The Prospective Implementation of the 2015 ATA Guidelines and Modified ATA Recurrence Risk Stratification System for Treatment of Differentiated Thyroid Cancer in a Canadian Tertiary Care Referral Setting

Low Mitotic Activity in Papillary Thyroid Cancer: A Marker for Aggressive Features and Recurrence

CONTEXT

The significance of low mitotic activity in papillary thyroid cancer (PTC) is largely undefined.

OBJECTIVE

We aimed to determine the behavioral landscape of PTC with low mitotic activity compared with that of no and high mitotic activity.

METHODS

A single-institution consecutive series of patients with PTC from 2018 to 2022 was reviewed. Mitotic activity was defined as no mitoses, low (1-2 mitoses/2 mm2) or high (≥3 mitoses/2 mm2) per the World Health Organization. The 2015 American Thyroid Association risk stratification was applied to the cohort, and clinicopathologic features were compared between groups. For patients with ≥6 months of follow-up, Cox regression analyses for recurrence were performed.

RESULTS

A total of 640 PTCs were included-515 (80.5%) no mitotic activity, 110 (17.2%) low mitotic activity, and 15 (2.3%) high mitotic activity. Overall, low mitotic activity exhibited rates of clinicopathologic features including vascular invasion, gross extrathyroidal extension, and lymph node metastases in between those of no and high mitotic activity. PTCs with low mitotic activity had higher rates of intermediate- and high-risk American Thyroid Association (ATA) risk stratification compared with those with no mitotic activity (P < .001). Low mitotic activity PTCs also had higher recurrence rates (15.5% vs 4.5%, P < .001). Low mitotic activity was associated with recurrence, independent of the ATA risk stratification (HR 2.96; 95% CI 1.28-6.87, P = .01).

CONCLUSION

Low mitotic activity is relatively common in PTC and its behavior lies within a spectrum between no and high mitotic activity. Given its association with aggressive clinicopathologic features and recurrence, low mitotic activity should be considered when risk stratifying patients with PTC for recurrence.

Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer

BACKGROUND

Various prognostic factors are expected to refine the American Thyroid Association recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making.

METHODS

We developed 2 predictive models for structural incomplete response (SIR) at the 1-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (ie, number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis.

RESULTS

Both model 1 and model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (model 1: IDI = 0.02, event NRI = 42.31%; model 2: IDI = 0.07, event NRI = 53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, model 2 maintained similar performance while model 1 did not significantly improve correct reclassification.

CONCLUSION

The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.

Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports

BACKGROUND

We aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports.

METHODS

We analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Nonstructured reports were narrative, while structured reports followed standardized formats. We developed ThyroPath, a rule-based Natural Language Processing pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstructured) for evaluation. The pipeline's performance was assessed using both strict and lenient criteria for accuracy, precision, recall, and F1-score; a metric that combines precision and recall evaluation.

RESULTS

In extraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for structured reports and 90% for unstructured reports, covering 18 thyroid cancer pathology features. In classification tasks, ThyroPath-extracted information demonstrated an overall accuracy of 93% in categorizing reports based on their corresponding guideline-based risk of recurrence: 76.9% for high-risk, 86.8% for intermediate risk, and 100% for both low and very low-risk cases. However, ThyroPath achieved 100% accuracy across all risk categories with human extracted pathology information.

CONCLUSIONS

ThyroPath shows promise in automating the extraction and risk recurrence classification of thyroid pathology reports at large scale. It offers a solution to laborious manual reviews and advancing virtual registries. However, it requires further validation before implementation.

Descriptive Analysis of Common Fusion Mutations in Papillary Thyroid Carcinoma in Hungary

Thyroid cancer is the most common type of endocrine malignancy. Papillary thyroid carcinoma (PTC) is its predominant subtype, which is responsible for the vast majority of cases. It is true that PTC is a malignant tumor with a very good prognosis due to effective primary therapeutic approaches such as thyroidectomy and radioiodine (RAI) therapy. However, we are often required to indicate second-line treatments to eradicate the tumor properly. In these scenarios, molecular therapies are promising alternatives, especially if specifically targetable mutations are present. Many of these targetable gene alterations originate from gene fusions, which can be found using molecular diagnostics like next-generation sequencing (NGS). Nonetheless, molecular profiling is far from being a routine procedure in the initial phase of PTC diagnostics. As a result, the mutation status, except for BRAF V600E mutation, is not included in risk classification algorithms either. This study aims to provide a comprehensive analysis of fusion mutations in PTC and their associations with clinicopathological variables in order to underscore certain clinical settings when molecular diagnostics should be considered earlier, and to demonstrate yet unknown molecular-clinicopathological connections. We conducted a retrospective fusion mutation screening in formalin-fixed paraffin-embedded (FFPE) PTC tissue samples of 100 patients. After quality evaluation by an expert pathologist, RNA isolation was performed, and then NGS was applied to detect 23 relevant gene fusions in the tumor samples. Clinicopathological data were collected from medical and histological records. To obtain the most associations from the multivariate dataset, we used the d-correlation method for our principal component analysis (PCA). Further statistical analyses, including Chi-square tests and logistic regressions, were performed to identify additional significant correlations within certain subsets of the data. Fusion mutations were identified in 27% of the PTC samples, involving nine distinct genes: RET, NTRK3, CCDC6, ETV6, MET, ALK, NCOA4, EML4, and SQSTM1. RET and CCDC6 fusions were associated with type of thyroidectomy, RAI therapy, smaller tumor size, and history of Hashimoto's disease. NCOA4 fusion correlated with sex, multifocality, microcarcinoma character, history of goiter, and obstructive pulmonary disease. EML4 fusion was also linked with surgical procedure type and smaller tumor size, as well as the history of hypothyroidism. SQSTM1 fusion was associated with multifocality and a medical history of thyroid/parathyroid adenoma. NTRK3 and ETV6 fusions showed significant associations with Hashimoto's disease, and ETV6, also with endometriosis. Moreover, fusion mutations were linked to younger age at the time of diagnosis, particularly the fusion of ETV6. The frequent occurrence of fusion mutations and their associations with certain clinicopathological metrics highlight the importance of integrating molecular profiling into routine PTC management. Early detection of fusion mutations can inform surgical decisions and therapeutic strategies, potentially improving clinical outcomes.

Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study

PURPOSE

The objective of this study was to train machine learning models for predicting the likelihood of recurrence in patients diagnosed with well-differentiated thyroid cancer. While thyroid cancer mortality remains low, the risk of recurrence is a significant concern. Identifying individual patient recurrence risk is crucial for guiding subsequent management and follow-ups.

METHODS

In this prospective study, a cohort of 383 patients was observed for a minimum duration of 10 years within a 15-year timeframe. Thirteen clinicopathologic features were assessed to predict recurrence potential. Classic (K-nearest neighbors, support vector machines (SVM), tree-based models) and artificial neural networks (ANN) were trained on three distinct combinations of features: a data set with all features excluding American Thyroid Association (ATA) risk score (12 features), another with ATA risk alone, and a third with all features combined (13 features). 283 patients were allocated for the training process, and 100 patients were reserved for the validation of stage.

RESULTS

The patients' mean age was 40.87 ± 15.13 years, with a majority being female (81%). When using the full data set for training, the models showed the following sensitivity, specificity and AUC, respectively: SVM (99.33%, 97.14%, 99.71), K-nearest neighbors (83%, 97.14%, 98.44), Decision Tree (87%, 100%, 99.35), Random Forest (99.66%, 94.28%, 99.38), ANN (96.6%, 95.71%, 99.64). Eliminating ATA risk data increased models specificity but decreased sensitivity. Conversely, training exclusively on ATA risk data had the opposite effect.

CONCLUSIONS

Machine learning models, including classical and neural networks, efficiently stratify the risk of recurrence in patients with well-differentiated thyroid cancer. This can aid in tailoring treatment intensity and determining appropriate follow-up intervals.

ATA risk stratification in papillary thyroid microcarcinoma has low positive predictive value when identifying recurrence

BACKGROUND

Rising incidence of papillary thyroid microcarcinomas (PTMC) has raised concerns for overdiagnosis. Utility of the American Thyroid Association Risk Stratification System (ATA-RSS) 2015 in predicting risk of disease recurrence in patients with PTMC was assessed.

METHODS

Electronic health records of patients who underwent total thyroidectomy were queried. ATA-RSS 2015 risk stratification was performed on those with PTMC, and validity for predicting disease recurrence was calculated.

RESULTS

With 10-year median follow up, recurrence was higher in PTMC patients with high/intermediate vs low ATA risk (33 ​% vs 4 ​%, p ​= ​0.002). Sensitivity of ATA-RSS for detecting recurrence was 60 ​%, specificity 90 ​%, PPV 33.3 ​%, NPV 96.6 ​%, and accuracy 88 ​%. When microscopic extrathyroidal extension (ETE) was excluded as an intermediate risk criterion, PPV improved to 50 ​% and accuracy improved to 92.5 ​% CONCLUSIONS: ATA-RSS 2015 predicts recurrence in PTMC with high NPV but low PPV. Exclusion of microscopic ETE improved PPV, which may help prevent overtreatment.

Prognostic Factors Improving ATA Risk System and Dynamic Risk Stratification in Low- and Intermediate-Risk DTC Patients

CONTEXT

American Thyroid Association (ATA) guidelines do not consider age at diagnosis as a prognostic factor on the estimation of the risk of persistent/recurrent disease in differentiated thyroid carcinoma (DTC) patients. While age at diagnosis has already been assessed in high-risk patients, it remains to be established in low- and intermediate-risk patients.

OBJECTIVE

The aim of our study was to investigate the role of age as a prognostic factor in the short- and long-term outcome of DTC patients classified at low and intermediate risk according to the ATA stratification risk system.

METHODS

We retrospectively evaluated 863 DTC patients (mean follow-up: 10 ± 6.2 years) 52% classified as low (449/863) and 48% as intermediate risk (414/863). For each ATA-risk class patients were divided into subgroups based on age at diagnosis (<55 or ≥55 years).

RESULTS

In the intermediate-risk group, patients aged 55 years or older had a higher rate of structural disease (11.6% vs 8.9%), recurrent disease (4.1% vs 0.7%), and death (4.1% vs 1%) when compared with younger patients (<55 years) (P = .007). Multivariate analysis confirmed that older age at diagnosis (odds ratio [OR] = 3.9; 95% CI, 1.9-8.6; P < .001) was an independent risk factor for worse long-term outcome together with response to initial therapy (OR = 13.0; 95% CI, 6.3-27.9; P < .001), and T (OR = 32; 95% CI, 1.4-7.1; P = .005) and N category (OR = 2.3; 95% CI, 1.1-5.0; P = .03). Nevertheless, a negative effect of older age was documented only in the subgroup of intermediate DTC patients with persistent structural disease after initial therapy. Indeed, the rate of worse long-term outcome rose from 13.3% in the whole population of intermediate DTC patients to 47.8% in patients with persistent structural disease after initial therapy (P < .001) and to 80% in patients older than 55 years and persistent structural disease after initial therapy (P = .02).

CONCLUSION

Our results suggest that age at diagnosis further predict individual outcomes in Intermediate-Risk DTC allowing ongoing management to be tailored accordingly.

Prospective Validation of ThyroSPEC Molecular Testing of Indeterminate Thyroid Nodule Cytology Following Diagnostic Pathway Optimization

Background: Molecular testing for cytologically indeterminate thyroid nodules (ITNs) is often reported with incomplete data on clinical assessment and ultrasound malignancy risk (USMR) stratification. This study aimed to clinically validate the diagnostic accuracy of a novel molecular test, assess the incremental preoperative malignancy risk of other clinical factors, and measure the impacts of introducing molecular testing at the population level. Methods: Comprehensive clinical data were collected prospectively for the first 615 consecutive patients with ITNs in a centralized health care system following implementation of a reflexive molecular test. Clinical data include patient history, method of nodule discovery, clinical assessment, USMR, cytology, molecular testing, and surgery or follow-up along with surgeon notes on surgical decision-making. Accuracy of molecular testing and the impact of the introduction of molecular testing were calculated. A multivariable regression model was developed to identify which clinical factors have the most diagnostic significance for ITNs. Results: A locally developed, low-cost molecular test achieved a negative predictive value (NPV) of 76-91% [confidence interval, CI 66-95%] and a positive predictive value (PPV) of 46-65% [CI 37-75%] in ITNs using only residual material from standard liquid cytology fine-needle aspiration (FNA). Sensitivity was highest (80%; [CI 63-92%]) in the American Thyroid Association (ATA) intermediate-suspicion ultrasound category, and lowest (46%; [CI 19-75%]) in the ATA high-suspicion ultrasound category. Following implementation of molecular testing, diagnostic yield increased by 14% (p = 0.2442) and repeat FNAs decreased by 24% (p = 0.05). Mutation was the primary reason for surgery in 76% of resected, mutation-positive patients. High-risk mutations were associated with a 58% (p = 0.0001) shorter wait for surgery. Twenty-six percent of patients with a negative molecular test result underwent surgery. Multivariable regression highlighted molecular testing and USMR as significantly associated with malignancy. Conclusions: Molecular testing improves preoperative risk stratification but requires further stratification for intermediate-risk mutations. Incorporation of clinical factors (especially USMR) with molecular testing may increase the sensitivity for detection of malignancy. Introduction of molecular testing offers some clinical benefits even in a low resection rate setting, and directly influences surgical decision-making. This study illustrates the importance of the local diagnostic pathway in ensuring appropriate integrated use of molecular testing for best outcomes.

How do BRAFV600E and TERT promoter mutations interact with the ATA and TNM staging systems in thyroid cancer?

Context

The American Thyroid Association risk stratification (ATA) and the American Joint Committee on Cancer Tumor Node Metastases (TNM) predict recurrence and mortality of differentiated thyroid cancer (DTC). BRAFV600E and TERT promoter mutations have been shown to correlate with the histopathological features and outcome of DTC. Our objectives were to study the correlation of these molecular markers with these clinicopathological-staging systems.

Patients and methods

We studied 296 unselected patients, 214 females and 82 males with a median age of 36 years (IQR 23.3-49.0). BRAFV600E and TERT promoter mutations were tested by PCR-based Sanger sequencing. Data were extracted from medical records and analysed using Chi-Square and Fisher Exact tests and Kaplan Meier analysis.

Results

Of 296 patients tested, 137 (46.3%) had BRAFV600E-positive tumors and 72 (24.3%) were positive for TERT promoter mutations. The BRAFV600E mutation did not correlate with the ATA and TNM staging, being non-significantly different in various stages of these systems and did not predict the development of persistent disease (PD) (P 0.12). Unlike BRAFV600E, TERT promoter mutations were more frequent in the ATA high-risk than in intermediate- or low-risk tumors (P 0.006) and in TNM stages III and IV than lower stages (P <0.0001). TERT promoter mutations also predicted the outcome, being present in 37.2% of patients with PD compared to only 15.4% in those without evidence of disease (P <0.0001). The same pattern was also seen when BRAFV600E and TERT promoter mutations were combined.

Conclusion

TERT promoter mutations alone or in combination with BRAFV600E mutation, but not BRAFV600E mutation alone, correlated well with the ATA and TNM staging and predicted development of PD, especially in higher stages of these systems.