Dataset Information

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.

ABSTRACT: To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics and a machine learning (ML) classifier to describe cases and controls in three different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P < 0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a seven-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals, and ML allows a fair prediction of case or control status based solely on the individual genetic background.

SUBMITTER: Brigante G

PROVIDER: S-EPMC9513665 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.

Brigante Giulia G Lazzaretti Clara C Paradiso Elia E Nuzzo Federico F Sitti Martina M Tüttelmann Frank F Moretti Gabriele G Silvestri Roberto R Gemignani Federica F Försti Asta A Hemminki Kari K Elisei Rossella R Romei Cristina C Zizzi Eric Adriano EA Deriu Marco Agostino MA Simoni Manuela M Landi Stefano S Casarini Livio L

European thyroid journal 20220912 5

To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics and a machine learning (ML) classifier to describe cases and controls in three different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) ...[more]

PMID: 35976137

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Project description:Importance:Thyroid nodules are common incidental findings. Ultrasonography and molecular testing can be used to assess risk of malignant neoplasm. Objective:To examine whether a model developed through automated machine learning can stratify thyroid nodules as high or low genetic risk by ultrasonography imaging alone compared with stratification by molecular testing for high- and low-risk mutations. Design, Setting, and Participants:This diagnostic study was conducted at a single tertiary care urban academic institution and included patients (n?=?121) who underwent ultrasonography and molecular testing for thyroid nodules from January 1, 2017, through August 1, 2018. Nodules were classified as high risk or low risk on the basis of results of an institutional molecular testing panel for thyroid risk genes. All thyroid nodules that underwent genetic sequencing for cytological results with Bethesda System categories III and IV were reviewed. Patients without diagnostic ultrasonographic images within 6 months of fine-needle aspiration or who received definitive treatment at an outside medical center were excluded. Main Outcomes and Measures:Thyroid nodules were categorized by the model as high risk or low risk using ultrasonographic images. Results were compared using genetic testing. Results:Among the 134 lesions identified in 121 patients (mean [SD] age, 55.7 [14.2] years; 102 women [84.3%]), 683 diagnostic ultrasonographic images were selected. Of the 683 images, 556 (81.4%) were used for training the model, 74 (10.8%) for validation, and 53 (7.8%) for testing. Most nodules had no mutation (75 [56.0%]), whereas 43 nodules (32.1%) had a high-risk mutation and 16 (11.9%) had an unknown or a low-risk mutation (?2?=?39.060; P?<?.001). In total, 228 images (33.4%) were of nodules classified as genetically high risk (n?=?43), and 455 (66.6%) were of low-risk nodules (n?=?91). The model performed with a sensitivity of 45% (95% CI,?23.1%-68.5%), a specificity of 97% (95% CI,?84.2%-99.9%), a positive predictive value of 90% (95% CI,?55.2%-98.5%), a negative predictive value of 74.4% (95% CI,?66.1%-81.3%), and an overall accuracy of 77.4% (95% CI,?63.8%-97.7%). Conclusions and Relevance:The study found that the model developed through automated machine learning could produce high specificity for identifying nodules with high-risk mutations on molecular testing. This finding shows promise for the diagnostic applications of machine learning interpretation of sonographic imaging of indeterminate thyroid nodules.

Dataset Information

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.

Publications

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.

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