Diagnostic performance of computed tomography features in detecting oropharyngeal squamous cell carcinoma extranodal extension.

OBJECTIVES: Accurate pre-treatment imaging determination of extranodal extension (ENE) could facilitate the selection of appropriate initial therapy for HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). Small studies have associated 7 CT features with ENE with varied results and agreement. This article seeks to determine the replicable diagnostic performance of these CT features for ENE. METHODS: Five expert academic head/neck neuroradiologists from 5 institutions evaluate a single academic cancer center cohort of 75 consecutive HPV + OPSCC patients. In a web-based virtual laboratory for imaging research and education, the experts performed training on 7 published CT features associated with ENE and then independently identified the "single most (if any) suspicious" lymph node and presence/absence of each of the features. Inter-rater agreement was assessed using percentage agreement, Gwet's AC1, and Fleiss' kappa. Sensitivity, specificity, and positive and negative predictive values were calculated for each CT feature based on histologic ENE. RESULTS: All 5 raters identified the same node in 52 cases (69%). In 15 cases (20%), at least one rater selected a node and at least one rater did not. In 8 cases (11%), all raters selected a node, but at least one rater selected a different node. Percentage agreement and Gwet's AC1 coefficients were > 0.80 for lesion identification, matted/conglomerated nodes, and central necrosis. Fleiss' kappa was always < 0.6. CT sensitivity for histologically confirmed ENE ranged 0.18-0.94, specificity 0.41-0.88, PPV 0.26-0.36, and NPV 0.78-0.96. CONCLUSIONS: Previously described CT features appear to have poor reproducibility among expert head/neck neuroradiologists and poor predictive value for histologic ENE. KEY POINTS: • Previously described CT imaging features appear to have poor reproducibility among expert head and neck subspecialized neuroradiologists as well as poor predictive value for histologic ENE. • Although it may still be appropriate to comment on the presence or absence of these CT features in imaging reports, the evidence indicates that caution is warranted when incorporating these features into clinical decision-making regarding the likelihood of ENE.

Parathyroid 4D CT: What the Surgeon Wants to Know.

Parathyroid four-dimensional (4D) CT is an increasingly used and powerful tool for preoperative localization of abnormal parathyroid tissue in the setting of primary hyperparathyroidism. Accurate and precise localization of a single adenoma facilitates minimally invasive parathyroidectomy, and localization of multiglandular disease aids bilateral neck exploration. However, many radiologists find the interpretation of these examinations to be an intimidating challenge. The authors review parathyroid 4D CT findings of typical and atypical parathyroid lesions and provide illustrative examples. Relevant anatomy, embryology, and operative considerations with which the radiologist should be familiar to provide clinically useful image interpretations are also discussed. The most important 4D CT information to the surgeon includes the number, size, and specific location of candidate parathyroid lesions with respect to relevant surgical landmarks; the radiologist's opinion and confidence level regarding what each candidate lesion represents; and the presence or absence of ectopic or supernumerary parathyroid tissue, concurrent thyroid pathologic conditions, and arterial anomalies associated with a nonrecurrent laryngeal nerve. The authors provide the radiologist with an accessible and practical approach to performing and interpreting parathyroid 4D CT images, detail what the surgeon really wants to know from the radiologist and why, and provide an accompanying structured report outlining the key information to be addressed. By accurately reporting and concisely addressing the key information the surgeon desires from a parathyroid 4D CT examination, the radiologist substantially impacts patient care by enabling the surgeon to develop and execute the best possible operative plan for each patient. ©RSNA, 2020.

Discriminating parathyroid adenoma from local mimics by using inherent tissue attenuation and vascular information obtained with four-dimensional CT: formulation of a multinomial logistic regression model.

PURPOSE: To identify a set of parameters, which are based on tissue enhancement and native iodine content obtained from a standardized triple-phase four-dimensional (4D) computed tomographic (CT) scan, that define a multinomial logistic regression model that discriminates between parathyroid adenoma (PTA) and thyroid nodules or lymph nodes. MATERIALS AND METHODS: Informed consent was waived by the institutional review board for this retrospective HIPAA-compliant study. Electronic medical records were reviewed for 102 patients with hyperparathyroidism who underwent triple-phase 4D CT and parathyroid surgery resulting in pathologically proved removal of adenoma from July 2010 through December 2011. Hounsfield units were measured in PTA, thyroid, lymph nodes, and aorta and were used to determine seven parameters characterizing tissue contrast enhancement. These were used as covariates in 10 multinomial logistic regression models. Three models with one covariate, four models with two covariates, and three models with three covariates were investigated. Receiver operating characteristic (ROC) analysis was performed to determine how well each model discriminated between adenoma and nonadenomatous tissues. Statistical differences between the areas under the ROC curves (AUCs) for each model pair were calculated, as well as sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. RESULTS: A total of 120 lesions were found; 112 (93.3%) lesions were weighed, and mean and median weights were 589 and 335 mg, respectively. The three-covariate models were significantly identical (P > .65), with largest AUC of 0.9913 ± 0.0037 (standard error), accuracy of 96.9%, and sensitivity, specificity, negative predictive value, and positive predictive value of 94.3%, 98.3%, 97.1%, and 96.7%, respectively. The one- and two-covariate models were significantly less accurate (P < .043). CONCLUSION: A three-covariate multinomial logistic model derived from a triple-phase 4D CT scan can accurately provide the probability that tissue is PTA and performs significantly better than models using one or two covariates.

Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.

Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.

BACKGROUND: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. METHODS: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. FINDINGS: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger. INTERPRETATION: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. FUNDING: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.

Parathyroid Computed Tomography: Pearls, Pitfalls, and Our Approach.

Parathyroid imaging is predominantly used for preoperative localization of parathyroid lesions in patients with the biochemical diagnosis of primary hyperparathyroidism. Although imaging algorithms vary, in the era of minimally invasive parathyroidectomy for single parathyroid adenomas, multiphase parathyroid computed tomography (CT) (4-dimensional CT) has emerged as a favored modality for presurgical mapping of parathyroid lesions. Implementation and correct interpretation of these studies can be challenging, although confidence and accuracy improve with experience and volume. This article reviews our approach to parathyroid imaging, focusing on pearls and pitfalls in parathyroid CT with ultrasound as a supportive and complementary modality.

Magnetic Resonance Imaging of Head and Neck Emergencies, a Symptom-Based Review, Part 1: General Considerations, Vision Loss, and Eye Pain.

Use of magnetic resonance (MR) imaging in the emergency department continues to increase. Although computed tomography is the first-line imaging modality for most head and neck emergencies, MR is superior in some situations and imparts no ionizing radiation. This article provides a symptom-based approach to nontraumatic head and neck pathologic conditions most relevant to emergency head and neck MR imaging, emphasizing relevant anatomy, "do not miss" findings affecting clinical management, and features that may aid differentiation from potential mimics. Essential MR sequences and strategies for obtaining high-quality images when faced with patient motion and other technical challenges are also discussed.

Magnetic Resonance Imaging of Head and Neck Emergencies, a Symptom-Based Review, Part 2: Ear Pain, Face Pain, and Fever.

The use of magnetic resonance (MR) imaging in the emergency department continues to increase. Although computed tomography is the first-line imaging modality for most head and neck emergencies, MR is superior in some situations and imparts no ionizing radiation. This article provides a symptom-based approach to nontraumatic head and neck pathologic conditions most relevant to emergency head and neck MR imaging, emphasizing relevant anatomy, "do not miss" findings affecting clinical management, and features that may aid differentiation from potential mimics. Essential MR sequences and strategies for obtaining high-quality images when faced with patient motion and other technical challenges are also discussed.

Utilization of a Virtual Information Session to Increase Engagement With Prospective Applicants in the Setting of COVID-19.

PURPOSE: This study aimed to assess the efficacy of a virtual information session hosted by a diagnostic radiology residency program at addressing applicant concerns about the 2020-2021 interview cycle and highlighting key aspects of the residency program. METHODS: Participants were recruited to attend the virtual information session over a 2-week period via social media and communication with medical school radiology interest groups. Attendees were able to submit questions or topics of interest prior to the session. The virtual information session was hosted by trainees and faculty from a radiology residency. Data regarding the demographics of the attendees and the efficacy of the session were obtained through interactive live polling during the virtual session and a voluntary anonymous postsession survey. RESULTS: A total of 171 attendees participated in the virtual information session. Of the attendees, 42% learned about the session from Twitter and 72% were fourth-year medical students applying for residency. Among topics addressed during the session, attendees indicated that they were most interested in learning about "Application strategies during COVID-19" during an in-session poll. On the post-session survey, 96% of attendees reported being more knowledgeable about the residency program culture and the breadth of research and educational opportunities. CONCLUSION: Given the virtual nature of the 2020-2021 residency application cycle, utilization of web-based platforms for recruitment will be essential. Virtual information sessions can be effective at providing insight into aspects of a residency program that are typically gained during the in-person interview experience.

Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment.

OBJECTIVES: Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS: A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS: There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION: When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.

Multidetector CT angiography in the evaluation of acute blunt head and neck trauma: a proposed acute craniocervical trauma scoring system.

PURPOSE: To determine the diagnostic yield of multidetector computed tomographic (CT) angiography in the evaluation of patients presenting to the emergency department with acute blunt head and neck trauma to assess for arterial injury and to propose a practical scoring system for the identification of patients at highest risk of arterial injury. MATERIALS AND METHODS: With institutional review board approval, Health Insurance Portability and Accountability Act compliance, and waived informed consent, a retrospective study was conducted of 830 consecutive patients who presented to the emergency department with acute blunt head and neck trauma over 9 years and were evaluated with multidetector CT angiography. Unenhanced CT scans and CT angiograms were reviewed for the presence of cervical interfacetal subluxations and/or dislocations, fractures, intracranial hemorrhage, and arterial injury. Medical records were reviewed for mechanism of injury (MOI). Multivariate logistic regression analysis was performed to identify independent predictors of an increased risk of arterial injury. RESULTS: Multidetector CT angiographic results showed injury to 118 arterial structures in 106 (12.8%) patients. Multivariate logistic regression analysis showed that the presence of cervical interfacetal subluxation/dislocations (44.4%; odds ratio [OR], 6.3; P < .0001), fracture lines reaching an arterial structure (22.1%; OR, 4.4; P < .0001), and high-impact MOIs (16.5%; OR, 3.1; P < .0001) were independent predictors of an increased risk of arterial injury and were used to construct a scoring system. Patients with scores of 2 and 3 (21.9% and 52.2%, respectively) were at highest risk of arterial injury. CONCLUSION: The proposed acute craniocervical trauma scoring system could be used as a guide to select blunt trauma patients for multidetector CT angiographic evaluation. Future validation of this scoring system is necessary.

Prevalence of traumatic dural venous sinus thrombosis in high-risk acute blunt head trauma patients evaluated with multidetector CT venography.

PURPOSE: To determine the prevalence of trauma-related dural venous sinus thrombosis (DVST) in high-risk patients with blunt head trauma who are examined with multidetector computed tomographic (CT) venography. MATERIALS AND METHODS: With institutional review board approval, HIPAA compliance, and waived informed consent, the authors retrospectively studied the findings in 195 consecutive patients who presented to the emergency department with acute blunt head trauma and were examined with multidetector CT venography because they were considered to be at high risk for DVST owing to the presence of a fracture near a dural venous sinus or jugular bulb or a high index of clinical suspicion. Nonenhanced CT images and CT venograms were reviewed for the presence of skull fractures, intracranial hemorrhage, and traumatic DVST. Magnetic resonance and nonenhanced CT images subsequently obtained in patients with traumatic DVST were assessed for hemorrhagic venous infarctions. Statistical analyses were performed by using Student t and Pearson chi(2) tests. RESULTS: Multidetector CT venography depicted thrombosis of 98 dural sinuses or jugular bulbs in 57 (40.7%) of the 140 patients with skull fractures extending to a dural sinus or jugular bulb. Fifty-four (55%) of the 98 sinuses or bulbs had occlusive thrombosis. DVST was seen in only those patients with skull fractures extending to a dural sinus or jugular bulb. Among the skull fractures extending to the transverse sinus, sigmoid sinus, or jugular bulb, those of the petrous temporal bone had a higher risk (50%, 36 of 72 fractures) of traumatic DVST than did those of the occipital bone (34% risk [32 of 93 fractures]) (P = .044). However, among the skull fractures extending to the superior sagittal sinus, those of the occipital bone had a higher risk (67% [eight of 12 fractures]) of traumatic DVST than did those of the parietal (39% risk [11 of 28 fractures]) and frontal (24% risk [four of 17 fractures]) bones (P = .065). Four (7%) patients with traumatic DVST had associated hemorrhagic venous infarctions, all secondary to occlusive DVST. CONCLUSION: In patients with blunt head trauma, multidetector CT venographic evaluation should be performed only if there is a fracture extending to a dural venous sinus or jugular bulb.

Chew on This: Emergency Imaging of the Oral Cavity and Salivary Glands-What the Clinician Really Needs to Know.

Nontraumatic emergencies of the oral cavity, sublingual and submandibular spaces, and salivary glands are common and those requiring imaging will most often be infectious in nature.1,2 However, noninfectious pathologies such as sialolithiasis, autoimmune sialoadenitis, and soft tissue swelling due to angioedema or hemorrhage are also important conditions that radiologists must be familiar with in order to inform the clinician of critical imaging findings that can have a profound impact on patient outcomes. To understand these pathologies, familiarity with the anatomy of these spaces is essential.