[THYROID HEMIAGENESIS: A CASE REPORT].

INTRODUCTION: Thyroid hemiagenesis is a rare congenital anomaly characterized by the absence of one thyroid lobe and the isthmus. This case report presents a 4-year-old girl with a history of prematurity. Incidentally, during a routine ultrasound evaluation of the neck, thyroid hemiagenesis was detected along with the presence of normal lymph nodes. The right thyroid lobe was absent, while the left thyroid lobe was preserved. No previous neck or thyroid surgeries were reported. DISCUSSION: This provides an overview of thyroid hemiagenesis, including its prevalence, predominant involvement of the left lobe, possible genetic and environmental factors, and associations with thyroid and extrathyroidal pathologies. Imaging modalities, such as ultrasound and scintigraphy, play a crucial role in diagnosing thyroid hemiagenesis and identifying additional thyroid gland abnormalities. Long-term follow-up and careful monitoring are recommended to assess thyroid function and identify potential structural abnormalities. The optimal therapeutic approach for thyroid hemiagenesis remains controversial, and further studies are needed to determine the clinical significance and long-term outcomes of this rare condition.

Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm.

BACKGROUND: At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment. METHODS: We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient's time to progression. We validated the prediction capacity of the algorithm by the Leave-One-Out cross-validation methodology. RESULTS: Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen's κ = 0.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R2 = 0.505). CONCLUSIONS: Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients' response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization.