Percutaneous thermal ablation is a safe and effective treatment for patients with ICCs and may be particularly valuable in unresectable patients, or those who have already undergone hepatic surgery. Tumor size and ablation modality were not associated with LTP, whereas primary tumors and superficially located tumors were more likely to subsequently recur.

Background Silent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports. Objective This study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center. Methods Both rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. The gold standard dataset includes 1000 radiology reports randomly retrieved from the 2 study sites (Mayo and Tufts) corresponding to patients with no prior or current diagnosis of stroke or dementia. 400 out of the 1000 reports were randomly sampled and double read to determine interannotator agreements. The gold standard dataset was equally split to 3 subsets for training, developing, and testing. Results Among the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively. Conclusions We adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.

Background: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. Method: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. Result: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. Conclusion: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

BACKGROUND AND PURPOSE: The significance of renal contrast on CT myelography is uncertain. This project examined different patient populations undergoing CT myelography for the presence of renal contrast to determine whether this finding is of diagnostic value in spontaneous intracranial hypotension. MATERIALS AND METHODS: Four groups of patients were analyzed for renal contrast on CT myelography. The control group underwent CT myelography for reasons other than spontaneous intracranial hypotension (n ϭ 47). Patients in study group 1 had spontaneous intracranial hypotension but CT myelography negative for dural CSF leak and CSF venous fistula (n ϭ 83). Patients in study group 2 had spontaneous intracranial hypotension and CT myelography positive for dural CSF leak (n ϭ 44). Patients in study group 3 had spontaneous intracranial hypotension and CT myelography suggestive of CSF venous fistula due to a hyperdense paraspinal vein (n ϭ 17, eleven surgically confirmed). RESULTS: Renal contrast was present on the initial CT myelography in 0/47 patients in the control group, 10/83 patients in group one, 1/44 patients in group 2, and 7/17 patients in group 3. Renal contrast on initial CT myelography in patients with suspected or surgically confirmed CSF venous fistula was significantly more likely than in patients with a dural CSF leak (P ϭ .0003). CONCLUSIONS: Renal contrast on initial CT myelography was seen only in patients with spontaneous intracranial hypotension. This was more common in confirmed/suspected CSF venous fistulas compared with dural leaks. Early renal contrast in patients with spontaneous intracranial hypotension should prompt scrutiny for a hyperdense paraspinal vein, and, if none is found, potentially advanced diagnostic studies.

With all four patients in our study having undetectable PSAs 12 months post ablation, and with no patient developing urinary incontinence due to the cryoablation, MRI-guided cryoablation appears to be a promising treatment option in patients who are poor surgical candidates due to prior pelvic surgery and/or radiation.

Possibly interested 14.29% 7.14% Interested 42.86% 21.43% Very interested 14.29% 50% Definitely planning on going into IR 21.43% 21.43%

Abstract. Peripheral arterial disease (PAD) management is exceptionally challenging. Despite advances in diagnostic and therapeutic technologies, long-term vessel patency and limb salvage rates are limited. Patients with PAD frequently require extensive workup with noninvasive tests and imaging to delineate their disease and help guide appropriate management. Ultrasound and computed tomography are commonly ordered in the workup of PAD. Magnetic resonance imaging (MRI), on the other hand, is less often acknowledged as a useful tool in this disease. Nevertheless, MRI is an important test that can effectively characterize atherosclerotic plaque, assess vessel patency in highly calcified disease, and measure lower extremity perfusion.

BACKGROUND Silent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports. OBJECTIVE This study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center. METHODS Both rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. RESULTS A total of 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively. CONCLUSIONS We adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.