Preoperative Prediction of Malignant Transformation of Sinonasal Inverted Papilloma Using MR Radiomics

Abstract

PurposeAccurate preoperative prediction of the malignant transformation of sinonasal inverted papilloma (IP) is essential for guiding biopsy, planning appropriate surgery and prognosis of patients. We aimed to investigate the value of MRI-based radiomics in discriminating IP from IP-transformed squamous cell carcinomas (IP-SCC).MethodsA total of 236 patients with IP-SCC (n=92) or IP (n=144) were enrolled and divided into a training cohort and a testing cohort. Preoperative MR images including T1-weighted, T2-weighted, and contrast enhanced T1-weighted images were collected. Radiomic features were extracted from MR images and key features were merged into a radiomic model. A morphological features model was developed based on MR morphological features assessed by radiologists. A combined model combining radiomic features and morphological features was generated using multivariable logistic regression. For comparison, two head and neck radiologists were independently invited to distinguish IP-SCC from IP. The area under the receiver operating characteristics curve (AUC) was used to assess the performance of all models.ResultsA total of 3948 radiomic features were extracted from three MR sequences. After feature selection, we saved 15 key features for modeling. The AUC, sensitivity, specificity, and accuracy on the testing cohort of the combined model based on radiomic and morphological features were respectively 0.962, 0.828, 0.94, and 0.899. The diagnostic ability of the combined model outperformed the morphological features model and also outperformed the two head and neck radiologists.ConclusionsA combined model based on MR radiomic and morphological features could serve as a potential tool to accurately predict IP-SCC, which might improve patient counseling and make more precise treatment planning.

MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors

“…Recently, Ramkumar et al (15) found that MRI-based texture analysis had the potential to classify SNIP and squamous cell carcinoma. Another previous study (16) showed that machine learning models based on MRI radiomics and morphological features achieved satisfactory predictive efficiency in differentiating SNIP from SNIP-transformed squamous cell carcinomas. Despite radiomics method has been utilized in prior studies to classify sinonasal tumors (17,18), and has promising performance, the most appropriate sequence and machine learning classifier for model construction remain undetermined.…”

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

“…In this field, machine learning (ML) algorithms are used to select the best features and develop and improve models, which have the potential to improve predictive power (8). In the last 2 years, studies regarding artificial intelligence in IP have gradually become a hot topic (9)(10)(11)(12)(13). In one study, Li et al (14) designed a deep learning framework through convolutional neural networks to automatically identify IP and NP with high AUC values of 0.95.…”