Comparison of the prognostic value of AJCC cancer staging system 7th and 8th editions for differentiated thyroid cancer

Patterns of cervical lymph node involvement in mono-focal papillary thyroid carcinoma patients with different capsular status: A multi-center analysis

OBJECTIVE

The characteristics of cervical lymph node involvement in papillary thyroid carcinoma (PTC) patients with different degree of capsular invasion remains unclear, especially for those with mono-focal lesion who have traditionally been considered as low neck metastasis risk subgroup.

STUDY DESIGN

Retrospective cohort study.

SETTING

Three academic teaching hospital.

METHODS

A total of 1276 mono-focal PTC patients were retrospectively analyzed.

RESULTS

Mono-focal PTC patients with extrathyroidal extension (ETE) showed significantly higher central lymph node metastasis (CLNM) rate than those without. For patients with no gross ETE (gETE), those with minimal ETE (mETE) also showed more commonly CLNM than those with encapsulated lesions. However, the lateral lymph node metastasis (LLNM) rates of patients with mETE and encapsulated tumors were comparable, both lower than that of patients with gETE. Age ≥40, male, and MTD ≥0.5 cm were identified as independent risk factors of CLNM for those with encapsulated tumors and were enrolled for creating a prediction model. In terms of LLNM, only MTD ≥1.0 cm was confirmed as independent risk factors of LLNM for patients with positive gETE.

CONCLUSIONS

The presence and degree of ETE may have different effects on the risk of central and lateral lymph node metastasis. gETE demonstrates a strong correlation with both CLNM and LLNM while mETE is only associated with CLNM in mono-focal PTC patients. A comprehensive model is established in the aim of predicting neck involvement according to the capsular status and the corresponding stratified risk factors, which may aid clinical decision-making for the management of neck regions.

Preoperative prognostic risk stratification model for papillary thyroid carcinoma based on clinical and ultrasound characteristics

BACKGROUND

The preoperative risk stratification for patients with papillary thyroid carcinoma (PTC) plays a crucial role in guiding individualized treatment. We aim to construct a predictive model that aids in distinguishing between patients with low-risk and high-risk PTC based on preoperative clinical and ultrasound characteristics.

MATERIALS AND METHODS

Patients who underwent open surgery and were diagnosed with PTC via a postoperative pathological report between January 2020 and December 2020 were retrospectively reviewed. Data including basic information, preoperative ultrasound characteristics, thyroid function, and postoperative pathology characteristics were obtained. Univariate logistic regression analysis and least absolute shrinkage and selection operator regression analysis were performed to screen candidate variables. Finally, the preoperative predictive model for PTC was established based on the results of the multivariate logistic regression analysis.

RESULTS

A total of 1,875 patients with PTC were enrolled. Eight variables (sex, age, number of foci, maximum tumor diameter on ultrasound, calcification, capsule, lymph node status on ultrasound, and thyroid peroxidase (TPO) antibody level) significantly associated with risk stratification were included in the predictive model. A nomogram was constructed for clinical utility. The model showed good discrimination, and the area under the curve was 0.777 [95% confidence interval (CI): 0.752-0.803] and 0.769 (95% CI: 0.729-0.809) in the training set and validation set, respectively. The calibration curve exhibited a rather good consistency with the perfect prediction. Furthermore, decision curve analysis and clinical impact curve showed that the model had good efficacy in predicting the prognostic risk of PTC.

CONCLUSIONS

The nomogram model based on preoperative indicators for predicting the prognostic stratification of PTC showed a good predictive value. This could aid surgeons in deciding on individualized precision treatments.