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 < BF < 4.77 × 10) for the ADC task, and from 0.64 and 0.70 (1.80 × 10 < BF < 5.69 × 10) 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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http://dx.doi.org/10.1038/s41598-024-78987-y | DOI Listing |
Sci Rep
December 2024
IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy.
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.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Provincial Clinical Research Center for Breast Cancer, Wuhan Clinical Research, Wuhan, 430070, People's Republic of China.
Background: Axillary lymph node (ALN) is the most common metastasis path for breast cancer, and ALN dissection directly affects the postoperative staging and prognosis of breast cancer patients. Therefore, additional research is needed to accurately predict ALN metastasis before surgery and construct predictive models to assist in surgical decision-making and optimize patient care.
Methods: We retrospectively analyzed the clinical data, radiomics, and pathomics of the patients diagnosed with breast cancer in the Breast Cancer Center of Hubei Cancer Hospital from January 2017 to December 2022.
Med Biol Eng Comput
December 2024
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518071, China.
Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed.
View Article and Find Full Text PDFBMC Cancer
December 2024
Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
Objective: This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM).
Methods: A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images.
Transl Oncol
January 2025
Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, PR China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China. Electronic address:
Objective: This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients.
Materials And Methods: This study involved 126 HCC patients who underwent hepatectomy and were followed for more than 5 years. Radiomic features were extracted from arterial-phase (AP) and portal venous-phase (PVP) MRI scans, whereas pathomic features were obtained from whole-slide images (WSIs) of the HCC patients.
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