Trauma CT evaluation prior to selective angiography in patients with traumatic injuries: negative predictive power and factors affecting its utility.

PURPOSE: To evaluate the predictive power of arterial injury detected on contrast-enhanced CT (trauma CT (tCT)) imaging obtained prior to selective angiography for treatment of patients with traumatic abdominal and pelvic injuries. MATERIALS AND METHODS: A retrospective chart review was performed of all patients who underwent angiography after undergoing contrast-enhanced CT imaging for the evaluation/treatment of traumatic injuries to the abdomen and pelvis between March 2014 and September 2018. Data collection included demographics, pertinent history and physical findings, CT and angiography findings, treatment information, and outcomes. RESULTS: Eighty-nine (63 males, mean age = 45.8 ± 20.5 years) patients that were found to have 102 traumatic injuries on tCT and subsequently underwent angiography met inclusion criteria for this study. Sixty-four injuries demonstrated evidence of traumatic vascular injury on initial tCT. A negative tCT was able to predict subsequent negative angiography in 83% of cases (negative predictive power = 83%). The ability of tCT to rule out a positive finding on subsequent angiography was also 83% (sensitivity = 83%). The average systolic blood pressure and hemoglobin concentration at the time of tCT were higher in patients who had positive tCT than in patients with negative tCT (p < 0.05 and p < 0.01, respectively). The average time to angiography was greater in patients whom had subsequent negative angiography than the patients who had subsequent positive angiography (p < 0.05). CONCLUSION: Contrast-enhanced CT imaging may be able to help stratify patients who may have subsequent negative angiograms. Hemodynamic factors may affect sensitivity of tCT. Shorter time to angiography may increase the chance of identifying the injury on subsequent angiography.

Safety and Efficacy of Sacroplasty for Sacral Fractures: A Systematic Review and Meta-Analysis.

PURPOSE: To evaluate the safety and efficacy of sacroplasty for the treatment of osteoporotic and malignant sacral fractures by performing a systematic review and meta-analysis of existing literature. MATERIALS AND METHODS: PubMed, Web of Science, and SCOPUS databases were searched from their inception until February 2018 for articles describing sacroplasty. Inclusion criteria were as follows: studies reporting > 5 patients, and pain assessment before and after the procedure recorded with visual analog scale (VAS). Demographic data, procedural details, technical success rates, VAS scores before and after the procedure, and procedural complications were recorded. A random-effects meta-analyses of the VAS pain score before the procedure, at 24-48 hours, at 6 months, and at 12 months were calculated. RESULTS: Nineteen studies (18 case series and 1 cohort study) were identified consisting of 861 total patients (682 women and 167 men; mean age 73.89 ± 9.73 years). Patients underwent sacroplasty for the following indications: sacral insufficiency fractures secondary to osteoporosis (n = 664), malignancy (n = 167), and nonspecified sacral insufficiency fractures (n = 30). Technical and clinical successes were achieved in 98.9% (852/861) and 95.7% (623/651) of patients undergoing sacroplasty, respectively. The pooled major complication rate was 0.3%, with 3 patients requiring surgical decompression for cement leakage. Random-effects meta-analyses demonstrated statistically significant differences in the VAS pain level at preprocedure, 24-48 hours, 6 months, and 12 months, with cumulative pain scores of 8.32 ± 0.01, 3.55 ± 0.01, 1.48 ± 0.01, and 0.923 ± 0.01, respectively. CONCLUSIONS: Sacroplasty appears safe and effective for pain relief in patients with osteoporotic or malignant sacral fractures, with statistically significant sustained improvement in VAS pain scores up to 12 months.

Percutaneous thrombin injection treatment of a gluteal pseudoaneurysm following radiofrequency ablation of a hip osteoid osteoma in a 6-year-old boy.

Osteoid osteomas are benign bone lesions that present with bone pain in children and young adults. Over the last 2 decades, radiofrequency ablation has become the mainstay of treatment and is now preferred over surgical resection. Major complications of the procedure are very rare, consisting mostly of local skin burns. We present a case of a child presenting with a gluteal pseudoaneursym following CT-guided radiofrequency ablation of an acetabular osteoid osteoma, which was then treated successfully with percutaneous thrombin injection.

Large pulmonary arteriovenous malformation lost to follow-up with 10 years of asymptomatic interval growth: A case report.

Pulmonary arteriovenous malformations, previously considered a rare condition, have been increasingly identified in asymptomatic patients over the past 2 decades. Usually congenital and associated with hereditary hemorrhagic telangiectasia, these fistulae result in right-to-left shunting of blood by abnormal communication of pulmonary arteries and veins lacking capillary beds. Clinical findings of right-to-left shunting in the presence of feeding and draining vessels identified on imaging confirm the diagnosis, for which the first-line therapy is embolization. This report highlights the presentation and management of a large asymptomatic PAVM detected incidentally in a patient who was lost to follow-up for 10 years and represented with acute hypoxic respiratory failure secondary to a viral infection with an interval increase of PAVM size.

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

Diagnostic value of PET imaging in clinically unresponsive patients.

Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts.

Readability assessment of internet-based patient education materials related to uterine artery embolization.

PURPOSE: To determine the readability of Internet-based patient education materials (IPEMs) created by United States hospitals and universities and clinical practices and miscellaneous health care-associated Web sites regarding uterine artery embolization (UAE) as a marker for IPEMs in general. METHODS AND METHODS: Two hundred unique Web sites were evaluated for patient-related articles on UAE. Web sites produced by US hospitals and universities and clinical practices, as well as miscellaneous health care-associated Web sites meeting the Health on the Net Foundation Code of Conduct criteria were included in the database. By using mathematical regression algorithms based on word and sentence length to quantitatively analyze reading materials for language intricacy, readability of 40 UAE-related IPEMs was assessed with four indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (GFOG). Scores were evaluated against national recommendations, and intergroup analysis was performed. RESULTS: None of the IPEMs were written at or below the sixth-grade reading level, based on FKGL. The mean readability scores were as follows: FRES, 43.98; FKGL, 10.76; SMOG, 13.63; and GFOG, 14.55. These scores indicate that the readability of UAE IPEMs is written at an advanced level, significantly above the recommended 6th grade reading level (P<.05) determined by the United States Department of Health and Human Services. CONCLUSIONS: IPEMs related to UAE generated by hospitals, clinical practices, and miscellaneous health care-associated Web sites are written above the recommended sixth grade level. IPEMs for other disease entities may also reflect similar results.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

Artificial intelligence in cardiac computed tomography.

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review.

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review.

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends.

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

Expedited TR Band Removal for Transradial Approach for Noncoronary Visceral Procedures: Initial Experience.

PURPOSE: For transradial interventions, most published studies report an initial Terumo Radial (TR) band placement time of 60 minutes, with gradual deflation over 30 to 90 minutes. We aimed to determine, retrospectively, whether TR band removal time could be expedited to 45 to 60 minutes, without adverse effects via an expedited single-step deflation protocol. METHODS: A total of 115 consecutive noncoronary visceral interventions that utilized TR band from September 2017 till February 2019 were retrospectively reviewed. Alternative single-step deflation protocol was utilized where the nursing staff was instructed to deflate the TR band in 1 step between 45 and 60 minutes; 79 patients (43 men, 36 women, mean age of 55.3 ± 13.6 years) underwent 115 transradial interventions. Mean procedure time was 49.8 ± 22.1 minutes, and mean fluoroscopy time was 18.5 ± 10.6 minutes. Data collected included patient demographics, procedure details, and nursing notes on complications including bleeding and reinflation of the TR band. Univariate and Multivariate analyses of independent variables were performed using a binary logistic regression model. All patients were followed up postoperatively before discharge and in clinic upon follow-up. RESULTS: The TR band was deflated at 51.3 ± 14.5 minutes, with successful removal achieved on the first attempt in 103 cases (90.3% primary technical success rate). In 12 cases, bleeding was noted upon initial deflation, secondary technical success was achieved when the band was reinflated for an additional mean time of 37.0 ± 19.1 minutes. There was 1 incidence of radial artery occlusion (0.8%) and 1 incidence of a grade 1 hematoma (0.8%). The only variable predictive of technical outcome upon initial band deflation on univariate binomial logistic regression was initial TR band removal time (P = .019). CONCLUSIONS: A single-step deflation protocol for TR band placement may be safe for nonocclusive patent hemostasis and may translate to even further shorten postprocedural hospital times for patients and cost savings for hospitals.

Endovascular Management of Hemarthrosis in Patients with Bleeding Diatheses: Systematic Review.

PURPOSE: In this systematic review, we report the safety and efficacy of embolization for treating hemarthrosis in patients with bleeding diatheses with or without a history of prior arthroplasty. MATERIALS AND METHODS: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. A structured search was performed in PubMed, Web of Science, MEDLINE, and SCOPUS databases of patients undergoing embolization for hemarthrosis. Demographic data, clinical history, angiographic findings, interventions, and outcomes were extracted and tabulated. RESULTS: The search identified 97 unique titles of which 15 were deemed relevant comprising 78 patients with coagulopathies undergoing 93 embolization procedures. Mean follow-up time was 29 months (range 3-116.5). In the 29 patients who underwent arthroplasty prior to embolization for hemarthrosis, there were 11 bleeding recurrences (37.9%). Of these patients who experienced recurrences, 10 underwent repeat embolization as coagulation factor replacement was not sufficient to address the re-bleeding. In the 49 patients with bleeding diatheses who underwent embolization for hemarthrosis with no history of prior arthroplasty, there were 11 recurrences (22.4%) and 4 of these patients underwent repeat embolization with favorable results. There were four major procedure-related complications. CONCLUSION: Transarterial embolization is safe and effective in addressing recurrent hemarthrosis in patients with bleeding diatheses with or without a history of arthroplasty of the affected joint.