We are analyzing https://link.springer.com/article/10.1007/s00330-018-5730-6.

Title:
Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features | European Radiology
Description:
Objective We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features. Methods A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis. Results The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (ZAUC = -3.029, ZACC = -4.553; p < 0.05). Conclusions Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours. Key Points • Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics. • A radiomics model helps clinicians to identify the histology of a sacral tumour. • CTE features should be preferred.
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Keywords {🔍}

article, pubmed, google, scholar, radiomics, features, sacral, cancer, cell, central, imaging, radiomic, chordoma, methods, lung, cas, selection, giant, tumour, based, glm, access, learning, radiol, radiology, classifiers, differentiation, computed, tomography, cte, lasso, classification, auc, acc, med, prognostic, privacy, cookies, content, analysis, machinelearning, mao, sgct, patients, performance, sacrum, machine, res, mri, eur,

Topics {✒️}

month download article/chapter receiver-operating characteristic curve radiomic machine-learning classifiers radiomics machine-learning classifiers 18f-fdg pet/ct radiotherapy-induced parotid shrinkage aid clinical decision-making related subjects enhanced computed tomography significant statistical expertise support vector machines machine learning methods giant cell tumor full article pdf institutional review board privacy choices/manage cookies joint fdg-pet 1007/s00330-017-5221-1 sun di salvatore mg lung cancer histology machine learning decoding tumour phenotypes peking university people nan hong advanced nasopharyngeal carcinoma european economic area radiologic risk models tumor radiomic heterogeneity histologic subtype classification ct image features article yin synchronous distant metastasis solitary pulmonary lesions o'regan kn di donato sl personalized medicine lymph node metastasis predict treatment response texture analysis based mri texture features conditions privacy policy ct texture analysis secondary malignant tumours soft-tissue sarcomas ethics declarations guarantor check access ieee access 2 instant access quantitative radiomics studies quantitative radiomic biomarkers

Questions {❓}

Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems?
Ji T, Guo W, Yang R, Tang X, Wang Y, Huang L (2017) What are the conditional survival and functional outcomes after surgical treatment of 115 patients with sacral chordoma?

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