A Closer Look at “Taller-Than-Wide” Thyroid Nodules: Examining Dimension Ratio to Predict Malignancy

2021 ◽  
pp. 019459982110513
Author(s):  
Aviva S. Mattingly ◽  
Julia E. Noel ◽  
Lisa A. Orloff

Objective To evaluate nodule height-to-width ratio as a continuous variable predicting likelihood of thyroid malignancy. Study Design Retrospective cohort study. Setting All study information was collected from a single academic tertiary care hospital. Methods Subjects included adult patients with thyroid nodules who underwent thyroid surgery between 2010 and 2020. The following variables were collected: patient demographics, nodule dimensions via ultrasound, fine-needle aspiration biopsy results, and surgical pathology results. Statistical analysis included logistic regression modeling malignancy with variables of interest. We used a receiver operating characteristic curve to assess the discriminatory value of variables. Results Height-to-width ratio, as a continuous variable, was associated with malignancy (with each 0.1 increase in ratio; odds ratio [OR], 1.25; 95% CI, 1.14-1.37). The same relationship was true for height-to-length ratio (OR, 1.36; 95% CI, 1.24-1.56). The area under the receiver operating characteristic curve for height-to-width ratio was 63.7%. In line with current emphasis on the transverse ultrasound view, we determined 4 different height-to-width ratio intervals: <0.8, 0.8 to <1.0, 1.0 to <1.5, and ≥1.5. Likelihood ratios of malignancy for each interval were 0.6, 1.0, 2.3, and 4.9, respectively. Conclusion Our results support the association between greater height-to-width ratio and malignancy but suggest that a multilevel rather than binary variable improves prediction. The likelihood ratios at different intervals give a more nuanced view of how height-to-width ratio predicts malignancy. With continuing review of guidelines for thyroid nodule biopsy, it is important to consider these data for any point total attributed to shape.

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.


2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.


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