A simple four-variable nomogram integrating age, ACR-TIRADS, BRAF V600E and Bethesda cytology for predicting occult central lymph node metastasis in papillary thyroid microcarcinoma: a preoperative risk-stratification tool

Introduction

Papillary thyroid microcarcinoma (PTMC), defined as papillary thyroid carcinoma (PTC) with a maximum diameter of ≤10 mm, has emerged as the most rapidly increasing subtype of thyroid malignancy worldwide (1,2). Improvements in high-resolution ultrasound and widespread screening have dramatically increased PTMC detection, with PTMCs now accounting for a substantial proportion of newly diagnosed PTCs in many Asian centers (3-6). Despite their typically indolent behavior, central lymph node metastases (CLNMs) occur in 20–65% of PTMC patients (7,8), creating substantial controversy regarding optimal management.

The 2015 American Thyroid Association (ATA) guidelines first endorsed active surveillance as an alternative to immediate surgery for selected low-risk PTMC, based on landmark prospective cohorts demonstrating excellent long-term outcomes with non-operative management (9-11).However, the same guidelines state that prophylactic central neck dissection may be considered in selected cN0 PTC patients with advanced primary tumors (T3/T4), clinically involved lateral neck nodes (cN1b), or when nodal information would guide further therapy (12). This juxtaposition creates an acute clinical dilemma: how to distinguish PTMC patients who can safely undergo active surveillance from those who harbor occult CLNM and would benefit from immediate surgery with CLND. The challenge is particularly pronounced in younger patients (<45 years), who have longer life expectancy and for whom the consequences of either over- or under-treatment are most consequential (13,14).

Existing clinical and ultrasonographic nomograms for PTMC/CLNM have shown variable, generally moderate-to-good discrimination across cohorts (15-19). The addition of ultrasound features classified by the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) (20-22) improves performance modestly. Recently, ultrasound radiomics approaches have achieved higher discrimination, with reported AUCs up to approximately 0.92 (23,24), but require specialized image-processing infrastructure that limits broad clinical deployment. Despite these advances, no existing model has integrated pre-operatively obtainable molecular and cytological markers—specifically BRAF V600E mutation status [from fine-needle aspiration (FNA)] and the Bethesda cytopathological classification—into a unified, clinically practical predictive framework.

This omission is notable given the substantial biological information these markers carry. BRAF V600E is the most common driver mutation in PTC and has been associated with more aggressive tumor behavior in the general PTC population (25,26). Bethesda categories V (suspicious for malignancy) and VI (malignant) provide pre-operative pathological grading that reflects cytomorphologic atypia (27). A recent 2024 PTMC study by Qiu et al. (28) noted that genetic mutations were not included and that preoperative FNA genotypes should be incorporated into future prediction models. This represents a clear and actionable gap in current research.

In this study, we aimed to develop and internally validate a simple, clinically deployable four-variable model integrating pre-operatively obtainable demographic, ultrasound, molecular, and cytological information for predicting occult CLNM in PTMC. We hypothesized that the addition of BRAF V600E status and Bethesda category to age and ACR-TIRADS would substantively improve risk discrimination over conventional clinico-sonographic models, particularly in the clinically critical subgroup of patients younger than 45 years. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2026-0352/rc) (29).

Methods

Study design and ethics

This was a single-center, retrospective observational study conducted at the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine (Fujian Provincial Second People’s Hospital), Fuzhou, China, between March 2020 and December 2022. The study protocol was reviewed and approved by the Institutional Review Board of the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine (No. SPHFJP-Y2025044-01). The requirement for informed consent was waived due to the retrospective nature of the study and the use of anonymized data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Patient selection

Patients with histopathologically confirmed PTC who underwent thyroidectomy with prophylactic or therapeutic CLND at our institution between March 2020 and December 2022 were screened for eligibility. Inclusion criteria were: (I) maximum tumor diameter ≤10 mm on pre-operative ultrasound (defining PTMC); (II) availability of pre-operative FNA cytology with Bethesda classification and BRAF V600E mutation testing; (III) standardized pre-operative ultrasound examination using a single GE HealthCare ultrasound system with a high-frequency linear probe (12 MHz); and (IV) complete histopathological reporting of CLND.

Exclusion criteria were: (I) prior thyroid ablation or radioactive iodine therapy; (II) coexisting non-papillary thyroid malignancy (e.g., medullary, anaplastic, or follicular carcinoma); (III) absence of central neck dissection precluding definitive CLNM determination; and (IV) missing key pre-operative variables.

Of an initial cohort of 266 patients, 5 were excluded due to missing key pre-operative variables, leaving 261 eligible patients. After applying the PTMC criterion (tumor size ≤10 mm), 131 patients with larger tumors were excluded, yielding the final analytic cohort of 130 patients (Figure 1).

Figure 1 Study flow diagram. From an initial cohort of 266 PTC patients, five were excluded for missing critical pre-operative variables and 131 for tumor size >10 mm on pre-operative ultrasound, yielding a final PTMC cohort of 130 patients. CLNM was confirmed in 73 patients (56.2%). The final Model E achieved a 5-fold cross-validated AUC of 0.686. Exploratory subgroup analyses with bootstrap resampling showed wide confidence intervals in patients younger than 45 years and in BRAF wild-type patients; in the latter subgroup BRAF is constant and the model reduces to three variables. Model E denotes the four-variable logistic-regression model integrating age, ACR-TIRADS category, BRAF V600E status, and Bethesda category. ACR-TIRADS, American College of Radiology Thyroid Imaging Reporting and Data System; AUC, area under the curve; CI, confidence interval; CLNM, central lymph node metastasis; PTC, papillary thyroid carcinoma; PTMC, papillary thyroid microcarcinoma; TCM, traditional Chinese medicine; US, ultrasound.

Variable definitions

Only pre-operatively obtainable variables were included as candidate predictors. Any post-operative information (e.g., Ki-67 index, definitive tumor capsule invasion status on permanent section) was strictly excluded to avoid temporal data leakage.

Clinical variables

Age (years), sex, and the presence of Hashimoto thyroiditis [HT, defined by serological criteria including elevated anti-thyroid peroxidase antibody (TPOAb) and/or anti-thyroglobulin antibody (TgAb) together with characteristic sonographic features] were extracted from electronic medical records. Pre-operative serum thyroid-stimulating hormone (TSH), TgAb, and TPOAb levels were obtained within 30 days prior to surgery.

Ultrasound variables

All pre-operative thyroid ultrasound examinations were performed using a single GE HealthCare ultrasound system (LOGIQ E NextGen, GE HealthCare, RRID: SCR_000985, USA) equipped with a high-frequency linear probe (12 MHz). Nodules were classified according to the ACR-TIRADS (2017 version) by board-certified ultrasonologists (20). Maximum tumor diameter was measured on the standard transverse plane.

Cytological and molecular variables

All patients underwent pre-operative ultrasound-guided FNA performed by experienced clinicians. FNA specimens were independently evaluated by certified cytopathologists and classified according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) (27). For BRAF V600E mutation analysis, cellular material from the FNA aspirate was tested using real-time PCR amplification refractory mutation system (ARMS-PCR). Both Bethesda classification and BRAF testing were performed pre-operatively on FNA material and were available before surgical decision-making.

Outcome variable

The primary outcome was the presence of CLNM, defined as histopathologically confirmed metastatic involvement of at least one lymph node in the central compartment (Level VI) of the neck. CLNM status was coded as binary (0 = negative, 1 = positive).

Statistical analysis

Continuous variables were presented as mean ± standard deviation (SD) or median [interquartile range (IQR)] based on the Shapiro-Wilk normality test. Categorical variables were presented as counts and percentages. Comparisons between CLNM-positive and CLNM-negative groups used Student’s t-test or Mann-Whitney U test for continuous variables, and chi-square test or Fisher’s exact test for categorical variables, as appropriate.

A multivariable logistic regression model (Model E) was constructed including four pre-operatively obtainable variables: age (continuous), ACR-TIRADS category, BRAF V600E status (mutated/wild-type), and Bethesda category. Model coefficients, odds ratios (ORs), and 95% confidence intervals (CIs) are reported. Internal validation used 5-fold stratified cross-validation. Model discrimination was assessed by AUC with 95% CI; calibration by calibration curves and the Hosmer-Lemeshow goodness-of-fit test; overall predictive accuracy by the Brier score (30). Decision curve analysis (DCA) was performed to evaluate net clinical benefit (31). A nomogram was constructed based on Model E coefficients as a visual decision-support tool.

Pre-specified subgroup analyses were conducted by age dichotomization (<45 vs. ≥45 years) and BRAF V600E status (mutated vs. wild-type). All statistical analyses were performed using Python 3.10 (scikit-learn 1.3, statsmodels 0.14, scipy 1.11). A two-sided P value <0.05 was considered statistically significant. To address the robustness of variable coding and model specification, we conducted several pre-specified sensitivity analyses: (I) collapsing the Bethesda category into a binary variable (VI vs. III–V) to address sparse-cell instability; (II) re-estimating the model after excluding BRAF V600E to quantify its incremental contribution; (III) adding TSH to the four-variable model to assess whether its univariable association persisted after adjustment; (IV) repeating the age-stratified analysis using an alternative cut-point of 55 years [consistent with the American Joint Committee on Cancer (AJCC) 8th-edition staging threshold] in addition to the primary 45-year cut-point; and (V) deriving bootstrap percentile 95% CIs (2,000 resamples) for the subgroup AUC estimates to reflect their small-sample uncertainty.

Results

Patient characteristics

A total of 130 patients with PTMC were included in the final analysis (Figure 1). The cohort consisted of 99 (76.2%) women, with a mean age of 46.4±11.9 years (range, 16–75 years). Sixty patients (46.2%) were younger than 45 years. The mean tumor diameter on pre-operative ultrasound was 6.7±1.7 mm. Of the 130 patients, 73 (56.2%) had histopathologically confirmed CLNM, while 57 (43.8%) did not. The two groups were balanced with respect to sex, HT, tumor size, and tumor location (all P>0.10; Table 1).

In univariable comparison, CLNM-positive patients were significantly younger than CLNM-negative patients (44.1±12.4 vs. 49.4±10.6 years, P=0.01). The ACR-TIRADS distribution differed between groups: 90.4% of CLNM-positive patients had TR5 nodules compared with 73.7% of CLNM-negative patients (P=0.02). BRAF V600E mutation was present in 73.1% (95/130) of all patients, with a numerically higher prevalence in the CLNM-negative group (80.7% vs. 67.1%, P=0.12). Bethesda VI cytology was more frequent in CLNM-positive patients (9.6% vs. 1.8%, P=0.10).

Univariable analysis

Univariable logistic regression identified three pre-operative variables significantly associated with CLNM at P<0.05 (Table 2): age (OR =0.961 per year, 95% CI: 0.932–0.991, P=0.01), ACR-TIRADS category (OR =3.367 per category increase, 95% CI: 1.268–8.946, P=0.01), and TSH (OR =1.579, 95% CI: 1.056–2.362, P=0.03). BRAF V600E (OR =0.488, P=0.09) and Bethesda category (OR =6.270, P=0.08) showed borderline associations with CLNM. Notably, the OR <1 for BRAF V600E indicated a paradoxical inverse association in this PTMC cohort. Tumor size, sex, HT, and antibody levels (TgAb, TPOAb) were not significantly associated with CLNM.

Multivariable model (Model E)

We constructed a multivariable logistic regression model (Model E) including four pre-operatively obtainable variables: age, ACR-TIRADS, BRAF V600E status, and Bethesda category (Table 3). Age was inversely associated with CLNM (adjusted OR =0.954 per year, 95% CI: 0.923–0.987, P=0.006), confirming that younger patients had a higher risk of CLNM. ACR-TIRADS demonstrated the strongest positive association (adjusted OR =3.484 per category, 95% CI: 1.259–9.645, P=0.02). BRAF V600E retained a borderline inverse association (adjusted OR =0.431, 95% CI 0.174–1.066, P=0.07), and Bethesda showed a borderline positive association (adjusted OR =6.815, 95% CI: 0.837–55.504, P=0.07).

Model E achieved a 5-fold cross-validated AUC of 0.686, with a Brier score of 0.215. Stepwise addition of variables showed: M1 (age only), AUC 0.638; M2 (+ ACR-TIRADS), AUC 0.673; M3 (+ BRAF V600E), AUC 0.677; and Model E (+ Bethesda), AUC 0.686.

Subgroup analysis

Pre-specified but exploratory subgroup analyses are summarised in Figure 2A. Because the subgroups are small, we derived non-parametric bootstrap percentile CIs (2,000 resamples) rather than relying on asymptotic intervals, which would be implausibly narrow at these sample sizes. Apparent discrimination was numerically higher in patients younger than 45 years (n=60; bootstrap median AUC 0.760, 95% CI: 0.574–0.888) than in patients aged 45 years or older (n=70; 0.599, 95% CI: 0.418–0.727), in whom the CI includes 0.5 and the model is therefore essentially non-discriminatory. In BRAF wild-type PTMC (n=35; CLNM-positive 24/35), the bootstrap median AUC was 0.864 (95% CI: 0.705–0.977); we emphasise that BRAF status is constant within this subgroup, so the model reduces to three effective variables. In BRAF V600E-mutated patients (n=95), discrimination was lower (0.600, 95% CI: 0.446–0.725). All subgroup CIs are wide and mutually overlapping; these estimates are hypothesis-generating, are equally compatible with overfitting within small strata, and require external validation before any subgroup-specific clinical inference is drawn. The stepwise ROC curves for M1 through Model E are shown in Figure 2B.

Figure 2 Subgroup analysis and stepwise ROC curves of Model E. (A) Forest plot of bootstrap-derived AUC with 95% percentile confidence intervals (2,000 resamples) for pre-specified but exploratory subgroups. ^†, the two subgroups showing numerically higher apparent discrimination than the overall cohort; these estimates are hypothesis-generating. (B) Stepwise ROC curves showing incremental contribution of each variable to Model E: M1 (age only, AUC 0.638), M2 (+ ACR-TIRADS, AUC 0.673), M3 (+ BRAF V600E, AUC 0.677), and Model E (+ Bethesda, AUC 0.686). Model E denotes the four-variable logistic-regression model integrating age, ACR-TIRADS category, BRAF V600E status, and Bethesda category. ACR-TIRADS, American College of Radiology Thyroid Imaging Reporting and Data System; AUC, area under the curve; CI, confidence interval; CV, cross-validation; PTMC, papillary thyroid microcarcinoma; ROC, receiver operating characteristic.

Model calibration, clinical utility, and nomogram

Model E showed acceptable calibration. The Hosmer-Lemeshow goodness-of-fit test was non-significant (P=0.43), and the calibration curve aligned closely with the ideal diagonal across the predicted probability range (Figure 3A). DCA demonstrated that Model E provided net clinical benefit across threshold probabilities ranging from approximately 0.31 to 0.95, exceeding both the “treat all” and “treat none” strategies in this clinically relevant range (Figure 3B). A nomogram based on Model E coefficients was constructed to allow direct estimation of individual CLNM probability from the four input variables (Figure 3C). Sensitivity analyses supported the robustness of the primary model while clarifying the limited role of BRAF V600E. Collapsing Bethesda to a binary variable yielded essentially unchanged discrimination (cross-validated AUC 0.696 vs. 0.686). Excluding BRAF V600E reduced the cross-validated AUC only marginally (0.688 vs. 0.696; ΔAUC 0.008), indicating that BRAF contributed negligibly to overall discrimination. Adding TSH did not improve performance (AUC 0.691). Using the alternative age cut-point of 55 years, discrimination in the younger subgroup was attenuated (age <55 cross-validated AUC 0.731) compared with the 45-year threshold (age <45 years: AUC 0.769; age ≥45 years: AUC 0.549), supporting 45 years as the more discriminative threshold in this cohort. Bootstrap resampling of the subgroup AUCs yielded wide CIs (age <45 years: median AUC 0.765, 95% CI: 0.595–0.881; BRAF wild-type: median AUC 0.867, 95% CI: 0.709–0.974), underscoring that these subgroup estimates are exploratory and hypothesis-generating. Full sensitivity-analysis results are provided in Table S1.

Figure 3 Calibration, decision curve analysis, and nomogram of Model E. (A) Calibration plot showing acceptable agreement between predicted and observed CLNM probabilities (Hosmer-Lemeshow P=0.43; Brier score 0.215). (B) Decision curve analysis demonstrating positive net benefit of Model E across a clinically relevant range of threshold probabilities. (C) Visual nomogram for clinical use: each variable contributes points, with the total mapped to predicted CLNM probability. Model E denotes the four-variable logistic-regression model integrating age, ACR-TIRADS category, BRAF V600E status, and Bethesda category. ACR-TIRADS, American College of Radiology Thyroid Imaging Reporting and Data System; CLNM, central lymph node metastasis; CV, cross-validation; PTMC, papillary thyroid microcarcinoma.

Discussion

Principal findings

In this single-center retrospective study of 130 PTMC patients, we developed and internally validated a simple four-variable nomogram—integrating age, ACR-TIRADS category, BRAF V600E status, and Bethesda category—for predicting occult CLNM. Three key findings emerged: (I) the four-variable model (Model E) achieved overall cross-validated discrimination of AUC 0.686, comparable to previously reported clinico-sonographic models built on much larger cohorts; (II) in exploratory subgroup analyses, the model showed higher apparent discrimination in patients younger than 45 years and in BRAF wild-type patients, although these estimates derive from small subgroups with wide CIs and require external validation; and (III) BRAF V600E was not independently associated with CLNM in this PTMC cohort; although the point estimate was in the inverse direction (adjusted OR 0.431, P=0.07), it did not reach statistical significance and is best regarded as an exploratory, hypothesis-generating observation.

Integration of molecular and cytological markers

Our results provide direct evidence for the additive value of combining pre-operative molecular and cytological markers with conventional clinico-sonographic variables. Stepwise model construction showed a modest progressive increase in discrimination from age alone (AUC 0.638), to age + ACR-TIRADS (AUC 0.673), to the addition of BRAF V600E (AUC 0.677), and finally Bethesda (AUC 0.686). The cumulative increase from M1 to Model E was 0.048 AUC units (approximately 0.05). To our knowledge, this is the first PTMC CLNM prediction model to jointly integrate BRAF V600E mutation status and Bethesda cytological classification as pre-operative predictors, directly addressing the gap highlighted by Qiu et al. (28), who noted that genetic mutations were not included and recommended incorporation of preoperative FNA genotypes in future prediction models.

The paradoxical inverse association of BRAF V600E in PTMC

One of the most noteworthy findings of our study is the inverse association between BRAF V600E mutation and CLNM within the PTMC subset (adjusted OR =0.431, 95% CI: 0.174–1.066, P=0.07), a pattern that contrasts with the predominantly positive association reported in studies of unrestricted PTC populations (25,26,32). Biologically, the very high BRAF V600E prevalence observed in our PTMC cohort (73.1%) is consistent with reports of high BRAF V600E prevalence in Han Chinese PTC populations (33). When a mutation becomes nearly ubiquitous within a tumor subtype, its capacity to discriminate aggressive from indolent phenotypes is necessarily attenuated. More broadly, TERT promoter mutations, particularly in conjunction with BRAF V600E, have been associated with aggressive PTC behavior (34-36). However, the molecular basis of the BRAF wild-type subgroup in our cohort remains undefined. Future multi-center studies with extended molecular profiling are warranted to test this hypothesis. Notably, recent evidence reinforces that the prognostic value of BRAF V600E in low-risk PTMC remains debated, with inconsistent associations for tumors below 2 cm (37); and although a recent meta-analysis of over 20,000 unrestricted PTC patients confirmed a significant but modest BRAF-lymph node association (OR =1.38, 95% CI: 1.17–1.61), this effect may be attenuated or reversed within the BRAF-saturated PTMC subset (38). Recent PTMC nomograms have likewise been built predominantly on clinical, sonographic, or routine laboratory variables rather than on BRAF status (39,40).

Subgroup-specific performance: bridging the active surveillance debate

A clinically relevant observation is the numerically higher apparent performance of Model E in patients younger than 45 years (bootstrap median AUC 0.760, 95% CI: 0.574–0.888) compared with older patients (0.599, 95% CI: 0.418–0.727). We stress that these CIs are wide and overlapping, and that this contrast is equally compatible with overfitting within small strata; it is therefore hypothesis-generating rather than established. This contrast is consequential because the active surveillance debate is most acute precisely in younger patients. Subsequent analyses of the original Japanese active surveillance cohorts have shown that younger patients (<40–45 years) experience higher rates of tumor enlargement, novel lymph node appearance, and progression to surgical intervention during observation (10,13,14). At the same time, the cumulative consequences of permanent thyroidectomy and lifelong levothyroxine replacement are most consequential in patients with the longest remaining life expectancy. A discriminative tool that performs well in patients younger than 45 years—where the choice between immediate surgery and active surveillance is most contested—therefore offers material clinical value (41).

Comparison with previous literature and the value of simplicity

Our model is intentionally simple, comprising only four variables, each routinely obtained during standard pre-operative workup. This simplicity contrasts with recent radiomics-based models that report higher AUCs (up to 0.92) (23,24,42) but require specialized image processing pipelines and methodological expertise that limit deployment outside tertiary referral centers. By demonstrating that a four-variable clinical–molecular–cytological model can achieve clinically meaningful discrimination—particularly in the subgroups of greatest clinical interest—our findings argue for a pragmatic approach to PTMC risk stratification that can be readily implemented at the point of care in any institution with FNA cytology and BRAF testing capability.

Strengths and limitations

The strengths of this study include the use of strictly pre-operative variables only (avoiding temporal data leakage), transparent 5-fold cross-validated discrimination assessment, comprehensive calibration and DCA evaluation, and pre-specified subgroup analyses targeting the clinically most relevant patient groups.

Several limitations warrant acknowledgment. First, this is a single-center retrospective study from a Chinese tertiary hospital; external validation in independent and ethnically diverse cohorts is essential before clinical implementation. Second, the sample size, although sufficient for stable estimation of a four-variable model under the events-per-variable (EPV) criterion (73 events, EPV =18) (43), limits power for detecting subtler effects in subgroup analyses. Third, ACR-TIRADS was extracted as the reported overall TR category rather than as the underlying 0–29 ACR feature score; only TR4 and TR5 nodules were represented, reflecting the malignancy-enriched surgical population. Fourth, TSH was univariably associated with CLNM (P=0.03) but did not retain independent significance in the multivariable model and was excluded for parsimony. Fifth, this study did not incorporate ultrasound radiomics features; future work integrating radiomics into the present framework is planned. Finally, the study was not designed to assess longitudinal outcomes such as disease recurrence or disease-specific survival. Sixth, and importantly, this cohort was drawn entirely from surgically treated patients who underwent CLND, yielding a CLNM prevalence (56.2%) substantially higher than that reported in unselected PTMC populations (20–40%). This spectrum bias limits direct generalizability to true active-surveillance candidates, and the model is therefore best positioned as a preoperative risk-stratification tool for patients already referred for surgical evaluation rather than as an active-surveillance triage instrument. Seventh, the cohort combined patients undergoing prophylactic and therapeutic CLND, and preoperative clinical nodal status (cN0 vs. cN1) was not systematically recorded; consequently, we cannot fully distinguish truly occult metastasis from clinically detectable nodal disease, and the term “occult” should be interpreted with this caveat. Eighth, the subgroup analyses were exploratory: the BRAF wild-type subgroup (n=35) effectively reduces the model to three variables, and bootstrap resampling yielded wide CIs (BRAF wild-type median AUC 0.867, 95% CI: 0.709–0.974; age <45 years median AUC 0.765, 95% CI: 0.595–0.881), so these estimates should be regarded as hypothesis-generating only. Consistent with this, the cohort was strongly enriched for higher-risk features: all nodules were ACR-TIRADS TR4 (16.9%) or TR5 (83.1%), and only a minority would plausibly have met conventional active-surveillance eligibility criteria; the observed CLNM prevalence (56.2%) far exceeds the 20–40% typically reported for surveillance-eligible PTMC, directly quantifying the spectrum bias.

Conclusions

We developed and internally validated a simple four-variable nomogram integrating age, ACR-TIRADS, BRAF V600E, and Bethesda cytology for pre-operative prediction of CLNM in PTMC. The model achieved an overall 5-fold cross-validated AUC of 0.686. In exploratory subgroup analyses, apparent discrimination was numerically higher in patients younger than 45 years and in BRAF wild-type cases, but with wide, overlapping bootstrap CIs; these subgroup estimates are hypothesis-generating and require external validation. The nomogram is parsimonious, interpretable, and uses only variables routinely obtained in standard pre-operative care, supporting its potential utility as a decision-support tool for individualized PTMC management. External multi-center validation is the necessary next step.

Acknowledgments

The authors thank the surgical pathology and clinical teams of the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine (Fujian Province Second People’s Hospital) for their dedicated work in specimen processing and pathological reporting, and all patients whose anonymized clinical data made this research possible.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-0352/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-0352/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2026-0352/prf

Funding: This work was supported by the program “Young and Middle-aged Medical and Health Talents of Fujian Province for Overseas Further Study”.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2026-0352/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine (No. SPHFJP-Y2025044-01) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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