Proteomic and metabolomic proof of concept for unified airways in chronic rhinosinusitis and asthma.

BACKGROUND: The pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) with comorbid asthma remains unclear. OBJECTIVE: To assess upper and lower airway unity and identify a possible common pathogenesis in CRSwNP with asthma. METHODS: This study analyzed the expression of proteins and metabolites in nasal lavage fluid cells (NLFCs) and induced sputum cells (ISCs). Differentially expressed proteins and their function-related metabolites in the upper and lower airways of patients having CRSwNP with or without asthma were identified; relevant signaling pathways were analyzed, and key pathway-related proteins were identified. Parallel reaction monitoring was used to verify these target proteins. RESULTS: Protein or metabolite expression between NLFCs and ISCs was highly correlated and conservative on the basis of expression profiles and weighted gene coexpression network analysis. There were 17 differentially coexpressed proteins and their function-related 13 metabolites that were identified in the NLFCs and ISCs of CRSwNP, whereas 11 proteins and 11 metabolites were identified in CRSwNP with asthma. An asthma pathway was involved in the copathogenesis of upper and lower airways in whether CRSwNP or CRSwNP with asthma. The asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase, as the core of the protein-metabolism interaction networks between the upper and lower airways, were both highly coexpressed in NLFCs and ISCs in patients having either CRSwNP or CRSwNP with asthma by parallel reaction monitoring validation. CONCLUSION: Proteomics and metabolomics reveal upper and lower airway unity. Asthma pathway-related proteins proteoglycan 2 and eosinophil peroxidase from the upper airway could be used to assess the potential risk of lower airway dysfunction in CRSwNP.

CT Radiomics-Based Nomogram for Predicting the Lateral Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Prospective Multicenter Study.

RATIONALE AND OBJECTIVES: This study is based on multicenter cohorts and aims to utilize computed tomography (CT) images to construct a radiomics nomogram for predicting the lateral neck lymph node (LNLN) metastasis in the papillary thyroid carcinoma (PTC) and further explore the biological basis under its prediction. MATERIALS AND METHODS: In the multicenter study, 1213 lymph nodes from 409 patients with PTC who underwent CT examinations and received open surgery and lateral neck dissection were included. A prospective test cohort was used in validating the model. Radiomics features were extracted from the CT images of each patient's LNLNs. Selectkbest, maximum relevance and minimum redundancy and the least absolute shrinkage and selection operator (LASSO) algorithm were used in reducing the dimensionality of radiomics features in the training cohort. Then, a radiomics signature (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. A nomogram was generated using the clinical risk factors of the patients and Rad-score. The nomograms' performance was analyzed in terms of accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUCs). The clinical usefulness of the nomogram was evaluated by decision curve analysis. Moreover, three radiologists with different working experiences and nomogram were compared to one another. Whole transcriptomics sequencing was performed in 14 tumor samples; the correlation of biological functions and high and low LNLN samples predicted by the nomogram was further investigated. RESULTS: A total of 29 radiomics features were used in constructing the Rad-score. Rad-score and clinical risk factors (age, tumor diameter, location and number of suspected tumors) compose the nomogram. The nomogram exhibited good discrimination performance of the nomogram for predicting LNLN metastasis in the training cohort (AUC, 0.866), internal test cohort (0.845), external test cohort (0.725), and prospective test cohort (0.808) and showed diagnostic capability comparable to senior radiologists, significantly outperforming junior radiologists (p < 0.05). Functional enrichment analysis suggested that the nomogram can reflect the ribosome-related structures of cytoplasmic translation in patients with PTC. CONCLUSION: Our radiomics nomogram provides a noninvasive method that incorporates radiomics features and clinical risk factors for predicting LNLN metastasis in patients with PTC.

Cervical lymphangioma in adults: A report of seven cases and review of the literature.

Background: Cervical lymphangioma usually occurs in children and is relatively rare in adults. The purpose of this study was to investigate the characteristics of cervical lymphangiomas in adults. Methods: We retrospectively reviewed the medical records and analyzed clinical data obtained from seven adult patients who suffered from cervical lymphangioma from January 2008 to June 2021. Results: Five (71.43%) of seven cases were asymptomatic and four (57.14%) lymphangiomas had a maximum diameter ≥ 10 cm. Each cervical lymphangioma was located adjacent to the carotid sheath. All the cervical lymphangiomas were completely surgically excised and were pathologically diagnosed as cystic lymphangioma. After a median 32 months follow-up period, no recurrence was observed. Conclusion: Cervical lymphangiomas from adults are often large asymptomatic masses infiltrating or adjacent to surrounding vital structures which brings many difficulties to the surgery, requiring an experienced surgeon to remove the mass.

Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.

Objectives: To develop and validate a Computed Tomography (CT) based radiomics nomogram for preoperative predicting of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC) patients. Methods: A total of 153 patients were randomly assigned to training and internal test sets (7:3). 46 patients were recruited to serve as an external test set. A radiologist with 8 years of experience segmented the images. Radiomics features were extracted from each image and Delta-radiomics features were calculated. Features were selected by using one way analysis of variance and the least absolute shrinkage and selection operator in the training set. K-nearest neighbor, logistic regression, decision tree, linear-support vector machine (linear -SVM), gaussian-SVM, and polynomial-SVM were used to build 6 radiomics models. Next, a radiomics signature score (Rad-score) was constructed by using the linear combination of selected features weighted by their corresponding coefficients. Finally, a nomogram was constructed combining the clinical risk factors with Rad-scores. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were performed on the three sets to evaluate the nomogram's performance. Results: 4 radiomics features were selected. The six models showed the certain value of radiomics, with area under the curves (AUCs) from 0.642 to 0.701. The nomogram combining the Rad-score and clinical risk factors (radiologists' interpretation) showed good performance (internal test set: AUC 0.750; external test set: AUC 0.797). Calibration curve and DCA demonstrated good performance of the nomogram. Conclusion: Our radiomics nomogram incorporating the radiomics and radiologists' interpretation has utility in the identification of ETE in PTC patients.