Patients

A retrospective analysis was performed on PTC patients who had undergone preoperative thyroid US elastography, BRAFV600E mutation diagnosis, and surgery at Jiangsu University Affiliated People's Hospital and traditional Chinese medicine hospital of Nanjing Lishui District between January 2014 and 2021. The enrolling process is displayed in Fig.1. 138 PTCs of 138 patients (mean age, 41.6311.36 [range, 2565] years) were analyzed in this study. The patients were divided into BRAFV600E mutation-free group (n=75) and BRAFV600E mutation group (n=63). Using a stratified sample technique at a 7:3 ratio, all patients were randomly assigned to either the training group (n=96) or the validation group (n=42). The following criteria were required for inclusion: postoperative pathology indicated PTC; preoperative thyroid US elastography evaluation; related US images and diagnostic outcomes; maximum nodule diameter>5mm, and<5cm; and unilateral and single focal lesion. The exclusion criteria included a maximum nodule diameter of>5cm and indistinct US imaging of nodules caused by artifacts. The clinical details of the enrolled patients were documented, including age, sex, nodule diameter, nodule location, nodular echo, nodule boundary, nodule internal and peripheral blood flow, nodule elastic grading, calcification, CLNM, and BRAFV600E mutation results. The Jiangsu University Affiliated People's Hospital and the traditional Chinese medicine hospital of Nanjing Lishui District Ethics Committee approved this study. Because it was retrospective in nature, it did not require written informed consent.

Schematic diagram of the patient selection. PTC, papillary thyroid carcinoma.

There were two ultrasonic devices used: the Philips Q5 (both Healthcare, Eindhoven, Netherlands) and the GE LOGIC E20 (GE Medical Systems, American General) (L12-5 linear array probe, frequency: 1014MHz).

To acquire longitudinal and transverse images of the thyroid nodules, continuous longitudinal and transverse scanning was done while the patients were supine. Blood flow in and around the nodule, strain elastic grading of the nodule, calcification, and CLNM were all visible on the coexisting diagram, which also included the nodule diameter, location, echo, and boundary.

The cross-sectional image's position and size of the sampling frame were adjusted, and the strain elastic imaging mode was activated. With an ROI that was larger than the nodules (generally more than two times), the nodules were placed in the middle of the elastic imaging zone. Pressure was applied steadily (range 12mm, 12 times/s) while the probe was perpendicular to the nodule. When the linear strain hint graph (green spring) suggested stability, the freeze key was pressed to get an elastic image; the ROI's color changed (green indicated soft; red indicated hard), and the nodule's hardness was determined based on elasticity. The elastic image was graded according to the following criteria: one point equals a nodular area that alternates between red, green, and blue; two points equal nodules that are partially red and partially green (mostly green, area>90%); three points equal a nodule area that is primarily green, with surrounding tissues visible in red; four points equal a nodule area that is primarily red, with the red area>90%; and five points equal a nodule area that is completely covered in red.

One week prior to surgery, thyroid US exams were conducted. US image segmentation was done manually. Using the ITK-SNAP program (http://www.itksnap.org), the ROIs were manually drawn on each image (Fig.2). The grayscale images were used to create a sketch outline of the tumor regions in the elastography US images.

(A) Ultrasound conventional B-mode image of papillary thyroid carcinoma. (B) corresponding ultrasound elastography image, with the circle,labeled A indicating a lesion region and the circle labeled B indicating a reference area. (C) Corresponding image after region of interest (ROIs) segmentation step.

Radiomic features were extracted using PyRadiomics (https://github.com/Radiomics/pyradiomics). A total of 479 radiomic features were recovered from each ROI's elastography US images. Among those included were first-order Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Gray Level Dependence Matrix (GLDM), and Neighbouring Gray Tone Difference Matrix (NGTDM) features, as well as features deduced from wavelet filter images containing first-order GLCM, GLRLM, GLSZM, GLDM, and NGTDM features.

The retrieved features were normalized using a standard scalar to reduce bias and overfitting in the study. The dataset was divided into training and validation cohorts. To make each characteristic substantially independent, the row spatial dimension of the feature matrix was reduced using the Pearson correlation coefficient (PCC). Every pair of features with a PCC of more than 0.80 was deemed redundant.

After PCC, recursive feature elimination (RFE) for feature selection was applied to the whole dataset using the Scikit-learn python module24 to choose representative features for the training cohort. During the RFE procedure, the following parameters were taken into consideration (cross-validation was set to stratifiedkfold with the number of splits being 10, the random state was set to 101, minimum features to select was set to 3, and accuracy was employed for the scoring.

The Support Vector Machine with the linear kernel (SVM_L), Support Vector Machine with radial basis function kernel (SVM_RBF), LogisticRegression (LR), Nave Bayes (NB), K-nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) classifiers were used to build the prediction models using the RFEs key features. All six algorithms were implemented using the Scikit-learn machine learning library24

The same feature sets were chosen and fed into the model during the validation process. Standard clinical statistics like the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy (ACC) were used to evaluate the model's performance on the training and validation datasets.

Python (version 3.7, https://www.python.org/ Accessed 8 July 2021) and IBM SPSS Statistics (Monk Ar, New York, New York State, USA.) for Windows version 26.0 were used for statistical analyses. Pearson's chi-square and Fisher's exact tests were used to compare the differences in categorical characteristics. The independent sample t-test was used for continuous factors with normal distribution, whereas the MannWhitney U test was used for continuous factors without normal distribution.

A twosided P<0.05 indicated statistically significant differences. PyRadiomics (version 2.2.0, https://github.com/Radiomics/pyradiomics Accessed 10 August 2021) and scikitlearn version 1.224 were used to extract radiomic features and build the prediction models. Each prediction model's AUC, sensitivity, specificity, ACC, NPV, and PPV were calculated.

Medcalc Statistical Software was used to calculate the six models AUCs and evaluate the predictions. The DeLong method was used to compare the AUCs of the six machine learning classifiers. To create calibration curves, the sci-kit-learn version 1.224 was used. R software (version 3.6.1, https://www.r-project.org) was used to perform the decision curve analysis.

The study was conducted in accordance with the Declaration of Helsinki and approved by the Jiangsu University-Affiliated Peoples Hospital and traditional Chinese medicine hospital of Nanjing Lishui District Ethics Committee.

Patient consent was waived by the Jiangsu University-Affiliated Peoples Hospital and traditional Chinese medicine hospital of Nanjing Lishui District ethics committee due to the retrospective nature of the study.

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Predicting BRAFV600E mutations in papillary thyroid carcinoma ... - Nature.com