Academic Journal
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
| العنوان: | 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 |
|---|---|
| المؤلفون: | Chen, Chen, Qin, Yuhui, Chen, Haotian, Cheng, Junying, He, Bo, Wan, Yixuan, Zhu, Dongyong, Gao, Fabao, Zhou, Xiaoyue |
| المساهمون: | innovative research group project of the national natural science foundation of china |
| المصدر: | European Radiology ; volume 32, issue 6, page 3819-3829 ; ISSN 1432-1084 |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2022 |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1007/s00330-021-08465-w |
| DOI: | 10.1007/s00330-021-08465-w.pdf |
| DOI: | 10.1007/s00330-021-08465-w/fulltext.html |
| الاتاحة: | http://dx.doi.org/10.1007/s00330-021-08465-w https://link.springer.com/content/pdf/10.1007/s00330-021-08465-w.pdf https://link.springer.com/article/10.1007/s00330-021-08465-w/fulltext.html |
| Rights: | https://creativecommons.org/licenses/by/4.0 ; https://creativecommons.org/licenses/by/4.0 |
| رقم الانضمام: | edsbas.CA81044D |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s00330-021-08465-w |
|---|