mean attenuation, noise power spectrum (NPS), and lung nodule contrast-to-noise ratio (CNR). Images were qualitatively analyzed by three radiologists blinded to detector type. Reproducibility was assessed with the intraclass correlation coefficient (ICC). McNemar, paired t, and Wilcoxon signed-rank tests were used to compare image quality. Results Thirty study subjects were evaluated (mean age, 55.0 years ± 8.7 [standard deviation]; 14 men). Of these patients, 10 had a normal body mass index (BMI) (BMI range, 18.5-24.9 kg/m2; group 1), 10 were overweight (BMI range, 25.0-29.9 kg/m2; group 2), and 10 were obese (BMI ≥30.0 kg/m2, group 3). PCD diagnostic quality was higher than EID diagnostic quality (P = .016, P = .016, and P = .013 for readers 1, 2, and 3, respectively), with significantly better NPS and image quality scores for lung, soft tissue, and bone and with fewer beam-hardening artifacts (all P < .001). Image noise was significantly lower for PCD images in all BMI groups (P < .001 for groups 1 and 3, P < .01 for group 2), with higher CNR for lung nodule detection (12.1 ± 1.7 vs 10.0 ± 1.8, P < .001). Inter- and intrareader reproducibility were good (all ICC > 0.800). Conclusion Initial human experience with dose-reduced PCD chest CT demonstrated lower image noise compared with conventional EID CT, with better diagnostic quality and lung nodule CNR. © RSNA, 2017 Online supplemental material is available for this article.

Abdominal Imaging with Contrast-enhanced Photon-counting CT: First Human Experience.

PURPOSE: To evaluate the performance of a prototype photon-counting detector (PCD) computed tomography (CT) system for abdominal CT in humans and to compare the results with a conventional energy-integrating detector (EID). MATERIALS AND METHODS: The study was HIPAA-compliant and institutional review board-approved with informed consent. Fifteen asymptomatic volunteers (seven men; mean age, 58.2 years ± 9.8 [standard deviation]) were prospectively enrolled between September 2 and November 13, 2015. Radiation dose-matched delayed contrast agent-enhanced spiral and axial abdominal EID and PCD scans were acquired. Spiral images were scored for image quality (Wilcoxon signed-rank test) in five regions of interest by three radiologists blinded to the detector system, and the axial scans were used to assess Hounsfield unit accuracy in seven regions of interest (paired t test). Intraclass correlation coefficient (ICC) was used to assess reproducibility. PCD images were also used to calculate iodine concentration maps. Spatial resolution, noise-power spectrum, and Hounsfield unit accuracy of the systems were estimated by using a CT phantom. RESULTS: In both systems, scores were similar for image quality (median score, 4; P = .19), noise (median score, 3; P = .30), and artifact (median score, 1; P = .17), with good interrater agreement (image quality, noise, and artifact ICC: 0.84, 0.88, and 0.74, respectively). Hounsfield unit values, spatial resolution, and noise-power spectrum were also similar with the exception of mean Hounsfield unit value in the spinal canal, which was lower in the PCD than the EID images because of beam hardening (20 HU vs 36.5 HU; P < .001). Contrast-to-noise ratio of enhanced kidney tissue was improved with PCD iodine mapping compared with EID (5.2 ± 1.3 vs 4.0 ± 1.3; P < .001). CONCLUSION: The performance of PCD showed no statistically significant difference compared with EID when the abdomen was evaluated in a conventional scan mode. PCD provides spectral information, which may be used for material decomposition.

Development of a separability index for task specific characterization of spectral computed tomography.

PURPOSE: In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions. METHOD: Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated. RESULTS: Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability. CONCLUSION: The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI.

INTRODUCTION: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics. MATERIAL AND METHODS: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models. RESULTS: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90. CONCLUSION: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population.

A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses.

OBJECTIVES: The aim of this study was to assess the interreader reliability and per-RCC sensitivity of high-resolution photon-counting computed tomography (PCCT) in the detection and characterization of renal masses in comparison to MRI. MATERIALS AND METHODS: This prospective study included 24 adult patients (mean age, 52 ± 14 years; 14 females) who underwent PCCT (using an investigational whole-body CT scanner) and abdominal MRI within a 3-month time interval and underwent surgical resection (partial or radical nephrectomy) with histopathology (n = 70 lesions). Of the 24 patients, 17 had a germline mutation and the remainder were sporadic cases. Two radiologists (R1 and R2) assessed the PCCT and corresponding MRI studies with a 3-week washout period between reviews. Readers recorded the number of lesions in each patient and graded each targeted lesion's characteristic features, dimensions, and location. Data were analyzed using a 2-sample t test, Fisher exact test, and weighted kappa. RESULTS: In patients with von Hippel-Lindau mutation, R1 identified a similar number of lesions suspicious for neoplasm on both modalities (51 vs 50, P = 0.94), whereas R2 identified more suspicious lesions on PCCT scans as compared with MRI studies (80 vs 56, P = 0.12). R1 and R2 characterized more lesions as predominantly solid in MRIs (R1: 58/70 in MRI vs 52/70 in PCCT, P < 0.001; R2: 60/70 in MRI vs 55/70 in PCCT, P < 0.001). R1 and R2 performed similarly in detecting neoplastic lesions on PCCT and MRI studies (R1: 94% vs 90%, P = 0.5; R2: 73% vs 79%, P = 0.13). CONCLUSIONS: The interreader reliability and per-RCC sensitivity of PCCT scans acquired on an investigational whole-body PCCT were comparable to MRI scans in detecting and characterizing renal masses. CLINICAL RELEVANCE STATEMENT: PCCT scans have comparable performance to MRI studies while allowing for improved characterization of the internal composition of lesions due to material decomposition analysis. Future generations of this imaging modality may reveal additional advantages of PCCT over MRI.

Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Skeletal involvement in Erdheim-Chester disease: Multimodality imaging features and association with the BRAFV600E mutation.

OBJECTIVE: The aim of this study was to characterize the distribution of skeletal involvement in Erdheim-Chester disease (ECD) by using radiography, computed tomography (CT), 18F-FDG positron emission tomography/computed tomography (PET/CT), and bone scans, as well as looking for associations with the BRAFV600E mutation. MATERIAL AND METHODS: Prospective study of 50 consecutive patients with biopsy-confirmed ECD who had radiographs, CT, 18F-FDG PET/CT, and Tc-99m MDP bone scans. At least two experienced radiologists with expertise in the relevant imaging studies analyzed the images. Summary statistics were expressed as the frequency with percentages for categorical data. Fisher's exact test, as well as odds ratios (OR) with 95 % confidence intervals (CI), were used to link imaging findings to BRAFV600E mutation. The probability for co-occurrence of bone involvement at different locations was calculated and graphed as a heat map. RESULTS: All 50 cases revealed skeletal involvement at different regions of the skeleton. The BRAFV600E mutation, which was found in 24 patients, was correlated with femoral and tibial involvement on 18F-FDG PET/CT and bone scan. The appearance of changes on the femoral, tibial, fibular, and humeral involvement showed correlation with each other based on heat maps of skeletal involvement on CT. CONCLUSION: This study reports the distribution of skeletal involvement in a cohort of patients with ECD. CT is able to detect the majority of ECD skeletal involvement. Considering the complementary nature of information from different modalities, imaging of ECD skeletal involvement is optimized by using a multi-modality strategy.

Automated detection of sclerotic metastases in the thoracolumbar spine at CT.

PURPOSE: To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm(3) in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed. RESULTS: Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). False-negative findings were most commonly (37 [40%] of 93) a result of endplate proximity, with 32 (34% of 93) caused by low CT attenuation. Marginal sclerosis caused by degenerative change (174 [28.1%] of 620 actual detections) was the most common cause of false-positive detections, followed by partial volume averaging with vertebral endplates (173 [27.9%] of 620) and pedicle cortex parallel to the axial imaging plane (121 [19.5%] 620). CONCLUSION: This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.

PURPOSE: Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. MATERIALS AND METHODS: This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. RESULTS: There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons. CONCLUSION: We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.