Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules

Çolakoğlu, Bülent; Aliş, Deniz; Yergin, Mert

doi:10.1155/2019/6328329

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…As our sample number was lower compared with that in common industrial cases, 2 clinical indices and 14 CT features were selected; 16 indicators were included in the statistical analysis according to prior clinical knowledge for prediction. ML has been shown to have a great advantage in tumor diagnosis as well as prognosis and recurrence prediction, with an increasing number of reports involving hepatic carcinoma (13), thyroid nodules (14), renal tumors (15) and colon cancer (16), among others. Previous studies have demonstrated that ML-based texture analysis has diagnostic accuracy ranging from 98.3 to 100% for neoplastic lesions of the abdomen.…”

Section: Discussionmentioning

confidence: 99%

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Jin

et al. 2021

Mol Clin Oncol

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Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.

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Section: Discussionmentioning

confidence: 99%

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Jin

et al. 2021

Mol Clin Oncol

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“…Their results indicated that the histogram feature was the most important parameter in classification, and both SVM (94.64%) and random forests (92.42%) achieved high accuracy. Colakoglu et al [ 85 ] attempted to differentiate benign and malignant thyroid nodules using texture analysis and random forest model construction. After testing the reproducibility of all texture features, they finally screened seven texture features from ultrasound images, including one histogram (HistPerc 99), one HOG (HogO8b2), four GRLMs (GrlmHRLNonUni, GrlmHMGLevNonUni, GrlmNRLNonUni, and GrlmZRLNonUni), and one GLCM (GlcmZ3AngScMom), in a random forest model.…”

Section: Radiomics In Thyroid Cancer and Nodule Classificationmentioning

confidence: 99%

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Cao

Zhong

Diao

et al. 2021

Cancers

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Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

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“…The authors in Reference 14 applied one ML technique which is Random Forrest Classifier (RFC) algorithm to perform classification of the thyroid nodules. Similarly, the study in Reference 15 run RFC and present the important classifiers in the textual dataset. The study in Reference 16 developed and validated a ML based radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules.…”

Section: Related Work and Motivationmentioning

confidence: 99%

On the identification of thyroid nodules using semi‐supervised deep learning

Türk

Özdemir

Zeydan

et al. 2021

Numer Methods Biomed Eng

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Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or expert‐based methods. For this reason, the solutions backed by Machine Learning (ML) algorithms are key to improve the detection rates of such rare cases. In this paper, we investigate the application of ML in the healthcare domain for the detection of rare thyroid nodules. The utilized dataset is collected from 636 distinct patients in 99 unique days in Turkey. In addition to the texture feature data of the Ultrasound (US), we have also included the scores of different assessment methods created by different health institutions (e.g., Korean, American and European thyroid societies) as additional features. For detection of extremely rare malign cases, we use auto‐encoder based neural network model. Through numerical results, it is shown that the auto‐encoder based model can result in an average Recall score of 0.98 and a Sensitivity score of 1.00 for detecting malign and non‐malign cases from the healthcare dataset outperforming the traditional classification algorithms that are trained after Synthetic Minority Oversampling Technique (SMOTE) oversampling.

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Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules

Cited by 13 publications

References 36 publications

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

On the identification of thyroid nodules using semi‐supervised deep learning

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