Endometrium CancerNCT06976333
Tumor-Infiltrating Lymphocytes in Endometrial Cancer
An observational study of Endometrium Cancer and Tumor Infiltration, run by Jagiellonian University at 1 site in Poland.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Endometrium Cancer, Tumor Infiltration
- What is being tested?
- Tumor-infliltrating lymphocyte (TIL) percentage, Grey level co-occurrence matrix (GLCM) and Fractal dimension (FD)
- How many people
- 52 participants
- Where
- 1 sitePoland
- Study dates
- 1 Dec 2024to 8 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Archival properly stored formalin-fixed paraffin-embedded tissue blocks from endometrial cancer tissue of women age at leat 18.
Who may be able to join
- confirmed diagnosis of EC
- adequate quality of archival material
- absence of prior neoadjuvant treatment
- complete medical documentation
Who cannot join
- none
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Tumor-Infiltrating Lymphocytes in Endometrial Cancer: Correlations With Tumor Grade, Stage, and Subcellular CD133, WNT-1, and mTOR Expression
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).
Read the detailed description
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).
- Conditions
- Endometrium Cancer, Tumor Infiltration
- Being tested
- Tumor-infliltrating lymphocyte (TIL) percentage, Grey level co-occurrence matrix (GLCM), Fractal dimension (FD)
- Keywords
- endometrial cancer, tumor-infiltrating lymphocytes, response biomarkers, artificial intelligence, immune microenvironment
- Medical subject headings
- Endometrial Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Tumor-infliltrating lymphocyte (TIL) percentageDiagnostic 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
| Study group | Role | What they receive |
|---|---|---|
| Low-grade Endometrial Cancer | Histological grade G1 and G2 Receives: Diagnostic Test: Tumor-infliltrating lymphocyte (TIL) percentage; Diagnostic Test: Grey level co-occurrence matrix (GLCM); Diagnostic Test: Fractal dimension (FD) | |
| High-grade Endometrial Cancer | Histological grade G3 Receives: Diagnostic Test: Tumor-infliltrating lymphocyte (TIL) percentage; Diagnostic Test: Grey level co-occurrence matrix (GLCM); Diagnostic Test: Fractal dimension (FD) |
What is being measured
The primary outcome is the main question the study is designed to answer.
TIL percentage [%]
TIL percentage is the area occupied by lymphocytes divided by the cancer area, expressed as a percentage [%]
Measured: up to 6 months
GLCM M1 (×10⁶)
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.
Measured: up to 6 months
FD
FD provides a statistical index of pattern complexity in geometric structures. Higher FD values thus indicate more structured and complex TIL distribution patterns
Measured: up to 6 months
GLCM M2 (×10³)
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.
Measured: up to 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 May 2025
- Study start1 Dec 2024
- Primary completion30 Apr 2025
- Study completion8 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endometrium CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 52 participants (actual)
Sponsor and investigators
- Lead sponsor
- Jagiellonian University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Iwona Magdalena Gawron, Jagiellonian University
- Investigators
- Milosz Pietrus, PhD, Study chair, Jagiellonian University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06976333
- Sponsor's protocol ID
- 118.0043.1.433.2024
- Status last verified
- May 2025
- First submitted
- 9 May 2025
- Last updated
- 16 May 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Tumor-Infiltrating Lymphocytes in Endometrial Cancer: Correlations With Tumor Grade, Stage, and Subcellular CD133, WNT-1, and mTOR Expression
- ClinicalTrials.gov ID
- NCT06976333
- Acronym / sponsor ID
- 118.0043.1.433.2024
- Conditions
- Endometrium Cancer, Tumor Infiltration
- Keywords
- endometrial cancer, tumor-infiltrating lymphocytes, response biomarkers, artificial intelligence, immune microenvironment
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 52 participants (actual)
- Lead sponsor
- Jagiellonian UniversityUniversity, hospital or other
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 30 Apr 2025 (actual)
- Study completion
- 8 May 2025 (actual)
- First posted
- 16 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed description
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).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Low-grade Endometrial Cancer | – | Diagnostic Test: Tumor-infliltrating lymphocyte (TIL) percentage, Diagnostic Test: Grey level co-occurrence matrix (GLCM), Diagnostic Test: Fractal dimension (FD)Histological grade G1 and G2 |
| High-grade Endometrial Cancer | – | Diagnostic Test: Tumor-infliltrating lymphocyte (TIL) percentage, Diagnostic Test: Grey level co-occurrence matrix (GLCM), Diagnostic Test: Fractal dimension (FD)Histological grade G3 |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Archival properly stored formalin-fixed paraffin-embedded tissue blocks from endometrial cancer tissue of women age at leat 18.
Key inclusion criteria (4 listed)
- confirmed diagnosis of EC
- adequate quality of archival material
- absence of prior neoadjuvant treatment
- complete medical documentation
Key exclusion criteria (1 listed)
- none
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Tumor-infliltrating lymphocyte (TIL) percentageTIL percentage was calculated as the area occupied by lymphocytes divided by the cancer area, expressed as a percentage [%] | Diagnostic test | High-grade Endometrial Cancer, Low-grade Endometrial Cancer |
| Grey level co-occurrence matrix (GLCM)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. | Diagnostic test | High-grade Endometrial Cancer, Low-grade Endometrial Cancer |
| Fractal dimension (FD)Quantification of the complexity of TIL | Diagnostic test | High-grade Endometrial Cancer, Low-grade Endometrial Cancer |
Primary outcome
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 [%]
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.
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
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 May 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Milosz Pietrus, PhD study chair
Jagiellonian University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Endometrium Cancer and Tumor Infiltration, run by Jagiellonian University at 1 site in Poland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Poland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
TIL percentage [%] (time frame: up to 6 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 May 2025.