The integration of artificial intelligence (AI) in healthcare, particularly in digital cytology, has the potential to enhance diagnostic accuracy and workflow efficiency. However, AI adoption remains limited due to technological and human-related barriers. Understanding the perceptions and experiences of healthcare professionals is essential for overcoming these challenges and facilitating effective AI implementation.

This study aimed to assess AI integration in digital cytology workflows by evaluating professionals' perspectives on its benefits, challenges, and requirements for successful adoption.

A survey was conducted among 150 professionals working in public and private healthcare settings in Italy, including laboratory technicians (35%), medical doctors (25%), biologists (20%), and specialists in diagnostic technical sciences (20%). Data were collected through a structured Computer-Assisted Web Interview (CAWI) and a Virtual Focus Group (VFG) to capture quantitative and qualitative insights on AI familiarity, perceived advantages, and barriers to adoption.

The findings indicated varying levels of AI familiarity among professionals. While many recognized AI's potential to improve diagnostic accuracy and streamline workflows, concerns were raised regarding resistance to change, implementation costs, and doubts about AI reliability. Participants emphasized the need for structured training and continuous support to facilitate AI adoption in digital cytology.

Addressing barriers such as resistance, cost, and trust is essential for the successful integration of AI in digital cytology workflows. Tailored training programs and ongoing professional support can enhance AI adoption, ultimately optimizing diagnostic processes and improving clinical outcomes.

Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some of these nodules may undergo fine-needle aspiration (FNA) biopsy to assess their nature, this procedure carries a risk of false negatives and inherent complications. To avoid the need for unnecessary biopsy examination, we explored a method for distinguishing the benign and malignant characteristics of thyroid TI-RADS 3-5 nodules based on deep-learning ultrasound images combined with computed tomography (CT).

Thyroid nodules, assessed as American College of Radiology (ACR) TI-RADS category 3-5 through conventional ultrasound, all of which had postoperative pathology results, were examined using both conventional ultrasound and CT before operation. We investigated the effectiveness of deep-learning models based on ultrasound alone, CT alone, and a combination of both imaging modalities using the following metrics: Area Under Curve (AUC), sensitivity, accuracy, and positive predictive value (PPV). Additionally, we compared the diagnostic efficacy of the combined methods with manual readings of ultrasound and CT.

A total of 768 thyroid nodules falling within TI-RADS categories 3-5 were identified across 768 patients. The dataset comprised 499 malignant and 269 benign cases. For the automatic identification of thyroid TI-RADS category 3-5 nodules, deep learning combined with ultrasound and CT demonstrated a significantly higher AUC (0.930; 95% CI: 0.892, 0.969) compared to the application of ultrasound alone AUC (0.901; 95% CI: 0.856, 0.947) or CT alone AUC (0.776; 95% CI: 0.713, 0.840). Additionally, the AUC of combined modalities surpassed that of radiologists'assessments using ultrasound alone AUCmean (0.725；95% CI:0.677, 0.773), CT alone AUCmean (0.617; 95% CI:0.564, 0.669). Deep learning method combined with ultrasound and CT imaging of thyroid can allow more accurate and precise classification of nodules within TI-RADS categories 3-5.

Background/Objectives: This study evaluates ChatGPT 4.0's ability to interpret thyroid ultrasound (US) reports using ACR-TI-RADS 2017 criteria, comparing its performance with different levels of US users. Methods: A team of medical experts, an inexperienced US user, and ChatGPT 4.0 analyzed 100 fictitious thyroid US reports. ChatGPT's performance was assessed for accuracy, consistency, and diagnostic recommendations, including fine-needle aspirations (FNA) and follow-ups. Results: ChatGPT demonstrated substantial agreement with experts in assessing echogenic foci, but inconsistencies in other criteria, such as composition and margins, were evident in both its analyses. Interrater reliability between ChatGPT and experts ranged from moderate to almost perfect, reflecting AI's potential but also its limitations in achieving expert-level interpretations. The inexperienced US user outperformed ChatGPT with a nearly perfect agreement with the experts, highlighting the critical role of traditional medical training in standardized risk stratification tools such as TI-RADS. Conclusions: ChatGPT showed high specificity in recommending FNAs but lower sensitivity and specificity for follow-ups compared to the medical student. These findings emphasize ChatGPT's potential as a supportive diagnostic tool rather than a replacement for human expertise. Enhancing AI algorithms and training could improve ChatGPT's clinical utility, enabling better support for clinicians in managing thyroid nodules and improving patient care. This study highlights both the promise and current limitations of AI in medical diagnostics, advocating for its refinement and integration into clinical workflows. However, it emphasizes that traditional clinical training must not be compromised, as it is essential for identifying and correcting AI-driven errors.

Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).

This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.

A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.

This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology care.

Thyroid nodules are common incidental findings. Most of them are benign, but many unnecessary fine-needle aspiration procedures, core biopsies, and even thyroidectomies or non-invasive treatments have been performed. To improve thyroid nodule characterization, the use of multiparametric ultrasound evaluation has been encouraged by most experts and several societies. In particular, US elastography for assessing tissue stiffness and CEUS for providing insight into vascularization contribute to improved characterization. Moreover, the application of AI, particularly machine learning and deep learning, enhances diagnostic accuracy. Furthermore, AI-based computer-aided diagnosis (CAD) systems, integrated into the diagnostic process, aid in risk stratification and minimize unnecessary interventions. Despite these advancements, challenges persist, including the need for standardized TIRADS, the role of US elastography in routine practice, and the integration of AI into clinical protocols. However, the integration of clinical information, laboratory information, and multiparametric ultrasound features remains crucial for minimizing unnecessary interventions and guiding appropriate treatments. In conclusion, ultrasound plays a pivotal role in thyroid nodule management. Open questions regarding TIRADS selection, consistent use of US elastography, and the role of AI-based techniques underscore the need for ongoing research. Nonetheless, a comprehensive approach combining clinical, laboratory, and ultrasound data is recommended to minimize unnecessary interventions and treatments.

The differential diagnosis between benign and malignant thyroid nodules continues to be a major challenge in clinical practice. The rising incidence of thyroid neoplasm and the low incidence of aggressive thyroid carcinoma, urges the exploration of strategies to improve the diagnostic accuracy in a pre-surgical phase, particularly for indeterminate nodules, and to prevent unnecessary surgeries. Only in 2022, the 5th WHO Classification of Endocrine and Neuroendocrine Tumors, and in 2023, the 3rd Bethesda System for Reporting Thyroid Cytopathology and the European Thyroid Association included biomarkers in their guidelines. In this review, we discuss the integration of biomarkers within the routine clinical practice for diagnosis of thyroid nodules and cancer.

The literature search for this review was performed through Pub Med, Science Direct, and Google Scholar. We selected 156 publications with significant contributions to this topic, with the majority (86, or 55.1%) published between January 2019 and March 2024, including some publications from our group during those periods. The inclusion criteria were based on articles published in recognized scientific journals with high contributions to the proposed topic. We excluded articles not emphasizing molecular biomarkers in refine the pre-surgical diagnosis of thyroid nodules.

We explored genetic biomarkers, considering the division of thyroid neoplasm into BRAF-like tumor and RAS-like tumor. The specificity of BRAF mutation in the diagnosis of papillary thyroid carcinoma (PTC) is nearly 100% but its sensitivity is below 35%. RAS mutations are found in a broad spectrum of thyroid neoplasm, from benign to malignant follicular-patterned tumors, but do not increase the ability to distinguish benign from malignant lesions. The overexpression of miRNAs is correlated with tumor aggressiveness, high tumor node metastasis (TMN) stage, and recurrence, representing a real signature of thyroid cancer, particularly PTC. In addition, associations between the expression levels of selected miRNAs and the presence of specific genetic mutations have been related with aggressiveness and worse prognosis.

The knowledge of genetic and molecular biomarkers has achieved a high level of complexity, and the difficulties related to its applicability determine that their implementation in clinical practice is not yet a reality. More studies with larger series are needed to optimize their use in routine practice. Additionally, the improvement of new techniques, such as liquid biopsy and/or artificial intelligence, may be the future for a better understanding of molecular biomarkers in thyroid nodular disease.

Thyroid cytopathology, particularly in cases of atypia of undetermined significance/follicular lesions of undetermined significance (AUS/FLUS), suffers from suboptimal sensitivity and specificity challenges. Recent advancements in digital pathology and artificial intelligence (AI) hold promise for enhancing diagnostic accuracy. This systematic review included studies that focused on diagnostic accuracy in AUS/FLUS cases using AI, whole slide imaging (WSI), or both.

Of the 176 studies from 2000 to 2023, 13 met the inclusion criteria. The datasets range from 145 to 964 WSIs, with an overall number of 494 AUS cases ranging from eight to 254. Five studies used convolutional neural networks (CNNs), and two used artificial neural networks (ANNs). The preparation methods included Romanowsky-stained smears either alone or combined with Papanicolaou-stained or H&E and liquid-based cytology (ThinPrep). The scanner models that were used for scanning the slides varied, including Leica/Aperio, Alyuda Neurointelligence Cupertino, and PANNORAMIC™ Desk Scanner. Classifiers used include Feedforward Neural Networks (FFNNs), Two-Layer Feedforward Neural Networks (2L-FFNNs), Classifier Machine Learning Algorithm (MLA), Visual Geometry Group 11 (VGG11), Gradient Boosting Trees (GBT), Extra Trees Classifier (ETC), YOLOv4, EfficientNetV2-L, Back-Propagation Multi-Layer Perceptron (BP MLP), and MobileNetV2.

The available studies have shown promising results in differentiating between thyroid lesions, including AUS/FLUS. AI can be especially effective in removing sources of errors such as subjective assessment, variation in staining, and algorithms. CNN has been successful in processing WSI data and identifying diagnostic features with minimal human supervision. ANNs excelled in integrating structured clinical data with image-derived features, particularly when paired with WSI, enhancing diagnostic accuracy for indeterminate thyroid lesions.

A combined approach using both CNN and ANN can take advantage of their strengths. While AI and WSI integration shows promise in improving diagnostic accuracy and reducing uncertainty in indeterminate thyroid cytology, challenges such as the lack of standardization need to be addressed.

Over the past millennia, the evaluation and management of thyroid nodules has essentially remained the same with thyroidectomy as the only reliable method to identify malignancy. However, in the last 30 years, technological advances have significantly improved diagnostic management of thyroid nodules. Advances in imaging have allowed development of a reliable risk- based stratification system to identify nodules at increased risk of malignancy. At the same time, sensitive imaging has caused collateral damage to the degree that we are now identifying and treating many small, low risk nodules with little to no clinical relevance.

To review the history of thyroid nodule evaluation with emphasis on recent changes and future pathways.

Literature review and discussion.

Thyroid ultrasound remains the best initial method to evaluate the thyroid gland for nodules. Different risk-of-malignancy protocols have been developed and introduced by different societies, reporting methods have been developed and improved each, with goals of improving the ability to recognize nodules requiring further intervention and minimizing excessive monitoring of those who do not. Once identified, cytological evaluation of nodules further enhances malignancy identification with molecular markers assisting in ruling out malignancies in indeterminate nodules preventing unneeded intervention. And all societies have urged avoidance of overdiagnosis and overtreatment of low-risk cancers of little to no clinical relevance.

In this review, we describe advancements in nodule evaluation and management, while emphasizing caution in overdiagnosing and overtreating low-risk lesions without clinical importance.

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases and further complicated by the inclusion of noninvasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTP. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye and (2) develop a deep learning model for multiclass segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable internuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available next-generation sequencing data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole-slide images of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all whole-slide image tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges.

To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues.

Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches.

These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

Technology allows us to predict a histopathological diagnosis, but the high costs prevent the large-scale use of these possibilities. The current liberal indication for surgery in benign thyroid conditions led to a rising frequency of incidental thyroid carcinoma, especially low-risk papillary micro-carcinomas.

We selected a cohort of 148 patients with thyroid nodules by ultrasound characteristics and investigated them by fine needle aspiration cytology (FNAC)and prospective BRAF collection for 70 patients. Also, we selected 44 patients with thyroid nodules using semi-quantitative functional imaging with an oncological, 99mTc-methoxy-isobutyl-isonitrile (99mTc-MIBI) radiotracer.

Following a correlation with final histopathological reports in patients who underwent thyroidectomy, we introduced the results in a machine learning program (AI) in order to obtain a pattern. For semi-quantitative functional visual pattern imaging, we found a sensitivity of 33%, a specificity of 66.67%, an accuracy of 60% and a negative predicting value (NPV) of 88.6%. For the wash-out index (WOind), we found a sensitivity of 57.14%, a specificity of 50%, an accuracy of 70% and an NPV of 90.06%.The results of BRAF in FNAC included 87.50% sensitivity, 75.00% specificity, 83.33% accuracy, 75.00% NPV and 87.50% PPV. The prevalence of malignancy in our small cohort was 11.4%.

We intend to continue combining preoperative investigations such as molecular detection in FNAC, 99mTc-MIBI scanning and AI training with the obtained results on a larger cohort. The combination of these investigations may generate an efficient and cost-effective diagnostic tool, but confirmation of the results on a larger scale is necessary.

Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice.

A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.

A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability.

Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.

This study aimed to evaluate the rates of necessary diagnostic and therapeutic procedures in a cohort of patients undergoing their first endocrine consultation for thyroid nodular disease.

This was an observational study conducted between January 1 and June 30, 2023, on patients undergoing their first endocrine consultation for thyroid nodular disease. Data were collected, including age, thyroid-stimulating hormone (TSH) concentration, reasons for performing thyroid ultrasound (US), and thyroid US reports. The US was performed at the time of the endocrine consultation according to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR-TIRADS) risk stratification system. Patients underwent further investigations/treatment or simple US follow-up as necessary.

A total of 373 patients with thyroid nodules were evaluated. Reasons for undergoing thyroid US were unrelated to suspected thyroid disease in 126 (33.8%), incidentalomas in 91 (24.4%), dysfunction or positivity for thyroid antibodies in 67 (18%), symptoms or visible nodules in 61 (16.3%), and family history of thyroid disease in 28 (7.5%). A total of 193 diagnostic or therapeutic procedures were performed in 133 (35.7%) patients [fine-needle aspiration (FNA): 121 (62.7%), surgery: 28 (14.5%), percutaneous ethanol injection: 20 (10.4%), scintigraphy: 10 (5.2%); thermal ablation: 7 (3.6%), and radioactive treatment: 7 (3.6%)].

In the present study only one-third of the patients undergoing endocrine consultation with first detected thyroid nodules required any diagnostic or therapeutic procedures.

Molecular tests can contribute to improve the preoperative diagnosis of thyroid nodules. Tests available are expensive and not adapted to different populations.

This study aimed to compare the cyto-histological genetic profile and to evaluate the reliability of molecular tests using ultrasound-guided fine needle aspiration cytology (US-FNAC) in accurately diagnosing differentiated thyroid carcinomas (DTCs) and predicting biologic behavior of papillary thyroid carcinomas (PTCs).

The series included 259 patients with paired cyto-histological samples totaling 518 samples. The genetic alterations were analyzed via PCR/Sanger sequencing. The association with clinicopathologic features was evaluated in PTCs.

From the 259 patients included, histologies were 50 (19.3%) benign controls and 209 (80.7%) DTC cases, from which 182 were PTCs; cytologies were 5.8% non-diagnostic, 18.2% benign, 39% indeterminate, and 37.1% malignant. In histology, indeterminate nodules (n = 101) were 22.8% benign and 77.2% malignant. Mutation frequencies in cytology and histology specimens were, respectively, TERTp: 3.7% vs. 7.9%; BRAF: 19.5% vs. 25.1%; and RAS: 11% vs. 17.5%. The overall cyto-histological agreement of the genetic mutations was 94.9%, with Cohen's k = 0.67, and in indeterminate nodules agreement was 95.7%, k = 0.64. The identified mutations exhibited a discriminative ability in diagnosing DTC with a specificity of 100% for TERTp and BRAF, and of 94% for RAS, albeit with low sensitivity. TERTp and BRAF mutations were associated with aggressive clinicopathological features and tumor progression in PTCs (p < 0.001). The obtained good cyto-histological agreement suggests that molecular analysis via US-FNAC may anticipate the genetic profile and the behavior of thyroid tumors, confirming malignancy and contributing to referring patients to surgery.

Thyroid nodules are very common, 5-15% of which are malignant. Despite the low mortality rate of well-differentiated thyroid cancer, some variants may behave aggressively, making nodule differentiation mandatory. Ultrasound and fine-needle aspiration biopsy are simple, safe, cost-effective and accurate diagnostic tools, but have some potential limits. Recently, machine learning (ML) approaches have been successfully applied to healthcare datasets to predict the outcomes of surgical procedures. The aim of this work is the application of ML to predict tumor histology (HIS), aggressiveness and post-surgical complications in thyroid patients. This retrospective study was conducted at the ENT Division of Eastern Piedmont University, Novara (Italy), and reported data about 1218 patients who underwent surgery between January 2006 and December 2018. For each patient, general information, HIS and outcomes are reported. For each prediction task, we trained ML models on pre-surgery features alone as well as on both pre- and post-surgery data. The ML pipeline included data cleaning, oversampling to deal with unbalanced datasets and exploration of hyper-parameter space for random forest models, testing their stability and ranking feature importance. The main results are (i) the construction of a rich, hand-curated, open dataset including pre- and post-surgery features (ii) the development of accurate yet explainable ML models. Results highlight pre-screening as the most important feature to predict HIS and aggressiveness, and that, in our population, having an out-of-range (Low) fT3 dosage at pre-operative examination is strongly associated with a higher aggressiveness of the disease. Our work shows how ML models can find patterns in thyroid patient data and could support clinicians to refine diagnostic tools and improve their accuracy.

To investigate the efficacy of ultrasound-guided radiofrequency ablation and its effect on thyroid function in patients with cystic-solid thyroid nodules.

We enrolled 90 patients with cystic-solid thyroid nodules and randomly assigned to either a control group (n = 37) or an observation group (n = 53). Patients in the observation group underwent ultrasound-guided radiofrequency ablation, while those in the control group were treated with ultrasound-guided lauromacrogol. Thyroid function was monitored, and complications were recorded for both groups, while nodule reduction rates were compared across a range of volumes and time periods.

One month after surgery, the observation group had a larger volume of nodules than the control group, while at 12 months, the volume of nodules in the observation group was smaller. (P < 0.05). Thyroid-stimulating hormone (TSH), free thyroxine 4 (FT4), and free triiodothyronine (FT3) levels were all within normal ranges after treatment in both groups and showed no significant differences from pre-treatment levels. (P > 0.05). There was no statistically significant difference between the total incidence of adverse reactions in the control group (8.11%) and the observation group (5.66%) (P > 0.05).

With a low incidence of postoperative adverse reactions, the ultrasound-guided radiofrequency ablation protocol in the clinical treatment of patients with cystic-solid thyroid nodules can effectively reduce the volume of solid thyroid nodules without affecting the thyroid function of patients and can achieve more ideal treatment effectiveness, and is deserving of promotion.

The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

The ectoenzyme CD73, encoded by the NT5E gene, has emerged as a potential prognostic and therapeutic marker for papillary thyroid carcinoma (PTC), which has increased in incidence in recent decades. Here, from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database, we extracted and combined clinical features, levels of NT5E mRNA, and DNA methylation of PTC samples and performed multivariate and random forest analyses to evaluate the prognostic relevance and the potential of discriminating between adjacent non-malignant and thyroid tumor samples. As a result, we revealed that lower levels of methylation at the cg23172664 site were independently associated with BRAF-like phenotype (p = 0.002), age over 55 years (p = 0.012), presence of capsule invasion (p = 0.007) and presence of positive lymph node metastasis (LNM) (p = 0.04). The methylation levels of cg27297263 and cg23172664 sites showed significant and inversely correlations with levels of NT5E mRNA expression (r = -0.528 and r = -0.660, respectively), and their combination was able to discriminate between adjacent non-malignant and tumor samples with a precision of 96%-97% and 84%-85%, respectively. These data suggest that combining cg23172664 and cg27297263 sites may bring new insights to reveal new subsets of patients with papillary thyroid carcinoma.

Assessment of thyroid nodules histopathology using AI is crucial for an accurate diagnosis. This systematic review analyzes recent works employing deep learning approaches for classifying thyroid nodules based on histopathology images, evaluating their performance, and identifying limitations.

Eligibility criteria focused on peer-reviewed English papers published in the last 5 years, applying deep learning to categorize thyroid histopathology images. The PubMed database was searched using relevant keyword combinations.

Out of 103 articles, 11 studies met inclusion criteria. They used convolutional neural networks to classify thyroid neoplasm. Most studies aimed for basic tumor subtyping; however, 3 studies targeted the prediction of tumor-associated mutation. Accuracy ranged from 77% to 100%, with most over 90%.

The findings from our analysis reveal the effectiveness of deep learning in identifying discriminative morphological patterns from histopathology images, thus enhancing the accuracy of thyroid nodule histopathological classification. Key limitations were small sample sizes, subjective annotation, and limited dataset diversity. Further research with larger diverse datasets, model optimization, and real-world validation is essential to translate these tools into clinical practice.