Risk Threshold Algorithm for Thyroid Nodule Management Demonstrates Increased Specificity and Diagnostic Accuracy as Compared With American College of Radiology Thyroid Imaging, Reporting and Data System; Society of Radiologists in Ultrasound; and American Thyroid Association Management Guidelines.

We studied diagnostic performance of an algorithm guiding thyroid nodule management using a malignancy risk model as compared with extant management guidelines. Single-institution, retrospective study was performed with sequential cases from pathology registry from 2012 to 2015. Seventy-eight patients were enrolled, with benign and malignant groups defined by aspiration results. Risk Threshold Algorithm determined management based off of a logistic regression model and a risk threshold. American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS), Society of Radiologists in Ultrasound (SRU), and American Thyroid Association (ATA) guidelines were used in comparison. Sensitivity, specificity, positive/negative predictive values, receiver operating characteristic (ROC) values were derived, with significance assessed via McNemar and permutation tests. Forty-four benign nodules and 40 papillary thyroid carcinomas were included. Risk Threshold Algorithm area under the ROC curve was 0.80 versus 0.59 (ACR TI-RADS), 0.49 (SRU), and 0.44 (ATA); all areas under the ROC curve differences were statistically significant. Risk Threshold Algorithm demonstrates sensitivity, specificity, positive predictive value, and negative predictive values of 63%, 91%, 86%, and 73% at the risk threshold maximizing diagnostic performance, compared with 85%, 39%, 56%, and 74% (ACR TI-RADS); 85%, 18%, 50%, and 57% (SRU); and 89%, 11%, 50%, and 83% (ATA). Sensitivity and specificity were significantly different between all groups except SRU versus TI-RADS. The Risk Threshold Algorithm, based on a malignancy risk model, demonstrates increased overall diagnostic accuracy as compared with ACR TI-RADS, SRU, and ATA management guidelines. Through eliminating unnecessary biopsy, patient anxiety, and morbidity can be reduced.

Diagnostic Performance of SRU and ATA Thyroid Nodule Classification Algorithms as Tested With a 1 Million Virtual Thyroid Nodule Model.

PURPOSE: The Society of Radiologists in Ultrasound (SRU 2005) and American Thyroid Association (ATA 2009 and ATA 2015) have published algorithms regarding thyroid nodule management. Kwak et al. and other groups have described models that estimate thyroid nodules' malignancy risk. The aim of our study is to use Kwak's model to evaluate the tradeoffs of both sensitivity and specificity of SRU 2005, ATA 2009 and ATA 2015 management algorithms. MATERIALS AND METHODS: 1,000,000 thyroid nodules were modeled in MATLAB. Ultrasound characteristics were modeled after published data. Malignancy risk was estimated per Kwak's model and assigned as a binary variable. All nodules were then assessed using the published management algorithms. With the malignancy variable as condition positivity and algorithms' recommendation for FNA as test positivity, diagnostic performance was calculated. RESULTS: Modeled nodule characteristics mimic those of Kwak et al. 12.8% nodules were assigned as malignant (malignancy risk range of 2.0-98%). FNA was recommended for 41% of nodules by SRU 2005, 66% by ATA 2009, and 82% by ATA 2015. Sensitivity and specificity is significantly different (< 0.0001): 49% and 60% for SRU; 81% and 36% for ATA 2009; and 95% and 20% for ATA 2015. CONCLUSION: SRU 2005, ATA 2009 and ATA 2015 algorithms are used routinely in clinical practice to determine whether thyroid nodule biopsy is indicated. We demonstrate significant differences in these algorithms' diagnostic performance, which result in a compromise between sensitivity and specificity.

Ultrasound-based clinical prediction rule model for detecting papillary thyroid cancer in cervical lymph nodes: A pilot study.

PURPOSE: To identify sonographic features of cervical lymph nodes (LNs) that are associated with papillary thyroid cancer (PTC) and to develop a prediction model for classifying nodes as metastatic or benign. METHODS: This retrospective study included the records of postthyroidectomy patients with PTC who had undergone cervical ultrasound and LN biopsy. LN location, size, shape, hilum, echopattern, Doppler flow, and microcalcifications were assessed. Model selection was used to identify features associated with malignant LNs and to build a predictive, binary-outcome, generalized linear mixed model. A cross-validated receiver operating characteristic analysis was conducted to assess the accuracy of the model for classifying metastatic nodes. RESULTS: We analyzed records from 71 LNs (23 metastatic) in 44 patients (16 with PTC). The predictive model included a nonhomogeneous echopattern (odds ratio [OR], 5.73; 95% confidence interval [CI], 1.07-30.74; p = 0.04), microcalcifications (OR, 4.91; 95% CI, 0.91-26.54; p = 0.06), and volume (OR, 2.57; 95% CI, 0.66-9.99; p = 0.16) as predictors. The model had an area under the curve of 0.74 (95% CI, 0.60-0.85), sensitivity of 65% (95% CI, 50% to 78%), and specificity of 85% (95% CI, 73% to 94%) at the Youden optimal cut point of 0.38. CONCLUSIONS: Nonhomogeneous echopattern, microcalcifications, and node volume were predictive of malignant LNs in patients with PTC. A larger sample is needed to validate this model.

Situational factors involved in college students' safer and risky sexual encounters.

OBJECTIVE: To ascertain the differences between the situational factors of risky and safer sexual encounters in college students. DESIGN: Descriptive. SETTING: Community-dwelling college students from a large Midwestern university. PARTICIPANTS: Participants were 84 college students ages 18 to 20 years. Fifty-five were female (67.5%), the majority (95.2%) were White, and all reported being heterosexual. MAIN OUTCOME MEASURE: Use of safer or risky practices in last or most memorable sexual encounter. RESULTS: Important situational characteristics of safer encounters were negative ambiance of the place, type of partner, unattractive qualities of the partner, communication with the partner, and having the encounter because of feeling desire. Important situational characteristics of risky encounters were a cozy ambiance, partner's attractiveness inducing desire, asking directly for the encounter, and encounters occurring after celebration or partying. CONCLUSIONS: Many characteristics were similar for both risk groups. However, practitioners can tailor interventions to make clients aware of factors that were more prevalent in risky sexual encounters, such as having the encounter with a boyfriend or girlfriend and having an encounter after drinking alcohol and partying.