Objectives: Endocrine neoplasms, as a general rule, show systemic, neuro-inflammatory and metabolic consequences, known as paraneoplastic syndrome. The comorbidity of thyroid tumors with neurological and autoimmune diseases prompt looking for common neuro-immuno-endocrine mechanisms of these disorders. While most TCs are well described, there is a gap in the literature after the isolation of oncocytic/Hürthle cell carcinoma (HCC), as a unique type due to immunoendocrine and metabolic features (low TSH-receptor expression and radioiodine avidity). The aim of this study was to collect clearly defined reports of HCC (as a separate entity) and to attempt determining common clinical symptoms and the usefulness of various diagnostic techniques (comprehensive critical review). This may be an introduction to modern treatment (patient-centered care) since the main cause of mortality is not local progression or metastases. Results: Until now, due to misnomenclature and data misinterpretation, HCC has been treated according to general standards (with overuse of TSH-ST and RIA). High thyroglobulin level, decreased total thyroxin (with normal FT3 and spontaneous decrease in TSH), hypercalcemia, as well as the "reverse flip-flop" phenomenon, as common symptoms, indicate the neuroendocrine origin of HCC. Sparse, well-documented lymph node metastases are another feature, although from few studies. Most studies omit the N stage. Whole-body [131]iodine and [18]F-fluorodeoxyglucose scintigraphy may be useful before FNAB. Fine-needle aspiration biopsy (FNAB), as a "gold standard" in early diagnosis of thyroid nodules, delays HCC diagnosis because of the inability to determine a benign/malignant nature. Conclusions: Final HCC outcome may be affected by various overlapping immunoendocrine factors (paraneoplastic effects). Due to very few thyroid function tests performed in HCC, we have proposed a set of basic laboratory analyses, core biopsy in HCC differentiation, and diagnostic chain for standardization. According to the review, adaptation and treatment of HCC based on existing standards for other thyroid cancers seem to be insufficient, and the risks outweigh the benefits. The key recommendations resulting from the 5th edition of the WHO Classification of Endocrine Neoplasms are only the beginning of refuting many myths and biases.

Thyroid cancer comprises biologically diverse entities ranging from largely indolent differentiated thyroid cancer (DTC) to aggressive poorly differentiated/anaplastic thyroid cancer and medullary thyroid cancer, generating a need for minimally invasive biomarkers that can be repeatedly sampled. This review summarizes recent advances in liquid biopsy for thyroid cancer, focusing on analytes and technologies spanning circulating tumor DNA (ctDNA)/cell-free DNA, circulating microRNAs (miRNAs), extracellular vesicles (EVs), and circulating tumor cells (CTCs). For ctDNA, we contrast qPCR/ddPCR and next-generation sequencing, tumor-informed versus tumor-agnostic strategies, the impact of low tumor fraction in DTC, clonal hematopoiesis confounding, and emerging methylation-based multi-cancer detection paradigms. For miRNAs, we highlight that bulk serum/plasma and EV-enriched compartments are not interchangeable and that regulated EV loading supports fraction-resolved biomarker development. We review recent translational EV-miRNA studies, including externally validated classifiers for metastatic disease and follicular-patterned/indeterminate nodules, and summarize the evolution of CTC research from enumeration to preoperative risk stratification and postoperative or radioiodine-related kinetics. We conclude with an indications-first framework that pairs analyte choice with clinical intent (preoperative diagnosis, initial risk stratification, response to treatment and minimal residual disease and identification of actionable alterations and resistance mechanisms) and prioritizes standardized workflows and prospective multicenter validation. Multi-analyte integration, epigenetic/fragmentomic cfDNA signals, and higher-resolution EV analytics are likely to accelerate clinical adoption, particularly in advanced thyroid cancer where circulating signal and therapeutic actionability are highest.

Objectives: Multimodal large language models (MLLMs) have shown potential for medical image classification. We evaluated four optimization strategies in two MLLMs-GPT-4o (gpt-4o-2024-08-06) and Gemini 2.5 Flash-Lite-for ultrasound-based thyroid nodule malignancy classification using two public datasets and a clinical cohort of nodules with atypia of undetermined significance (AUS) cytology. Methods: Text prompting, few-shot learning, fine-tuning, and a hybrid strategy combining fine-tuning with few-shot learning were evaluated for each model. Performance was assessed using the Digital Database of Thyroid Images (DDTI; n = 80), a 1000-image test subset of TN5000, and an institutional AUS cohort with surgical pathology (n = 84). In the AUS cohort, the best-performing strategy was compared with the consensus classification of three endocrinologists and the American Thyroid Association (ATA) ultrasound risk stratification. Results: For GPT-4o, the hybrid strategy achieved the highest area under the receiver operating characteristic curve (AUC) in DDTI (0.866), TN5000 (0.689), and the AUS cohort (0.836). In the AUS cohort, its specificity was higher than that of endocrinologist consensus and ATA risk stratification when only high-suspicion nodules were classified as malignant (95.1% vs. 70.7% and 70.7%; p = 0.002 and p = 0.001, respectively), while sensitivity did not differ significantly (72.1% vs. 74.4% and 79.1%, respectively; both p > 0.05). However, the hybrid model misclassified 12 of 43 malignant nodules, corresponding to a false-negative rate of 27.9%. When high- and intermediate-suspicion ATA categories were classified as malignant, ATA sensitivity increased to 83.7% and specificity decreased to 56.1%; the hybrid model had a higher AUC than ATA risk stratification (0.836 vs. 0.749; p = 0.017). For Gemini 2.5 Flash-Lite, few-shot learning, fine-tuning, and the hybrid strategy did not improve AUC relative to text prompting in any dataset. Conclusions: The hybrid strategy produced the most consistent performance gains for GPT-4o across the three datasets but did not improve Gemini 2.5 Flash-Lite. The optimized GPT-4o model achieved high specificity in the diagnostically challenging AUS cohort, although its false-negative rate limits its use as a stand-alone diagnostic tool. Further validation in larger, prospective multicenter cohorts is required before clinical use.