Blood immune indexes can predict lateral lymph node metastasis of thyroid papillary carcinoma

Geriatric Nutritional Risk Index (GNRI) and Prognostic Nutritional Index (PNI) Before Treatment as the Predictive Indicators for Bone Metastasis in Prostate Cancer Patients

Objective

Inflammation and nutritional status are involved in the occurrence and progression of cancer. The purpose of this study was to investigate the relationship of nutritional status indices (geriatric nutritional risk index (GNRI), neutrophil to albumin ratio (NAR), prognostic nutritional index (PNI)), and comprehensive inflammatory indices (pan-immune inflammation value (PIV), systemic immune inflammation index (SII), and system inflammation response index (SIRI)) and bone metastasis of prostate cancer.

Methods

A retrospective analysis was performed on 888 prostate cancer patients treated in Meizhou People's Hospital from November 2017 to December 2022. Clinical characteristics were collected, including age, body mass index (BMI), bone metastasis, and GNRI, NAR, PNI, PIV, SII, and SIRI levels. The optimal cutoff values of these indices were calculated by receiver operating characteristic (ROC) curve, and the relationship between these indices and bone metastasis was analyzed.

Results

There were 836 (94.1%) cases were ≥60 years old, indicating that the majority of prostate cancer patients were elderly men. There were 640 (72.1%) patients without bone metastasis and 248 (27.9%) patients with bone metastasis. The levels of GNRI and PNI in patients with bone metastasis were significantly lower than those without, while NAR, PIV, SII, and SIRI were not statistically significant. And the levels of GNRI and PNI in patients with multiple bone metastasis were significantly lower than those with single bone metastasis. When bone metastasis was taken as the endpoint of GNRI and PNI, the critical value of GNRI was 97.05 (sensitivity 55.2%, specificity 67.5%, area under the ROC curve (AUC) = 0.639), the PNI cutoff value was 44.925 (sensitivity 51.2%, specificity 67.2%, AUC = 0.634), and the AUC of GNRI plus PNI was 0.647.

Conclusion

Prostate cancer is more common in older men; about a quarter of patients have bone metastasis. GNRI and PNI have predictive efficacy in bone metastasis and multiple bone metastasis of prostate cancer, but NAR, PIV, SII, and SIRI do not.

Construction of a predictive model for cervical lymph node metastasis in papillary thyroid carcinoma

Background

In oncology, the relationships among cervical central lymph node metastasis (CLNM), biochemical tests, and ultrasound characteristics in patients with papillary thyroid cancer (PTC) remain controversial. This association is currently not well supported by evidence, which emphasizes the need for further research. Understanding the connection between CLNM, biochemical testing, and ultrasound features is crucial for clinical practice and public health efforts. Research on this topic is still underway and is now receiving much interest. Our goal was to create and verify a basic cervical lymph node metastasis prediction model.

Methods

In this retrospective cohort study, 685 individuals diagnosed with PTC from First Hospital of Shanxi Medical University (n = 560) and Changzhi Heping Hospital (n = 125) participated in the research from January 2020 to October 2022. Patients were randomly assigned to a training set (n=392), an internal test set (n=168), or an external test set (n=125). Comprehensive clinical information, serological indices, and ultrasonography features were obtained for every participant. LASSO (Least Absolute Shrinkage and Selection Operator) and BSR (Best Subset Regression) to select features for model construction. A logistic regression model with filtered variables was constructed. A nomogram was developed based on six risk factors. Receiver operating characteristic (ROC) curves, decision curve analysis, and calibration curves were used to assess the predictive accuracy, clinical utility, and discriminative ability of the nomogram.

Results

Of the 560 individuals, 54.3% (304/560) did not have lymph node metastases, whereas 45.7% (256/560) did. Age, male, nodule size, multifocal lesions, capsular contact or invasion and ill-defined margins were determined to be risk variables via BSR and multivariate logistic analysis. Nomograms were created using these six risk indicators. The prediction model of CLNM had an AUC of 0.884 (95% CI 0.851, 0.916). Both the internal and the external validation results were highly encouraging. Confirming the model's stability and applicability in different data environments.

Conclusion

We developed a predictive model and nomogram for CLNM in PTC patients, which demonstrated robust performance. This model can guide surgical planning, potentially reducing complications and improving outcomes.

Inflammation and Thyroid Cancer: Deciphering the Role of Blood Immune Indexes

BACKGROUND

Inflammation within tumor microenvironments has been correlated to numerous malignancies. This study aims to explore its significance in thyroid cancer (TC).

METHODS

Retrospective analysis of 157 thyroid carcinomas and 40 benign cases involved initial univariate analysis. The value of neutrophils/value of lymphocytes (NLR), value of platelets/value of lymphocytes (PLR), value of lymphocytes/value of monocytes (LMR), and value of platelets × value of neutrophils/value of lymphocytes (SII) indexes were related to TC characteristics and number and location of involved lymph nodes using χ2 or Fischer's exact tests for categorical variables and Student's t-tests for continuous ones. A 1:1 propensity score matching balanced malignant and benign TC groups based on age, sex, and tumor size was used. Post-matching, a multivariate logistic model integrated sex, age, Central lymph node metastases (CLNM), and SII index. Statistically significant immune index values underwent ROC curve analysis for determining cut-offs. Among the 157 malignant TC, median test and density plots were performed.

RESULTS

The SII index emerged as a predictor of malignancy in both univariate and multivariate analyses (p-value = 0.0202). The ROC curve indicated a cut-off SII value of 465.71, (specificity = 58% [95% CI: 0.43-0.73]; sensitivity = 80% [95% CI: 0.68-0.93]). Median SII index values for tumor sizes of 1 and >1 were 522.8 and 654.8, respectively (p-value = 0.016). When central lymph nodes metastases(CLNMs) was considered (CLNM = 0 vs. CLNM > 0), median SII values were 530.7 and 1121.7, respectively (p-value = 0.011).

CONCLUSIONS

The SII index appears to be a valuable tool in the presence of TC, showing correlations with malignancy, tumor size, and CLNMs.

Utilizing innovative two curves in nomogram

Objective

Nomograms are valuable tools in clinical research for predicting patient outcomes. Understanding threshold values within these models is crucial for assessing the model's effectiveness and practical application in clinical environments.

Methods

We developed two novel interpretive curves to enhance the utility of nomograms. These curves were designed to provide clear visualization of how clinical prediction models perform across various thresholds. The curves are applied to two case studies to demonstrate their practical application and efficacy.

Results

In both examples, the novel curves successfully highlighted critical threshold values and revealed changes in prediction accuracy across these thresholds. This enhanced the understanding of the nomogram's performance, providing clinicians with more informative decision-making tools.

Conclusions

The introduction of these interpretive curves allows for a more nuanced understanding of nomogram-based predictions, offering insights into threshold effects that can inform clinical decisions.

Systemic Immune-Inflammation Index and Systemic Inflammation Response Index Predict the Response to Radioiodine Therapy for Differentiated Thyroid Cancer

Purpose

This research sought to evaluate the clinical value of systemic immune-inflammation index and systemic inflammation response index in predicting the response to radioactive iodine (RAI) therapy in individuals diagnosed with differentiated thyroid cancer.

Patients and Methods

This retrospective study included 406 patients with differentiated thyroid cancer who received initial RAI therapy and follow-up from December 2019 to December 2023. Patients were divided into two groups based on imaging and serum indicators to evaluate the response to radioactive iodine treatment: the ER group (excellent response) and the non-ER group (suboptimal response). Systemic immune-inflammation index and systemic inflammation response index were calculated based on peripheral blood cell counts before treatment. Multivariable logistic regression analysis was used to assess the independent associations of these indices with the therapeutic response to radioiodine treatment. Receiver operating characteristic (ROC) curves were graphed and the area under the curve (AUC) was calculated to evaluate their predictive ability.

Results

Compared to the ER group, patients in the non-ER group had significantly elevated systemic immune-inflammation index and systemic inflammation response index levels (p < 0.001). After adjusting for confounding factors, there was a significant association between these indices and the response to radioactive iodine treatment in patients with differentiated thyroid cancer. The optimal cutoff values for predicting the response to RAI treatment were 668.91 for systemic immune-inflammation index (AUC=0.692, sensitivity 58.2%, specificity 73.1%, 95% CI: 0.639-0.745, p < 0.001) and 0.47 for systemic inflammation response index (AUC=0.664, sensitivity 85.6%, specificity 42.7%, 95% CI: 0.612-0.717, p < 0.001).

Conclusion

Systemic immune-inflammation index and systemic inflammation response index could be valuable for predicting the response to RAI treatment in individuals diagnosed with differentiated thyroid cancer. Further research is needed to explore their practical utility, and these novel inflammation markers could serve as adjunct tools in clinical practice.

The Association of Pretreatment Systemic Immune Inflammatory Response Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR) with Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma

Objective

Immunoinflammatory response can participate in the development of cancer. To investigate the relationship between pretreatment systemic immune inflammatory response index (SII), systemic inflammatory response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and lymph node metastasis in patients with papillary thyroid carcinoma (PTC).

Methods

A retrospective analysis was performed on 547 PTC patients treated in Meizhou People's Hospital from January 2018 to December 2021. Clinicopathological data were collected, including gender, age, Hashimoto's thyroiditis, maximum tumor diameter, extra-membrane infiltration, disease stage, BRAF V600E mutation, pretreatment inflammatory index levels, and lymph node metastasis. The optimal cutoff values of SII, SIRI, NLR, PLR and LMR were calculated by receiver operating characteristic (ROC) curve, and the relationship between inflammatory indexes and other clinicopathological features and lymph node metastasis was analyzed.

Results

There were 303 (55.4%) PTC patients with lymph node metastasis. The levels of SII, SIRI, NLR, and PLR in patients with lymph node metastasis were significantly higher than those in patients without lymph node metastasis, while the levels of LMR were significantly lower than those in patients without lymph node metastasis (all p<0.05). When lymph node metastasis was taken as the endpoint, the critical value of SII was 625.375, the SIRI cutoff value was 0.705, the NLR cutoff value was 1.915 (all area under the ROC curve >0.6). The results of regression logistic analysis showed that age <55 years old (OR: 1.626, 95% CI: 1.009-2.623, p=0.046), maximum tumor diameter >1cm (OR: 2.681, 95% CI: 1.819-3.952, p<0.001), BRAF V600E mutation (OR: 2.709, 95% CI: 1.542-4.759, p=0.001), SII positive (≥625.375/<625.375, OR: 2.663, 95% CI: 1.560-4.546, p<0.001), and NLR positive (≥1.915/<1.915, OR: 1.808, 95% CI: 1.118-2.923, p=0.016) were independent risk factors for lymph node metastasis of PTC.

Conclusion

Age <55 years old, maximum tumor diameter >1cm, BRAF V600E mutation, SII positive, and NLR positive were independent risk factors for lymph node metastasis in PTC.

Association between blood inflammatory indicators and prognosis of papillary thyroid carcinoma: a narrative review

Background and Objective

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer, accounting for up to 85-90% of cases, with the best overall prognosis and mostly inert tumors. However, some tumors are aggressive, causing metastasis, recurrence, and other bad outcomes. Preoperative inflammation indices, such as lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII) in peripheral blood, have recently gained attention as nonspecific markers of inflammatory response in thyroid. In this study, we reviewed the interactions between preoperative inflammatory factors and outcomes in patients with PTC.

Methods

This is a narrative review. We searched for English articles published between January 2014 and December 2023 on PubMed and Web of Science to identify how do these blood indicators affect the prognosis of patients with papillary thyroid cancer.

Key Content and Findings

All retrievable indicators that have predictive significance for the prognosis of PTC were included, and the prognosis mainly included tumor-node-metastasis (TNM) staging, survival rate, recurrence, clinical and pathological risk factors such as lymph node metastasis (LNM), etc. From the general evidence, the prognostic predictive value of cell count alone was unknown, and low LMR was usually associated with poor prognosis, high NLR and high platelet-to-lymphocyte ratio (PLR) usually indicated poor prognosis.

Conclusions

These minimally invasive, low-cost, and easily obtainable blood indicators provide convenience for precise prognosis management of PTC patients, but many of the findings are conflicting and need to be validated by prospective studies that are more multi-sample, multi-centre and incorporate factors such as age that affect the immune response.

Clinical and molecular features of progressive papillary thyroid microcarcinoma

In recent decades, the prevalence of thyroid cancer has risen substantially, with papillary thyroid microcarcinoma (PTMC) constituting over 50% of cases. Although most PTMCs exhibit indolent growth and a favorable prognosis, some present an increased risk of recurrence and an unfavorable prognosis due to high-risk characteristics such as lymph node metastasis, extrathyroidal extension, and distant metastasis. The early identification of clinically progressing PTMC remains elusive. In this review, the authors summarize findings from PTMC progression-related literature, highlighting that factors such as larger tumor size, cervical lymph node metastasis, extrathyroidal extension, younger age, higher preoperative serum thyroid-stimulating hormone levels, family history, and obesity positively correlate with PTMC progression. The role of multifocality in promoting PTMC progression; however, remains contentious. Furthermore, recent studies have shed light on the impact of mutations, such as BRAF and TERT mutations, on PTMC progression. Researchers have identified several mRNAs, noncoding RNAs, and proteins associated with various features of PTMC progression. Some studies propose that peripheral and tumor tissue-infiltrating immune cells could serve as biomarkers for the clinical progression of PTMC. Collectively, these clinical and molecular features offer a rationale for the early detection and the development of precision theranostic strategies of clinically progressive PTMC.

Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis

Objectives

To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN).

Methods

Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results

Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value.

Conclusion

Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.

Risk stratification of papillary thyroid cancers using multidimensional machine learning

BACKGROUND

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.

MATERIALS AND METHODS

The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system.

RESULTS

The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively.

CONCLUSION

This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

BACKGROUND

The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).

METHODS

A retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.

RESULTS

Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.

CONCLUSION

The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.