Development and validation of a population-based model for predicting the regional lymph node metastasis in adolescent differentiated thyroid carcinoma

Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database

INTRODUCTION

Although the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node metastasis as an additional prognostic factor is essential for predicting outcomes in LSCC patients.

METHODS

This retrospective study included patients diagnosed with LSCC between 2004 and 2018 and was based on data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The primary endpoint of the study was cancer-specific survival (CSS), and demographic characteristics, tumor characteristics, and treatment regimens were incorporated into the predictive model. The study focused on the value of indicators related to pathological lymph node testing, including the lymph node ratio (LNR), regional node positivity (RNP), and lymph node examination count (RNE), in the prediction of cancer-specific survival in LSCC. A prognostic model was established using a multivariate Cox regression model, and the model was evaluated using the C index, Kaplan-Meier, the Akaike information criterion (AIC), decision curve analysis (DCA), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive efficacy of different models was compared.

RESULTS

A total of 14,200 LSCC patients (2004-2018) were divided into training and validation cohorts. The 10-year CSS rate was approximately 50%, with no significant survival differences between cohorts (p = 0.8). The prognostic analysis revealed that models incorporating LNR, RNP, and RNE demonstrated superior performance over the TNM model. The LNR and RNP models demonstrated better model fit, discrimination, and reclassification, with AUC values of 0.695 (training) and 0.665 (validation). The RNP and LNR models showed similar predictive performance, significantly outperforming the TNM and RNE models. Calibration curves and decision curve analysis confirmed the clinical utility and net benefit of the LNR and RNP models in predicting long-term CSS for LSCC patients, highlighting their value in clinical decision-making.

CONCLUSION

This study confirms that RNP status is an independent prognostic factor for CSS in LSCC, with predictive efficacy comparable to LNR, with both models enhancing survival prediction beyond TNM staging.

Construction and validation of a nomogram for predicting lateral lymph node metastasis in pediatric and adolescent with differentiated thyroid carcinoma

BACKGROUND

Limited research has been conducted to specifically investigate the identification of risk factors and the development of prediction models for lateral lymph node metastasis (LNM) in pediatric and adolescent differentiated thyroid carcinoma (DTC) populations, despite its significant association with unfavorable prognosis.

METHODS

This study entails a retrospective analysis of the clinical characteristics exhibited by pediatric and adolescent patients who have been diagnosed with DTC. The data utilized for this analysis was sourced from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the time frame from 2000 to 2020. Furthermore, the study incorporates patients who were treated at the Departments of Breast and Thyroid Surgery in the Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine, as well as The General Hospital of Western Theater Command, during the period from 2010 to 2020.

RESULTS

A cohort of 2631 patients from the SEER database, along with an additional 339 patients from our departments who met the specified inclusion criteria, were included in this study. Subsequently, four clinical variables, namely age, tumor size, multifocality, and extrathyroidal invasion, were identified as being significantly associated with lateral LNM in pediatric and adolescent DTC patients. These variables were then utilized to construct a nomogram, which demonstrated effective discrimination with a concordance index (C-index) of 0.731. Furthermore, the performance of this model was validated through both internal and external assessments, yielding C-index values of 0.721 and 0.712, respectively. Afterward, a decision curve analysis was conducted to assess the viability of this nomogram in predicting lymph node metastasis.

CONCLUSION

The current investigation has effectively constructed a nomogram model utilizing visualized multipopulationsal data. Our findings demonstrate a significant association between various clinical characteristics and lateral LNM in pediatric and adolescent DTC patients. These outcomes hold substantial significance for healthcare practitioners, as they can employ this model to inform individualized clinical judgments for the pediatric and adolescent cohorts.

Application of 18F-FDG PET-CT Images Based Radiomics in Identifying Vertebral Multiple Myeloma and Bone Metastases

Purpose

The purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods.

Methods

This retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively.

Results

A total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively).

Conclusion

The radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.