Introduction:

Thyroid cancer is the most common malignant tumor originating in endocrine organs (more than 92%) and comprises a group of tumors that are clinically and epidemiologically different. In recent years, the use of predictive models has increased in medical practice to determine the best behavior in patients with tumors of the thyroid gland.

Objective:

To develop a probabilistic model for predicting recurrence in patients with thyroid cancer.

Methods:

A longitudinal prospective study was carried out at the "Dr. Carlos J Finlay" Central Military Hospital, from January 2015 to February 2020. 63 patients who entered the study by simple random sampling with replacement were included; a predictive model was made using a binary logistic regression in program R.

Results:

The most affected age group was between 40 and 59 years old, female sex predominated and papillary carcinoma, vascularization and irregularity were the most detected ultrasound elements. The Wald statistic was significant with a normal distribution in all variables analyzed, which indicates that their coefficients are different from 0 and should be included in the model. The variable with the greatest influence on the recurrence rate turned out to be cell differentiation.

Conclusions:

The final binary logistic regression model had an adequate goodness of fit and discrimination was very good, with an acceptable receiving operator area under the curve.

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