Does a three-degree hypoechogenicity grading improve ultrasound thyroid nodule risk stratification and affect the TI-RADS 4 category? A retrospective observational study

ABSTRACT Objective: The aim of this study was to determine whether classifying hypoechogenicity in three degrees (mild, moderate, and marked) could improve the distinction between benign and malignant nodules and whether such an approach could influence Category 4 of the Thyroid Imaging Reporting and Data System (TI-RADS). Materials and methods: In total, 2,574 nodules submitted to fine needle aspiration, classified by the Bethesda System, were retrospectively assessed. Further, a subanalysis considering solid nodules without any additional suspicious findings (n = 565) was performed with the purpose of evaluating mainly TI-RADS 4 nodules. Results: Mild hypoechogenicity was significantly less related to malignancy (odds ratio [OR]: 1.409; CI: 1.086-1.829; p = 0.01), compared to moderate (OR: 4.775; CI: 3.700-6.163; p < 0.001) and marked hypoechogenicity (OR: 8.540; CI: 6.355-11.445; p < 0.001). In addition, mild hypoechogenicity (20.7%) and iso-hyperechogenicity (20.5%) presented a similar rate in the malignant sample. Regarding the subanalysis, no significant association was found between mildly hypoechoic solid nodules and cancer. Conclusion: Stratifying hypoechogenicity into three degrees influences the confidence in the assessment of the rate of malignancy, indicating that mild hypoechogenicity has a unique low-risk biological behavior that resembles iso-hyperechogenicity, but with minor malignant potential when compared to moderate and marked hypoechogenicity, with special influence on the TI-RADS 4 category

Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.

The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, China) and currently spreading across six continents causing a considerable harm to patients, with no specific tools until now to provide prognostic outcomes. Thus, the aim of this study is to evaluate possible findings on chest CT of patients with signs and symptoms of respiratory syndromes and positive epidemiological factors for COVID-19 infection and to correlate them with the course of the disease. In this sense, it is also expected to develop specific machine learning algorithm for this purpose, through pulmonary segmentation, which can predict possible prognostic factors, through more accurate results. Our alternative hypothesis is that the machine learning model based on clinical, radiological and epidemiological data will be able to predict the severity prognosis of patients infected with COVID-19. We will perform a multicenter retrospective longitudinal study to obtain a large number of cases in a short period of time, for better study validation. Our convenience sample (at least 20 cases for each outcome) will be collected in each center considering the inclusion and exclusion criteria. We will evaluate patients who enter the hospital with clinical signs and symptoms of acute respiratory syndrome, from March to May 2020. We will include individuals with signs and symptoms of acute respiratory syndrome, with positive epidemiological history for COVID-19, who have performed a chest computed tomography. We will assess chest CT of these patients and to correlate them with the course of the disease. Primary outcomes:1) Time to hospital discharge; 2) Length of stay in the ICU; 3) orotracheal intubation;4) Development of Acute Respiratory Discomfort Syndrome. Secondary outcomes:1) Sepsis; 2) Hypotension or cardiocirculatory dysfunction requiring the prescription of vasopressors or inotropes; 3) Coagulopathy; 4) Acute Myocardial Infarction; 5) Acute Renal Insufficiency; 6) Death. We will use the AUC and F1-score of these algorithms as the main metrics, and we hope to identify algorithms capable of generalizing their results for each specified primary and secondary outcome

Likelihood of malignancy in thyroid nodules according to a proposed Thyroid Imaging Reporting and Data System (TI-RADS) classification merging suspicious and benign ultrasound features

ABSTRACT Objective The aim of this study was to describe the ultrasound features of benign and malignant thyroid nodules and evaluate the likelihood of malignancy associated with each feature according to the Bethesda System for Reporting Thyroid Cytopathology and histopathology. With this analysis, we propose a new TI-RADS classification system. Materials and methods The likelihood of malignancy from ultrasound features were assessed in 1413 thyroid nodules according to the Bethesda System for Reporting Thyroid Cytopathology and histopathological findings. A score was established by attributing different weights to each ultrasound feature evaluated. Results Features positively associated with malignancy in bivariate analysis received a score weight of +1. We attributed a weight of +2 to features which were independently associated with malignancy in a multivariate analysis and +3 for those associated with the highest odds ratio for malignancy (> 10.0). Hence, hypoechogenicity (graded as mild, moderate or marked, according to a comparison with the overlying strap muscle), microcalcification and irregular/microlobulated margin received the highest weights in our scoring system. Features that were negatively associated with malignancy received weights of -2 or -1. In the proposed system a cutoff score of 2 (sensitivity 97.4% and specificity 51.6%) was adopted as a transition between probably benign (TI-RADS 3) and TI-RADS 4a nodules. Overall, the frequency of malignancy in thyroid nodules according to the categories was 1.0% for TI-RADS 3, 7.8% for TI-RADS 4a, 35.3% for TI-RADS 4b, and 84.7% for TI-RADS 5. Conclusion A newly proposed TI-RADS classification adequately assessed the likelihood of malignancy in thyroid nodules