RadioGraphics Update: Parathyroid CT and Primary Hyperparathyroidism.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

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.

Low-Energy Virtual Monochromatic Dual-Energy Computed Tomography Images for the Evaluation of Head and Neck Squamous Cell Carcinoma: A Study of Tumor Visibility Compared With Single-Energy Computed Tomography and User Acceptance.

OBJECTIVE: Dual-energy computed tomography (CT) 40-keV virtual monochromatic images (VMIs) have been reported to improve visualization of head and neck squamous cell carcinoma, but a direct comparison to single-energy CT (SECT) is lacking, and there is debate regarding subjective user preference. We compared 40-keV VMIs with SECT and performed a subjective evaluation of their utility and acceptability for clinical use. METHODS: A total of 60 dual-energy CT and 60 SECT scans from 2 different institutions were evaluated. Tumor conspicuity was evaluated objectively using absolute and relative attenuation and subjectively by 3 head and neck specialists and 3 general radiologists. RESULTS: Tumors had significantly higher absolute and relative attenuation on 40-keV VMIs (P < 0.0001). Subjectively, the 40-keV VMIs improved visualization, with substantial (κ, 0.61-0.80) to almost perfect (κ, 0.81-1) interrater agreements. CONCLUSIONS: The 40-keV VMIs improve tumor visibility objectively and subjectively both by head and neck specialists and general radiologists.

Pediatric Optic Nerve Meningioma: Diagnostic and Therapeutic Challenges.

A 13-year-old female presented with left unilateral proptosis, blurry vision, and diplopia. Clinical examination showed left sided visual acuity of 20/50, limited extraocular movement, 5-mm proptosis, and optic disc edema. CT and MRI displayed a large, intraconal, well-demarcated soft tissue mass with inferotemporal displacement of the optic nerve. The imaging appearance was unusual and diagnosis remained uncertain. Histopathologic analysis of the biopsy specimen confirmed the diagnosis of atypical syncytial meningioma. The tumor cells were positive for both androgen and progesterone receptors and the Ki67 stain was positive (proliferation index of 8%). The patient was treated with proton beam radiation therapy (total dose 50.4 GyE) that suppressed tumor growth and has preserved visual acuity to date (20/40). Differential diagnosis and approaches to therapy are explored.

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.

Prospective validation of the computed tomographic angiography spot sign score for intracerebral hemorrhage.

BACKGROUND AND PURPOSE: Intracerebral hemorrhage (ICH) results in high mortality and morbidity for patients. Previous retrospective studies correlated the spot sign score (SSSc) with ICH expansion, mortality, and clinical outcome among ICH survivors. We performed a prospective study to validate the SSSc for the prediction of ICH expansion, mortality, and clinical outcome among survivors. METHODS: We prospectively included consecutive patients with primary ICH presenting to a single institution for a 1.5-year period. All patients underwent baseline noncontrast computed tomography (CT) and multidetector CT angiography performed within 24 hours of admission and a follow-up noncontrast CT within 48 hours after the initial CT. The ICH volume was calculated on the noncontrast CT images using semiautomated software. The SSSc was calculated on the multidetector CT angiographic source images. We assessed in-hospital mortality and modified Rankin Scale at discharge and at 3 months among survivors. A multivariate logistic regression analysis was performed to determine independent predictors of hematoma expansion, in-hospital mortality, and poor clinical outcome. RESULTS: A total of 131 patients met the inclusion criteria. Of the 131 patients, a spot sign was detected in 31 patients (24%). In a multivariate analysis, the SSSc predicted significant hematoma expansion (odds ratio, 3.1; 95% confidence interval, 1.77-5.39; P≤0.0001), in-hospital mortality (odds ratio, 4.1; 95% confidence interval, 2.11-7.94; P≤0.0001), and poor clinical outcome (odds ratio, 3; 95% confidence interval, 1.4-4.42; P=0.004). In addition, the SSSc was an accurate grading scale for ICH expansion, modified Rankin Scale at discharge, and in-hospital mortality. CONCLUSIONS: The SSSc demonstrated a strong stepwise correlation with hematoma expansion and clinical outcome in patients with primary ICH.

Late Radiation-Induced Carotid Artery Stenosis and Stroke in Pediatric Patient Treated With Proton Radiation Therapy for Skull-Base Chordoma.

Radiation vasculopathy is a well-recognized late complication of radiation therapy. We present a case of a stroke 29 years after high-dose proton radiation therapy for skull-base chordoma due to occlusion of bilateral internal carotid arteries.

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.