Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.

Objective: This study aimed to develop, validate, and test a comprehensive radiomics prediction model using clinical data and contrast-enhanced multiphasic computed tomography (CT) scans for differentiating between atypical parotid carcinomas (PCAs) and pleomorphic adenomas (PAs) within a multicenter cohort.

Materials And Methods: The study involved 218 patients diagnosed with either PAs (n=162) or atypical PCAs (n=56) (no invasion of adjacent tissues or lymph node metastases) across three anonymized hospitals, divided into a training set (n=175) and a validation set (n=43). Clinical features and radiological findings were used to develop a clinical model. Radiomics features were extracted from multi-phase contrast-enhanced CT, with feature selection achieved through statistical methods and the least absolute shrinkage and selection operator (LASSO). Radiomics signature were developed using a Light Gradient Boosting Decision Tree (LightGBM) model. A radiomics nomogram integrating significant clinical risk factors with the radiomics signature was created, with external validation conducted on an independent dataset of 32 patients from two additional hospitals.

Results: In the training set, the multiphase models (model, model and model) demonstrated significantly superior predictive performance compared to the arterial-phase-only model (model) (DeLong's test, p=0.04-0.02). However, no significant differences emerged between the models in the validation or independent testing sets (p > 0.05). Based on recall and F1-score evaluations in the independent testing set, model was selected for integration with clinical risk factors to develop a radiomics nomogram. This nomogram demonstrated excellent diagnostic performance, achieving AUCs of 1.000 (training), 0.854 (validation) and 0.783 (independent testing), accuracies of 1.000, 0.864 and 0.750, and F1-scores of 1.000, 0.914 and 0.826, respectively. Key discriminative features - cluster shade, run-length non-uniformity and first-order mean, extracted via wavelet or exponential filters - significantly differentiated atypical PCAs from PAs.

Conclusion: The CT-based radiomics nomogram, supplemented by machine learning, effectively differentiates atypical PCAs from PAs, presenting a non-invasive diagnostic tool that could guide treatment decisions and reduce the need for invasive procedures.