The effect of the area proportion of the metastatic lesion within the central metastatic lymph node on response to therapy in papillary thyroid carcinoma

Whether Detection of Gene Mutations Could Identify Low- or High-Risk Papillary Thyroid Microcarcinoma? Data from 393 Cases Using the Next-Generation Sequencing

Objective

The objective of this study is to explore the utilization of next-generation sequencing (NGS) technology in evaluating the likelihood of identifying individuals with papillary thyroid microcarcinoma (PTMC ≤10 mm) who are at high or low risk.

Design

NGS was used to analyze 393 formalin-fixed, paraffin-embedded tissues of PTC tumors, all of which were smaller than 15 mm.

Results

The study found that bilateralism, multifocality, intrathyroidal spread, and extrathyroidal extension were present in 84 (21.4%), 153 (38.9%), 16 (4.1%), and 54 (13.7%) cases, respectively. Metastasis of cervical lymph nodes was identified in 226 (57.5%) cases and 96 (24.4%) cases with CLNM >5. Out of the total number of cases studied, 8 cases (2.3%) showed signs of tumor recurrence, all of which were localized and regional. Genetic alterations were detected in 342 cases (87.0%), with 336 cases revealing single mutations and 6 cases manifesting compound mutations. 332 cases (84.5%) had BRAFV600E mutation, 2 cases had KRASQ61K mutation, 2 cases had NRASQ61R mutation, 8 cases had RET/PTC1 rearrangement, 3 cases had RET/PTC3 rearrangement, and 1 case had TERT promoter mutation. Additionally, six individuals harbored concurrent mutations in two genes. These mutations were of various types and combinations: BRAFV600E and NRASQ61R (n = 2), BRAFV600E and RET/PTC3 (n = 2), BRAFV600E and RET/PTC1 (n = 1), and BRAFV600E and TERT promoter (n = 1). The subsequent analysis did not uncover a significant distinction in the incidence of gene mutation or fusion between the cN0 and cN1 patient cohorts. The presence of BRAFV600E mutation and CLNM incidence rates were found to be positively correlated with larger tumor size in PTMC. Our data showed that gene mutations did not appear to have much to do with high-risk papillary thyroid microcarcinoma (PTMC). However, when we looked at tumor size, we found that if the tumor was at least 5 millimeters in size, there was a higher chance of it being at high risk for PTM (P < 0.001, odds ratio (OR) = 2.55, 95% confidence interval (CI): 1.57-4.14). Identification of BRAFV600E mutation was not demonstrated to be significantly correlated with advanced clinicopathological characteristics, although it was strongly associated with a bigger tumor diameter (OR = 4.92, 95% CI: 2.40-10.07, P < 0.001).

Conclusion

In clinical practice, BRAFV600E mutation does not consistently serve as an effective biomarker to distinguish high-risk PTMC or predict tumor progression. The size of the tumor has a significant correlation with its aggressive characteristics. PTMC with a diameter of ≤5 mm should be distinguished and targeted as a unique subset for specialized treatment.

The safety and efficacy of delayed surgery by simulating clinical progression of observable papillary thyroid microcarcinoma: a retrospective analysis of 524 patients from a single medical center

Objective

When active surveillance (AS) is developed in the patients with low-risk papillary thyroid microcarcinoma (PTMC), a medical center needs to ensure the delayed operation that is caused by PTMC clinical progression to have the same prognosis as that of immediate operation. The objective of this study was to investigate the efficacy of delayed surgery by simulating clinical progression (tumor size enlargement and appearance of lymph node metastasis) of PTMCs with AS in a single medical center.

Methods

We retrospectively analyzed the response to therapy in 317 papillary thyroid carcinoma patients treated with total thyroidectomy and post-operative radioactive iodine ablation. They were classified into three groups according to tumor size (group A ≤0.5 cm; group B >0.5 cm and ≤1 cm; group C >1 cm and ≤1.5 cm) or two groups according to the presence (cN1) or absence (cN0) of the clinical lymph node (LN) metastasis. Groups C and cN1 were regarded as simulated clinical progression of observational PTMC and the operation for them was assumed to be "delayed surgery". However, Groups A, B and cN0 were regarded as no clinical progression and the operation for them was considered as immediate surgery.

Results

There were no significantly differences in excellent response to therapy and recurrence-free survival not only among the group A, B and C, but also between the group cN0 and cN1. In other words, these insignificant differences were found between immediate and simulated "delayed" surgeries.

Conclusion

For the PTMC patients suitable for AS, the oncological outcomes were also excellent even if surgery was delayed until after the presence of clinical progression, according to our clinical simulation. Furthermore, we consider that it was feasible for medical centers to assess the ability to implement AS for PTMC patients by retrospectively analyzing their own previous clinical data using the described simulation.

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer

BACKGROUND

Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development.

METHODS

This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models.

RESULTS

Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy.

CONCLUSIONS

The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.