Endometriosis is one of the most common benign gynecological conditions with a prevalence of 6.6% to 16.2% among women of reproductive age in the United States. It is defined as the occurrence of hormone-responsive endometrial tissue outside of the uterine cavity. However, the pathophysiology of endometriosis is poorly understood. Intestinal endometriosis causing large bowel obstruction is rare despite being the second most common extragenital site of endometriotic implantation. In the adult population, intestinal endometriosis is a clinical challenge because it can be mistaken for other acute obstructive diseases, such as colorectal carcinoma. Computed tomography lacks specificity in the detection of bowel wall abnormalities that cause a large bowel obstruction, and endoscopy does not show an intraluminal mass. The gold standard diagnostic procedures are laparoscopy and biopsy, with laparoscopy used for surgical resection of the abnormal tissue if necessary.

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

Importance

Electronic health record (EHR) clinical decision support (CDS) tools can provide evidence-based feedback at the point of care to reduce low-value imaging. Success of these tools has been limited partly due to lack of engagement by busy clinicians.

Objective

Measure the impact of a time-saving quality improvement intervention to increase engagement with a CDS tool for low back pain imaging ordering.

Design, setting and participants

We conducted a quasi-experimental difference-in-differences analysis at (BLINDED), examining back pain imaging orders from 29 May 2015 to 07 January 2016. The intervention site was (BLINDED) Emergency Medicine/Urgent Care Center (n=5736) and control sites included all other (BLINDED) hospitals and clinics (n=1621). In May 2015, the Department of Health Services installed a CDS tool that triggered a survey when clinicians ordered an imaging test, generating an 'appropriateness score' based on the American College of Radiology guidelines. Clinicians often bypassed the tool, resulting in 'unscored' tests.

Intervention

To increase clinician engagement with the tool and decrease the rate of unscored imaging tests, a new policy was implemented at the intervention site on 15 August 2015. If clinicians completed the CDS survey and scored an appropriateness score >3, they could forego a previously mandatory telephone call for pre-imaging utilisation review with the radiology department.

Main outcomes and measures

We used EHR data to measure pre-post-intervention differences in: (1) percentage of unscored tests and (2) percentage of tests with high appropriateness scores (>7).

Results

Percentage of unscored tests decreased from 69.4% to 10.4% at the intervention site and from 50.6% to 34.8% at the control sites (between-group difference: -23.3%, p<0.001). Percentage of high scoring tests increased from 26.5% to 75.0% at the intervention site and from 17.2% to 22.7% at the control sites (between-group difference: 19%, p<0.001).

Conclusion

Workflow time-saving interventions may increase physician engagement with CDS tools and have potential to improve practice patterns.

The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing worldwide and is projected to become a major etiology of cirrhosis and hepatocellular carcinoma (HCC). HCC occurs more commonly in NAFLD patients who develop cirrhosis, though HCC is known to occur in the setting of noncirrhotic NAFLD as well. This is of particular importance given that the American College of Radiology (ACR) CT/MRI Liver Reporting and Data System (LI-RADS) algorithm may only be applied to a certain population of patients, and this population does not include those with noncirrhotic NAFLD. Conventional ultrasound (US) has long been in use for HCC surveillance, but contrast-enhanced US (CEUS) is a relatively newer modality, growing in use for assessment of liver lesions, and its use in HCC diagnosis has been formalized with CEUS LI-RADS. The use of US and CEUS in the assessment of liver lesions in NAFLD patients involves the consideration of certain particular nuances, and familiarity with these considerations will continue increasing in importance as the disease becomes more common.

For patients with thyroid nodules, the ability to detect and diagnose a malignant nodule is the key to creating an appropriate treatment plan. However, assessments of ultrasound images do not accurately represent malignancy, and often require a biopsy to confirm the diagnosis. Deep learning techniques can classify thyroid nodules from ultrasound images, but current methods depend on manually annotated nodule segmentations. Furthermore, the heterogeneity in the level of magnification across ultrasound images presents a significant obstacle to existing methods. We developed a multi-scale, attention-based multiple-instance learning model which fuses both global and local features of different ultrasound frames to achieve patient-level malignancy classification. Our model demonstrates improved performance with an AUROC of 0.785 (p<0.05) and AUPRC of 0.539, significantly surpassing the baseline model trained on clinical features with an AUROC of 0.667 and AUPRC of 0.444. Improved classification performance better triages the need for biopsy.

Primary vulvar and vaginal cancers are rare female genital tract malignancies which are staged using the 2009 International Federation of Gynecology and Obstetrics (FIGO) staging. These cancers account for approximately 2,700 deaths annually in the USA. The most common histologic subtype of both vulvar and vaginal cancers is squamous cell carcinoma, with an increasing role of the human papillomavirus (HPV) in a significant number of these tumors. Lymph node involvement is the hallmark of FIGO stage 3 vulvar cancer while pelvic sidewall involvement is the hallmark of FIGO stage 3 vaginal cancer. Imaging techniques include computed tomography (CT), positron emission tomography (PET)-CT, magnetic resonance imaging (MRI), and PET-MRI. MRI is the imaging modality of choice for preoperative clinical staging of nodal and metastatic involvement while PET-CT is helpful with assessing response to neoadjuvant treatment and for guiding patient management. Determining the pretreatment extent of disease has become more important due to modern tailored operative approaches and use of neoadjuvant chemoradiation therapy to reduce surgical morbidity. Moreover, imaging is used to determine the full extent of disease for radiation planning and for evaluating treatment response. Understanding the relevant anatomy of the vulva and vaginal regions and the associated lymphatic pathways is helpful to recognize the potential routes of spread and to correctly identify the appropriate FIGO stage. The purpose of this article is to review the clinical features, pathology, and current treatment strategies for vulvar and vaginal malignancies and to identify multimodality diagnostic imaging features of these gynecologic cancers, in conjunction with its respective 2009 FIGO staging system guidelines.

Objective

Molecular testing can refine the diagnosis for the 20% of thyroid fine-needle aspiration biopsies that have indeterminate cytology. We assessed the diagnostic accuracy of molecular testing based on ultrasound risk classification.

Methods

This retrospective cohort study analyzed all thyroid nodules with indeterminate cytology at an academic US medical center (2012-2016). All indeterminate nodules underwent reflexive molecular testing with the Afirma Gene Expression Classifier (GEC). Radiologists performed blinded reviews to categorize each nodule according to the American Thyroid Association (ATA) ultrasound classification and the American College of Radiology Thyroid Imaging, Reporting and Data System. GEC results and diagnostic performance were compared across ultrasound risk categories.

Results

Of 297 nodules, histopathology confirmed malignancy in 65 (22%). Nodules by ATA classification were 8% high suspicion, 44% intermediate, and 48% low/very low suspicion. A suspicious GEC result was more likely in ATA high-suspicion nodules (81%) than in nodules of all other ATA categories (57%; P = .04). The positive predictive value (PPV) of GEC remained consistent across ultrasound categories (ATA high suspicion, 64% vs all other ATA categories, 48%; P = .39). The ATA high-suspicion category had higher specificity than a suspicious GEC result (93% vs 51%; P < .01). A suspicious GEC result did not increase specificity for the ATA high-suspicion category.

Conclusion

The PPV of molecular testing remained consistent across ultrasound risk categories. However, a suspicious GEC result was very likely in ATA high-suspicion nodules and did not improve specificity in this sonographic category.

Purpose To investigate Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) approximations of target lesion tumor burden by comparing categorical treatment response according to conventional RECIST versus actual tumor volume measurements of RECIST target lesions. Materials and Methods This is a retrospective cohort study of individuals with metastatic renal cell carcinoma enrolled in a clinical trial (from 2003 to 2017) and includes individuals who underwent baseline and at least one follow-up chest, abdominal, and pelvic CT study and with at least one target lesion. Target lesion volume was assessed by (a) Vmodel, a spherical model of conventional RECIST 1.1, which was extrapolated from RECIST diameter, and (b) Vactual, manually contoured volume. Volumetric responses were determined by the sum of target lesion volumes (Vmodel-sum TL and Vactual-sum TL, respectively). Categorical volumetric thresholds were extrapolated from RECIST. McNemar tests were used to compare categorical volume responses. Results Target lesions were assessed at baseline (638 participants), week 9 (593 participants), and week 17 (508 participants). Vmodel-sum TL classified more participants as having progressive disease (PD), compared with Vactual-sum TL at week 9 (52 vs 31 participants) and week 17 (57 vs 39 participants), with significant overall response discordance (P < .001). At week 9, 25 (48%) of 52 participants labeled with PD by Vmodel-sum TL were classified as having stable disease by Vactual-sum TL. Conclusion A model of RECIST 1.1 based on a single diameter measurement more frequently classified PD compared with response assessment by actual measured tumor volume. Keywords: Urinary, Kidney, Metastases, Oncology, Tumor Response, Volume Analysis, Outcomes Analysis ClinicalTrials.gov registration no. NCT01865747 © RSNA, 2023 Supplemental material is available for this article.