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PurposeWe present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.Materials and methodsIn our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.ResultsProbabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.ConclusionWe present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record are utilized as "truth" to which upstream diagnoses generated by radiology are compared. The described system automatically extracts and compares patient medical data to characterize concordance between clinical sources. Initial results are presented in the context of breast imaging, matching 18 101 radiologic interpretations with 301 pathology diagnoses and achieving a precision and recall of 84% and 92%, respectively. The presented data-driven method highlights the challenges of integrating multiple data sources and the application of information extraction tools to facilitate healthcare quality improvement.

Tuberous sclerosis complex is a multisystem genetic syndrome often affecting the central nervous system. The purpose of the current study was to identify topographical patterns in the distribution specific to epileptogenic (n = 37) and nonepileptogenic (n = 544) tubers throughout the brain for a cohort of 23 tuberous sclerosis complex patients with a history of seizures. Tubers localized to the inferior parietal lobes, middle frontal lobes, middle temporal lobes, or central sulcus regions were associated with a high frequency of epileptogenic tubers. Epileptogenic tubers occurred statistically more frequently within the inferior parietal lobe and within the central sulcus region in children younger than 1 or between 1 and 3 years old, respectively. Results imply seizure activity in tuberous sclerosis complex patients can be associated with the location of cortical tubers.

Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record are utilized as “truth” to which upstream diagnoses generated by radiology are compared. The described system automatically extracts and compares patient medical data to characterize concordance between clinical sources. Initial results are presented in the context of breast imaging, matching 18 101 radiologic interpretations with 301 pathology diagnoses and achieving a precision and recall of 84% and 92%, respectively. The presented data-driven method highlights the challenges of integrating multiple data sources and the application of information extraction tools to facilitate healthcare quality improvement.

BackgroundDynamic susceptibility contrast (DSC)-MRI is a well-established perfusion MR imaging technique for estimating relative cerebral blood volume (CBV) in primary brain tumors; however, tumors localized to regions with naturally elevated perfusion, including cortical tissue and common vascular territories, make evaluation of tumor vascularity difficult to assess. In the current study, we have constructed a large-scale radiographic atlas of CBV to assess treatment response to bevacizumab in individual patients with recurrent glioblastoma.MethodsZ-score normalized CBV maps were registered to stereotactic atlas space in 450 patients with brain tumors. A CBV atlas was created by calculating the voxel-wise mean and variability in CBV. MRI and CBV maps from 32 recurrent glioblastoma patients were then obtained prior to and following treatment with bevacizumab, registered to and compared with the CBV atlas. The volume of tumor tissue with elevated CBV, percentage of enhancing tumor with elevated CBV, and the mean and maximum change in normalized CBV intensity relative to the atlas were computed.ResultsVoxel-wise comparison of individual patient CBV maps to the atlas allowed delineation of elevated tumor perfusion from artery and normal cortical tissue. An atlas-defined hypervascular tumor blood volume greater than 2.35 cc prior to treatment, 0.14 cc after treatment, and a decrease in atlas-defined hypervascular tumor volume less than 80% following treatment were characteristic of a shorter PFS and OS. Traditional measures of CBV were not predictive of PFS or OS.ConclusionsThis study highlights the advantages of large-scale population maps to identify abnormal biological tissues.

Purpose Office-based laboratories (OBLs), or outpatient interventional suites, are rapidly gaining traction with both patients and interventionalists. Compared with hospitals, OBLs can provide greater convenience for patients, allow physicians greater autonomy, and potentially generate more revenue under a lower cost structure. The aim of this article is to discuss logistics and finances of running an OBL using the authors' institution as a guide for interventional radiologists seeking to establish their own OBL. Materials and Methods Interventional radiology (IR) procedures performed at an OBL from the authors' academic institution were reviewed from January 2017 to December 2020. Yearly revenues and expenses related to IR procedures were recorded. Results The number and complexity of IR procedures performed in the OBL increased over time with nearly 10-fold growth of revenue from 2017 to 2020. Conclusion Most IR procedures performed in the hospital are feasible in the OBL setting, and by increasing patient volume and variety over time, an individual OBL can develop into a robust patient care center capable of generating revenue. [ABSTRACT FROM AUTHOR]

Minimally invasive image-guided interventions have changed the face of procedural medicine. For these procedures, safety and efficacy depend on precise needle placement. Needle targeting devices help improve the accuracy of needle placement, but their use has not seen broad penetration. Some of these devices are costly and require major modifications to the clinical workflow. In this article, we developed a low-cost, disposable, and easy-to-use angulation tracking device, which was based on a redesigned commercial passive needle holder. The new design provided real-time angulation information for needle tracking. In this design, two potentiometers were used as angulation sensors, and they were connected to two axes of the passive needle holder's arch structure through a 3 D-printed bridge structure. A control unit included an Arduino Pro Mini, a Bluetooth module, and two rechargeable batteries. The angulation was calculated and communicated in real time to a novel developed smartphone app, where real-time angulation information was displayed for guiding the operator to position the needle to the planned angles. The open-air test results showed that the average errors are 1.03° and 1.08° for left–right angulation and head–foot angulation, respectively. The animal cadaver tests revealed that the novel system had an average angular error of 3.2° and a radial distance error of 3.1 mm. The accuracy was comparable with some commercially available solutions. The novel and low-cost needle tracking device may find a role as part of a real-time precision approach to both planning and implementation of image-guided therapies. [ABSTRACT FROM AUTHOR]

The odds of dying brain tissue (infarction) from lack of blood supply is reduced in cases of closure of the internal carotid artery when the posterior communicating artery on the same side is open. The circle of Willis, a linked circle of arteries connecting the two internal carotid arteries, can provide circulation to areas served by the internal carotid arteries when one of them is closed. When 29 patients with confirmed blockage or narrowing of one or both carotid arteries were followed, four of six cerebral infarctions occurred in patients with same-side posterior communicating arteries less than 1 millimeter (mm) in diameter, and 10 of 13 occurred in patients with no flow through the posterior communicating artery. No infarcts occurred in those with artery diameters 1 mm or more. No other artery in the circle of Willis showed an association with cerebral infarct.

Sepsis caused by Escherichia (E.) coli infection may lead to high blood levels of bilirubin and kernicterus. Kernicterus is a destructive brain disease marked by high levels of bilirubin. The case report of a full-term infant with kernicterus details the diagnosis, treatment and outcome of an initially healthy infant who was brought back to the hospital at four days of age with fever and jaundice. The infant was found to have kernicterus, caused by E. coli infection. He was treated with exchange transfusions and antibiotics. He was later found to have a glucose-6-phosphate dehydrogenase (G6PD) deficiency. A neurological examination at four months of age showed motor development problems and possible hearing loss. Magnetic resonance images (MRI) of the infant's brain revealed damage consistent with that caused by high levels of bilirubin in the blood.

Purpose: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. Methods: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. Results: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set. Conclusion: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89). [ABSTRACT FROM AUTHOR]