---
title: "Ultrasound Radiomics for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thy"
id: "frontiers-in-immunology-15-noninvasive-immune-inflammatory-profiling-by-ultrasound-radiomics-for"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-noninvasive-immune-inflammatory-profiling-by-ultrasound-radiomics-for"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1912827"
published_at: "2026-09-18T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Ultrasound Radiomics for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thy
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-noninvasive-immune-inflammatory-profiling-by-ultrasound-radiomics-for
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1912827)
- **Published At:** 2026-09-18T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The article title reports use of **ultrasound radiomics** for noninvasive immune-inflammatory profiling to predict **central lymph node metastasis (CLNM)** in **papillary thyroid carcinoma (PTC)** prior to surgery. - The approach is described as **preoperative** and **noninvasive**, emphasizing imaging-derived radiomic analysis rather than tissue biopsy. - The phrasing indicates a focus on immune‑inflammatory features detectable or inferred through ultrasound radiomics, applied to assessing nodal metastatic risk in PTC. - The source text provided here contains only website navigation and the article title; specific study design, patient population, imaging protocols, radiomic features, analytical methods, model performance metrics, validation, clinical implications, and conclusions were not reported in the provided source extract. - Because detailed methods and results are not present in the supplied content, no outcome data, statistical results, or recommendations can be summarized from this source.
## Clinical Analysis & Structured Key Points
Frontiers | Noninvasive immune-inflammatory profiling by ultrasound radiomics for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma ORIGINAL RESEARCH article Front. Immunol. , 18 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1912827 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Editor & Reviewers Edited by Z J Zebo Jiang Reviewed by X F Xin Fan K B Kangkana Baishya Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article Figure 8 View in article Table 1 Baseline characteristics of the study cohorts. View in article Table 2 Diagnostic metrics and incremental validation of the four progressive models. View in article Table 3 BRAF-stratified Spearman correlations between DLR-score and immune-inflammatory indices. View in article Table 4 Covariate-adjusted partial Spearman correlations between DLR-score and immune-inflammatory indices stratified by BRAF status. View in article ORIGINAL RESEARCH article Front. Immunol. , 18 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1912827 Noninvasive immune-inflammatory profiling by ultrasound radiomics for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma M S Meng Sun 1 * J G Jiening Gao 1 Q S Qian Sui 1,2 M L Meng Li 1 Y W Yi Wang 1 R H Ruoling Han 1 * 1. Department of Ultrasound, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China 2. Department of Clinical Laboratory, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China Article metrics View details Abstract Background: Central lymph node metastasis in clinically node-negative papillary thyroid carcinoma is regulated by the tumor immune microenvironment, yet existing studies have addressed molecular, systemic immune, and local imaging signals in isolation without examining their cross-scale associations. Methods: A retrospective cohort of 1,000 cN0 PTC patients was enrolled. BRAF V600E mutation status, six peripheral blood immune-inflammatory indices, and an ultrasound radiomics deep learning score were integrated into four progressive LightGBM models. Cross-scale Spearman correlations, BRAF-stratified Fisher z-transformation tests, and TCGA-THCA immune deconvolution analysis were performed. Results: The three-scale integrated model achieved a validation AUC of 0.824, with all DeLong incremental comparisons reaching statistical significance. The DLR-score was significantly correlated with all six immune-inflammatory indices after Bonferroni correction. BRAF-positive subgroup exhibited significantly stronger imaging-immune coupling for NLR and SII, and this enhanced coupling persisted after adjustment for age, tumor size, multifocality, and Hashimoto’s thyroiditis in partial correlation analyses. TCGA tissue immune infiltration patterns were directionally consistent with peripheral observations. Calibration assessment confirmed probability accuracy across the predicted range, and decision curve analysis demonstrated sustained net benefit superiority of the integrated model over single-scale alternatives within the clinically relevant 10% to 50% threshold interval. Conclusion: Three-scale noninvasive immune signals provide complementary CLNM prediction and exhibit directionally consistent cross-scale statistical associations at the correlation level, suggesting that they may serve as noninvasive surrogates reflecting partially overlapping aspects of the same tumor immune dysregulation process, although confirmation of the underlying biological mechanisms requires prospective studies incorporating direct tissue-level immune profiling. 1 Introduction Papillary thyroid carcinoma (PTC) is the most common type of thyroid malignancy worldwide, characterized by a constantly rising incidence rate, placing it in the topmost causes of cancer amongst women ( 1 ). Despite the generally favorable prognosis of PTC, central lymph node metastasis (CLNM) remains a core risk factor for recurrence and reoperation, occurring at a covert rate of 30% to 60% in patients whose preoperative ultrasound assessment indicates clinically node-negative (cN0) status ( 2 ). In the body of research obtained through clinical trials and molecular studies, CLNM can be understood not only in terms of spread-related reasons but also from a biological perspective linked to the tumor microenvironment immunology. In this context, the BRAF V600E mutation promotes immune escape by triggering MAPK signaling and is highly correlated with increased risk of lymph node metastasis ( 3 ). Immune dysregulation at a molecular level is similarly expressed by the body’s systemic circulation. Immune-inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII), have been found to be independently correlated with the risk for developing lymph node metastasis in patients suffering from PTC ( 4 ). A systematic review with network meta-analysis encompassing multiple inflammatory biomarkers has confirmed the independent predictive value of these indices in PTC prognostic assessment ( 5 ). A systematic review and meta-analysis restricted to differentiated thyroid carcinoma pooled 7,599 patients and found no significant association between preoperative NLR, LMR, PLR and disease-free survival, indicating that the prognostic value of these indices across populations remains contested ( 6 ). Other types of thyroid cancers have also displayed promising biomarker activity when it comes to these immune-inflammatory markers ( 7 ), while new research has highlighted the potential for using pre-operative inflammation biomarkers as predictors of central metastasis in patients diagnosed with cN0 PTC ( 8 ). In addition, patients affected by PTC who also suffer from Hashimoto’s thyroiditis show clear differences in metastatic tendencies depending on their specific immune condition ( 9 ), thus providing further support to the hypothesis about the involvement of the immune system host condition in PTC metastatic regulation. The collected multi-scale data suggests that mutations caused by BRAF, systemic immune-inflammatory disturbance, and tumor microenvironment remodeling might be different representations of a common immune process at different scales of biology. Regarding the technical aspect of imaging analysis using ultrasound radiomics, there is a wealth of information suggesting that prediction of metastasis in PTC can be performed with high sensitivity and specificity. This technology was first pioneered based on the validation of radiomic nomograms based on shear wave ultrasound and their additional value in assessing the staging of cervical lymph node staging ( 10 ). Afterwards, multi-modal ultrasound imaging fusions were used ( 11 ) along with machine learning methods that utilize the SHAP approach to interpret results ( 12 ). As for other imaging techniques, there is additional information that can be provided using contrast-enhanced ultrasound radiomics compared to traditional gray scale images ( 13 ). Furthermore, development of contrast-enhanced nomograms based on radiomic model has enabled expansion of the technological boundaries ( 14 ). The generalizability of radiomic signatures to various patient groups is confirmed by external validation performed in several institutes ( 15 ), whereas subgroup analysis provides further proof of the scores’ consistency in heterogeneous populations ( 16 ). The structured approach towards the development of prediction scenarios includes integration of clinical-radiomic models ( 17 – 19 ); accurate prognosis within subgroups of cN0 papillary thyroid microcarcinoma ( 20 – 22 ); fusion strategies using multimodal imaging ( 23 , 24 ); and independent investigation of metastatic spread into lateral neck compartments ( 25 ). These all serve as multidimensional clinical evidence that supports the utility of radiomics in preoperative management of PTC. For the development of technical paradigms, there is an emergence of methods such as end-to-end deep learning ( 26 ), super-resolution reconstruction techniques ( 27 ), automatic key-frame selection ( 28 ), and prediction via AI integrated multi-modal systems ( 29 ), thereby shifting the method of prediction from manual feature engineering to representation learning. Predictive targets have also become broader than the lymph node metastasis status prediction and have shifted toward evaluating the risk for recurrence ( 30 ) and classifying tumor aggression ( 31 ). Meanwhile, combining the two approaches on the dataset level ( 32 ), along with their verification through cross-modality using CT radiomics ( 33 , 34 ), has further widened the scope of usage for imaging biomarkers of PTC. Nevertheless, examining all of these developments through a unified perspective, one can identify an inherent structural problem with the current approach that involves both deep-learning-driven image segmentation ( 35 ) and multi-source feature fusion nomograms ( 36 ). However, the multi-source properties have consistently been conceived to represent parallel sources, arranged within classifiers ( 37 ), where the focus on engineering was on achieving maximum predictability while ignoring whether the signals obtained from different levels show any intrinsic biological connections. Based on the principles of immunology, correlating quantitative parameters of ultrasound phenotypes and tumor immune microenvironments are biologically plausible. Prior studies based on the foundations of radiomics have shown that quantitative analyses using medical imaging allow for the non-invasive decoding of tumor phenotypes through quantitative features that correspond to tumor heterogeneity ( 38 ). The “images are data” principle recognizes that medical imaging not only serves as diagnostic tools but also as high-dimensional data representing the tumor microenvironment ( 39 ). In the light of this theory, several studies conducted among different forms of cancers have revealed correlations quantitatively between radiomics and the conditions of the immune microenvironment. For instance, in breast cancer cases, there have been measurable correlations of radiomic features with the distribution patterns of immune cells within the tumors ( 40 ). Radiomic signature from MRI has shown potential ability to evaluate the infiltration of tumor-associated macrophages in glioma and predict the efficacy of immunotherapy ( 41 ). There have been systematic efforts recently towards establishing the mechanisms behind which AI contributes to precision immunotherapy using the combination of immunogenomics, radiomics, and pathomics ( 42 ). The aforementioned cross-cancer data provide theoretical justification for the core hypothesis that ultrasonic radiomics can be used to evaluate the immune-inflammatory profile of PTC. Nevertheless, in the current PTC literature, there is a lack of research efforts that have considered molecular and systemic as well as local noninvasive immune assessment. Based on the above background information of previous research and the existing academic gap, this study applied a retrospective cohort of 1,000 patients with cN0 PTC in the Fourth Hospital of Hebei Medical University along with a non-invasive tri-scale immune signal cross-corroboration framework. This system aimed to incorporate the BRAF V600E mutation test result, peripheral immune-inflammatory indices (NLR, PLR, LMR, SII, SIRI, and PIV), and an ultrasound radiomics deep learning score (DLR-score), which includes three scores for non-invasive immune-associated indicators, into an CLNM predictive model before surgery. It was also improved to measure the separate contribution of each scale through the progressive model design with four groups, alongside the DeLong incremental method. Furthermore, it was designed to assess the associations among cross-scales using the Spearman correlation analysis of radiomic and immune-inflammatory indicators, BRAF-stratified moderation effect assessment, and validation of tissue-immune infiltrates through TCGA-THCA dataset. As opposed to the existing paradigm that treats predictive performance optimization as the end goal, the key advance of the present study lies in shifting from a predictive focus to a relational one. The research investigates whether directionally consistent statistical associations exist among immune-related signals captured at different biological scales, rather than establishing direct mechanistic causation. Given the retrospective observational design, the cross-scale analyses are positioned as hypothesis-generating evidence intended to motivate subsequent prospective functional validation. The present approach is expected to provide a methodological reference for studying PTC metastasis from an immunological perspective and to offer preliminary multi-scale evidence that may inform individualized preoperative decision-making for cN0 PTC patients. 2 Materials and methods 2.1 Study population This was a single-center retrospective observational study approved by the Ethics Committee of the Fourth Hospital of Hebei Medical University (approval number 2023KY078). Since this is a retrospective study, informed consent was waived. The study enrolled patients who underwent surgery between January 2019 and June 2024 and met all of the following criteria: pathologically confirmed PTC, preoperative ultrasound assessment of cN0, thyroidectomy combined with central lymph node dissection, availability of a complete blood routine within one week before surgery, and retrievable preoperative grayscale thyroid ultrasound DICOM images from the PACS system. Exclusion criteria included those patients with other malignancies, neck surgery or radiation therapy before, any infection occurring in the previous two weeks before surgery, immunosuppressant drugs, hematologic disorders, and ultrasound images of inadequate quality. The number of records to be exported was 1,120 cases, out of which only 1,000 cases qualified based on the eligibility criteria. Based on the chronological order of the surgeries, 731 cases from January 2019 to December 2022 were selected for the training data, whereas 269 cases from January 2023 to June 2024 were deliberately held out as a temporal validation set. Temporal splitting was adopted in preference to random splitting because it more rigorously tests generalizability by exposing the model to patients from a later calendar period who may reflect secular trends in referral patterns, imaging protocols, and surgical practice, thereby mimicking the real-world clinical deployment scenario in which a model trained on historical data is applied to future patients. CLNM was defined as the presence of at least one positive lymph node in the central compartment (level VI) on postoperative paraffin-section pathology. Since 2020, the institute has regularly been performing BRAF V600E screening for PTC samples via real-time fluorescence-based PCR, yielding an 85.2% BRAF test coverage rate in the cohort (852 of 1,000 patients). The missingness of BRAF results was predominantly driven by the institutional adoption timeline of routine molecular testing rather than by patient-level clinical characteristics, constituting a mechanism-based missing pattern tied to calendar period. Given that BRAF V600E is a somatic tumor mutation whose status cannot be reliably predicted from routine clinical or hematological variables, standard multiple imputation was considered methodologically inappropriate for this context, as imputing a binary molecular marker from clinically unrelated predictors would introduce uncertain assumptions and potentially distort the very associations under investigation. For the predictive modeling framework, a missing indicator approach was adopted whereby patients with unavailable BRAF results were encoded as BRAF-negative in the mutation status variable together with a companion binary missing indicator set to 1, enabling the model to distinguish BRAF-negative patients from those with unknown status and to retain the full 1,000-patient cohort for training and validation without discarding cases or imputing unverifiable mutation status. For the BRAF-stratified cross-scale correlation analyses, which required definitive BRAF classification, a complete-case analytical strategy was adopted within the 852-patient sub-cohort with available test results (BRAF-positive n = 505, BRAF-negative n = 347). To further evaluate the contribution of BRAF information, a sensitivity analysis was performed using Model D reconstructed without either BRAF status or the missing indicator as input variables. The overall study design and analytical framework are illustrated in Figure 1 . Figure 1 Study design and analytical framework. 2.2 Clinical and immune-inflammatory variables The clinical variables were collected on the basis of the criteria set forth in the management guidelines provided by the American Thyroid Association in 2015 ( 43 ). These include age, gender, maximum tumor diameter (ultrasound-measured, mm), multifocality, capsular invasion, concurrent Hashimoto’s thyroiditis (pathologically determined), and ACR TI-RADS classification. BRAF V600E mutation status, acting as the molecular-level marker, was assessed using postoperative pathologic findings. Peripheral blood immune-inflammatory indices were calculated from routine complete blood counts (neutrophil count N, lymphocyte count L, monocyte count M, and platelet count PLT, all in ×10 9 /L) collected under standardized fasting morning conditions between 6:00 and 8:00 AM within seven days prior to surgery, thereby minimizing circadian and postprandial fluctuations in leukocyte subpopulations. Patients receiving systemic corticosteroids or immunosuppressive agents at the time of sampling were excluded per the eligibility criteria. Although overt infections within two weeks before surgery were excluded, subclinical chronic inflammatory conditions and baseline metabolic profiles such as diabetes mellitus, obesity, and dyslipidemia were not systematically recorded in the retrospective dataset and could not be adjusted for, representing a residual confounding source acknowledged in the limitations. Following the original definition of the systemic immune-inflammation index ( 44 ), six indices were computed as shown in Equations 1 , 2 : These indices were categorized into two tiers according to their compositional complexity and immunological interpretation. The three binary ratios (NLR, PLR, and LMR) each capture the balance between two circulating cell lineages and primarily reflect the equilibrium between innate pro-inflammatory effectors and adaptive immune surveillance at the systemic level. The three composite indices (SII, SIRI, and PIV) integrate three to four cell lineages into a single metric, providing a more comprehensive representation of the overall systemic immune-inflammatory burden. It should be noted that all six indices are derived from peripheral blood absolute counts of circulating leukocyte subsets and platelets, and as such they serve as surrogate markers of systemic broad-spectrum inflammation rather than direct indicators of local adaptive immunity, antigen-specific T cell responses, or tumor immune evasion mechanisms within the thyroid microenvironment. The rationale for their inclusion in the present framework is not to claim equivalence with tissue-level immune profiling, but to test the hypothesis that systemic immune-inflammatory perturbations captured by routine hematological parameters exhibit directionally consistent associations with local imaging heterogeneity features tha
## Related Clinical Research

- [Automated OCR extraction of genomic biomarkers (Oncotype DX) to reduce oncology data latency](https://medichelpline.com/clinical-feed/pubmed-42763830.md) (DOI: 10.1007/s10552-026-02252-y)
- [Inflammatory Biomarkers and Risk of Postmenopausal ER-Positive Breast Cancer](https://medichelpline.com/clinical-feed/british-journal-of-cancer-0-inflammatory-biomarkers-and-risk-of-postmenopausal-oestrogen-receptor-positive.md)
- [Pretrained gene representations transfer mean expression more than spatial patterns in virtual spa](https://medichelpline.com/clinical-feed/biorxiv-10-pretrained-gene-representations-transfer-mean-expression-more-broadly-than.md)
- [Pemetrexed Stabilizes BRCA1 and Enhances Radiosensitivity in Triple‑Negative Breast Cancer](https://medichelpline.com/clinical-feed/pubmed-42757486.md) (DOI: 10.3892/or.2026.9194)
- [Educating Patients on ‘Asian Glow’ and Alcohol Risks in East Asian Populations](https://medichelpline.com/clinical-feed/stat-news-0-opinion-doctors-should-educate-patients-about-asian-glow-and-drinking-risks.md)

## Navigation
- [← Back to Oncology Feed](https://medichelpline.com/clinical-feed/oncology.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.