Classification of benign and malignant bone lesions on CT imagesusing support vector machine: A comparison of kernel functions

Kumar, Rishav; Suhas, M. V.

doi:10.1109/rteict.2016.7807941

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Most studies enrolled consecutive or randomized cases and avoided case-control designs, with reasonable exclusions. Six papers (16,20,23,38,40,47) involved selective inclusion of cases, three papers (20,21,38) were case-control studies, and two papers (28,43) did not specify the type of study, which could lead to potential case selection bias. Three papers (25,32,43) were unable to derive a diagnostic fourfold table due to missing data.…”

Section: Risk Of Bias Assessment Resultsmentioning

confidence: 99%

“…The 31 original studies included in our systematic review were published mainly between 2021 and 2022, covering 382,371 samples, of which 141,315 were malignant bone tumor samples. The countries of publication contained China (15,16,29,30,34,35,38,43,45,48), the USA (23), Korea (33, 49), Germany (39, 40), Italy (22,26,27), Japan (44), India (28,37), Spain (32), Thailand (31), and Saudi Arabia (24). The type of study was mainly retrospective.…”

Section: Basic Characteristics Of the Included Literaturementioning

confidence: 99%

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The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

Li,

Dong,

Yuan

2023

Front. Oncol.

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BackgroundMalignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches.ResultsThe inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set.ConclusionMachine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.

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Section: Risk Of Bias Assessment Resultsmentioning

confidence: 99%

Section: Basic Characteristics Of the Included Literaturementioning

confidence: 99%

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

Li,

Dong,

Yuan

2023

Front. Oncol.

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“…19 Models for the analysis of bone lesions on cross-sectional imaging have also been developed. CADx systems for differentiating between malignant and benign vertebral osseous lesions on computed tomography (CT) were presented by Kumar and Suhas, 20 who used an SVM classifier, and Mishra and Suhas, 21 who used a random forest classification system based on extracted Haralick texture features.…”

Section: Bone Tumor Diagnosismentioning

confidence: 99%

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Gorelik

Chong

Lin

2020

Semin Musculoskelet Radiol

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Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

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“…However, research using radiomics nomograms for bone tumours is relatively limited. Textural analysis of CT imaging has been applied for assessment of bone lesions, but the accuracy was low (77.8-86%) [9][10][11]. These studies nonetheless provided a new approach to bone tumour diagnosis using quantitative imaging.…”

Section: Introductionmentioning

confidence: 99%

A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours

et al. 2021

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Background We sought to evaluate the performance of a computed tomography (CT)-based radiomics nomogram we devised in distinguishing benign from malignant bone tumours. Methods Two hundred and six patients with bone tumours were spilt into two groups: a training set (n = 155) and a validation set (n = 51). A feature extraction process based on 3D Slicer software was used to extract the radiomics features from unenhanced CT images, and least absolute shrinkage and selection operator logistic regression was used to calculate the radiomic score to generate a radiomics signature. A clinical model comprised demographics and CT features. A radiomics nomogram combined with the clinical model and the radiomics signature was constructed. The performance of the three models was comprehensively evaluated from three aspects: identification ability, accuracy, and clinical value, allowing for generation of an optimal prediction model. Results The radiomics nomogram comprised clinical and radiomics signature features. The nomogram model displayed good performance in training and validation sets with areas under the curve of 0.917 and 0.823, respectively. The areas under the curve, decision curve analysis, and net reclassification improvement showed that the radiomics nomogram model could obtain better diagnostic performance than the clinical model and achieve greater clinical net benefits than the clinical and radiomics signature models alone. Conclusions We constructed a combined nomogram comprising a clinical model and radiomics signature as a noninvasive preoperative prediction method to distinguish between benign and malignant bone tumours and assist treatment planning.

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Classification of benign and malignant bone lesions on CT imagesusing support vector machine: A comparison of kernel functions

Cited by 8 publications

References 7 publications

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours

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