Development of machine learning models to predict lymph node metastases in major salivary gland cancers

Regional disease control in nasal vestibule squamous cell carcinoma: Systematic review and meta-analysis

OBJECTIVES

To systematically analyze the regional disease control of nasal vestibule squamous cell carcinoma (NVSCC) after primary treatment.

METHODS

The study was performed according to the PRISMA guidelines, searching on Scopus, PubMed/MEDLINE, Cochrane Library, and Google Scholar databases.

RESULTS

A total of 37 studies with 1861 patients (70.5 % males; median age of 58.2 years) were included. Overall, the cumulative regional recurrence (RR) rate was 10.7 % (95 %CI: 8.4 %-13.5 %) with a median follow-up of 51.7 months. The RR rate in clinical node negative patients who didn't undergo any elective neck treatment was 13.7 % (n = 548, 95 %CI: 9.7 %-19.0 %). When stratified by T stage, regardless of the classification used, RR rate was 10.4 % (n = 750, 95 % CI: 7.9 %-13.5 %) for T1-T2 tumors and 25.5 % (n = 102, 95 % CI: 16.4 %-37.4 %) for T3-T4 tumors.

CONCLUSION

Elective neck treatment does not seem justified in patients with early-stage primary NVSCC. However, for locally advanced tumors, elective neck treatment should be strongly considered.

Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features

OBJECTIVE

Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through conventional diagnostic strategies. The aim of this study was to develop machine learning (ML) models capable of identifying papillary thyroid carcinomas using preoperative variables.

METHODS

Patients with thyroid nodules undergoing thyroid surgery were enrolled in a retrospective monocentric study. Six 2-class supervised ML models were developed to predict papillary thyroid carcinoma, by sequentially incorporating clinical-immunological, ultrasonographic, cytological, and radiomic variables.

RESULTS

Out of 186 patients, 92 nodules (49.5 %) were papillary thyroid carcinomas in the histological report. The Area Under the Curve (AUC) ranged from 0.41 to 0.61 using only clinical-immunological variables. All ML models exhibited an increased performance when ultrasound variables were included (AUC: 0.95-0.97). The addition of cytological (AUC: 0.86-0.97) and radiomic (AUC: 0.88-0.97) variables did not further improve ML models' performance.

CONCLUSION

ML algorithms demonstrated low accuracy when trained with clinical-immunological data. However, the inclusion of radiological data significantly improved the models' performance, while cytopathological and radiomics data did not further improve the accuracy.

LEVEL OF EVIDENCE

Level 4.

Seizing the fate of lymph nodes in immunotherapy: To preserve or not?

Lymph node dissection has been a long-standing diagnostic and therapeutic strategy for metastatic cancers. However, questions over myriad related complications and survival outcomes are continuously debated. Immunotherapy, particularly neoadjuvant immunotherapy, has revolutionized the conventional paradigm of cancer treatment, yet has benefited only a fraction of patients. Emerging evidence has unveiled the role of lymph nodes as pivotal responders to immunotherapy, whose absence may contribute to drastic impairment in treatment efficacy, again posing challenges over excessive lymph node dissection. Hence, centering around this theme, we concentrate on the mechanisms of immune activation in lymph nodes and provide an overview of minimally invasive lymph node metastasis diagnosis, current best practices for activating lymph nodes, and the prognostic outcomes of omitting lymph node dissection. In particular, we discuss the potential for future comprehensive cancer treatment with effective activation of immunotherapy driven by lymph node preservation and highlight the challenges ahead to achieve this goal.

Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer?

Background and objective

Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI)-based language processing model using deep learning to create human-like text dialogue. It has been a popular source of information covering vast number of topics including medicine. Patient education in head and neck cancer (HNC) is crucial to enhance the understanding of patients about their medical condition, diagnosis, and treatment options. Therefore, this study aims to examine the accuracy and reliability of ChatGPT in answering questions regarding HNC.

Methods

154 head and neck cancer-related questions were compiled from sources including professional societies, institutions, patient support groups, and social media. These questions were categorized into topics like basic knowledge, diagnosis, treatment, recovery, operative risks, complications, follow-up, and cancer prevention. ChatGPT was queried with each question, and two experienced head and neck surgeons assessed each response independently for accuracy and reproducibility. Responses were rated on a scale: (1) comprehensive/correct, (2) incomplete/partially correct, (3) a mix of accurate and inaccurate/misleading, and (4) completely inaccurate/irrelevant. Discrepancies in grading were resolved by a third reviewer. Reproducibility was evaluated by repeating questions and analyzing grading consistency.

Results

ChatGPT yielded "comprehensive/correct" responses to 133/154 (86.4%) of the questions whereas, rates of "incomplete/partially correct" and "mixed with accurate and inaccurate data/misleading" responses were 11% and 2.6%, respectively. There were no "completely inaccurate/irrelevant" responses. According to category, the model provided "comprehensive/correct" answers to 80.6% of questions regarding "basic knowledge", 92.6% related to "diagnosis", 88.9% related to "treatment", 80% related to "recovery - operative risks - complications - follow-up", 100% related to "cancer prevention" and 92.9% related to "other". There was not any significant difference between the categories regarding the grades of ChatGPT responses (p=0.88). The rate of reproducibility was 94.1% (145 of 154 questions).

Conclusion

ChatGPT generated substantially accurate and reproducible information to diverse medical queries related to HNC. Despite its limitations, it can be a useful source of information for both patients and medical professionals. With further developments in the model, ChatGPT can also play a crucial role in clinical decision support to provide the clinicians with up-to-date information.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.