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Machine Learning Differentiates Between Benign and Malignant Parotid Tumors With Contrast-Enhanced Ultrasound
Jie Shan1, Yifei Yang2, Hualian Liu2
1Resident, Department of Oral and Maxillofacial Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Background:
Contrast-enhanced ultrasound (CEUS) is frequently used to distinguish benign parotid tumors (BPTs) from malignant parotid tumors (MPTs). Introducing machine learning may enable clinicians to preoperatively diagnose parotid tumors precisely.
Purpose:
We aimed to estimate the diagnostic capability of machine learning in differentiating BPTs from MPTs.
Study Design, Setting, And Sample:
A retrospective cohort study was conducted at the Third Affiliated Hospital of Soochow University. Patients who underwent parotidectomy and CEUS for untreated parotid tumors were included. Patients with recurrent tumors, inadequate specimens, or chemoradiotherapy were excluded.
Predictor Variable:
Predictor variable was preoperative diagnosis coded as BPTs and MPTs based on the support vector machine (SVM) algorithms, laboratory, and CEUS variables.
Main Outcome Variable(S):
Outcome variable was pathological diagnosis coded as BPTs and MPTs.
Covariates:
Covariate was demographics.
Analyses:
A senior surgeon labeled patients' tumors as BPTs or MPTs, creating a clinical diagnosis. Patients were randomly divided into training (70%) and testing (30%) sets. After developing the SVM models using the training set, we evaluated their diagnostic performance on the testing set with the area under the receiver-operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. Delong's test was used to compare the AUC of SVM models, laboratory, and CEUS variables.
Results:
The sample included 48 patients, and the testing set comprised 12 (25%) BPTs and 3 (6.25%) MPTs. Three CEUS variables (width, arrival time, and time to peak) and 3 laboratory variables (lymphocyte count, D-dimer, prognostic nutritional index) were identified through recursive feature elimination. Tested on the testing set, the SVM models with linear, polynomial, and radial kernels showed identical performance (AUC = 0.972, accuracy = 93.3%, positive predictive value = 75%, negative predictive value = 100%, sensitivity = 100%, specificity = 91.7%). They had larger AUC than SVM with sigmoid kernel (P = .18), width (P = .03), lymphocyte count (P = .02), D-dimer (P < .01), prognostic nutritional index (P = .03), arrival time (P = .02), time to peak (P = .04), CEUS diagnosis (P < .01), and clinical diagnosis (P < .01).
Conclusion And Relevance:
The SVM algorithm differentiated BPTs from MPTs better than laboratory and CEUS variables.
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