Skip to main content
OpenTrials
Completed

NCT Number: NCT06976333

Tumor-Infiltrating Lymphocytes in Endometrial Cancer

Endometrial cancer (EC) is a leading cancer among women globally. The tumor microenvironment in EC is characterized by complex interactions between cancer cells and immune components. Among these proteins, CD133, WNT-1, and mTOR have emerged as key molecular markers with potential prognostic and therapeutic implications in EC. Understanding the association between these molecular alterations and the immune contexture of EC can provide valuable insights into EC biology and lead to the identification of novel therapeutic targets.

In this study, the spatial organization of tumor-infiltrating lymphocytes (TILs) in EC and their correlations with tumor grade, stage, and subcellular CD133, WNT-1, and mTOR expression were investigated. Artificial intelligence-assisted image analysis was performed to quantify TIL metrics, including TIL percentage, grey level co-occurrence matrix (GLCM M1 and M2) parameters, and fractal dimension (FD).

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Endometrium Cancer Endometrial Neoplasms Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genital Diseases Genital Diseases, Female Genital Neoplasms, Female Neoplasms Neoplasms by Site Tumor Infiltration Urogenital Diseases Urogenital Neoplasms Uterine Diseases Uterine Neoplasms

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Jagiellonian University

Krakow, Poland

About this study

The study was conducted using properly stored archival formalin-fixed paraffin-embedded tissue blocks. The inclusion criteria required a confirmed diagnosis of EC, adequate quality of archival material, absence of prior neoadjuvant treatment, and complete medical documentation. Tumor staging followed the FIGO classification system based on surgical protocols and pathomorphological examination results. For analytical purposes, patients results were stratified into two groups based on tumor grade: a low-grade group (grade 1 and 2) and a high-grade group (grade 3). Cancer cells and lymphocytes were identified using Hover-Net, a state-of-the-art nucleic segmentation and classification algorithm. Detected cells were categorized into six categories: unlabeled, neoplastic (cancer), inflammatory (TILs, i.e., lymphocytes and plasma cells), connective, necrosis, and non-neoplastic. To estimate cancer areas from cancer cell segmentation masks, a novel block-processing algorithm optimized for large image analysis, was developed. For each tissue sample, the TIL percentage as the area occupied by lymphocytes divided by the cancer area, expressed as a percentage, was calculated. TIL distribution maps were constructed using tissue segmentation masks, cancer region masks, and TIL segmentation masks. Spatial TIL metrics were subsequently calculated based on GLCM analysis and FD. After grey level co-occurrence matrix (GLCM) calculation, different weights were applied to each matrix element to derive two measures: M1 and M2, representing areas with low and high intensities, respectively. Lower M1 and higher M2 values characterized more structured images with distinct TIL patterns. FD provided a statistical index of pattern complexity in geometric structures. A curve with an FD close to 1 resembles an ordinary line (simple structure), while curves with higher FD values exhibit convoluted spatial arrangements resembling spaces. Higher FD values thus indicate more structured and complex TIL distribution patterns.

Data were analyzed using Dell Statistica software v13.3 (TIBCO Software Inc., Palo Alto, California, United States) and MedCalc Statistical Software v19.2.6 (MedCalc Software, Ostend, Belgium).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • confirmed diagnosis of EC
  • adequate quality of archival material
  • absence of prior neoadjuvant treatment
  • complete medical documentation

Exclusion criteria

  • none

Treatment and study plan

Tumor-infliltrating lymphocyte (TIL) percentage

Diagnostic Test

TIL percentage was calculated as the area occupied by lymphocytes divided by the cancer area, expressed as a percentage [%]

Grey level co-occurrence matrix (GLCM)

Diagnostic Test

The GLCM is a second-order statistical method for texture feature extraction. Structured images typically contain numerous pixel pairs with co-occurring low- and high-intensity values. After GLCM calculation, different weights were applied to each matrix element to derive two measures: M1 and M2, representing areas with low and high intensities, respectively. Lower M1 and higher M2 values characterized more structured images with distinct TIL patterns.

Fractal dimension (FD)

Diagnostic Test

Quantification of the complexity of TIL

Primary outcomes

  1. TIL percentage [%]

    Time frame: up to 6 months

    TIL percentage is the area occupied by lymphocytes divided by the cancer area, expressed as a percentage [%]

  2. GLCM M1 (×10⁶)

    Time frame: up to 6 months

    GLCM is a second-order statistical method for texture feature extraction. Structured images typically contain numerous pixel pairs with co-occurring low- and high-intensity values. After GLCM calculation, different weights were applied to each matrix element to derive two measures: M1 and M2, representing areas with low and high intensities, respectively. Lower M1 and higher M2 values characterize more structured images with distinct TIL patterns. GLCM M1 values are scaled and expressed in millions (×10⁶), with lower values indicating more structured TIL patterns.

  3. FD

    Time frame: up to 6 months

    FD provides a statistical index of pattern complexity in geometric structures. Higher FD values thus indicate more structured and complex TIL distribution patterns

  4. GLCM M2 (×10³)

    Time frame: up to 6 months

    GLCM is a second-order statistical method for texture feature extraction. Structured images typically contain numerous pixel pairs with co-occurring low- and high-intensity values. After GLCM calculation, different weights were applied to each matrix element to derive two measures: M1 and M2, representing areas with low and high intensities, respectively. Lower M1 and higher M2 values characterize more structured images with distinct TIL patterns. GLCM M2 values are scaled and expressed in thousands (×10³), with higher values indicating increased TIL clustering.

Sponsors and collaborators

Lead sponsor

Jagiellonian University

Other

Registry information

Official study title

Tumor-Infiltrating Lymphocytes in Endometrial Cancer: Correlations With Tumor Grade, Stage, and Subcellular CD133, WNT-1, and mTOR Expression

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
May 16, 2025
Registry last updated
May 16, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.