IMPORTANCE Thyroid nodules are common incidental findings. Ultrasonography and molecular testing can be used to assess risk of malignant neoplasm.OBJECTIVE To examine whether a model developed through automated machine learning can stratify thyroid nodules as high or low genetic risk by ultrasonography imaging alone compared with stratification by molecular testing for high-and low-risk mutations.DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted at a single tertiary care urban academic institution and included patients (n = 121) who underwent ultrasonography and molecular testing for thyroid nodules from January 1, 2017, through August 1, 2018. Nodules were classified as high risk or low risk on the basis of results of an institutional molecular testing panel for thyroid risk genes. All thyroid nodules that underwent genetic sequencing for cytological results with Bethesda System categories III and IV were reviewed. Patients without diagnostic ultrasonographic images within 6 months of fine-needle aspiration or who received definitive treatment at an outside medical center were excluded.MAIN OUTCOMES AND MEASURES Thyroid nodules were categorized by the model as high risk or low risk using ultrasonographic images. Results were compared using genetic testing. RESULTS Among the 134 lesions identified in 121 patients (mean [SD] age, 55.7 [14.2] years; 102 women [84.3%]), 683 diagnostic ultrasonographic images were selected. Of the 683 images, 556 (81.4%) were used for training the model, 74 (10.8%) for validation, and 53 (7.8%) for testing. Most nodules had no mutation (75 [56.0%]), whereas 43 nodules (32.1%) had a high-risk mutation and 16 (11.9%) had an unknown or a low-risk mutation (χ 2 = 39.060; P < .001). In total, 228 images (33.4%) were of nodules classified as genetically high risk (n = 43), and 455 (66.6%) were of low-risk nodules (n = 91). The model performed with a sensitivity of 45% (95% CI, 23.1%-68.5%), a specificity of 97% (95% CI, 84.2%-99.9%), a positive predictive value of 90% (95% CI, 55.2%-98.5%), a negative predictive value of 74.4% (95% CI, 66.1%-81.3%), and an overall accuracy of 77.4% (95% CI, 63.8%-97.7%). CONCLUSIONS AND RELEVANCEThe study found that the model developed through automated machine learning could produce high specificity for identifying nodules with high-risk mutations on molecular testing. This finding shows promise for the diagnostic applications of machine learning interpretation of sonographic imaging of indeterminate thyroid nodules.

Background There is a paucity of literature characterizing outcomes in older adult patients with head and neck cancer (HNC). This study aims to describe patients from this group, their adherence to National Comprehensive Cancer Network (NCCN) adjuvant treatment guidelines, and the impact of guideline adherence on overall survival (OS). Methods In this retrospective cohort study, we reviewed all patients ≥80 years old with HNC who underwent surgery with curative intent from 2008 to 2016. Adherence to NCCN guidelines was determined in blinded fashion, and quality metrics and OS were compared. Results One hundred fifty‐nine patients met inclusion criteria. The majority of patients (n = 94, 59%) underwent treatment in accordance with NCCN recommendations while 65 (41%) deviated from NCCN guidelines. The two cohorts did not demonstrate a difference in 2‐year OS (62% vs 66%, P = .50). Conclusion Older adult patient outcomes were not different when treatment deviated from NCCN guidelines.

Background The role of next generation sequencing (NGS) for identifying high risk mutations in thyroid nodules following fine needle aspiration (FNA) biopsy continues to grow. However, ultrasound diagnosis even using the American College of Radiology’s Thyroid Imaging Reporting and Data System (TI-RADS) has limited ability to stratify genetic risk. The purpose of this study was to incorporate an artificial intelligence (AI) algorithm of thyroid ultrasound with object detection within the TI-RADS scoring system to improve prediction of genetic risk in these nodules. Methods Two hundred fifty-two nodules from 249 patients that underwent ultrasound imaging and ultrasound-guided FNA with NGS with or without resection were retrospectively selected for this study. A machine learning program (Google AutoML) was employed for both automated nodule identification and risk stratification. Two hundred one nodules were used for model training and 51 reserved for testing. Three blinded radiologists scored the images of the test set nodules using TI-RADS and assigned each nodule as high or low risk based on the presence of highly suspicious imaging features on TI-RADS (very hypoechoic, taller-than-wide, extra-thyroidal extension, punctate echogenic foci). Subsequently, the TI-RADS classification was modified to incorporate AI for T4 nodules while treating T1-3 as low risk and T5 as high risk. All diagnostic predictions were compared to the presence of a high-risk mutation and pathology when available. Results The AI algorithm correctly located all nodules in the test dataset (100% object detection). The model predicted the malignancy risk with a sensitivity of 73.9%, specificity of 70.8%, positive predictive value (PPV) of 70.8%, negative predictive value (NPV) of 73.9% and accuracy of 72.4% during the testing. The radiologists performed with a sensitivity of 52.1 ± 4.4%, specificity of 65.2 ± 6.4%, PPV of 59.1 ± 3.5%, NPV of 58.7 ± 1.8%, and accuracy of 58.8 ± 2.5% when using TI-RADS and sensitivity of 53.6 ± 17.6% (p=0.87), specificity of 83.3 ± 7.2% (p=0.06), PPV of 75.7 ± 8.5% (p=0.13), NPV of 66.0 ± 8.8% (p=0.31), and accuracy of 68.7 ± 7.4% (p=0.21) when using AI-modified TI-RADS. Conclusions Incorporation of AI into TI-RADS improved radiologist performance and showed better malignancy risk prediction than AI alone when classifying thyroid nodules. Employing AI in existing thyroid nodule classification systems may help more accurately identifying high-risk nodules.

Objectives1) review benefits and risks of cannabis use, with emphasis on otolaryngic disease processes; 2) define and review the endocannabinoid signaling system (ESS); and 3) review state and federal regulations for the use and research of cannabis and ESS modulators.MethodsThis manuscript is a review of the current literature relevant to the stated objectives.ResultsCannabis (marijuana) use is increasing. It is the most widely used illicit substance in the world. There is increasing interest in its therapeutic potential due to changing perceptions, new research, and legislation changes controlling its use. The legal classification of cannabis is complicated due to varied and conflicting state and federal laws. There are currently two synthetic cannabinoid drugs that are FDA approved. Current indications for use include chemotherapy‐related nausea and vomiting, cachexia, and appetite loss. Research has demonstrated potential benefit for use in many other pathologies including pain, inflammatory states, and malignancy. Data exists demonstrating potential antineoplastic benefit in oral, thyroid, and skin cancers.ConclusionsESS modulators may play both a causal and therapeutic role in several disorders seen in otolaryngology patients. The use of cannabis and cannabinoids is not without risk. There is a need for further research to better understand both the adverse and therapeutic effects of cannabis use. With increasing rates of consumption, elevated public awareness, and rapidly changing legislation, it is helpful for the otolaryngologist to be aware of both the adverse manifestations of use and the potential therapeutic benefits when talking with patients.

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ObjectiveTo assess the validity of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI‐RADS) for evaluating thyroid nodules in children.MethodsPatients aged <19 years with thyroid nodule(s) evaluated by ultrasound (US) from 2007–2018 at a tertiary children's hospital were included. Two radiologists scored de‐identified thyroid US images using ACR TI‐RADS (from 1, “benign” to 5, “highly suspicious”). The radiologists recorded size and rated vascularity for each nodule. Ultrasound findings were compared to pathology results (operative cases, n = 91) and clinical follow‐up without disease progression (non‐operative cases, n = 15).ResultsThyroid images from 115 patients were reviewed. Nine patients were excluded due to the absence of an evaluable nodule. Forty‐seven benign and 59 malignant nodules were included. Median age at ultrasound was 15 years (range 0.9–18 years). Twenty (18.9%) patients were male. There was moderate agreement between TI‐RADS levels assigned by the two raters (kappa = 0.57, p < 0.001). When the raters' levels were averaged, >3 as the threshold for malignancy correctly categorized the greatest percentage of nodules (68.9%). Eleven (18.6%) malignant nodules received a TI‐RADS level of 2 (n = 3) or 3 (n = 8). Sensitivity, specificity, and positive and negative predictive values were 81.4%, 53.2%, 68.6%, and 69.4%, respectively. Although not part of TI‐RADS, vascularity was similar between benign and malignant nodules (p = 0.56).ConclusionIn a pediatric population, TI‐RADS can help distinguish between benign and malignant nodules with comparable sensitivity and specificity to adults. However, the positive and negative predictive values suggest TI‐RADS alone cannot eliminate the need for FNA.Level of Evidence3 Laryngoscope, 133:2394–2401, 2023

ImportanceVenous thromboembolism (VTE) is a severe complication after free tissue transfer to the head and neck (H&amp;amp;N). Enoxaparin 30 mg twice daily (BID) is a common regimen for chemoprophylaxis. However, differences in enoxaparin metabolism based on body weight may influence its efficacy and safety profile.ObjectiveTo assess the association between BMI and postoperative VTE and hematoma rates in patients treated with prophylactic enoxaparin 30 mg BID.Design, Setting, and ParticipantsThis was a retrospective review of a prospectively collected cohort from 2012 to 2022. Postoperative VTE, hematoma, and free flap pedicle thrombosis were recorded within 30 days of index surgery. The setting was a tertiary academic referral center. Participants included patients undergoing H&amp;amp;N reconstruction with free flaps that received fixed-dose subcutaneous enoxaparin 30 mg BID postoperatively. Statistical analysis was conducted from April to May 2022.Main Outcomes and MeasuresOutcomes include incidence of VTE, hematoma, and flap pedicle thrombosis events within 30 days of the surgery. Univariate and multivariable regression models were used to evaluate associations between BMI and other patient factors with these outcomes.ResultsAmong the 765 patients included, 262 (34.24%) were female; mean (SD) age was 60.85 (12.64) years; and mean (SD) BMI was 26.36 (6.29). The rates of VTE and hematoma in the cohort were 3.92% (30 patients) and 5.09% (39 patients), respectively. After adjusting for patient factors, BMI was the only factor associated with VTE (OR, 1.07; 95% CI, 1.015-1.129). Obesity (BMI &amp;gt;30) was associated with increased odds of VTE (OR, 2.782; 95% CI, 1.197-6.564). Hematoma was not associated with BMI (OR, 0.988; 95% CI, 0.937-1.041). Caprini score of at least 9 was not associated with VTE (OR, 1.259; 95% CI, 0.428-3.701).Conclusions and RelevanceThis cohort study found that obesity was associated with an increased risk of VTE in patients after microvascular H&amp;amp;N reconstruction and while on standard postoperative chemoprophylaxis regimens. This association may suggest insufficient VTE prophylaxis in this group and a potential indication for weight-based dosing.

Objective Upper airway stimulation (UAS) is used to treat patients with moderate to severe obstructive sleep apnea (OSA). The aim of this study is to report the incidence and potential predictors of elevated central and mixed apnea index (CMAI) after UAS. Study Design Retrospective chart review of patients undergoing UAS. Setting Tertiary care center. Subjects and Methods Included patients underwent UAS for OSA at our institution between 2014 and 2018. Data collected included demographic information, implantation records, and pre- and postoperative polysomnography (PSG) results. CMAI ≥5 was considered elevated. Post hoc univariate analysis was performed to evaluate factors associated with elevated CMAI. Results In total, 141 patients underwent UAS at our institution. This included 94 men and 47 women with a mean age of 61.2 ± 11.0 years and a mean body mass index of 29.1 ± 3.9 kg/m2. Five patients had an elevated CMAI after surgery during UAS titration. Demographics, comorbid conditions, and device settings were not associated with an elevated postoperative CMAI ( P > .05). Conclusion The occurrence of an elevated CMAI after surgery may represent treatment-emergent events. Demographics, comorbid conditions, and UAS device settings were not associated with central and mixed apneic events. Level of Evidence 4