Adolescent health and adaptation in Canada: Examination of gender and age aspects of the healthy immigrant effect

Introduction A longstanding and widely held assumption is that immigrants suffer from ill health and adaptation problems. Yet recent studies show that immigrants report the same or better state of health compared to their native-born counterparts. This phenomenon, known as the healthy immigrant effect, has been found in studies of specific health conditions of adults. The present study focuses instead on adolescents and extends its examination of the healthy immigrant effect, measuring both health and adaptation.Methods Using data from population samples in the Canadian Community Health Survey (2007), foreign-born immigrant adolescents (n?=?920) were compared to non-immigrant adolescents (n?=?13,572) for their self-report to questionnaire items for health (general health, mental health, chronic illnesses with psychosomatic symptoms, and psychological illnesses) and adaptation (daily life stress, life satisfaction, and sense of belonging). Adolescents? gender, age, and length of residence were analyzed for the effects.Results Immigrant adolescents were better than non-immigrant peers on the four health measures, and did not differ from non-immigrants for the three adaptation measures despite having less household income and more family members in the household. Immigrant girls exhibited more resilient adaptability, while young immigrant boys and older non-immigrant girls displayed some potential vulnerability. Length of residence, on the other hand, did not contribute to differences for the health and adaptation of immigrant adolescents.Conclusions The healthy immigrant effect was confirmed in a community population sample of adolescents in Canada. Foreign-born immigrant adolescents experience better health, as well as good adaptation equal to their native-born peers. These outcomes call for further research on sustaining good health and adaptation of the immigrant population, in particular by providing age-related effective services and prevention strategies

Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network

Abstract To assess the performance of deep convolutional neural network (CNN) to discriminate malignant and benign thyroid nodules < 10 mm in size and compare the diagnostic performance of CNN with those of radiologists. Computer-aided diagnosis was implemented with CNN and trained using ultrasound (US) images of 13,560 nodules ≥ 10 mm in size. Between March 2016 and February 2018, US images of nodules < 10 mm were retrospectively collected at the same institution. All nodules were confirmed as malignant or benign from aspirate cytology or surgical histology. Diagnostic performances of CNN and radiologists were assessed and compared for area under curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Subgroup analyses were performed based on nodule size with a cut-off value of 5 mm. Categorization performances of CNN and radiologists were also compared. A total of 370 nodules from 362 consecutive patients were assessed. CNN showed higher negative predictive value (35.3% vs. 22.6%, P = 0.048) and AUC (0.66 vs. 0.57, P = 0.04) than radiologists. CNN also showed better categorization performance than radiologists. In the subgroup of nodules ≤ 5 mm, CNN showed higher AUC (0.63 vs. 0.51, P = 0.08) and specificity (68.2% vs. 9.1%, P < 0.001) than radiologists. Convolutional neural network trained with thyroid nodules ≥ 10 mm in size showed overall better diagnostic performance than radiologists in the diagnosis and categorization of thyroid nodules < 10 mm, especially in nodules ≤ 5 mm

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma.

PURPOSE:Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). METHODS:Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). RESULTS:The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649-0.770) in the training cohort and was 0.621 (95% CI: 0.560-0.682) in the validation cohort. CONCLUSIONS:The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was slightly lower in the validation cohort, our study shows that US radiomic features of the primary tumor alone have the potential to predict lateral LNM

Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma.

PURPOSE:To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC). METHODS:From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109). RESULTS:Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively. CONCLUSION:Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size

Comparison of metabolites obtained by ¹H-NMR spectroscopy between patient groups classified by the presence of lymph node metastasis and lateral lymph node metastasis.

<p>Comparison of metabolites obtained by ¹H-NMR spectroscopy between patient groups classified by the presence of lymph node metastasis and lateral lymph node metastasis.</p

Patient demographics and clinicopathologic characteristics.

<p>Patient demographics and clinicopathologic characteristics.</p

Application of metabolomics in prediction of lymph node metastasis in papillary thyroid carcinoma - Fig 1

<p>(Left) OPLA-DA score plot for lymph node metastasis. Red dots represent patients with lymph node metastasis and black dots represents patient without lymph node metastasis. The x-axis is the first component from OPLS-DA and the y-axis is the corresponding orthogonal score. (Right) OPLS-DS loading S-plot for lymph node metastasis. The x-axis is the covariation and the y-axis is the corresponding orthogonal score. The metabolites situated at the upper right or lower left sections are statistically relevant and represent possible discriminating variables.</p