AUTHOR=Du Lianze , Yuan Qinghai , Han Qinghe 

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

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1151455

DOI=10.3389/fneur.2023.1151455

ISSN=1664-2295

ABSTRACT=Background and purpose: Inverted Papilloma (IP) and Nasal polyp(NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve,and to analyze its diagnostic efficacy.
Materials and methods: A total of 74 cases of IP and 55 cases of NP were retrospectively analyzed in this study. A stratified random sample of 129 patients (80%) was used as the training set (59 IP and 44 NP); the remaining data were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-WI, and contrast-enhanced T1-WI(CE-T1WI) images were collected. Radiomic features were extracted from MR images. The Pearson correlation coefficient (PCC) method is used to reduce the dimensionality of the features. Then the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, radiomics parameters with non-zero coefficients were combined into a rad scoring formula to build machine learning models Calculate the AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the model. Finally, Decision Curve Analysis (DCA) is used to evaluate the clinical usefulness of the model.
Results: There were significant differences in age, nasal bleeding and hyposmia between the two lesions (P< 0.05). A total of 1906 radiomic features were extracted from two MR sequences. After feature selection, saving 12 key features for modeling. The AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were respectively 0.9121, 0.828, 0.9091 and 0.899.
Conclusion: A new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.