Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography

Wu, Hongxun; Deng, Zhaohong; Zhang, Bingjie; Liu, Qianyun; Chen, Junyong

doi:10.2214/ajr.15.15813

Cited by 51 publications

(38 citation statements)

References 29 publications

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“…The quality of the included studies is summarized in online supplementary Table 2. The risk of bias from patient selection was judged to be high or unclear in 13 of the included studies: 4 studies limited the nodule size within a certain scope [16,17,21,25]; 5 studies excluded difficult-to-diagnose nodules [15,[25][26][27]31]; and 4 studies were unclear about whether there were selected co-horts and inappropriate exclusions [14,19,23,29]. The risk of bias from the reference standard was considered to be unclear in 2 of the included studies [14,23].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…The flow diagram of the literature search is shown in Figure 1. Nineteen studies with 4,781 nodules used in external validation sets were included in the study, including 6 studies on classic machine learning-based CAD sys-DOI: 10.1159/000504390 tems [14][15][16][17][18][19] and 13 studies on deep learning-based CAD systems [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The general characteristics of the included studies are shown in Table 1, and the detailed characteristics are demonstrated in online supplementary Table 1 (see www.karger.com/doi/10.1159/000504390 for all online suppl.…”

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

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Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning-and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

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Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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“…However, when the diagnostic performances of quantitative analysis and visual assessment by radiologists were compared in predicting thyroid malignancy, quantitative analysis showed relatively inferior diagnostic performance compared to traditional grayscale US . A number of machine learning algorithms using US images have been investigated for the differential diagnosis of thyroid nodules and have shown conflicting results when diagnostic performances were compared between these algorithms and visual assessment by radiologists . The convolutional neural network (CNN) is a typical type of deep learning technique with fully trainable models and is accepted as a state‐of‐the‐art method .…”

Section: Introductionmentioning

confidence: 99%

“…13,14 A number of machine learning algorithms using US images have been investigated for the differential diagnosis of thyroid nodules and have shown conflicting results when diagnostic performances were compared between these algorithms and visual assessment by radiologists. [15][16][17][18][19][20] The convolutional neural network (CNN) is a typical type of deep learning technique with fully trainable models and is accepted as a state-of-the-art method. 21,22 Ma et al 23 first attempted a CNN-based method to classify thyroid nodules and the method was shown to be eligible for thyroid nodule diagnosis, but their study did not enroll patients in a clinical setting and did not compare diagnostic performances between CNN and radiologists.…”

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confidence: 99%

Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Lee

Yoon

et al. 2019

Head & Neck

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Background We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic performance of CNN with that of experienced radiologists. Methods Between May 2012 and February 2015, 589 thyroid nodules in 519 patients were diagnosed as benign or malignant by surgical excision. Experienced radiologists retrospectively reviewed the US of the thyroid nodules in a test set. CNNs were trained and tested using retrospective data of 439 and 150 US images, respectively. Diagnostic performances were compared between the two groups. Results Of the 589 thyroid nodules, 396 were malignant and 193 were benign. The area under the curve (AUC) for diagnosing thyroid malignancy was 0.805‐0.860 for radiologists. The AUCs for diagnosing thyroid malignancy for the three CNNs were 0.845, 0.835, and 0.850. There was no significant difference in AUC between radiologists and CNNs. Conclusions CNNs showed comparable diagnostic performance compared to experienced radiologists in differentiating thyroid malignancy on US.

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“…After the introduction of many and different computational systems [76], a new class of applications has recently emerged in the health care debate: the decision support systems (DSS) that embed predictive models that have been developed by means of machine learning (ML) methods and techniques. These systems, which for the sake of brevity we will call ML-DSS, have recently raised a strong interest among the medical practitioners of almost every corner of the world in virtue of their high accuracy at an unprecedented extent [26,37], in some cases even allegedly capable of outperforming human experts (e.g., [50,93]). This is reflected by the stance of influential commentators and medical experts that have recently shared their thoughts from some of the most impacted journals of the medical community (e.g.…”

Section: Motivations and Backgroundmentioning

confidence: 99%

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

Cabitza

Ciucci

Rasoini

2018

Lecture Notes in Information Systems and Organisation

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This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.

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Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography

Abstract: The machine learning algorithms underperformed with respect to the experienced radiologist's readings used to construct them, and the RBF-NN outperformed the other machine learning algorithm models.

Cited by 51 publications

References 29 publications

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

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