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Endometrial cancer tissue features clusterization by kurtosis MRI.

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Title: Endometrial cancer tissue features clusterization by kurtosis MRI.
Authors: Maiuro, Alessandra1,2 (AUTHOR), Di Stadio, Francesca1 (AUTHOR), Palombo, Marco3,4 (AUTHOR), Ciardiello, Andrea5 (AUTHOR), Satta, Serena6 (AUTHOR), Pernazza, Angelina6 (AUTHOR), Leopizzi, Martina6 (AUTHOR), Rocca, Carlo Della6 (AUTHOR), Catalano, Carlo6 (AUTHOR), Manganaro, Lucia6 (AUTHOR), Capuani, Silvia2,7 (AUTHOR) silvia.capuani@cnr.it
Source: Medical Physics. May2025, Vol. 52 Issue 5, p2898-2908. 11p.
Subjects: Endometrial cancer, Diffusion magnetic resonance imaging, Prognosis, Tissue differentiation, Magnetic resonance imaging, Histopathology, Measuring instruments, Tissue analysis
Abstract: Background: Endometrial cancer (EC) is one of the most common gynecological malignancies and the second most common gynecological malignancy cause of death in women. Heterogeneous tissues with different grades of complexity and different diffusion properties characterize the EC. Several diffusion magnetic resonance imaging (DMRI) protocols have been used to perform a non‐invasive and global evaluation of EC for diagnostic and prognostic purposes. However, the association of a single value for the diffusion coefficient to an EC tissue could be a severe limit for developing a DMRI virtual histology protocol. Purpose: This study evaluates the potential of diffusion kurtosis imaging (DKI) and tissue multiple diffusion clusterization in detecting the specific features of healthy/cancer tissue that can be useful in EC diagnosis and prognosis. Methods: Thirty‐eight subjects were analyzed: 18 with a final diagnosis of EC and 20 healthy, asymptomatic, with no history of endometrial pathology and uterine tumor pathology. Diffusion‐weighted Spin‐Echo Echo‐Planar Imaging (DW‐EPI) with TR/TE = 2000 ms/77 ms was used at 3T using six different b‐values: (500, 800, 1000, 1500, 2000, and 2500)s/mm2 along three gradient directions (x, y, z). The decay of the signal in each voxel was used to obtain clusters of different diffusion compartments reflecting tissue heterogeneity. Moreover, using the Kurtosis representation, the parametric maps of the apparent kurtosis (K) and diffusivity (D) coefficients were obtained. The statistical analysis of the differences in the mean value of the parameters obtained in the selected regions of interest (ROIs) in tumor area (T) peritumor area (PT) and healthy tissue was carried out using a Kruskal–Wallis Test. A p‐value < 0.05 indicated a statistically significant difference. To validate DKI and multiple diffusion clusterization in the detection of EC and healthy tissue, DMRI results were compared with EC histology. A ROC curve analysis was performed to evaluate the performance of the clustering feature in differentiating healthy and tumoral tissues. Results: K discriminates the peritumor area (PT) of the tumor from the healthy tissues (p < 0.05) and the area inside the EC (cancerous tissue, p < 0.05). This result is validated and explained by the diffusion clustering, which shows a great variability in K for pathological compared to healthy subjects. Moreover, the standard deviation of K in the cluster defined by the highest K/D ratio differentiates T and H ROIs. Conclusions: K as well as diffusion clusterization are sensitive to the different microstructural organizations in EC and healthy tissue, promoting themself as a potential tool for the diagnosis and prognosis of EC. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Endometrial cancer tissue features clusterization by kurtosis MRI.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Maiuro%2C+Alessandra%22&quot;&gt;Maiuro, Alessandra&lt;/searchLink&gt;&lt;relatesTo&gt;1,2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Di+Stadio%2C+Francesca%22&quot;&gt;Di Stadio, Francesca&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Palombo%2C+Marco%22&quot;&gt;Palombo, Marco&lt;/searchLink&gt;&lt;relatesTo&gt;3,4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Ciardiello%2C+Andrea%22&quot;&gt;Ciardiello, Andrea&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Satta%2C+Serena%22&quot;&gt;Satta, Serena&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Pernazza%2C+Angelina%22&quot;&gt;Pernazza, Angelina&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Leopizzi%2C+Martina%22&quot;&gt;Leopizzi, Martina&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Rocca%2C+Carlo+Della%22&quot;&gt;Rocca, Carlo Della&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Catalano%2C+Carlo%22&quot;&gt;Catalano, Carlo&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Manganaro%2C+Lucia%22&quot;&gt;Manganaro, Lucia&lt;/searchLink&gt;&lt;relatesTo&gt;6&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Capuani%2C+Silvia%22&quot;&gt;Capuani, Silvia&lt;/searchLink&gt;&lt;relatesTo&gt;2,7&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; silvia.capuani@cnr.it&lt;/i&gt;
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Medical+Physics%22&quot;&gt;Medical Physics&lt;/searchLink&gt;. May2025, Vol. 52 Issue 5, p2898-2908. 11p.
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  Data: Background: Endometrial cancer (EC) is one of the most common gynecological malignancies and the second most common gynecological malignancy cause of death in women. Heterogeneous tissues with different grades of complexity and different diffusion properties characterize the EC. Several diffusion magnetic resonance imaging (DMRI) protocols have been used to perform a non‐invasive and global evaluation of EC for diagnostic and prognostic purposes. However, the association of a single value for the diffusion coefficient to an EC tissue could be a severe limit for developing a DMRI virtual histology protocol. Purpose: This study evaluates the potential of diffusion kurtosis imaging (DKI) and tissue multiple diffusion clusterization in detecting the specific features of healthy/cancer tissue that can be useful in EC diagnosis and prognosis. Methods: Thirty‐eight subjects were analyzed: 18 with a final diagnosis of EC and 20 healthy, asymptomatic, with no history of endometrial pathology and uterine tumor pathology. Diffusion‐weighted Spin‐Echo Echo‐Planar Imaging (DW‐EPI) with TR/TE = 2000 ms/77 ms was used at 3T using six different b‐values: (500, 800, 1000, 1500, 2000, and 2500)s/mm2 along three gradient directions (x, y, z). The decay of the signal in each voxel was used to obtain clusters of different diffusion compartments reflecting tissue heterogeneity. Moreover, using the Kurtosis representation, the parametric maps of the apparent kurtosis (K) and diffusivity (D) coefficients were obtained. The statistical analysis of the differences in the mean value of the parameters obtained in the selected regions of interest (ROIs) in tumor area (T) peritumor area (PT) and healthy tissue was carried out using a Kruskal–Wallis Test. A p‐value &lt; 0.05 indicated a statistically significant difference. To validate DKI and multiple diffusion clusterization in the detection of EC and healthy tissue, DMRI results were compared with EC histology. A ROC curve analysis was performed to evaluate the performance of the clustering feature in differentiating healthy and tumoral tissues. Results: K discriminates the peritumor area (PT) of the tumor from the healthy tissues (p &lt; 0.05) and the area inside the EC (cancerous tissue, p &lt; 0.05). This result is validated and explained by the diffusion clustering, which shows a great variability in K for pathological compared to healthy subjects. Moreover, the standard deviation of K in the cluster defined by the highest K/D ratio differentiates T and H ROIs. Conclusions: K as well as diffusion clusterization are sensitive to the different microstructural organizations in EC and healthy tissue, promoting themself as a potential tool for the diagnosis and prognosis of EC. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.17718
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        Text: English
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              Text: May2025
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