Deep Small RNA Sequencing of BRAF V600E Mutated Papillary Thyroid Carcinoma With Lymph Node Metastasis.
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ABSTRACT:
SUBMITTER: Yusof AM
PROVIDER: S-EPMC6791926 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Deep Small RNA Sequencing of <i>BRAF</i> V600E Mutated Papillary Thyroid Carcinoma With Lymph Node Metastasis.
Yusof Azliana Mohamad AM Jamal Rahman R Saidin Sazuita S Muhammad Rohaizak R Suhaimi Shahrun Niza Abdullah SNA Rose Isa Mohamed IM Nazarie Wan Fahmi Wan WFW Fu Francis Tieng Yew FTY Mutalib Nurul-Syakima Ab NA
Frontiers in genetics 20191008
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Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.
| S-EPMC10733258 | biostudies-literature
Project description:BackgroundThe aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).MethodsThis research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score.ResultsThe multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63).ConclusionsThe suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.
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Project description:BackgroundThyroid cancer is prone to early lymph node metastasis (LNM), and patients with large volume LNM (LVLNM) tend to have a poorer prognosis. The aim of this study was to predict LVLNM in before surgery based on radiomics and deep learning (DL).MethodsA multicenter retrospective study was performed, including 854 papillary thyroid carcinoma (PTC) patients from three centers. Radiomics features were extracted. Logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), ExtraTrees, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms were used to construct radiomics models. AlexNet, DenseNet121, inception_v3, ResNet50, and transformer algorithms were used to construct DL models. The receiver operating characteristic (ROC) curve was employed to select the better-performing model. A combined model was then created by merging radiomics features and DL features. The least absolute shrinkage and selection operator (LASSO) method was utilized to identify metabolites and radiomics features with non-zero coefficients. The performance of the models was evaluated using area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and F1-score.ResultsA total of 1,357 radiomics features were extracted. Among the radiomics models, the ExtraTrees model demonstrated the optimal diagnostic capabilities with an AUC of 0.787 [95% confidence interval (CI): 0.715-0.858], and DenseNet121 DL model demonstrated the optimal diagnostic capabilities with an AUC of 0.766 (95% CI: 0.683-0.848). Furthermore, the combined model, named the Thy-DL-Radiomics model, exhibited an AUC of 0.839 (95% CI: 0.758-0.920) in the internal validation set and 0.789 (95% CI: 0.718-0.859) in the external validation set.ConclusionsA radiomics-DL features integrated model can predict LVLNM in PTC patients and provide guidance for personalized treatment.
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Project description:BackgroundFew highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development.MethodsThis study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models.ResultsBinary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy.ConclusionsThe deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.
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Project description: Not available
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Project description:To identify papillary thyroid carcinoma (PTC)-associated transcripts, we compared the gene expression profiles of three Serial Analysis of Gene Expression libraries generated from thyroid tumors and a normal thyroid tissue.Selected transcripts were validated in a panel of 57 thyroid tumors using quantitative PCR (qPCR). An independent set of 71 paraffin-embedded sections was used for validation using immunohistochemical analysis. To determine if PTC-associated gene expression could predict lymph node involvement, a separate cohort of 130 primary PTC (54 metastatic and 76 nonmetastatic) was investigated. The BRAF(V600E) mutational status was compared with qPCR data to identify genes that might be regulated by abnormal BRAF/MEK/extracellular signal-regulated kinase signaling.We identified and validated new PTC-associated transcripts. Three genes (CST6, CXCL14, and DHRS3) are strongly associated with PTC. Immunohistochemical analysis of CXCL14 confirmed the qPCR data and showed protein expression in PTC epithelial cells. We also observed that CST6, CXCL14, DHRS3, and SPP1 were associated with PTC lymph node metastasis, with CST6, CXCL14, and SPP1 being positively correlated with metastasis and DHRS3 being negatively correlated. Finally, we found a strong correlation between CST6 and CXCL14 expression and BRAF(V600E) mutational status, suggesting that these genes may be induced subsequently to BRAF activation and therefore may be downstream in the BRAF/MEK/extracellular signal-regulated kinase signaling pathway.CST6, CXCL14, DHRS3, and SPP1 may play a role in PTC pathogenesis and progression and are possible molecular targets for PTC therapy.
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Project description:BackgroundThe BRAF V600E mutation is the most common genetic variant in papillary thyroid cancer (PTC), but the relationship between the BRAF V600E mutation in PTC and cervical lymph node metastasis (LNM) remains controversial.ObjectiveTo estimate risk factors for neck nodal metastasis in PTC with BRAF V600E mutation.PatientsA total of 292 patients diagnosed with BRAF V600E mutation related PTC were admitted.DesignIn this retrospective study, data from 292 patients, including clinical, molecular, and ultrasonic characteristics, were analyzed. Univariate and multivariate logistic regression analyses were applied to identify risk factors for LNM in PTC with the BRAF V600E mutation.ResultsIn the univariate analysis of all PTC patients with the BRAF V600E mutation, the LNM was found to be significantly associated with age (P = 0.010), size (P = 0.000), bilaterality (P = 0.000), multifocality (P = 0.002), LNM in ultrasound (US) (P = 0.000), and capsular invasion (P = 0.010). In ultrasonic image characteristics, margin (P = 0.036), shape (P = 0.046), and microcalcification (P = 0.002) were significantly associated with LNM. In multivariate analysis, LNM in PTCs with BRAF V600E mutation was significantly associated with age ≤ 45 years (OR = 1.869, P = 0.020, 95% CI = 1.106 - 3.158), size ≥ 1cm (OR = 3.131, P = 0.001, 95% CI = 1.578 - 6.212), LNM in US (OR = 6.962, P = 0.000, 95% CI = 2.924 - 16.572), bilaterality (OR = 1.626, P = 0.007, 95% CI = 1.142 - 2.314), ill-defined margins in US (OR = 1.980, P = 0.033, 95% CI = 1.057 - 3.709), and microcalcification in US (OR = 2.786, P = 0.002, 95% CI = 1.464 - 5.303).ConclusionThis study revealed that several significant risk factors for LNM in PTCs with the BRAF V600E mutation included: age ≤ 45 years, size ≥ 1cm, LNM in US, bilaterality, ill-defined margins in US, and microcalcification in US.
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Project description:BackgroundThe objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).MethodsA 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.ResultsAmong 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.ConclusionThe 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.
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Project description:Lin28 is involved in the progression of several types of tumors. Data collected from clinical trials have suggested that Lin28 expression is correlated with poor prognosis in thyroid carcinoma. The present study was conducted to investigate the association between Lin28 expression and the clinicopathological parameters of papillary thyroid carcinoma (PTC). Accordingly, the clinical data and diagnostic results from 237 patients with PTC were collected. Immunohistochemical staining was performed to evaluate the Lin28 expression levels in thyroid tissue samples. Associations between the expression levels and clinicopathological parameters were evaluated. Lin28 was expressed in 96/237 (40.5%) of PTC specimens. Compared with patients with no Lin28 expression, patients with expression had higher rates of lymph node metastasis (P<0.001) and larger tumors (P=0.011). Multivariate analysis revealed that Lin28 was associated with lymph node metastasis. Next, bioinformatics analysis was performed based using the Gene Expression Omnibus database and The Cancer Genome Atlas database. Lin28 expression was associated with aggressive tumor characteristics, such as lymph node metastasis and larger tumors. In conclusion, the present study revealed that Lin28 expression served as a risk factor for lymph node metastasis. Accordingly, Lin28 expression may be used as a prognostic marker to predict lymph node metastasis in patients with PTC. In addition, Lin28 may serve as a therapeutic target in the management of this tumor type, which may help improve patient outcomes.
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Project description:Radioactive iodine-refractory metastatic differentiated thyroid cancer (RAIR) is associated with a poor prognosis. Multikinase inhibitors have demonstrated improvement in progression-free but not overall survival in such patients, but usage is limited by significant adverse effects and the development of resistance. Clinical research has demonstrated improvement in progression-free survival with the combined use of the BRAF/MEK inhibitor in patients with metastatic melanoma and anaplastic thyroid cancer with the BRAFV600E mutation and has shown promise in redifferentiation of BRAF-positive RAIR differentiated thyroid cancer. A 58-year-old woman went to her primary care physician for a growing mass on the left side of her neck. CT imaging noted a 6 x 8 x 6 cm mixed cystic and solid mass and lymphadenopathy. Core biopsy subsequently showed metastatic papillary thyroid cancer (Stage III, PT4a/PN1b), and she underwent a total thyroidectomy with left neck dissection. She then received 204mCi 131I post-total thyroidectomy. Unfortunately, her thyroglobulin continued to increase post-radioactive iodine (RAI) treatment, indicating persistent and/or recurrent thyroid cancer. An RAI-131 whole-body scan on the thyrogen protocol showed no significant RAI uptake. A fluorodeoxyglucose (FDG)-positron emission tomography (PET) CT scan was then performed, which showed recurrent metastatic disease with hypermetabolism noted in the left thyroid bed and FDG-avid bilateral cervical lymph nodes and pulmonary nodules. Given these findings, her cancer was classified as radioactive iodine refractory (RAIR). Molecular testing indicated the BRAFV600E mutation. After a discussion with the patient, it was decided to initiate therapy with a BRAF inhibitor (dabrafenib 150 mg twice a day) and MEK inhibitor (trametinib 2 mg once a day) in an attempt to redifferentiate RAIR. Repeat RAI-131 thyrogen whole body scan one month after initiation of therapy demonstrated left level 2 cervical lymphadenopathy radioiodine uptake. The patient subsequently received 216 mCi 131I treatment given evidence of redifferentiation. Her post-treatment scan indicated additional uptake in a left lower lobe pulmonary nodule as well as a left paratracheal mass indicating successful RAI-131 uptake by metastases. Her thyroglobulin level, six months post-RAI, decreased to 4.0 indicating an encouraging response. Further surveillance, including imaging studies, is planned. This case illustrates the re-differential potential for dabrafenib and trametinib treatment in patients with BRAFV600E-mutated RAIR differentiated thyroid cancer. This therapy has been shown to be successful in small series of patients and could potentially be offered to RAIR patients with the BRAFV600E mutation as an alternative to multikinase treatment given its favorable side-effect profile.
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