Diagnostic methods for distinguishing benign and malignant tumors
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Imaging-Based Diagnostic Methods for Benign vs. Malignant Tumors
Radiomics and Deep Learning in Soft Tissue Tumors
Radiomics and deep learning (DL) methods using MRI and CT imaging have shown high accuracy in distinguishing benign from malignant soft tissue tumors. These approaches analyze quantitative imaging features and use machine learning models to classify tumors, achieving combined sensitivity and specificity as high as 0.84 and 0.88, respectively. The integration of multimodal data and rigorous study designs are recommended to further improve diagnostic accuracy and clinical utility 1458.
MRI and Ultrasound for Breast Tumors
MRI and ultrasound (US) are widely used for differentiating benign and malignant breast tumors. MRI alone or in combination with US provides higher diagnostic accuracy, especially in patients with dense breast tissue. The combined approach yields the highest sensitivity and negative predictive value, making it valuable for early breast cancer detection . Diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) MRI techniques also offer high sensitivity and specificity, with no significant difference in diagnostic accuracy between the two methods .
Simplified MRI Criteria for Soft Tissue Tumors
A systematic MRI approach using tumor size, signal intensity heterogeneity, and depth can help non-experts distinguish between benign and malignant soft tissue tumors. The combination of these features, especially signal intensity and size, improves diagnostic accuracy, with specificity up to 85% .
Multiparametric MRI and Machine Learning for Orbital Tumors
For orbital tumors, machine learning models that integrate multiparametric MRI data—including dynamic contrast-enhanced, DWI, IVIM, and morphological imaging—achieve high diagnostic performance. A streamlined model using a select set of features can provide nearly the same accuracy as more complex models, making it practical for clinical use .
CT-Based Radiomics and Biopsy for Bone Tumors
CT-based radiomics nomograms, which combine clinical and imaging features, are effective for distinguishing benign from malignant bone tumors, with areas under the curve (AUC) exceeding 0.8. In pediatric cases, CT-guided needle biopsy is highly accurate, with sensitivity and specificity close to 100%, and imaging findings are crucial for interpreting indeterminate biopsy results 810.
Biomarker-Based Diagnostic Methods
Serum Protein Biomarkers for Ovarian Tumors
Serum biomarkers, particularly CA125, play a dominant role in distinguishing malignant from benign ovarian tumors. Combining CA125 with other markers such as HE4 and CA72.4 can further improve diagnostic accuracy, especially for differentiating between stages of malignancy .
Conclusion
Modern diagnostic methods for distinguishing benign from malignant tumors rely heavily on advanced imaging techniques—such as MRI, US, CT, and radiomics—as well as machine learning models that integrate clinical and imaging data. These approaches offer high sensitivity and specificity across various tumor types and anatomical locations. Additionally, serum biomarkers remain important for certain tumors, such as ovarian masses. Combining multiple diagnostic modalities and leveraging artificial intelligence can further enhance diagnostic accuracy and support clinical decision-making.
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