Multicenter Quantification of Radiation Exposure and Associated Risks for Prostatic Artery Embolization in 1476 Patients.

Background Prostatic artery embolization (PAE) is a safe, minimally invasive angiographic procedure that effectively treats benign prostatic hyperplasia; however, PAE-related patient radiation exposure and associated risks are not completely understood. Purpose To quantify radiation dose and assess radiation-related adverse events in patients who underwent PAE at multiple centers. Materials and Methods This retrospective study included patients undergoing PAE for any indication performed by experienced operators at 10 high-volume international centers from January 2014 to May 2021. Patient characteristics, procedural and radiation dose data, and radiation-related adverse events were collected. Procedural radiation effective doses were calculated by multiplying kerma-area product values by an established conversion factor for abdominopelvic fluoroscopy-guided procedures. Relationships between cumulative air kerma (CAK) or effective dose and patient body mass index (BMI), fluoroscopy time, or radiation field area were assessed with linear regression. Differences in radiation dose stemming from radiopaque prostheses or fluoroscopy unit type were assessed using two-sample t tests and Wilcoxon rank sum tests. Results A total of 1476 patients (mean age, 69.9 years ± 9.0 [SD]) were included, of whom 1345 (91.1%) and 131 (8.9%) underwent the procedure with fixed interventional or mobile fluoroscopy units, respectively. Median procedure effective dose was 17.8 mSv for fixed interventional units and 12.3 mSv for mobile units. CAK and effective dose both correlated positively with BMI (R2 = 0.15 and 0.17; P < .001) and fluoroscopy time (R2 = 0.16 and 0.08; P < .001). No radiation-related 90-day adverse events were reported. Patients with radiopaque implants versus those without implants had higher median CAK (1452 mGy [range, 900-2685 mGy] vs 1177 mGy [range, 700-1959 mGy], respectively; P = .01). Median effective dose was lower for mobile than for fixed interventional systems (12.3 mSv [range, 8.5-22.0 mSv] vs 20.4 mSv [range, 13.8-30.6 mSv], respectively; P < .001). Conclusion Patients who underwent PAE performed with fixed interventional or mobile fluoroscopy units were exposed to a median effective radiation dose of 17.8 mSv or 12.3 mSv, respectively. No radiation-related adverse events at 90 days were reported. © RSNA, 2024 See also the editorial by Mahesh in this issue.

Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.

OBJECTIVE: To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more reliable evaluation of its fitness in prostate regions that were not identified as suspicious for cancer by a human-reader in pre- and intra-biopsy imaging analysis. METHODS: Low-level pre-biopsy MRI biomarkers from targeted and non-targeted biopsy locations were extracted and statistically tested for representativeness against biomarkers from non-biopsied prostate regions. A probabilistic machine learning classifier was optimized to map biomarkers to their core-level pathology, followed by extrapolation of pathology scores to non-biopsied prostate regions. Goodness-of-fit was assessed at targeted and non-targeted biopsy locations for the post-biopsy individual patient. RESULTS: Our experiments showed high predictability of imaging biomarkers in differentiating histopathology scores in thousands of non-targeted core-biopsy locations (ROC-AUCs: 0.85-0.88), but also high variability between patients (Median ROC-AUC [IQR]: 0.81-0.89 [0.29-0.40]). CONCLUSION: The sparseness of prostate biopsy data makes the validation of a whole gland risk mapping a non-trivial task. Previous studies i) focused on targeted-biopsy locations although biopsy-specimens drawn from systematically scattered locations across the prostate constitute a more representative sample to non-biopsied regions, and ii) estimated prediction-power across predicted instances (e.g., biopsy specimens) with no patient distinction, which may lead to unreliable estimation of model fitness to the individual patient due to variation between patients in instance count, imaging characteristics, and pathologies. SIGNIFICANCE: This study proposes a personalized whole-gland prostate cancer risk mapping post-biopsy to allow clinicians to better stage and personalize focal therapy treatment plans.

INTEGRATING PROSTATE SPECIFIC ANTIGEN DENSITY BIOMARKER INTO DEEP LEARNING PROSTATE MRI LESION SEGMENTATION MODELS.

Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance. Deep convolution neural networks have been widely utilized for lesion segmentation. However, these methods fail to achieve a high Dice coefficient because of the large variations in lesion size and location within the gland. To address this problem, we integrate the clinically-meaningful prostate specific antigen density (PSAD) biomarker into the deep learning model using feature-wise transformations to condition the features in latent space, and thus control the size of lesion prediction. We tested our models on a public dataset with 214 annotated mpMRI scans and compared the segmentation performance to a baseline 3D U-Net model. Results demonstrate that integrating the PSAD biomarker significantly improves segmentation performance in both Dice coefficient and centroid distance metric.

Prediction of Adverse Pathology at Radical Prostatectomy in Grade Group 2 and 3 Prostate Biopsies Using Machine Learning.

PURPOSE: There is ongoing clinical need to improve estimates of disease outcome in prostate cancer. Machine learning (ML) approaches to pathologic diagnosis and prognosis are a promising and increasingly used strategy. In this study, we use an ML algorithm for prediction of adverse outcomes at radical prostatectomy (RP) using whole-slide images (WSIs) of prostate biopsies with Grade Group (GG) 2 or 3 disease. METHODS: We performed a retrospective review of prostate biopsies collected at our institution which had corresponding RP, GG 2 or 3 disease one or more cores, and no biopsies with higher than GG 3 disease. A hematoxylin and eosin-stained core needle biopsy from each site with GG 2 or 3 disease was scanned and used as the sole input for the algorithm. The ML pipeline had three phases: image preprocessing, feature extraction, and adverse outcome prediction. First, patches were extracted from each biopsy scan. Subsequently, the pre-trained Visual Geometry Group-16 convolutional neural network was used for feature extraction. A representative feature vector was then used as input to an Extreme Gradient Boosting classifier for predicting the binary adverse outcome. We subsequently assessed patient clinical risk using CAPRA score for comparison with the ML pipeline results. RESULTS: The data set included 361 WSIs from 107 patients (56 with adverse pathology at RP). The area under the receiver operating characteristic curves for the ML classification were 0.72 (95% CI, 0.62 to 0.81), 0.65 (95% CI, 0.53 to 0.79) and 0.89 (95% CI, 0.79 to 1.00) for the entire cohort, and GG 2 and GG 3 patients, respectively, similar to the performance of the CAPRA clinical risk assessment. CONCLUSION: We provide evidence for the potential of ML algorithms to use WSIs of needle core prostate biopsies to estimate clinically relevant prostate cancer outcomes.

Atlas-based Semantic Segmentation of Prostate Zones.

Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical information in the form of a probabilistic prostate zone atlas and utilizes a dynamically controlled hyperparameter to combine the atlas with the semantic segmentation result. In addition to providing significantly improved segmentation performance, this hyperparameter is capable of being dynamically adjusted during the inference stage to provide users with a mechanism to refine the segmentation. We validate our approach using an external test dataset and demonstrate Dice similarity coefficient values (mean±SD) of 0.91±0.05 for the CG and 0.77±0.16 for the PZ that significantly improves upon the baseline segmentation results without the atlas. All code is publicly available on GitHub: https://github.com/OnofreyLab/prostate_atlas_segm_miccai2022.

Cumulative diagnostic imaging radiation exposure in premature neonates.

BACKGROUND: To date, there has been limited work evaluating the total cumulative effective radiation dose received by infants in the neonatal intensive care unit. Most previous publications report that the total radiation dose received falls within the safe limits but does not include all types of ionizing radiation studies typically performed on this vulnerable patient population. We aimed to provide an estimate of the cumulative effective ionizing radiation dose (cED) in microSieverts (µSv) received by premature infants ≤32 weeks from diagnostic studies performed throughout their NICU stay, and predictors of exposures. METHODS: Retrospective chart review from 2004-2011. Data included demographics, gestational age (GA), birth weight (BW), length of stay (LOS), clinical diagnosis, and radiological studies. RESULTS: 1045 charts were reviewed. Median GA = 30.0 weeks (SD 2.7, range 22.0-32.6). Median BW = 1340.0 grams (SD 445.4, range 420-2470). Median number of radiographic studies = 9 (SD 28.5, range 0-210). Median cED = 162µSv (range 0-9248). The cED was positively associated with LOS (p < 0.001) and inversely correlated with GA (p < 0.001) and BW (p < 0.001). Infants with intestinal perforation had the highest median cED 1661µSv compared to 162µSv for others (p < 0.001). CONCLUSION: Our results provide an estimate of the cumulative effective radiation dose received by premature infants in a level 4 neonatal intensive care unit from all radiological studies involving ionizing radiation and identifies risk factors and predictors of such exposure. Radiation exposure in NICU is highest among the most premature and among infants who suffer from intestinal perforation.

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically. METHODS: In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR). RESULTS: 73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations. CONCLUSION: Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.

Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions.

The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.

Effect of Reader and Patient Parameters on the Performance of Last-Image-Hold for Fluoroscopic Grading of Vesicoureteric Reflux.

OBJECTIVE. The purpose of this study is to determine the effect of different reader and patient parameters on the degree of agreement and the rate of misclassification of vesicoureteric reflux grading on last-image-hold frames in relation to spot-exposed frames from voiding cystourethrography (VCUG) as well as to determine the nature of reflux misclassification on last-image-hold frames. MATERIALS AND METHODS. Blinded readers conducted a retrospective evaluation of last-image-hold and spot-exposed frames of the renal fossae from 191 sequential VCUG examinations performed during a five-year period. Kappa tests were used to determine the agreement between reflux gradings and to assess the impact of reader and patient parameters. Pearson product-moment correlations were used to evaluate the effect of patient parameters on reader level of certainty regarding reflux grading. RESULTS. We measured almost perfect overall agreement for more experienced readers and substantial overall agreement for less experienced readers. Point estimates of overall misclassification were less than 2% for more experienced readers and less than 4% for less experienced readers. The readers' level of certainty about reflux grading had a positive impact on agreement values and misclassification rates. Experienced readers' most common misclassification was assigning reflux a grade of 3 on a spot-exposed frame and a grade of 2 on an equivalent last-image-hold frame. Inexperienced readers' most common misclassification involved missing reflux altogether. CONCLUSION. Instances of grade 2 reflux on last-image-hold frames may warrant supplemental evaluation with spot-exposed frames. Otherwise, a reader's level of certainty regarding reflux grading on a last-image-hold frame may help determine whether a supplemental spot-exposed frame would be beneficial.

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require accurate registration of the post-implantation CT images to the pre-implantation functional and structural magnetic resonance imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where the extraction of the cortical surface is difficult due to the missing parts of the skull and artifacts introduced from the electrodes. In this paper, we present a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. We propose learning a model of locally oriented appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Utilizing a database of clinical epilepsy imaging data to train and test our approach, we demonstrate that our method using locally oriented image appearance both more accurately extracts the brain surface and better localizes electrodes on the post-operative brain surface compared to standard, non-oriented appearance modeling. In addition, we compare our method to a standard atlas-based segmentation approach and to a U-Net-based deep convolutional neural network segmentation method.

Assessment of a modified technique for air delivery during fluoroscopic-monitored pneumatic intussusception reduction.

BACKGROUND: Intussusception is one of the most common causes of acute abdominal emergency in children. Image-monitored pressure reduction is the first line of treatment. OBJECTIVE: We report on a modified technique of air delivery during fluoroscopic-monitored pneumatic intussusception reductions, and compare it with an established technique. MATERIALS AND METHODS: We modified the Shiels intussusception reduction device so that the air used for intussusception reduction is delivered not by the device's insufflator bulb, but rather by the hospital medical air supply system, eliminating the need for continuous pumping of the insufflator bulb during the procedure. Subsequently, we retrospectively compared sequential fluoroscopy-monitored pneumatic intussusception reduction procedures performed in patients younger than 18 years using either the standard or modified devices, evaluating technical procedure parameters (i.e. median procedure time length, fluoroscopy time length and radiation dose) and patient outcomes (i.e. number of complete intussusception reductions, number of incomplete intussusception reductions, number of intussusception recurrences within 3 days of the procedure, number of procedures followed by surgery). RESULTS: We found no statistically significant differences between procedures performed with the standard and modified techniques. CONCLUSION: The device modification allows for increased operator comfort. Evaluated procedure parameters and patient outcomes appear similar to those of the standard technique.

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention.

Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

Knowledge of Density and Screening Ultrasound.

To determine breast density awareness and attitudes regarding supplemental breast ultrasound screening since implementation of the nation's first breast density notification law, Connecticut Public Act 09-41. A self-administered survey was distributed at a Connecticut academic breast imaging center between February 2013 and February 2014. Women with prior mammography reports describing heterogeneous or extremely dense breast tissue were invited to participate when presenting for screening mammography, screening ultrasound, or both. Data were collected on breast density awareness, history of prior ultrasounds, attitudes toward ultrasound and breast-cancer risk, and demographics. Data were collected from 950 completed surveys. The majority of surveyed women (92%) were aware of their breast density, and 77% had undergone a prior screening ultrasound. Forty-three percent of participants who were aware of their breast density also expressed increased anxiety about developing breast cancer due to having dense breast tissue. Caucasian race and higher education were significantly associated (p < 0.05) with knowledge of personal breast density (93% and 95%, respectively) and having a prior screening breast ultrasound (79% and 80%, respectively). Patients with less than a college degree (82%) were significantly more likely to rely exclusively on their provider's recommendation regarding obtaining screening ultrasound (p < 0.05). Breast density awareness is strongly associated with higher education, higher income, and Caucasian race. Non-Caucasian patients and those with less than a college education rely more heavily on their physicians' recommendations regarding screening ultrasound. Among women aware of their increased breast density, nearly half reported associated increased anxiety regarding the possibility of developing breast cancer.

LEARNING NONRIGID DEFORMATIONS FOR CONSTRAINED POINT-BASED REGISTRATION FOR IMAGE-GUIDED MR-TRUS PROSTATE INTERVENTION.

This paper presents and validates a low-dimensional nonrigid registration method for fusing magnetic resonance imaging (MRI) and trans-rectal ultrasound (TRUS) in image-guided prostate biopsy. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. Conventional clinical practice uses TRUS to guide prostate biopsies when there is a suspicion of cancer. Pre-procedural MRI information can reveal lesions and may be fused with intra-procedure TRUS imaging to provide patient-specific, localization of lesions for targeting. The state-of-the-art MRI-TRUS nonrigid image fusion process relies upon semi-automated segmentation of the prostate in both the MRI and TRUS images. In this paper, we develop a fast, automated nonrigid registration approach to MRI-TRUS fusion based on a statistical deformation model of intra-procedural deformations derived from a clinical sample.

The clinical impact of gynecologic MRI.

OBJECTIVE. This study assessed the clinical impact of pelvic MRI performed after the diagnosis of an indeterminate pelvic mass on ultrasound or CT. MATERIALS AND METHODS. The radiologic records of 567 patients who underwent pelvic MRI at our hospital from 2004 to 2006 were reviewed. Of these patients, 214 patients underwent pelvic MRI for evaluation of a gynecologic mass detected on a preceding ultrasound or CT examination; this group of patients constituted the basis of our study. The imaging and clinical records from the database were used for our analysis. The medical records were reviewed for the impact of the radiologic findings on patient treatment, and the results were tabulated for the findings of the first modality, whether the first modality provided a diagnosis, what management plan would be made according to the first modality, and what management plan would be made as a result of the MRI. The adequacy of the imaging study was assessed on the basis of either obtaining an accurate exact diagnosis or ascertaining at the minimum whether the mass was benign or malignant. Further endpoints included specificity and sensitivity of the individual modalities in the diagnosis of a specific gynecologic mass and whether clinical management was altered. Exact binomial CIs were computed for individual proportions. RESULTS. The clinical management of the patient was altered as a result of MRI in 77% of the cases (CI = 0.70-0.82). Surgery was avoided in 36% (CI = 0.29-0.43), and surgery was changed to a more appropriate method (laparoscopy vs laparotomy, involvement or not of a gynecologic oncologist) in an additional 17% (CI = 0.12-0.23). CONCLUSION. Without having undergone MRI, many of the women and girls in this study would have undergone unnecessary surgery; a more costly type of surgery; or long-term follow-up with the associated financial costs, personal and physical costs, and mental costs from the resultant anxiety of an unresolved indeterminate mass.

Using SUV as a guide to 18F-FDG dose reduction.

UNLABELLED: This article explores how one can lower the injected (18)F-FDG dose while maintaining validity in comparing standardized uptake values (SUVs) between studies. Variations of the SUV within each lesion were examined at different acquisition times. METHODS: Our protocol was approved by either the Human Investigation Committee or the Institutional Review Board. All 120 PET datasets were acquired continuously for 180 s per bed position in list mode and were reconstructed to obtain 30-, 60-, 90-, 120-, 150-, and 180-s-per-bed-position PET images with registration to a single set of nondiagnostic CT images. Qualitative assessment of the images was performed separately for correlation. The SUV measurements of each lesion were computed and normalized to the 180-s acquisition values to create a stabilization factor. These stabilization factors were used to demonstrate a predictable trend of stabilization over time. The variances of the stabilization factors over the entire dataset, composed of several tumor types over a range of sizes, were compared for each time point with the corresponding 150-s time point using a 2-sided F test, which has similar values to the 180-s time point. RESULTS: The variance of the data decreased with increasing acquisition time and with increasing dose but leveled off for sufficiently long acquisitions. CONCLUSION: Through the statistical analysis of SUVs for increasing acquisition times and visual evaluation of the plots, we developed and hereby propose an algorithm that can be used to seek the maximum reduction in administered (18)F-FDG dose while preserving the validity of SUV comparisons.

Volumetric intraoperative brain deformation compensation: model development and phantom validation.

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy.

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician.

The utility of I-123 pretherapy scan in I-131 radioiodine therapy for thyroid cancer.

BACKGROUND: There is a growing belief that a pretherapy scan yields little or no additional information that would impact on radioiodine ablation dosing. In addition, there is some concern regarding on the stunning effect of a pretherapy scan, especially when I-131 radioisotope is used for imaging. We hypothesized that a pretherapy scan provides invaluable information on the amount of thyroid remnant, sometimes indicating the need for two-step I-131 ablation. It may also detect unsuspected local lymph node involvement or distant metastases, indicating the requirement for a higher I-131 dose after thyroidectomy. The aim of this study was to evaluate how effective pretherapy scans are for guiding I-131 therapy planning and augmenting information provided in the pathology reports for thyroidectomy specimens. METHODS: We reviewed 122 patients who underwent I-123 pretherapy scan and I-131 radioablation at Yale New Haven Hospital between January 2006 and August 2007. The percentage of neck uptake and whole-body images were acquired 24 hours following the administration of 51.8 MBq (1.4 mCi) of I-123 NaI. A 24-hour uptake of >3% was used as the cutoff to determine whether there was a greater than desired quantity of thyroid remnant, which would require a two-step treatment protocol. Furthermore, attention was paid to identifying cervical lymph nodes, which may not have presented themselves in the euthyroid state at the time of thyroidectomy. Additional clinical information provided by pretherapy scans was computed as percentages with 95% confidence intervals by using adjusted Wald intervals. RESULTS: Overall, the pretherapy scans provided additional critical information in 25% of the cases (31/122; 95% CI: 18%-34%). For cases demonstrating >3% uptake with midline lymph nodes, the pretherapy scan provided additional information in 50% of the cases (8/16; 95% CI: 28%-72%). CONCLUSIONS: Our study demonstrated that I-123 pretherapy scans provide valuable information with regard to unsuspected lymph nodes or distant metastases, indicating the requirement for a significantly higher I-131 dose, and unexpected large thyroid remnants, suggesting the need for two-step ablation. We should take advantage of stimulated pretherapy scans and adjust the treatment dosing accordingly.

Gender-based divergence of cardiovascular outcomes in asymptomatic patients with type 2 diabetes: results from the DIAD study.

Gender differences in cardiovascular outcomes were compared in asymptomatic men and women with type 2 diabetes (T2DM) in the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study. Of 1123 participants, 290 men and 271 women were randomised to screening with stress myocardial perfusion imaging (MPI); 311 men and 251 women were randomised to no screening. Follow-up was 4.8±0.9 years for the occurrence of cardiac events (CE; cardiac death or non-fatal myocardial infarction). The frequency of abnormal screening was similar in men (24%) and women (19%), (p=0.2), although women trended to have smaller MPI abnormalities. CE rates were lower in women than men (1.7% vs. 3.8%, p=0.04). No CEs occurred in 17 high-risk (UKPDS risk engine) women, whereas 14 (11.2%) occurred in 125 high-risk men. Asymptomatic women with T2DM have significantly better cardiac outcomes than their male counterparts and represent a subgroup for which screening for coronary artery disease does not appear warranted.