The prognostic value of lymph node metastasis and the eighth edition of AJCC for patients with anaplastic thyroid cancer

Anaplastic thyroid cancer: Genetic roles, targeted therapy, and immunotherapy

Anaplastic thyroid cancer (ATC) stands as the most formidable form of thyroid malignancy, presenting a persistent challenge in clinical management. Recent years have witnessed a gradual unveiling of the intricate genetic underpinnings governing ATC through next-generation sequencing. The emergence of this genetic landscape has paved the way for the exploration of targeted therapies and immunotherapies in clinical trials. Despite these strides, the precise mechanisms governing ATC pathogenesis and the identification of efficacious treatments demand further investigation. Our comprehensive review stems from an extensive literature search focusing on the genetic implications, notably the pivotal MAPK and PI3K-AKT-mTOR signaling pathways, along with targeted therapies and immunotherapies in ATC. Moreover, we screen and summarize the advances and challenges in the current diagnostic approaches for ATC, including the invasive tissue sampling represented by fine needle aspiration and core needle biopsy, immunohistochemistry, and 18F-fluorodeoxyglucose positron emission tomography/computed tomography. We also investigate enormous studies on the prognosis of ATC and outline independent prognostic factors for future clinical assessment and therapy for ATC. By synthesizing this literature, we aim to encapsulate the evolving landscape of ATC oncology, potentially shedding light on novel pathogenic mechanisms and avenues for therapeutic exploration.

Combined radiation and chemotherapy versus monotherapy for anaplastic thyroid cancer: A SEER retrospective analysis

Background

The effect of combined radiation and chemotherapy (combination therapy) versus monotherapy on anaplastic thyroid carcinoma (ATC) has not yet been clear.

Methods

We identified 516 ATC patients during 2010-2015 from the Surveillance, Epidemiology and End Results (SEER) database and evaluated their survival outcome using the Kaplan-Meier method, Cox regression analysis and propensity score matching (PSM) technique.

Results

The median overall survival (OS) among the entire cohort was 3 months (95 % confidence interval [CI], 2.58-3.42 months), and the 6- and 12-month OS rates were 29 % (95 % CI, 25.01%-32.88 %) and 13 % (95 % CI, 10.60%-16.58 %), respectively. Multivariable analysis demonstrated that ATC patients not receiving radiotherapy or chemotherapy were unquestionably associated with worse OS (hazard ratio [HR] 3.000, 95 % CI, 2.390-3.764) and cancer-specific survival (CSS) (HR = 3.107, 95 % CI, 2.388-4.043), compared with those receiving combination therapy. However, combination therapy did not predict better prognosis compared with monotherapy (all P > 0.05). After PSM, the median OS and CSS were also not significantly improved in patients undergoing chemoradiotherapy versus chemotherapy alone (OS, P = 0.382; CSS, P = 0.420) or radiotherapy alone (OS, P = 0.065; CSS, P = 0.251).

Conclusion

Combination therapy, compared to monotherapy, does not have the expected improvement in survival beyond the benefits achievable with each single-modality treatment, necessitating further prospective research to tailor its treatment management.

Clinical features and imaging examination assessment of cervical lymph nodes for thyroid carcinoma

BACKGROUNDS

The purpose of this study is to investigate the relationship between clinical characteristics and cervical lymph node metastasis (LNM) in patients with thyroid carcinoma, as well as estimate the preoperative diagnosis values of ultrasound (US) and contrast enhanced computed tomography (CECT) examinations on the neck for detection of cervical LNM in thyroid carcinoma.

METHODS

A retrospective analysis of 3 026 patients with surgically proven thyroid carcinoma was conducted. Patients' clinical characteristics, including gender, age, tumor size, bilateral lesions, multifocality, adenomatous nodules, Hashimoto's thyroiditis (HT), and extrathyroidal extension, were collected to explore their association with cervical LNM in thyroid carcinoma. Preoperative assessments for central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) were conducted through US and CECT. The diagnostic value of US, CECT and US combined with CECT for detection of LNM located in various cervical compartments was estimated based on the pathological results.

RESULTS

The risk of cervical LNM was higher in thyroid cancer patients who were male, age < 55 years old, tumor size > 10 mm, bilateral lesions, and extrathyroidal extension, while multifocality, adenomatous nodules and HT had no significant effect on LNM. US, CECT and US combined with CECT all had a higher sensitivity to LLNM (93.1%, 57.8%, 95.4%) than to CLNM (32.3%, 29.0%, 43.4%). US and CECT had a high specificity to both CLNM and LLNM (94.3-97.8%).

CONCLUSION

Preoperative clinical characteristics and imaging examinations on patients with thyroid carcinoma are crucial to the evaluation of cervical lymph nodes and conducive to individualizing surgical treatments by clinicians. US combined with CECT are superior to single US or CECT alone in detection of CLNM and LLNM.

Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma

BACKGROUND

To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC).

METHODS

Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models.

RESULTS

For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model.

CONCLUSIONS

Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.

A novel prognostic model based on log odds of positive lymph nodes to predict outcomes of patients with anaplastic thyroid carcinoma after surgery

OBJECTIVE

The eighth version of the American Joint Committee on Cancer (8th AJCC) system for anaplastic thyroid carcinoma (ATC) added lymph node (LN) metastasis as the staging element. This study aimed to explore the association between LN status and ATC's prognosis, identify the optimal LN index and establish a novel prognostic model.

DESIGN AND PATIENTS

Data of 199 ATC patients after surgery were collected from the Surveillance, Epidemiology and End Results (SEER) database, then randomly divided into training and validation cohorts.

MEASUREMENTS

We compared the prognostic value of AJCC N status, number of positive LN (PLNN), ratio of LN (LNR) and log odds of positive LN (LODDS). We conducted univariate and multivariate Cox analyses to determine the independent prognostic factors for ATC, and constructed a novel prognostic model. The concordance index (C-index), area under the receiver-operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA) were used to assess the nomogram's predictive performance.

RESULTS

LODDS showed the highest accuracy among four LN systems to predict overall survival (OS) for ATC. In the training cohort, the C-index of the LODDS-based nomogram was 0.738. The AUCs were 0.813, 0.850 and 0.869 for predicting 1-, 2- and 3-year OS, respectively. The calibration plots and DCA indicated the great clinical applicability of the model. The above results were verified in the validation cohort.

CONCLUSIONS

LODDS showed better predictive performance than other LN schemes in ATC. The LODDS-incorporated nomogram has the potential to more precisely predict the prognosis for ATC patients than the AJCC system.

Thyroid Cancer and COVID-19: Prospects for Therapeutic Approaches and Drug Development

Thyroid cancer is the most prevalent endocrine malignancy and the reported incidence of thyroid cancer has continued to increase in recent years. Since 2019, coronavirus disease 2019 (COVID-19) has been spreading worldwide in a global pandemic. COVID-19 aggravates primary illnesses and affects disease management; relevant changes include delayed diagnosis and treatment. The thyroid is an endocrine organ that is susceptible to autoimmune attack; thus, thyroid cancer after COVID-19 has gradually attracted attention. Whether COVID-19 affects the diagnosis and treatment of thyroid cancer has also attracted the attention of many researchers. This review examines the literature regarding the influence of COVID-19 on the pathogenesis, diagnosis, and treatment of thyroid cancer; it also focuses on drug therapies to promote research into strategies for improving therapy and management in thyroid cancer patients with COVID-19.