Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database. 91 radiomics features were extracted from ADC maps and T1C images, and 566 pathomic features from cell detections and cell density maps at four different resolutions. Spearman's correlation and Bayes Factor analysis were used to evaluate radio-pathomic correlations. Significant cross-scale correlations were found, with strengths ranging from 0.57 to 0.89 in absolute value (9.47 × 10<sup>4</sup> < BF < 4.77 × 10<sup>14</sup>) for the ADC task, and from 0.64 and 0.70 (1.80 × 10<sup>4</sup> < BF < 5.69 × 10<sup>5</sup>) for the T1C task. Most significant and high cross-scale associations were observed between ADC textural features and features from cell density maps. Correlations involving morphometric features and ADC and T1C first-order features were also observed, reflecting variations in tumor aggressiveness and tissue composition. These findings suggest that correlating radiomic features from ADC and T1C features with histopathological features can enhance understanding of EC intratumoral heterogeneity.

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Publication Details

Journal
Scientific Reports
Published
2024-12-28
DOI
https://doi.org/10.1038/s41598-024-78987-y
Citations
4
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.62

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article

Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma

Valentina Brancato, Nunzia Garbino, Marco Aiello, Carlo Cavaliere et al.
4 citations
Scientific Reports
Radiomics and Machine Learning in Medical Imaging
1.62
article

Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma

Valentina Brancato, Nunzia Garbino, Marco Aiello, Carlo Cavaliere, Marco Salvatore
article en
4 citations

Abstract

Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database. 91 radiomics features were extracted from ADC maps and T1C images, and 566 pathomic features from cell detections and cell density maps at four different resolutions. Spearman's correlation and Bayes Factor analysis were used to evaluate radio-pathomic correlations. Significant cross-scale correlations were found, with strengths ranging from 0.57 to 0.89 in absolute value (9.47 × 10<sup>4</sup> < BF < 4.77 × 10<sup>14</sup>) for the ADC task, and from 0.64 and 0.70 (1.80 × 10<sup>4</sup> < BF < 5.69 × 10<sup>5</sup>) for the T1C task. Most significant and high cross-scale associations were observed between ADC textural features and features from cell density maps. Correlations involving morphometric features and ADC and T1C first-order features were also observed, reflecting variations in tumor aggressiveness and tissue composition. These findings suggest that correlating radiomic features from ADC and T1C features with histopathological features can enhance understanding of EC intratumoral heterogeneity.

Scientific ReportsVol. 14(1)
Synlab Czech (Czechia) (CZ), SDN Istituto di Ricerca Diagnostica e Nucleare (IT)
Ministero della Salute, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 15%
Radiomics and Machine Learning in Medical Imaging
1.62
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