Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values

Abstract

Objective We used radiomics feature–based machine learning classifiers of apparent diffusion coefficient (ADC) maps to differentiate small round cell malignant tumors (SRCMTs) and non-SRCMTs of the nasal and paranasal sinuses. Materials A total of 267 features were extracted from each region of interest (ROI). Datasets were randomized into two sets, a training set (∼70%) and a test set (∼30%). We performed dimensional reductions using the Pearson correlation coefficient and feature selection analyses (analysis of variance [ANOVA], relief, recursive feature elimination [RFE]) and classifications using 10 machine learning classifiers. Results were evaluated with a leave-one-out cross-validation analysis. Results We compared the AUC for all the pipelines in the validation dataset using FeAture Explorer (FAE) software. The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUCs with ten features. When the “one-standard error” rule was used, FAE produced a simpler model with eight features, including Perc.01%, Perc.10%, Perc.90%, Perc.99%, S(1,0) SumAverg, S(5,5) AngScMom, S(5,5) Correlat, and WavEnLH_s-2. The AUCs of the training, validation, and test datasets achieved 0.995, 0.902, and 0.710, respectively. For ANOVA, the pipeline with the auto-encoder classifier yielded the highest AUC using only one feature, Perc.10% (training/validation/test datasets: 0.886/0.895/0.809, respectively). For the relief, the AUCs of the training, validation, and test datasets that used the LRLasso classifier using five features (Perc.01%, Perc.10%, S(4,4) Correlat, S(5,0) SumAverg, S(5,0) Contrast) were 0.892, 0.886, and 0.787, respectively. Compared with the RFE and relief, the results of all algorithms of ANOVA feature selection were more stable with the AUC values higher than 0.800. Conclusions We demonstrated the feasibility of combining artificial intelligence with the radiomics from ADC values in the differential diagnosis of SRCMTs and non-SRCMTs and the potential of this non-invasive approach for clinical applications. Key Points • The parameter with the best diagnostic performance in differentiating SRCMTs from non-SRCMTs was the Perc.10% ADC value. • Results of all the algorithms of ANOVA feature selection were more stable and the AUCs were higher than 0.800, as compared with RFE and relief. • The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUC.

Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging

“…Recent studies have demonstrated that segmentation repeatability is essential in terms of feature stability, for it is heavily influenced by different MRI protocols and machines [ 21 , 33 , 34 , 35 , 36 ]. In this paper, we downloaded the segmented data outlined on the TCIA website by using automatic image segmentation and manual supervision, and we performed the experiment using the FAE software, which is a publicly available tool for radiomics models and is applied to many fields [ 37 , 38 , 39 , 40 , 41 ]. Thus, all the experiment results are robust and replicable.…”

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…Most studies have combined AI with CT 20,21,35,44,46,47,53,56,69,80 . Other studies have combined AI with MRI, 33,39,40,45 whole‐slide imaging (WSI), 14,15,51 endoscopic images, 82,90 or positron emission tomography (PET)‐CT 34 …”

“…Some studies used AI to differentiate anterior ethmoidal artery location, 20 identify middle turbinate pneumatization (concha bullosa), 21 and recognize and calculate the volume of the inferior turbinate and maxillary sinus. 53 Most studies have combined AI with CT. 20,21,35,44,46,47,53,56,69,80 Other studies have combined AI with MRI, 33,39,40,45 whole-slide imaging (WSI), 14,15,51 endoscopic images, 82,90 or positron emission tomography (PET)-CT. 34 Some studies directly compared AI with specialist physicians and showed equivalence or superiority of AI over specialist physicians 14,69,80,90 ; it was found that AI required much less time to diagnose. 14 Some studies have combined 2 or more algorithms to solve this problem.…”

Differentiation of Sinonasal NKT From Diffuse Large B-Cell Lymphoma Using Machine Learning and MRI-Based Radiomics

“…Radiomics performs high-throughput mining and quantification of medical images, extracts, and analyses of almost infinite advanced quantitative features by identifying tumor heterogeneity and microenvironment, thus contributing to the detection, diagnosis, and assessment of cancer 17,18 . The applications of radiomics for the head and neck have demonstrated promising results in the differential diagnosis of malignant tumors 19–23 . To the best of our knowledge, no studies yet use the approach of radiomics to identify these 2 types of sinonasal NHLs.…”

“…17,18 The applications of radiomics for the head and neck have demonstrated promising results in the differential diagnosis of malignant tumors. [19][20][21][22][23] To the best of our knowledge, no studies yet use the approach of radiomics to identify these 2 types of sinonasal NHLs.…”