Histogram analysis in diffusion-weighted imaging in differentiating breast masses
Abstract Background Gray-scale histogram analysis has been submitted to evaluate the heterogeneity of the diffusion distribution among different sorts of tumors in the body. Measures obtained from apparent diffusion coefficient (ADC) histograms reflect the histopathological heterogeneity, distributi...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | The Egyptian Journal of Radiology and Nuclear Medicine |
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| Online Access: | https://doi.org/10.1186/s43055-024-01415-8 |
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| author | Dina EL-Metwally Amina Ahmed Ahmed Sultan Shohanda Abdelmonem Mahmoud Eltelbany |
| author_facet | Dina EL-Metwally Amina Ahmed Ahmed Sultan Shohanda Abdelmonem Mahmoud Eltelbany |
| author_sort | Dina EL-Metwally |
| collection | DOAJ |
| description | Abstract Background Gray-scale histogram analysis has been submitted to evaluate the heterogeneity of the diffusion distribution among different sorts of tumors in the body. Measures obtained from apparent diffusion coefficient (ADC) histograms reflect the histopathological heterogeneity, distributions of cellular density, and tissue degeneration. This can supply a more credible base for recognition, categorization, and prognosis assessment of benign and malignant tumors. The aim of this work was to assess the role of ADC histogram analysis in differentiating benign from malignant breast lesions. Results Among ADC histogram parameters, there was significant difference between benign and malignant lesions regarding to ADC mean being 1.59 ± 0.32 for benign tumors versus 0.871 ± 0.29 for malignant tumors (P value < 0.001), ADC minimum being 1.09 ± 0.44 for benign lesions versus 0.432 ± 0.327 malignant lesions (P value < 0.001), ADC maximum being 1.92 ± 0.387 for benign lesions versus 1.27 ± 0.390 for malignant lesions (P value < 0.001), and kurtosis being 3.71 ± 2.54 for benign lesions versus 6.23 ± 3.82 for malignant lesions (P value = 0.007). Among ADC histogram parameters, ADC mean had the highest diagnostic performance with AUC (0.959), specificity (95.7%), and accuracy (93.3%). Conclusion ADC histogram analysis is used as sensitive and specific technique in differentiating benign from malignant breast lesions with ADC mean showing the highest diagnostic performance among ADC histogram parameters. |
| format | Article |
| id | doaj-art-5edba1cf95a646e08b1ed15a03df30e6 |
| institution | Kabale University |
| issn | 2090-4762 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | The Egyptian Journal of Radiology and Nuclear Medicine |
| spelling | doaj-art-5edba1cf95a646e08b1ed15a03df30e62024-12-29T12:14:08ZengSpringerOpenThe Egyptian Journal of Radiology and Nuclear Medicine2090-47622024-12-0155111210.1186/s43055-024-01415-8Histogram analysis in diffusion-weighted imaging in differentiating breast massesDina EL-Metwally0Amina Ahmed Ahmed Sultan1Shohanda Abdelmonem Mahmoud Eltelbany2Faculty of Medicine, Mansoura UniversityFaculty of Medicine, Mansoura UniversityMinistry of HealthAbstract Background Gray-scale histogram analysis has been submitted to evaluate the heterogeneity of the diffusion distribution among different sorts of tumors in the body. Measures obtained from apparent diffusion coefficient (ADC) histograms reflect the histopathological heterogeneity, distributions of cellular density, and tissue degeneration. This can supply a more credible base for recognition, categorization, and prognosis assessment of benign and malignant tumors. The aim of this work was to assess the role of ADC histogram analysis in differentiating benign from malignant breast lesions. Results Among ADC histogram parameters, there was significant difference between benign and malignant lesions regarding to ADC mean being 1.59 ± 0.32 for benign tumors versus 0.871 ± 0.29 for malignant tumors (P value < 0.001), ADC minimum being 1.09 ± 0.44 for benign lesions versus 0.432 ± 0.327 malignant lesions (P value < 0.001), ADC maximum being 1.92 ± 0.387 for benign lesions versus 1.27 ± 0.390 for malignant lesions (P value < 0.001), and kurtosis being 3.71 ± 2.54 for benign lesions versus 6.23 ± 3.82 for malignant lesions (P value = 0.007). Among ADC histogram parameters, ADC mean had the highest diagnostic performance with AUC (0.959), specificity (95.7%), and accuracy (93.3%). Conclusion ADC histogram analysis is used as sensitive and specific technique in differentiating benign from malignant breast lesions with ADC mean showing the highest diagnostic performance among ADC histogram parameters.https://doi.org/10.1186/s43055-024-01415-8ADC histogramBenignMalignantADC meanKurtosis |
| spellingShingle | Dina EL-Metwally Amina Ahmed Ahmed Sultan Shohanda Abdelmonem Mahmoud Eltelbany Histogram analysis in diffusion-weighted imaging in differentiating breast masses The Egyptian Journal of Radiology and Nuclear Medicine ADC histogram Benign Malignant ADC mean Kurtosis |
| title | Histogram analysis in diffusion-weighted imaging in differentiating breast masses |
| title_full | Histogram analysis in diffusion-weighted imaging in differentiating breast masses |
| title_fullStr | Histogram analysis in diffusion-weighted imaging in differentiating breast masses |
| title_full_unstemmed | Histogram analysis in diffusion-weighted imaging in differentiating breast masses |
| title_short | Histogram analysis in diffusion-weighted imaging in differentiating breast masses |
| title_sort | histogram analysis in diffusion weighted imaging in differentiating breast masses |
| topic | ADC histogram Benign Malignant ADC mean Kurtosis |
| url | https://doi.org/10.1186/s43055-024-01415-8 |
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