Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system.

PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS). MATERIALS AND METHODS: 378 thyroid nodules from 320 patients were included in this retrospective evaluation. All nodules had ultrasound images and had undergone fine needle aspiration (FNA). 147 nodules were Bethesda V or VI (suspicious or diagnostic for malignancy), and 231 were Bethesda II (benign). Three radiologists assigned features according to the AI TI-RADS lexicon (same categories and features as the American College of Radiology TI-RADS) to each nodule based on ultrasound images. FNA recommendations using AI TI-RADS and ACR TI-RADS were then compared and sensitivity and specificity for each RSS were calculated. RESULTS: Across three readers, mean sensitivity of AI TI-RADS was lower than ACR TI-RADS (0.69 vs 0.72, p < 0.02), while mean specificity was higher (0.40 vs 0.37, p < 0.02). Overall total number of points assigned by all three readers decreased slightly when using AI TI-RADS (5,998 for AI TI-RADS vs 6,015 for ACR TI-RADS), including more values of 0 to several features. CONCLUSION: AI TI-RADS performed similarly to ACR TI-RADS while eliminating point assignments for many features, allowing for simplification of future TI-RADS versions.

Increasing utilization of contrast-enhanced ultrasound during abdominal biopsies: impact of an educational training program.

RATIONAL AND OBJECTIVES: To increase utilization of contrast-enhanced ultrasound (CEUS) during ultrasound-guided targeted liver biopsies. MATERIAL AND METHODS: Two educational training interventions performed to increase use of CEUS. First, 14 radiologists (fellowship-trained in Abdominal Imaging) given didactic teaching and case presentations on the use of CEUS. Second, hands-on teaching on how to use CEUS provided to the same group. To determine the efficacy of these two interventions, radiologists completed anonymous surveys to determine the level of understanding and acceptability of using CEUS before and 6 months after CEUS training. In addition, the percentage of CEUS assisted liver biopsies was compared for the 6 months before and 6 months after the training. RESULTS: Pre-training survey completed by 11 radiologists and post-training survey completed by 9 radiologists. Before training, 11% survey responders use CEUS routinely, whereas 89% never or rarely used it. After training, 54% of respondents were new users and 100% reported they planned to use CEUS in the future. Unfamiliarity (71%) was the main reason for not using it. After training, 25% reported lack of comfort with using CEUS as the main reason for not using CEUS. During six months before training, CEUS was administered in 6% (10/172) of targeted liver biopsies. Six months after training, CEUS was used nearly twice as often (10%, 16/160, P = 0.09, 1-sided Boschloo test). The number of radiologists using CEUS increased to 57% (8/14) after training compared to 20% (3/14, P = 0.03, 1-sided Boschloo) before training. CONCLUSION: Educational training intervention increases use of CEUS during ultrasound-guided targeted liver biopsies.

Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation.

Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research. However, ML tools require large, well-labeled datasets, the curation of which is time-consuming and labor-intensive. The purpose of our study was to develop and test a deep-learning-based tool to facilitate and automate the data annotation process for thyroid nodules; we named our tool Multistep Automated Data Labelling Procedure (MADLaP). MADLaP was designed to take multiple inputs including pathology reports, ultrasound images, and radiology reports. Using multiple step-wise 'modules' including rule-based natural language processing, deep-learning-based imaging segmentation, and optical character recognition, MADLaP automatically identified images of a specific thyroid nodule and correctly assigned a pathology label. The model was developed using a training set of 378 patients across our health system and tested on a separate set of 93 patients. Ground truths for both sets were selected by an experienced radiologist. Performance metrics including yield (how many labeled images the model produced) and accuracy (percentage correct) were measured using the test set. MADLaP achieved a yield of 63 % and an accuracy of 83 %. The yield progressively increased as the input data moved through each module, while accuracy peaked part way through. Error analysis showed that inputs from certain examination sites had lower accuracy (40 %) than the other sites (90 %, 100 %). MADLaP successfully created curated datasets of labeled ultrasound images of thyroid nodules. While accurate, the relatively suboptimal yield of MADLaP exposed some challenges when trying to automatically label radiology images from heterogeneous sources. The complex task of image curation and annotation could be automated, allowing for enrichment of larger datasets for use in machine learning development.

Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. METHODS: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of radiologists. The algorithm was further tested with 378 nodules imaged with ultrasound machines from different manufacturers and product types than the training cases. Four experienced radiologists were requested to evaluate the nodules for comparison with deep learning. RESULTS: The Area Under Curve (AUC) of the deep learning algorithm and four radiologists were calculated with parametric, binormal estimation. For the deep learning algorithm, the AUC was 0.69 (95% CI: 0.64-0.75). The AUC of radiologists were 0.63 (95% CI: 0.59-0.67), 0.66 (95% CI:0.61-0.71), 0.65 (95% CI: 0.60-0.70), and 0.63 (95%CI: 0.58-0.67). CONCLUSION: In the new testing dataset, the deep learning algorithm achieved similar performances with all four radiologists. The relative performance difference between the algorithm and the radiologists is not significantly affected by the difference of ultrasound scanner.

Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiologists' overall impression, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults. METHODS. This retrospective study included 139 patients (median age 17.5 years; 119 female patients, 20 male patients) evaluated from January 1, 2004, to September 18, 2020, who were 21 years old and younger with a thyroid nodule on ultrasound with definitive pathologic results from fine-needle aspiration and/or surgical excision to serve as the reference standard. A single nodule per patient was selected, and one transverse and one longitudinal image each of the nodules were extracted for further evaluation. Three radiologists independently characterized nodules on the basis of their overall impression (benign vs malignant) and ACR TI-RADS. A previously developed deep learning algorithm determined for each nodule a likelihood of malignancy, which was used to derive a risk level. Sensitivities and specificities for malignancy were calculated. Agreement was assessed using Cohen kappa coefficients. RESULTS. For radiologists' overall impression, sensitivity ranged from 32.1% to 75.0% (mean, 58.3%; 95% CI, 49.2-67.3%), and specificity ranged from 63.8% to 93.9% (mean, 79.9%; 95% CI, 73.8-85.7%). For ACR TI-RADS, sensitivity ranged from 82.1% to 87.5% (mean, 85.1%; 95% CI, 77.3-92.1%), and specificity ranged from 47.0% to 54.2% (mean, 50.6%; 95% CI, 41.4-59.8%). The deep learning algorithm had a sensitivity of 87.5% (95% CI, 78.3-95.5%) and specificity of 36.1% (95% CI, 25.6-46.8%). Interobserver agreement among pairwise combinations of readers, expressed as kappa, for overall impression was 0.227-0.472 and for ACR TI-RADS was 0.597-0.643. CONCLUSION. Both ACR TI-RADS and the deep learning algorithm had higher sensitivity albeit lower specificity compared with overall impressions. The deep learning algorithm had similar sensitivity but lower specificity than ACR TI-RADS. Interobserver agreement was higher for ACR TI-RADS than for overall impressions. CLINICAL IMPACT. ACR TI-RADS and the deep learning algorithm may serve as potential alternative strategies for guiding decisions to perform fine-needle aspiration of thyroid nodules in children.

Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.

BACKGROUND: There is a sparsity of data evaluating outcomes of patients with Liver Imaging Reporting and Data System (LI-RADS) (LR)-M lesions. PURPOSE: To compare overall survival (OS) and progression free survival (PFS) between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) meeting LR-M criteria and to evaluate factors associated with prognosis. STUDY TYPE: Retrospective. SUBJECTS: Patients at risk for HCC with at least one LR-M lesion with histologic diagnosis, from 8 academic centers, yielding 120 patients with 120 LR-M lesions (84 men [mean age 62 years] and 36 women [mean age 66 years]). FIELD STRENGTH/SEQUENCE: A 1.5 and 3.0 T/3D T1 -weighted gradient echo, T2 -weighted fast spin-echo. ASSESSMENT: The imaging categorization of each lesion as LR-M was made clinically by a single radiologist at each site and patient outcome measures were collected. STATISTICAL TESTS: OS, PFS, and potential independent predictors were evaluated by Kaplan-Meier method, log-rank test, and Cox proportional hazard model. A P value of <0.05 was considered significant. RESULTS: A total of 120 patients with 120 LR-M lesions were included; on histology 65 were HCC and 55 were iCCA. There was similar median OS for patients with LR-M HCC compared to patients with iCCA (738 days vs. 769 days, P = 0.576). There were no significant differences between patients with HCC and iCCA in terms of sex (47:18 vs. 37:18, P = 0.549), age (63.0 ± 8.4 vs. 63.4 ± 7.8, P = 0.847), etiology of liver disease (P = 0.202), presence of cirrhosis (100% vs. 100%, P = 1.000), tumor size (4.73 ± 3.28 vs. 4.75 ± 2.58, P = 0.980), method of lesion histologic diagnosis (P = 0.646), and proportion of patients who underwent locoregional therapy (60.0% vs. 38.2%, P = 0.100) or surgery (134.8 ± 165.5 vs. 142.5 ± 205.6, P = 0.913). Using multivariable analysis, nonsurgical compared to surgical management (HR, 4.58), larger tumor size (HR, 1.19), and higher MELD score (HR, 1.12) were independently associated with worse OS. DATA CONCLUSION: There was similar OS in patients with LR-M HCC and LR-M iCCA, suggesting that LR-M imaging features may more closely reflect patient outcomes than histology. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.

Hepatic Artery Infusion Pumps: A Surgical Toolkit for Intraoperative Decision-Making and Management of Hepatic Artery Infusion-Specific Complications.

BACKGROUND: Hepatic artery infusion (HAI) is a liver-directed therapy that delivers high-dose chemotherapy to the liver through the hepatic arterial system for colorectal liver metastases and intrahepatic cholangiocarcinoma. Utilization of HAI is rapidly expanding worldwide. OBJECTIVE AND METHODS: This review describes the conduct of HAI pump implantation, with focus on common technical pitfalls and their associated solutions. Perioperative identification and management of common postoperative complications is also described. RESULTS: HAI therapy is most commonly performed with the surgical implantation of a subcutaneous pump, and placement of its catheter into the hepatic arterial system for inline flow of pump chemotherapy directly to the liver. Intraoperative challenges and abnormal hepatic perfusion can arise due to aberrant anatomy, vascular disease, technical or patient factors. However, solutions to prevent or overcome technical pitfalls are present for the majority of cases. Postoperative HAI-specific complications arise in 22% to 28% of patients in the form of pump pocket (8%-18%), catheter (10%-26%), vascular (5%-10%), or biliary (2%-8%) complications. The majority of patients can be rescued from these complications with early identification and aggressive intervention to continue to deliver safe and effective HAI therapy. CONCLUSIONS: This HAI toolkit provides the HAI team a reference to manage commonly encountered HAI-specific perioperative obstacles and complications. Overcoming these challenges is critical to ensure safe and effective pump implantation and delivery of HAI therapy, and key to successful implementation of new programs and expansion of HAI to patients who may benefit from such a highly specialized treatment strategy.

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLLs) on CT pulmonary angiographic (CTPA) examinations. METHODS: This retrospective study included 1 month of consecutive CTPA examinations from a multisite teleradiology practice. Visual classification (VC) software analyzed images for the presence (+) or absence (-) of SLLs (>1 cm, >20 Hounsfield units). Separately, a natural language processing (NLP) algorithm evaluated corresponding reports for description (+) of an SLL or lack thereof (-). Studies containing possible missed SLLs (VC+/NLP-) were reviewed by three abdominal radiologists in a two-step adjudication process to confirm if an SLL was missed by the interpreting radiologist. The number of VC+/NLP- cases, the number of images needing radiologist review, and the number of cases with confirmed missed SLLs were recorded. Interobserver agreement for SLLs was calculated for the radiologist readers. RESULTS: A total of 2,573 CTPA examinations were assessed, and 136 were classified as potentially containing missed SLLs (VC+/NLP-). After radiologist review, 13 cases with missed SLLs were confirmed, representing 0.5% of analyzed CT studies. Using AI, the ratio of CT studies requiring review to missed SLLs identified was 10:1; the ratio without the help of AI would be at least 66:1. Among the 136 cases reviewed by radiologists, interobserver agreement for SLLs was excellent (κ = 0.91). CONCLUSIONS: AI can accelerate meaningful peer review by rapidly assessing thousands of examinations to identify potentially clinically significant errors. Although radiologist involvement is necessary, the amount of effort required after initial AI screening is dramatically reduced.

Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications.

Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been widely described in the literature, with reported performance of AI tools matching or in some instances surpassing radiologists' performance. As these data have accumulated, products for classification and risk stratification of thyroid nodules on ultrasound have become commercially available. This article reviews FDA-approved products currently on the market, with a focus on product features, reported performance, and considerations for implementation. The products perform risk stratification primarily using a Thyroid Imaging Reporting and Data System (TIRADS), though may provide additional prediction tools independent of TIRADS. Key issues in implementation include integration with radiologist interpretation, impact on workflow and efficiency, and performance monitoring. AI applications beyond nodule classification, including report construction and incidental findings follow-up, are also described. Anticipated future directions of research and development in AI tools for thyroid nodules are highlighted.

Management of Incidental Thyroid Nodules on Chest CT: Using Natural Language Processing to Assess White Paper Adherence and Track Patient Outcomes.

OBJECTIVE: The purpose of this study was to develop a natural language processing (NLP) pipeline to identify incidental thyroid nodules (ITNs) meeting criteria for sonographic follow-up and to assess both adherence rates to white paper recommendations and downstream outcomes related to these incidental findings. METHODS: 21583 non-contrast chest CT reports from 2017 and 2018 were retrospectively evaluated to identify reports which included either an explicit recommendation for thyroid ultrasound, a description of a nodule ≥ 1.5 cm, or description of a nodule with suspicious features. Reports from 2018 were used to train an NLP algorithm called fastText for automated identification of such reports. Algorithm performance was then evaluated on the 2017 reports. Next, any patient from 2017 with a report meeting criteria for ultrasound follow-up was further evaluated with manual chart review to determine follow-up adherence rates and nodule-related outcomes. RESULTS: NLP identified reports with ITNs meeting criteria for sonographic follow-up with an accuracy of 96.5% (95% CI 96.2-96.7) and sensitivity of 92.1% (95% CI 89.8-94.3). In 10006 chest CTs from 2017, ITN follow-up ultrasound was indicated according to white paper criteria in 81 patients (0.8%), explicitly recommended in 46.9% (38/81) of patients, and obtained in less than half of patients in which it was appropriately recommended (17/35, 48.6%). DISCUSSION: NLP accurately identified chest CT reports meeting criteria for ITN ultrasound follow-up. Radiologist adherence to white paper guidelines and subsequent referrer adherence to radiologist recommendations showed room for improvement.

Comparison of clinical efficacy, subjective user experience, and safety for two different core biopsy needles, the Achieve® and Marquee®.

PURPOSE: To compare clinical efficacy, subjective radiologist preference, and complication rates for two different core biopsy needles, the Achieve® and Marquee®. METHODS: Retrospective review included consecutive patients who underwent 18 gauge non-targeted core liver biopsy, 30 with Achieve® (Merit Medical) and 30 with Marquee® (BD Bard) Pathologist (blinded to needle type) reviewed specimen total length, maximum width, and portal triad count. Sixteen radiologists subjectively rated (1 to 5(best)) each needle for cocking, firing, recoil, chamber exposure, handling, and overall. A medical records search of all (targeted and non-targeted) core liver biopsies 1/1/17-9/30/2020 compared rates of major (requiring transfusion and/or embolization) and minor (self-limited bleeding) hemorrhagic complications. Comparison between needle types was performed using t-test. RESULTS: For Achieve® and Marquee® needles, the respective mean (SD) for total length(mm) was 29.7(7.0) and 31.9(4.6), p = 0.1; max width(mm) was 0.78(0.1) and 0.85(0.1), p < 0.01; and number of portal triads was 15.3(5.3) and 17.3(5.3), p = 0.2. Radiologists subjectively preferred the Marquee® for several measures including cocking, chamber exposure, and overall (p < 0.02 for each), while the needles were rated similarly for firing, recoil, and handling. Review of 800 cases showed no difference in major (1.0% Achieve®, 1.9% Marquee®, p = 0.5) or minor (1.5% Achieve®, 0.5% Marquee®, p = 0.3) rates of hemorrhagic complications. CONCLUSION: Liver biopsy specimens were significantly wider with Marquee® compared to Achieve®. Radiologists preferred the Marquee® for multiple tactile measures, while the major complication rate was not significantly different. While both needles have a similar side-notch design, the Marquee® needle demonstrates better sample quality and higher user preference, without compromising safety.

Needle types used in abdominal cross-sectional interventional radiology: a survey of the Society of Abdominal Radiology emerging technology commission.

PURPOSE: To identify commonly used needle types in cross-sectional interventional radiology (CSIR) and to review features and safety profiles of those needles. METHODS: Members of the Society of Abdominal Radiology (SAR) emerging technologies commission (ETC) on CSIR were sent a 13-question survey about what needles they use for common CSIR procedures: random and targeted solid organ biopsy, ultrasound-guided paracentesis, and ultrasound-guided thyroid fine needle aspiration (FNA). Results were compiled with descriptive statistics, and features of the most commonly used needles were reviewed. RESULTS: 19 surveys were completed (response rate 57.6%, 19/33) from 16 institutions. For solid organ biopsies, the majority of respondents reported using an 18-gauge needle with an automatic firing mechanism and a variable throw length option. The most commonly used needle for both random and targeted biopsies was the Argon BioPince (26.3%, 5/19) The three most commonly used needles for solid organ biopsies all featured automatic firing, variable throw length options, and 18-gauge size. A 5 French Cook Yueh needle was most the most commonly used paracentesis needle (36.8%, 7/19). For thyroid FNA, all respondents used spinal needles, and 25-gauge was the most common size (72.2%, 13/18). CONCLUSION: Abdominal radiologists use a variety of needles when performing common interventional procedures. Members of the SAR CSIR ETC commonly use automatic, 18-gauge, variable throw length needles for solid organ biopsies, 5 French catheter style needles for paracentesis, and 25-gauge spinal needles for thyroid FNA.

Hepatic Arterial Infusion Pumps: What the Radiologist Needs to Know.

Hepatic arterial infusion (HAI) entails the surgical implantation of a subcutaneous pump to deliver chemotherapeutic agents directly to the liver in the setting of primary or secondary liver cancer. The purpose of HAI chemotherapy is to maximize hepatic drug concentrations while minimizing systemic toxicity, facilitating more effective treatment. HAI is used in combination with systemic chemotherapy and can be considered in several clinical scenarios, including adjuvant therapy, conversion of unresectable disease to resectable disease, and unresectable disease. Radiologists are key members of the multidisciplinary team involved in the selection and management of these patients with complex liver disease. As these devices begin to be used at more sites across the country, radiologists should become familiar with the guiding principles behind pump placement, expected imaging appearances of these devices, and potential associated complications. The authors provide an overview of HAI therapy, with a focus on the key imaging findings associated with this treatment that radiologists may encounter. ©RSNA, 2021.

Using Radiographic Domain for Evaluating Indications in Abdominal Wall Transplantation.

BACKGROUND: There is currently no description of abdominal domain changes in small bowel transplantation population or consensus of criteria regarding which patients are at high risk for immediate postoperative abdominal wall complications or would benefit from abdominal wall vascularized composite allotransplantation. METHODS: A retrospective chart review was performed on 14 adult patients receiving intestinal or multivisceral transplantation. Preoperative and postoperative computed tomography scans were reviewed, and multiple variables were collected regarding abdominal domain and volume and analyzed comparing postoperative changes and abdominal wall complications. RESULTS: Patients after intestinal or multivisceral transplantation had a mean reduction in overall intraperitoneal volume in the immediate postoperative period from 9031 cm3 to 7846 cm3 (P = 0.314). This intraperitoneal volume was further reduced to an average of 6261 cm3 upon radiographic evaluation greater than 1 year postoperatively (P = 0.024). Patients with preexisting abdominal wound (P = 0.002), radiation, or presence of ostomy (P = 0.047) were significantly associated with postoperative abdominal wall complications. No preoperative radiographic findings had a significant association with postoperative abdominal wall complications. CONCLUSIONS: Computed tomography imaging demonstrates that intestinal and multivisceral transplant patients have significant reduction in intraperitoneal volume and domain after transplantation in the acute and delayed postoperative setting. Preoperative radiographic abdominal domain was not able to predict patients with postoperative abdominal wall complications. Patients with abdominal wounds, ostomies, and preoperative radiation therapy were associated with acute postoperative abdominal complications and may be considered for need of reconstructive techniques including abdominal wall transplantation.

Computed Tomography for Abdominal Pain: Do Radiology Reports Answer the Clinical Question?

RATIONALE AND OBJECTIVES: To assess whether abdominopelvic computed tomography (CT) radiology reports directly address a cause for pain when pain is included in the scan indication. MATERIALS AND METHODS: Four hundred and ninety-five random abdominopelvic CT reports containing "pain" in the indication were retrospectively reviewed. The position of pain descriptors within the indication, the presence of an oncology-related indication in addition to pain and whether a cause for pain was addressed in the impression were recorded. Linguistic analysis of indication and impression sections was performed. Comparisons between reports that addressed pain and those that did not were conducted using Chi-square, Fisher exact, and two-tailed t-tests. RESULTS: A cause for pain was addressed in 454 of 495 (91.7%) report impressions. Indications with both oncology-related and pain-related descriptors were less likely to have pain directly addressed (χ2 (1, N = 495) = 16.4, p < .001). There was no significant association between where pain appeared within the indication and whether pain was addressed (χ2 (1, N = 495) = 3.2, p = .07). Whether an impression conveyed a normal result did not influence if pain was addressed (p = .49). Impression word count and complexity were higher in the addressed group compared to the not addressed group (word count 66.6 vs. 51.9, p= .02, Composite grade level 30.1 vs. 25.3, p= .02). CONCLUSION: Radiologists at our institution consistently addressed a cause for pain on abdominopelvic CTs when pain was in the indication. However, oncology patients who also had an indication of pain were less likely to have a cause for pain addressed. Impression complexity was high for all reports, though higher in those where pain was addressed.

How frequently does hepatocellular carcinoma develop in at-risk patients with a negative liver MRI examination with intravenous Gadobenate dimeglumine?

OBJECTIVE: To determine the rate of development of clinically significant liver nodules (LR-4, LR-5, LR-M) after a negative MRI in an HCC screening population. METHODS: This retrospective study included patients at risk of developing HCC requiring imaging surveillance who had undergone multiphase Gadobenate dimeglumine-enhanced MRI that was negative and had follow up LI-RADS compliant multiphase CTs or MRIs for at least 12 months or positive follow-up within 12 months. Follow-up examinations were classified as negative (no nodules or only LR-1 nodules) or positive (nodule other than LR-1). Time-to-first positive examination, types of nodules, and cumulative incidence of nodule development were recorded. RESULTS: 204 patients (mean age 58.9 ± 10.2 years, 128 women), including 172 with cirrhosis, were included. Based CT/MRI follow-up (median 35 months, range 12-80 months), the overall cumulative incidence of developing a nodule was 10.5%. Cumulative incidence of nodule development was: 0.5% at 6-9 months and 2.1% at 12 ± 3 months, including one LR-4 nodule, one LR-M nodule, and two LR-3 nodules. The cumulative incidence of clinically significant nodule development was 1.1% at 9-15 months. 70% (143/204) of patients also underwent at least one US follow-up, and no patient developed a positive US examination following index negative MRI. CONCLUSION: Clinically significant liver nodules develop in 1.1% of at-risk patients in the first year following negative MRI. While ongoing surveillance is necessary for at-risk patients, our study suggests than longer surveillance intervals after a negative MRI may be reasonable and that further research is needed to explore this possibility.

Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency.

PURPOSE: To compare the effectiveness of different reporting templates using the ACR Thyroid Imaging Reporting and Data System (TI-RADS) for thyroid ultrasound. METHODS: In this retrospective study, four radiologists implemented ACR TI-RADS while dictating 20 thyroid ultrasounds for each of four different templates: free text, minimally structured, fully structured, fully structured and automated (embedded software automatically sums TI-RADS points, correlates with nodule size, and inserts appropriate recommendation into report impression). In total, 80 reports were constructed per template type. Frequencies of different errors related to ACR TI-RADS were recorded: errors in point assignment, point addition, risk-level assignment, and recommendation. Reporting times were recorded, and a survey about using the template was administered. Differences in error rates were compared using χ2 and Fisher's exact tests, and differences in reporting times were compared using Kruskal-Wallis tests. RESULTS: Across all readers, errors were identified in 27.5% of reports (22 of 80) for the free text template, 28.8% (23 of 80) for the minimally structured template, 18.8% (15 of 80) for the fully structured template, and 0% (0 of 80) for the fully structured and automated template (P < .0001). Frequency of each error type (number assignment, addition, TR categorization, recommendation) decreased across the four templates (P < .0005 to P < .005). Median reporting times for the less complex templates were 210 to 240 seconds, whereas the median automated template reporting time was 180 seconds (P = .41). Radiologists subjectively preferred using the automated template. CONCLUSION: A structured reporting template for thyroid ultrasound that automatically executed steps of ACR TI-RADS resulted in fewer reporting errors for radiologists.

Implementation of a Hepatic Artery Infusion Program: Initial Patient Selection and Perioperative Outcomes of Concurrent Hepatic Artery Infusion and Systemic Chemotherapy for Colorectal Liver Metastases.

BACKGROUND: Hepatic artery infusion (HAI) combined with systemic chemotherapy is a treatment strategy for patients with unresectable liver-only or liver-dominant colorectal liver metastases (CRLM). Although HAI has previously been performed in only a few centers, this study aimed to describe patient selection and initial perioperative outcomes during implementation of a new HAI program. METHODS: The study enrolled patients with CRLM selected for HAI after multi-disciplinary review November 2018-January 2020. Demographics, prior treatment, and perioperative outcomes were assessed. Objective hepatic response was calculated according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. RESULTS: During a 14-month period, 21 patients with CRLM underwent HAI pump placement. Of these 21 patients, 20 (95%) had unresectable disease. Most of the patients had synchronous disease (n = 18, 86%) and had received prior chemotherapy (n = 20, 95%) with extended treatment cycles (median 16; interquartile range, 8-22; range, 0-66). The median number of CRLMs was 7 (range, 2-40). Operations often were performed with combined hepatectomy (n = 4, 19%) and/or colectomy/proctectomy (n = 11, 52%). The study had no 90-day mortality. The overall surgical morbidity was 19%. The HAI-specific complications included pump pocket seroma (n = 2), hematoma (n = 1), surgical-site infection (n = 1), and extrahepatic perfusion (n = 1). HAI was initiated in 20 patients (95%). The hepatic response rates at 3 months included partial response (n = 4, 24%), stable disease (n = 9, 53%), and progression of disease (n = 4, 24%), yielding a 3-month hepatic disease control rate (DCR) of 76%. CONCLUSION: Implementation of a new HAI program is feasible, and HAI can be delivered safely to selected patients with CRLM. The initial response and DCR are promising, even for patients heavily pretreated with chemotherapy.

Using the American College of Radiology Thyroid Imaging Reporting and Data System at the Point of Care: Sonographer Performance and Interobserver Variability.

The purpose of this study was to assess inter-observer variability and performance when sonographers assign features to thyroid nodules on ultrasound using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Fifteen sonographers retrospectively evaluated 100 thyroid nodules and assigned features to each nodule according to ACR TI-RADS lexicon. Ratings were compared with one another and to a gold standard using Fleiss' and Cohen's kappa statistics, respectively. Sonographers were also asked subjective questions regarding their comfort level assessing each feature, and opinions were compared with performance using a mixed effects model. Sonographers demonstrated only slight agreement for margin (κ = 0.18, 95% confidence interval [CI]: 0.16-0.20) and large comet tail artifact (κ = 0.08, 95% CI: 0.06-0.10) but better performance for macrocalcification (κ = 0.41, 95% CI: 0.39-0.43) and no echogenic foci (κ = 0.52, 95% CI: 0.50-0.54). Sonographer comfort level with different feature assignments did not statistically correlate with performance for a given feature. In conclusion, sonographers using ACR TI-RADS to assign thyroid nodule features on ultrasound demonstrate a range of agreement across features, with margin and large comet tail artifact showing the most variability. These results highlight potential areas of focus for sonographer education efforts as ACR TI-RADS continues to be implemented in radiology departments.