PMID- 37537230 OWN - NLM STAT- MEDLINE DCOM- 20230807 LR - 20231121 IS - 2045-2322 (Electronic) IS - 2045-2322 (Linking) VI - 13 IP - 1 DP - 2023 Aug 3 TI - Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography. PG - 12604 LID - 10.1038/s41598-023-39747-6 [doi] LID - 12604 AB - The most common BRAF mutation is thymine (T) to adenine (A) missense mutation in nucleotide 1796 (T1796A, V600E). The BRAF(V600E) gene encodes a protein-dependent kinase (PDK), which is a key component of the mitogen-activated protein kinase pathway and essential for controlling cell proliferation, differentiation, and death. The BRAF(V600E) mutation causes PDK to be activated improperly and continuously, resulting in abnormal proliferation and differentiation in PTC. Based on elastography ultrasound (US) radiomic features, this study seeks to create and validate six distinct machine learning algorithms to predict BRAF(V6OOE) mutation in PTC patients prior to surgery. This study employed routine US strain elastography image data from 138 PTC patients. The patients were separated into two groups: those who did not have the BRAF(V600E) mutation (n = 75) and those who did have the mutation (n = 63). The patients were randomly assigned to one of two data sets: training (70%), or validation (30%). From strain elastography US images, a total of 479 radiomic features were retrieved. Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified tenfold cross-validation were used to decrease the features. Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naive Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAF(V600E). The accuracy (ACC), the area under the curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curves of the machine learning algorithms were used to evaluate their performance. 1 in circle The machine learning algorithms' diagnostic performance depended on 27 radiomic features. 2 in circle AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 (95% confidence interval [CI]: 0.65-0.91), 0.87 (95% CI 0.73-0.95), 0.91(95% CI 0.79-0.98), 0.92 (95% CI 0.80-0.98), 0.93 (95% CI 0.80-0.98), and 0.98 (95% CI 0.88-1.00), respectively. 3 in circle There was a significant difference in echogenicity,vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAF(V600E) and PTC patients without BRAF(V600E). Machine learning algorithms based on US elastography radiomic features are capable of predicting the likelihood of BRAF(V600E) in PTC patients, which can assist physicians in identifying the risk of BRAF(V600E) in PTC patients. Among the six machine learning algorithms, the support vector machine with radial basis function (SVM_RBF) achieved the best ACC (0.93), AUC (0.98), SEN (0.95), SPEC (0.90), PPV (0.91), and NPV (0.95). CI - (c) 2023. Springer Nature Limited. FAU - Agyekum, Enock Adjei AU - Agyekum EA AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. AD - School of Medicine, Jiangsu University, Zhenjiang, 212013, China. FAU - Wang, Yu-Guo AU - Wang YG AD - Department of Ultrasound, Traditional Chinese Medicine Hospital of Nanjing Lishui District, Nanjing, China. FAU - Xu, Fei-Ju AU - Xu FJ AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. FAU - Akortia, Debora AU - Akortia D AD - School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. FAU - Ren, Yong-Zhen AU - Ren YZ AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. AD - School of Medicine, Jiangsu University, Zhenjiang, 212013, China. FAU - Chambers, Kevoyne Hakeem AU - Chambers KH AD - School of Medicine, Jiangsu University, Zhenjiang, 212013, China. FAU - Wang, Xian AU - Wang X AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. FAU - Taupa, Jenny Olalia AU - Taupa JO AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. AD - School of Medicine, Jiangsu University, Zhenjiang, 212013, China. FAU - Qian, Xiao-Qin AU - Qian XQ AD - Ultrasound Medical Laboratory, Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China. yz_tyz1030@126.com. LA - eng PT - Journal Article PT - Randomized Controlled Trial PT - Research Support, Non-U.S. Gov't DEP - 20230803 PL - England TA - Sci Rep JT - Scientific reports JID - 101563288 RN - EC 2.7.11.1 (Proto-Oncogene Proteins B-raf) SB - IM MH - Humans MH - Thyroid Cancer, Papillary/diagnostic imaging/genetics/pathology MH - *Thyroid Neoplasms/diagnostic imaging/genetics/pathology MH - *Elasticity Imaging Techniques MH - Proto-Oncogene Proteins B-raf/genetics MH - Bayes Theorem MH - *Carcinoma, Papillary/pathology MH - Mutation MH - Machine Learning PMC - PMC10400539 COIS- The authors declare no competing interests. EDAT- 2023/08/04 01:08 MHDA- 2023/08/07 06:42 PMCR- 2023/08/03 CRDT- 2023/08/03 23:20 PHST- 2023/05/31 00:00 [received] PHST- 2023/07/30 00:00 [accepted] PHST- 2023/08/07 06:42 [medline] PHST- 2023/08/04 01:08 [pubmed] PHST- 2023/08/03 23:20 [entrez] PHST- 2023/08/03 00:00 [pmc-release] AID - 10.1038/s41598-023-39747-6 [pii] AID - 39747 [pii] AID - 10.1038/s41598-023-39747-6 [doi] PST - epublish SO - Sci Rep. 2023 Aug 3;13(1):12604. doi: 10.1038/s41598-023-39747-6.