Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging

Sharma, Garima; Saran, Sonal; Saxena, Sudhir; Goyal, Tarun

doi:10.1016/j.jcot.2022.101899

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…MRI plays a key role in aiding clinicians in discriminating between benign vs. primary malignant or metastatic bone tumours [ 143 ]. The conventional pulse sequences [ 20 , 144 ], diffusion-weighted imaging (DWI) [ 145 , 146 ] with matched apparent diffusion coefficient (ADC) maps [ 147 , 148 ] as well as dynamic contrast sequences (DCE) [ 149 , 150 ] can predict potential malignancy with good reliability. However, some imaging features of benign vs. malignant bone lesions can overlap and this makes formulating a differential diagnosis challenging [ 151 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Ong

Zhu

Tan

et al. 2023

Cancers

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An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

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Section: Discussionmentioning

confidence: 99%

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Ong

Zhu

Tan

et al. 2023

Cancers

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“…It can help with the differentiation between benign and malignant bone tumors by providing insight into the cellularity of lesions, where malignant tumors have markedly increased cellularity which can lead to more restricted diffusion reflected by high DWI signal and a low apparent diffusion coefficient (ADC) value (5)(6)(7). Dynamic contrast-enhanced MRI (DCE-MRI) has also been widely explored in bone tumor evaluation based on lesion enhancement pattern, which is shown semi-quantitatively with evaluation of the time intensity curve and quantitatively with pharmacokinetic model determination (8,9). While the use of DWI and/or DCE-MRI for bone tumor evaluation still requires familiarity with potential diagnostic pitfalls due to technical challenges and other confounding biochemical factors, such as the large amount of lipids contained in the bone marrow (3,10).…”

Section: Introductionmentioning

confidence: 99%

Preliminary exploration of amide proton transfer weighted imaging in differentiation between benign and malignant bone tumors

Li,

Lin,

Zhang

et al. 2024

Front. Oncol.

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PurposeTo explore the value of 3D amide proton transfer weighted imaging (APTWI) in the differential diagnosis between benign and malignant bone tumors, and to compare the diagnostic performance of APTWI with traditional diffusion-weighted imaging (DWI).Materials and methodsPatients with bone tumors located in the pelvis or lower limbs confirmed by puncture or surgical pathology were collected from January 2021 to July 2023 in the First Affiliated Hospital of Zhengzhou University. All patients underwent APTWI and DWI examinations. The magnetization transfer ratio with asymmetric analysis at the frequency offset of 3.5 ppm [MTRasym(3.5 ppm)] derived by APTWI and the apparent diffusion coefficient (ADC) derived by DWI for the tumors were measured. The Kolmogorou-Smirnou and Levene normality test was used to confirm the normal distribution of imaging parameters; and the independent sample t test was used to compare the differences in MTRasym(3.5 ppm) and ADC between benign and malignant bone tumors. In addition, the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different imaging parameters in differentiation between benign and malignant bone tumors. P<0.05 means statistically significant.ResultsAmong 85 bone tumor patients, 33 were benign and 52 were malignant. The MTRasym(3.5 ppm) values of malignant bone tumors were significantly higher than those of benign tumors, while the ADC values were significantly lower in benign tumors. ROC analysis shows that MTRasym(3.5 ppm) and ADC values perform well in the differential diagnosis of benign and malignant bone tumors, with the area under the ROC curve (AUC) of 0.798 and 0.780, respectively. Combination of MTRasym(3.5 ppm) and ADC values can further improve the diagnostic performance with the AUC of 0.849 (sensitivity = 84.9% and specificity = 73.1%).ConclusionMTRasym(3.5 ppm) of malignant bone tumors was significantly higher than that of benign bone tumors, reflecting the abnormal increase of protein synthesis in malignant tumors. APTWI combined with DWI can achieve a high diagnostic efficacy in differentiation between benign and malignant bone tumors.

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Advances in Musculoskeletal Tumor Imaging

Murthy,

Thakur,

Ghasemi

et al. 2024

Seminars in Roentgenology

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Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging

Cited by 5 publications

References 21 publications

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Preliminary exploration of amide proton transfer weighted imaging in differentiation between benign and malignant bone tumors

Advances in Musculoskeletal Tumor Imaging

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