RT Journal Article
SR Electronic
T1 Prediction of squamous cell carcinoma cases from squamous cell hyperplasia in throat lesions using CT radiomics model
JF Saudi Medical Journal
JO Saudi Med J
FD Prince Sultan Military Medical City
SP 284
OP 292
DO 10.15537/smj.2021.42.3.20200617
VO 42
IS 3
A1 Osama A. Khodrog
A1 Fengzhi Cui
A1 Nannan Xu
A1 Qinghe Han
A1 Jianhua Liu
A1 Tingting Gong
A1 Qinghai Yuan
YR 2021
UL http://smj.org.sa/content/42/3/284.abstract
AB Objectives: To differentiate squamous cell hyperplasia (SCH) (benign) from squamous cell carcinoma (SCC) malignant) using textural features extracted from CT images and thereby, facilitate the preoperative medical diagnosis and treatment of throat cancers without the need for sample biopsies.Methods: In total, 100 throat cancer patients were selected for this retrospective study. The cases were collected from the Second Hospital of Jilin University, Changchun, China, from June 2017 to January 2019. The patients were separated into a training and validation cohort consisting of 70 and 30 cases, respectively. The Artificial Intelligence Kit software (A.K. software) was used to extract the radiomics features from the CT images. These features were further processed using the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods to obtain a subset of optimal features. The radiomics model was validated based on area-under-the-curve (AUC) values, accuracy, specificity, and sensitivity using the R-studio software.Results: The diagnostic accuracy, specificity, PPV, NPV, and AUC values obtained for the training cohort was 0.91, 0.9, 0.93, 0.9, and 0.96 CT angiography (CTA), 0.93, 0.93, 0.95, 0.90, and 0.96 computed tomography normal (CTN), and 0.92, 0.87, 0.91, 0.96, and 0.96 CT venogram (CTV). These values were subsequently confirmed in the validation cohort.Conclusion: The radiomics-based prediction model proposed in this study successfully differentiated between SCH and SCC throat cancers using CT imaging, thereby facilitating the development of accurate preoperative diagnosis based on specific biomarkers and cancer phenotypes.