Background/Objectives: Thyroid nodules are a very common finding, mostly benign but sometimes malignant, and thus require accurate diagnosis. Ultrasound and fine needle biopsy are the most widely used and reliable diagnostic methods to date, but they are sometimes limited in addressing benign from malignant nodules, mainly with regard to ultrasound, by the operator’s experience. Radiomics, quantitative feature extraction from medical images and machine learning offer promising avenues to improve diagnosis. The aim of this work was to develop a machine learning model based on thyroid ultrasound images to classify nodules into benign and malignant classes. Methods: For this purpose, images of ultrasonography from 142 subjects were collected. Among these subjects, 40 patients (28.2%) belonged to the class “malignant” and 102 patients (71.8%) belonged to the class “benign”, according to histological diagnosis from fine-needle aspiration. This image set was used for the training, cross-validation and internal testing of three different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic feature could capture the disease heterogeneity among the two groups. Three models consisting of four ensembles of machine learning classifiers (random forests, support vector machines and k-nearest neighbor classifiers) were developed for the binary classification task of interest. The best performing model was then externally tested on a cohort of 21 new patients. Results: The best model (ensemble of random forest) showed Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (%) of 85 (majority vote), 83.7 ** (mean) [80.2–87.2], accuracy (%) of 83, 81.2 ** [77.1–85.2], sensitivity (%) of 70, 67.5 ** [64.3–70.7], specificity (%) of 88, 86.5 ** [82–91], positive predictive value (PPV) (%) of 70, 66.5 ** [57.9–75.1] and negative predictive value (NPV) (%) of 88, 87.1 ** [85.5–88.8] (* p < 0.05, ** p < 0.005) in the internal test cohort. It achieved an accuracy of 90.5%, a sensitivity of 100%, a specificity of 86.7%, a PPV of 75% and an NPV of 100% in the external testing cohort. Conclusions: The model constituted of four ensembles of random forest classifiers could identify all the malignant nodes and the consistent majority of benign in the external testing cohort.

In recent years, the medical field has witnessed the rapid expansion and refinement of omics and imaging technologies, which have profoundly transformed patient surveillance and monitoring strategies, with stage-adapted protocols and cross-sectional imaging important in high-risk follow-up. In the melanoma context, diagnostic imaging plays a pivotal role in disease staging, follow-up and evaluation of therapeutic response. Moreover, the emergence of Artificial Intelligence (AI) has further driven the transition toward precision medicine, emphasizing the complexity and individuality of each patient: AI/Radiomics pipelines are increasingly supporting lesion characterization and response prediction within clinical workflows. Consequently, it is essential to emphasize the significant potential of quantitative imaging techniques and radiomic applications, as well as the role of AI in improving diagnostic accuracy and enabling personalized oncologic treatment. Early evidence demonstrates increased sensitivity and specificity, along with a reduction in unnecessary biopsies and imaging procedures, within selected care approaches. In this review, we will outline the current clinical guidelines for the management of melanoma patients and use them as a framework to explore and evaluate advanced imaging approaches and their potential contributions. Specifically, we compare the recommendations of major societies such as NCCN, which advocates more intensive imaging for stages IIB–IV; ESMO and AIOM, which recommend symptom-driven surveillance; and SIGN, which discourages routine imaging in the absence of clinical suspicion. Furthermore, we will describe the latest imaging technologies and the integration of AI-based tools for developing predictive models to actively support therapeutic decision-making and patient care. The conclusions will focus on the prospective role of novel imaging modalities in advancing precision oncology, improving patient outcomes and optimizing the allocation of clinical resources. Overall, the current evidences support a stage-adapted surveillance strategy (ultrasound ± elastography for lymph node regions, targeted brain MRI in high-risk patients, selective use of DECT or total-body MRI) combined with rigorously validated AI-based decision support systems to personalize follow-up, streamline workflows and optimize resource utilization.

Background/Objectives: Diagnostic examinations that cutaneous melanoma (CM) patients undergo during follow-up can generate various emotional states related to the possibility of recurrence or disease progression. Understanding the emotions and perceptions of patients during the wait for diagnostic exams is crucial for improving care. This study aims to explore the experiences of CM patients awaiting follow-up diagnostic investigations. Methods: This is a qualitative descriptive inductive, single-center study, structured according to COREQ criteria. The sample of CM patients was recruited at the Radiology and Diagnostic Imaging Unit of the San Gallicano Dermatological Institute in Rome. Data were collected through semi-structured interviews and analyzed using Framework Analysis, as described by Ritchie and Spencer. Results: A total of 30 patients with an average age of 57.4 were recruited. The interviews revealed the following four themes: waiting influences the emotional experience; the double face of family support; strategies of support during the waiting period; and waiting requires attention to the person and the environment. Conclusions: This study revealed that waiting for a diagnostic exam for cutaneous melanoma (CM) is often characterized by anxiety and worry. Healthcare professionals need to implement positive coping strategies, balanced family support, and improved communication to enhance patient care and satisfaction in oncology settings. Recognizing and addressing patients’ emotional dynamics is crucial for improving care. Training healthcare staff, providing psychological interventions, and leveraging technological innovation can improve the waiting experience, foster a more welcoming and reassuring environment, and enhance patients’ psychological well-being and satisfaction with care pathways.

Background: Hashimoto’s thyroiditis (HT) is an autoimmune disease that progressively leads to thyroid gland destruction and hypothyroidism. Patients produce antibodies against two thyroid-specific antigens: thyroglobulin and thyroid peroxidase. As a result of the abnormal amount of thyroid hormone produced and its autoimmune etiology, patients with HT display a higher frequency of either autoimmune or non-autoimmune dermatological disorders. On ultrasound examination, HT can be detected by observing an enlarged thyroid gland with hypoechoic parenchymal regions or by observing a small gland indicating atrophy and diffusely heterogeneous parenchyma. Changes in the parenchymal vascularity can be observed as decreased or increased levels of vascularization. Objective: This study aimed to analyze the role of clinical data and ultrasound evaluation of patients with HT in suggesting an association with dermatological disorders. Patients and methods: Complete dermatological and endocrinological assessments as well as ultrasonography of the thyroid and lymphnodes were performed in HT patients. Results: The data obtained showed that a family history of thyroid disease, increased diameter of the inferior thyroid artery, and the presence of cervical lymphadenopathy were more frequent in the group of patients with dermatological diseases. Taken together, our results suggest the usefulness of ultrasound in determining a possible association between HT and dermatological disorders.

Background/Objectives: Diagnostic examinations that cutaneous melanoma (CM) patients undergo during follow-up can generate various emotional states related to the possibility of recurrence or disease progression. Understanding the emotions and perceptions of patients during the wait for diagnostic exams is crucial for improving care. This study aims to explore the experiences of CM patients awaiting follow-up diagnostic investigations. Methods: This is a qualitative descriptive inductive, single-center study, structured according to COREQ criteria. The sample of CM patients was recruited at the UOSD of Radiology and Diagnostic Imaging ISG of the IFO in Rome. Data was collected through semi-structured interviews and analyzed using Framework Analysis, as described by Ritchie and Spencer. Results: A total of 30 patients with an average age of 57.4 were recruited. The interviews revealed the following four themes: waiting influences the emotional experience; the double face of family support; strategies of support during the waiting period; waiting requires attention to the person and the environment. Conclusions: Healthcare professionals need to recognize and address patients' emotional dynamics to improve care. Training healthcare staff, psychological intervention, and technological innovation can improve the waiting experience, foster a more welcoming and reassuring environment, and enhance patients' psychological well-being and satisfaction with care pathways.