Papillary thyroid microcarcinoma with and without nodal metastasis: A comparative analysis

OBJECTIVES

To assess the demographics and clinical factors of papillary thyroid microcarcinoma (PTMC) patients in Saudi Arabia and compared and analyzed the differences between the patients with and without lymph node metastasis (LNM). Papillary thyroid microcarcinoma (PTMC) is a common thyroid cancer and is not usually detectable clinically but found incidentally after pathologic evaluation of thyroid tissue following surgery for benign thyroid disorders. However, these tumors have a significant risk of LNM.

METHODS

All PTMC patients who underwent surgery at King Abdulaziz University Hospital, King Fahad Medical City, and King Abdulaziz Medical City from 2012 to 2022 were included. The incidence rate of LNM was 9.17%. The patients' average age was 44.05. Most of the patients were female.

RESULTS

Prevalence of LNM among PTMC patients is 9.17% (n=31). The PTMC patients showed the following significant risk factors for LNM: higher Bethesda class, type of pathology, extrathyroidal extension, extracapsular extension, lymphovascular invasion, and residual tumors in patients who had received radioactive iodine. Presence of thyroiditis, multifocality, goitrous thyroid, neural invasion, and tumor size were unrelated to the LNM in the PTMC patients.

CONCLUSION

Higher Bethesda class, pathology type, extrathyroidal extension, extracapsular extension, lymphovascular invasion, and RAI-treated residual tumors were strongly linked to LNM.

Utility of the Milan System for Reporting Salivary Gland Cytopathology in Parotid Gland Masses: The Experience of Two Tertiary Centers

Background The application of fine needle aspiration (FNA) in parotid masses via the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) enhances the diagnosis of these lesions alongside radiological investigations.  Objectives Our objective was to assess the risk of malignancy, sensitivity, specificity, and false positive and negative results for each category of the MSRSGC. Additionally, we assessed the level of agreement between the FNA results using MSRSGC and post-resection histopathological diagnosis. Methods We conducted a retrospective chart review of parotid gland masses that received FNA and postoperative pathological diagnosis at King Saud University Medical City and King Fahad Medical City between 2018 and 2022. We summarized the categorical variables using frequencies and percentages. Results A total of 172 cases met the inclusion criteria. Males encompassed 102 patients (59.3%) of the study sample, and 89 (51.7%) of parotid masses were on the left side. The risk of malignancy for the MSRSGC categories was 37.5% (Category I), 9.0% (II), 50.0% (III), 4.7% (IVa), 50.0% (IVb), 100.0% (V), and 71.0% (VI). FNA had an overall success rate of 81%. The sensitivity was 64% and specificity was 94% for non-neoplastic masses. For benign masses, the sensitivity was 91% and specificity was 66%; however, the sensitivity was 40% and specificity was 97% for malignant lesions. We found that the percentage of agreement between the FNA and final pathology was 80%. Conclusion FNA using MSRSGC is a valuable preoperative clinical tool. However, the low sensitivity rates based on the diagnosis of malignant lesions should alert clinicians not to be overly reliant on biopsy results and instead defer to definitive surgical management.

DICER1 Syndrome in Twins With Ovarian Sertoli-Leydig Cell Tumor and Papillary Thyroid Carcinoma

DICER1 syndrome is a rare autosomal dominant syndrome resulting in benign and malignant tumors in various organs with tumors in endocrine organs (pituitary, thyroid, adrenal, ovaries, and pancreas). Here we present a rare case of 18-year-old twin sisters with DICER1 syndrome who presented at the age of 15 years with hirsutism, deepening of the voice, and amenorrhea. They were diagnosed with a Sertoli-Leydig cell tumor of the ovary and underwent unilateral oophorectomy, with no evidence of recurrence or metastasis during follow-up. Genetic analysis showed the same germline DICER1 mutation in both cases. They also had large multinodular goiters (nodule size ranging from 1.0 to 2.3 cm) nodules were increasing in size. Fine needle aspiration cytology (FNAC) of thyroid nodules for both the sisters showed atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), and they both underwent total thyroidectomy revealing papillary thyroid carcinoma. No pituitary lesion was observed in the brain magnetic resonance imaging (MRI) of either of them. A chest CT scan showed bilateral sub-pleural benign-looking nodules in both patients. The twin sisters developed some features, such as Sertoli-Leydig cell tumor, multinodular goiter, and papillary thyroid carcinoma, and had positive genetic tests for DICER1 germline mutation. The father and paternal grandfather had a family history of papillary thyroid carcinoma. Both patients require active surveillance due to the increased risk of developing tumors in multiple organs associated with this disease.

Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model's accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.