How Do Cancer-Specific Computed Tomography Protocols Compare With the American College of Radiology Dose Index Registry? An Analysis of Computed Tomography Dose at 2 Cancer Centers.

BACKGROUND: Little guidance exists on how to stratify radiation dose according to diagnostic task. Changing dose for different cancer types is currently not informed by the American College of Radiology Dose Index Registry dose survey. METHODS: A total of 9602 patient examinations were pulled from 2 National Cancer Institute designated cancer centers. Computed tomography dose (CTDI vol ) was extracted, and patient water equivalent diameter was calculated. N-way analysis of variance was used to compare the dose levels between 2 protocols used at site 1, and three protocols used at site 2. RESULTS: Sites 1 and 2 both independently stratified their doses according to cancer indications in similar ways. For example, both sites used lower doses ( P < 0.001) for follow-up of testicular cancer, leukemia, and lymphoma. Median dose at median patient size from lowest to highest dose level for site 1 were 17.9 (17.7-18.0) mGy (mean [95% confidence interval]) and 26.8 (26.2-27.4) mGy. For site 2, they were 12.1 (10.6-13.7) mGy, 25.5 (25.2-25.7) mGy, and 34.2 (33.8-34.5) mGy. Both sites had higher doses ( P < 0.001) between their routine and high-image-quality protocols, with an increase of 48% between these doses for site 1 and 25% for site 2. High-image-quality protocols were largely applied for detection of low-contrast liver lesions or subtle pelvic pathology. CONCLUSIONS: We demonstrated that 2 cancer centers independently choose to stratify their cancer doses in similar ways. Sites 1 and 2 dose data were higher than the American College of Radiology Dose Index Registry dose survey data. We thus propose including a cancer-specific subset for the dose registry.

Appendiceal Neuroendocrine Neoplasms: A Comprehensive Review.

ABSTRACT: Appendiceal neuroendocrine neoplasm (NEN) is the most common adult appendiceal malignant tumor, constituting 16% of gastrointestinal NENs. They are versatile tumors with varying morphology, immunohistochemistry, secretory properties, and cancer genomics. They are slow growing and clinically silent, to begin with, or present with features of nonspecific vague abdominal pain. Most acute presentations are attributed clinically to appendicitis, with most cases detected incidentally on pathology after an appendectomy. Approximately 40% of them present clinically with features of hormonal excess, which is likened to the functional secretory nature of their parent cell of origin. The symptoms of carcinoid syndrome render their presence clinically evident. However, slow growing and symptomatically silent in its initial stages, high-grade neuroendocrine tumors and neuroendocrine carcinomas of the appendix are aggressive and usually have hepatic and lymph node metastasis at presentation. This review article focuses on imaging characteristics, World Health Organization histopathological classification and grading, American Joint Committee on Cancer/Union or International Cancer Control, European Neuroendocrine Tumor Society staging, European Neuroendocrine Tumor Society standardized guidelines for reporting, data interpretation, early-stage management protocols, and advanced-stage appendiceal NENs. Guidelines are also set for the follow-up and reassessment. The role of targeted radiotherapy, chemotherapy, and high-dose somatostatin analogs in treating advanced disease are discussed, along with types of ablative therapies and liver transplantation for tumor recurrence. The search for newer location-specific biomarkers in NEN is also summarized. Regarding the varying aggressiveness of the tumor, there is a scope for research in the field, with plenty of data yet to be discovered.

Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.

Neurofibromatosis from Head to Toe: What the Radiologist Needs to Know.

Neurofibromatosis type 1 (NF1) and neurofibromatosis type 2 (NF2) are autosomal dominant inherited neurocutaneous disorders or phakomatoses secondary to mutations in the NF1 and NF2 tumor suppressor genes, respectively. Although they share a common name, NF1 and NF2 are distinct disorders with a wide range of multisystem manifestations that include benign and malignant tumors. Imaging plays an essential role in diagnosis, surveillance, and management of individuals with NF1 and NF2. Therefore, it is crucial for radiologists to be familiar with the imaging features of NF1 and NF2 to allow prompt diagnosis and appropriate management. Key manifestations of NF1 include café-au-lait macules, axillary or inguinal freckling, neurofibromas or plexiform neurofibromas, optic pathway gliomas, Lisch nodules, and osseous lesions such as sphenoid dysplasia, all of which are considered diagnostic features of NF1. Other manifestations include focal areas of signal intensity in the brain, low-grade gliomas, interstitial lung disease, various abdominopelvic neoplasms, scoliosis, and vascular dysplasia. The various NF1-associated abdominopelvic neoplasms can be categorized by their cellular origin: neurogenic neoplasms, interstitial cells of Cajal neoplasms, neuroendocrine neoplasms, and embryonal neoplasms. Malignant peripheral nerve sheath tumors and intracranial tumors are the leading contributors to mortality in NF1. Classic manifestations of NF2 include schwannomas, meningiomas, and ependymomas. However, NF2 may have shared cutaneous manifestations with NF1. Lifelong multidisciplinary management is critical for patients with either disease. The authors highlight the genetics and molecular pathogenesis, clinical and pathologic features, imaging manifestations, and multidisciplinary management and surveillance of NF1 and NF2. Online supplemental material is available for this article. ©RSNA, 2022.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Performance of Multidetector Computed Tomography and Negative Versus Positive Enteric Contrast for Evaluation of Gastrointestinal Neuroendocrine Neoplasms.

BACKGROUND: Routine computed tomography (CT) scans are thought to have poor performance for detection of gastrointestinal (GI) neuroendocrine neoplasms (NENs), which leads to delayed workup. Detection of even 1 bowel tumor can guide diagnostic workup and management. The purposes of this study were to assess the accuracy of multidetector computed tomography (MDCT) and to compare negative versus positive enteric contrast in detecting at least 1 GI tumor per patient with suspected or confirmed diagnosis of a NEN. METHODS: This retrospective study included 107 patients with intravenous and oral contrast (65 positive, 40 negative, and 2 no oral contrast) abdominopelvic MDCT. Two abdominal radiologists independently analyzed the CTs for detection and localization of bowel NENs. Surgical pathology was considered the reference standard. Analyses included κ and summary statistics, McNemar test, Pearson χ2 test, and Fisher exact test. RESULTS: Among the 107 CT scans, there were 30 pathology negative studies and 77 studies with positive pathology for GI NEN. Interreader agreement for CT evaluation was substantial (κ = 0.61). At least 1 GI NEN per patient was detected with 51% to 53% sensitivity, 87% to 93% specificity, 91% to 95% positive predictive value (PPV), 42% negative predictive value, and 63% accuracy for each reader, and 57% accuracy when only the concordant (ie, matching) results of the 2 readers were considered. Computed tomography scans with negative enteric contrast had significantly higher sensitivity for concordant results than CTs with positive enteric contrast (58% vs 30%, P = 0.01). Specificity (100% vs 95%, P = 0.5), PPV (100% vs 93%, P = 0.49), negative predictive value (39% vs 39%, P = 0.99), and accuracy (67% vs 51%, P = 0.10) were not significantly different for negative versus positive enteric contrast for the concordant results. There was no significant difference in GI NEN localization between the readers. CONCLUSIONS: Routine MDCT with either positive or negative enteric contrast can detect at least 1 GI tumor per patient with more than 90% PPV and more than 50% accuracy in patients suspected of GI NEN. Using negative enteric contrast improves sensitivity for GI NEN versus positive enteric contrast. In addition, there is high accuracy in localizing the bowel tumor with positive or negative enteric contrast, which may guide surgery. Radiologists should have heightened awareness that evaluating such scans closely may lead to detection of primary bowel NENs at a higher rate than previously reported.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques.

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.

Calcified Splenic Lesions: Pattern Recognition Approach on CT With Pathologic Correlation.

OBJECTIVE. Incidental splenic lesions, often found on CT images of the abdomen, may often be ignored or mischaracterized. Calcified splenic lesions are often presumed to be granulomas; however, understanding the broader differential diagnostic considerations can be useful. CONCLUSION. Determining the cause of splenic lesions is essential to guide appropriate management; the pattern of calcification together with other imaging and clinical findings can aid with differentiation.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Improved Computed Tomography Contrast Injection Rates Through Implantable Chest Power Ports.

OBJECTIVE: The aim of this study was to optimize chest port contrast injections using stepwise improvements. METHODS: Ex vivo injections were tested. Two hundred scans using power port injections were then evaluated. RESULTS: The highest flow rate was achieved using a 19G access needle, larger diameter tubing, and warmed contrast.The mean injection rates in baseline and postimprovement groups were 2.7 ± 0.4 and 4.8 ± 0.4 mL/s, respectively (P < .0001). CONCLUSION: Component optimization of the port apparatus can maximize contrast flow rates.

Quantification and homogenization of image noise between two CT scanner models.

Feedback from radiologists indicated that differences in image appearance and noise impeded reading of post-contrast computed tomography (CT) scans from an updated CT scanner that was recently added to a fleet of existing scanners from the same vendor, despite using identically named reconstruction algorithms. The goals of this work were to quantify and possibly standardize image quality on the new and an existing scanner using phantom images. Three months of daily quality control images were analyzed to determine the mean CT number and noise magnitude in a water phantom. Next, subtraction images from the uniformity section of an American College of Radiology CT phantom were used to generate noise power spectra for both scanners. Then, a semi-anthropomorphic liver phantom was imaged with both scanners in triplicate using identical body protocols to quantify differences CT number and noise magnitude. Finally, the scanner dependence of CT number and noise magnitude on material attenuation was quantified using a multi-energy CT phantom with 15 material inserts. Significant differences between scanners were determined using a paired or Welch's t test as appropriate. In daily quality control images, the new scanner exhibited slightly higher CT number (0.697 vs. 0.412, P < 0.001, n = 85) and slightly lower noise magnitude (4.85 vs. 4.94, P < 0.001, n = 85). Measured NPS was not significantly different between the existing and new scanners. Interestingly, it was observed that the noise magnitude from the new scanner increased with increasing material attenuation in both the liver (P = 0.008) and multi-energy (P < 0.001) phantoms. Using an alternate reconstruction algorithm with the new scanner eliminated this deviation at high material attenuations. While standard noise evaluation in a water phantom was unable to discern differences between the scanners, more comprehensive testing with higher attenuation materials allowed for the characterization and homogenization of image quality.

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT.

Purpose To evaluate colorectal cancer hepatic metastasis detection and characterization between reduced radiation dose (RD) and standard dose (SD) contrast material-enhanced CT of the abdomen and to qualitatively compare between filtered back projection (FBP) and iterative reconstruction algorithms. Materials and Methods In this prospective study (from May 2017 through November 2017), 52 adults with biopsy-proven colorectal cancer and suspected hepatic metastases at baseline CT underwent two portal venous phase CT scans: SD and RD in the same breath hold. Three radiologists, blinded to examination details, performed detection and characterization of 2-15-mm lesions on the SD FBP and RD adaptive statistical iterative reconstruction (ASIR)-V 60% series images. Readers assessed overall image quality and lesions between SD FBP and seven different iterative reconstructions. Two nonblinded consensus reviewers established the reference standard using the picture archiving and communication system lesion marks of each reader, multiple comparison examinations, and clinical data. Results RD CT resulted in a mean dose reduction of 54% compared with SD. Of the 260 lesions (233 metastatic, 27 benign), 212 (82%; 95% confidence interval [CI]: 76%, 86%) were detected with RD CT, whereas 252 (97%; 95% CI: 94%, 99%) were detected with SD (P < .001); per-lesion sensitivity was 79% (95% CI: 74%, 84%) and 94% (95% CI: 90%, 96%) (P < .001), respectively. Mean qualitative scores ranked SD images as higher quality than RD series images, and ASIR-V ranked higher than ASIR and Veo 3.0. Conclusion CT evaluation of colorectal liver metastases is compromised with modest radiation dose reduction, and the use of iterative reconstructions could not maintain observer performance. © RSNA, 2018.

Comparison of Abdominal Computed Tomographic Enhancement and Organ Lesion Depiction Between Weight-Based Scanner Software Contrast Dosing and a Fixed-Dose Protocol in a Tertiary Care Oncologic Center.

OBJECTIVE: This study aimed to evaluate the quality of enhancement and solid-organ lesion depiction using weight-based intravenous (IV) contrast dosing calculated by injector software versus fixed IV contrast dose in oncologic abdominal computed tomographic (CT) examinations. METHODS: This institutional review board-exempt retrospective cohort study included 134 patients who underwent single-phase abdominal CT before and after implementation of weight-based IV contrast injector software. Patient weight, height, body mass index, and body surface area were determined. Two radiologists qualitatively assessed examinations (4 indicating markedly superior to -4 indicating markedly inferior), and Hounsfield unit measurements were performed. RESULTS: Enhancement (estimated mean, -0.05; 95% confidence interval [CI], -0.19 to 0.09; P = 0.46) and lesion depiction (estimated mean, -0.01; 95% CI, -0.10 to 0.07; P = 0.79) scores did not differ between CT examinations using weight-based IV contrast versus fixed IV contrast dosing when a minimum of 38.5 g of iodine was used. However, the scores using weight-based IV contrast dosing were lower when the injector software calculated and delivered less than 38.5 g of iodine (estimated mean, -0.81; 95% CI, -1.06 to -0.56; P < 0.0001). There were no significant differences in measured Hounsfield units between the CT examinations using weight-based IV contrast dosing versus fixed IV contrast dosing. CONCLUSIONS: Oncologic CT image quality was maintained or improved with weight-based IV contrast dosing using injector software when using a minimum amount of 38.5 g of iodine.

HIV-related Malignancies and Mimics: Imaging Findings and Management.

The risk of developing malignancy is higher in patients with human immunodeficiency virus (HIV) infection than in non-HIV-infected patients. Several factors including immunosuppression, viral coinfection, and high-risk lifestyle choices lead to higher rates of cancer in the HIV-infected population. A subset of HIV-related malignancies are considered to be acquired immunodeficiency syndrome (AIDS)-defining malignancies, as their presence confirms the diagnosis of AIDS in an HIV-infected patient. The introduction of highly active antiretroviral therapy (HAART) has led to a significant drop in the rate of AIDS-defining malignancies, including Kaposi sarcoma, non-Hodgkin lymphoma, and invasive cervical carcinoma. However, non-AIDS-defining malignancies (eg, Hodgkin lymphoma, lung cancer, hepatocellular carcinoma, and head and neck cancers) now account for an increasing number of cancer cases diagnosed in HIV-infected patients. Although the number has decreased, AIDS-defining malignancies account for 15%-19% of all deaths in HIV-infected patients in the post-HAART era. Most HIV-related malignancies in HIV-infected patients manifest at an earlier age with a more aggressive course than that of non-HIV-related malignancies. Understanding common HIV-related malignancies and their specific imaging features is crucial for making an accurate and early diagnosis, which impacts management. Owing to the weakened immune system of HIV-infected patients, other entities such as various infections, particularly opportunistic infections, are prevalent in these patients. These processes can have confounding clinical and imaging manifestations that mimic malignancy. This article reviews the most common AIDS-defining and non-AIDS-defining malignancies, the role of imaging in their diagnosis, and the imaging mimics of malignancies in HIV-infected patients. ©RSNA, 2018.

Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions.

OBJECTIVE: The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP). METHODS AND MATERIALS: Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed. RESULTS: Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P < 0.05). Adaptive statistical iterative reconstruction-V 90% showed superior LCD and had the highest CNR in the liver, aorta, and, pancreas, measuring 7.32 ± 3.22, 11.60 ± 4.25, and 4.60 ± 2.31, respectively, compared with the next best series of ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P <0.0001). Veo 3.0 and ASIR 80% had the best and worst spatial resolution, respectively. CONCLUSIONS: Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.

Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction.

PURPOSE: To qualitatively and quantitatively compare abdominal computed tomography (CT) images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0. MATERIALS AND METHODS: This retrospective study was approved by our institutional review board and was Health Insurance Portability and Accountability Act compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75-mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference (RP), and Veo 3.0 20% RP. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions.The images were reviewed by 3 independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale.Multiple 2-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation. RESULTS: The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference, 0.43; 95% confidence interval [95% CI], 0.25-0.6; P < 0.0001), overall image quality (mean difference, 0.87; 95% CI, 0.62-1.13; P < 0.0001) and qualitative resolution (mean difference, 0.9; 95% CI, 0.69-1.1; P < 0.0001). Although the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest. CONCLUSION: Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations, such as focal lesion detection, in the oncology setting.

Imaging of Hereditary Tumors of the Female Genital System.

Cancers of the female genital system, particularly endometrial and ovarian cancers, can be associated with hereditary cancer syndromes such as hereditary breast and ovarian cancer and Lynch syndrome. Cancers that are found in the setting of a hereditary cancer syndrome are often unique in presentation, clinical features, and pathologic profiles when compared with sporadic tumors. This article reviews the hereditary cancer syndromes associated with gynecological malignancies, as well as the imaging findings and staging system of endometrial and ovarian cancers. These associations are important for proper patient screening, diagnosis, and treatment.

Qualitative Slow Blood Flow in Lower Extremity Deep Veins on Doppler Sonography: Quantitative Assessment and Preliminary Evaluation of Correlation With Subsequent Deep Venous Thrombosis Development in a Tertiary Care Oncology Center.

OBJECTIVES: To determine whether the qualitative sonographic appearance of slow deep venous flow in the lower extremities correlates with quantitative slow flow and an increased risk of deep venous thrombosis (DVT) in oncology patients. METHODS: In this Institutional Review Board-approved retrospective study, we reviewed lower extremity venous Doppler sonographic examinations of 975 consecutive patients: 482 with slow flow and 493 with normal flow. The subjective slow venous flow and absence of initial DVT were confirmed by 2 radiologists. Peak velocities were recorded at 3 levels. Each patient was followed for DVT development. The associations between DVT and the presence of slow venous flow were examined by the Fisher exact test; a 2-sample t test was used for peak velocity and DVT group comparisons. The optimal cutoff peak velocity for correlation with the radiologists' perceived slow flow was determined by the Youden index. RESULTS: Deep venous thrombosis development in the slow-flow group (21 of 482 [4.36%]) was almost doubled compared with patients who had normal flow (11 of 493 [2.23%]; P = .0456). Measured peak venous velocities were lower in the slow-venous flow group (P < .001). Patients with subsequent DVT did not have a significant difference in venous velocities compared with their respective patient groups. The sum of 3 venous level velocities resulted in the best cutoff for dichotomizing groups into normal versus slow venous flow. CONCLUSIONS: Qualitative slow venous flow in the lower extremities on Doppler sonography accurately correlates with quantitatively slower flow, and this preliminary evaluation suggests an associated mildly increased rate of subsequent DVT development in oncology patients.