Early Breast CancerNCT07842068
Artificial Intelligence (AI) - Based Models for Biomarkers Prediction
An observational study of Early Breast Cancer, run by Hellenic Cooperative Oncology Group.
Trial summary
Despite the improvements in therapeutic approaches, breast cancer remains the most commonly occurring cancer and leading cause of cancer death in women worldwide. Breast cancer treatment decisions are based mainly on the presence or absence of established prognostic and predictive biomarkers, mainly the hormone… Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Early Breast Cancer
- How many people
- 3,648 participants
- Where
- No sites listed yet
- Study dates
- 10 Dec 1996to 23 Jul 2026
- 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: patients with operable breast cancer treated with dds-CT including taxanes and anthracyclines. Adjuvant hormonal and radiation treatment were administered, as indicated
Who may be able to join
- Age 18 and above
- Histologically confirmed BC
- All treated with adjuvant dose-dense sequential chemotherapy (dds-CT)
- Tumor tissue specimen (FFPE) availability
Who cannot join
- not adequate, and unsuitable tissue for IHC, FISH and analysis
About this study
Official title: Biomarker Prediction Through Deep Learning Approaches From Histopathological Whole Slide Images in Early Breast Cancer
Despite the improvements in therapeutic approaches, breast cancer remains the most commonly occurring cancer and leading cause of cancer death in women worldwide. Breast cancer treatment decisions are based mainly on the presence or absence of established prognostic and predictive biomarkers, mainly the hormone receptors, estrogen (ER) and progesterone (PgR), the human epidermal growth factor receptor 2 oncoprotein (HER2) in correlation with ki67 proliferation index.
Currently, PCR-based assays, next-generation sequencing (NGS), and immunohistochemistry (IHC), followed by in situ hybridization (ISH) when needed, are used for biomarker testing. Ιn an effort to overpass technical limitations, human subjectivity which may yield different results between different observers leading to inappropriate treatment and complications that can adversely affect patient quality of life, alternative, cost-effective approaches using AI-based digital pathology analysis of histopathology images has received significant attention last years. Digital pathology uses whole-slide image scanners and combined with artificial intelligence (AI) algorithms intend to improve diagnostic accuracy, enabling rapid patient stratification and optimal treatment regimens, thereby improving patient outcomes. Identification of invasive breast tumors and lymph node metastasis, evaluation of hormonal status, breast cancer grading and mitotic count evaluation, biomarkers identification based on images of the tumor microenvironment have also been enhanced by AI quantitative analysis. AI has promising results so far. More studies are, however, needed in order to overcome obstacles and before it is validated for patient care in the clinical setting.
Read more
DL technology has been implemented in clinical pathology in an attempt to automate the way pathologists evaluate hematoxylin and eosin (H/E) stained slides, but also to attain information such as molecular background and protein expression, given that genetic variations could likely be reflected in the morphology of cancer cells, and although not detectable to the human eye, these changes can be recognized by advanced DL algorithms and then correlated with a specific molecular variation or protein expression.
For the purpose of our study, we will use FFPE tumor blocks from patients enrolled in 7 randomized and observational studies conducted by the Hellenic Cooperative Oncology Group (HeCOG) to define via NGS their underlying mutational profile. Subsequently, a DL algorithm will be trained in order to examine if any genetic alterations found in breast cancer could be detected by the DL model in H/E whole section slides, which would suggest that these variants reflect in the morphology of the cancer cells.
Accordingly, we will explore if our algorithm could be trained to identify tumor infiltrating lymphocytes (TILs) by a similar approach using the annotated by our pathologist TILs regions on the H/E slides, as despite their importance, TILs scoring seems to be differentiated among pathologists because of substantial interobserver variation. This fact raises an urgent need for the exploration of novel methods for a more accurate scoring system. Indeed, the International Immuno-Oncology Working Group suggested a computational assessment of TILs based on deep learning models. The evaluation of biomarkers related to TILs, such as CD8, in IHC-stained images has also shown significant potential when DL approaches are used, yielding promising results in no biased TILs evaluation and providing us with valuable details about their distribution and spatial relationships .
Since there are still limitations and things to enhance and more data and parameters need to be incorporated to create more accurate and powerful models, we aim to improve TILs evaluation and therapeutic response prediction in breast cancer, through our deep learning model with the use of H/E whole slide images and digitalized IHC-stained CD8 images for lymphocytes' automatic detection for better quantification of immune response.
Additionally, and apart from ER and PgR IHC-stained slides digitalization, we intend also to digitalize our archive's breast cancer IHC sections stained with HER2 ab, which have already been evaluated by our experienced pathologist, as well as HER2/TOP2A/CEN17 FISH slides. Subsequently, we intend to develop an automated deep learning (DL) - based image analysis algorithm with the ultimate goal of HER2 evaluation with a more accurate than human scoring manner, with the advantage of scoring every cell within a sample specifically to better identify patients with low-level HER2 expression given that T-DXd seems to be efficacious in this patient population . In this way, we will improve diagnostic accuracy by enabling pathologists to review high-resolution digital slides, apply advanced image analysis algorithms, and reduce inter-observer variability, for an accurate HER2 expression in breast cancers.
Read the detailed description
From 1996 until 2020, 5.320 eligible patients with operable breast cancer were treated, within the context of 3 randomized and 4 observational clinical studies with dds-CT including taxanes and anthracyclines Adjuvant hormonal and radiation treatment were administered, as indicated (Table 1).
All patients have signed a study-specific written informed consent before randomization, consenting for the trial and permitting the use of their biological material for future research purposes. All studies were conducted in accordance with the Declaration of Helsinki.
IHC and FiSH data from patients enrolled in the above randomized phase III trials conducted by the Hellenic Cooperative Oncology Group (HeCOG) will be retrospectively reviewed.
Briefly, tumor material is examined mainly on tissue microarrays (TMA). Although, there is a HER2 assessment from local pathology laboratories, all tumors are re-evaluated centrally for ER, PgR and HER2 according to ASCO/CAP guidelines. Tumors are subtyped with immunohistochemistry (IHC4) for ER, PgR, HER2 and Ki67. FISH is performed for the assessment of HER2 gene status. IHC for EGFR and CK5 is used for the classification of basal-like tumors; the expression of CD8 on TILs is also evaluated. Moreover, we investigate the mutational profile of Greek women with breast cancer, via the application of DNA Next Generation Sequencing (NGS) technologies, relative also to patient outcome. All H\&E and IHC-stained slides will be digitalized for the investigation of the potential of a deep learning-based system for automated biomarker prediction.
Patient demographic, clinicopathological and treatment data are available in all cases.
- Conditions
- Early Breast Cancer
- Keywords
- Artificial Intelligence model, FISH images, digitalised H/E slides, digitalised IHC slides
- Medical subject headings
- Breast Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Development and Evaluation of an AI-based predictive algorithm
Evaluation of the efficiency of the AI-predictive algorithm, by determining key metrics such as sensitivity and specificity
Measured: Through study completion, 2 years
Secondary outcomes (2)
Development of an AI-predictive algorithm from H/E and IHC digitalized slides
All H\&E and IHC-stained slides will be digitalized for the investigation of the potential of a deep learning-based system for automated biomarker prediction
Measured: Through study completion, 2 years
Development of an AI-based algorithm from FISH images
An AI-based algorithm will be developed from FISH images to assess spatial patterns and cell-to-cell heterogeneity in breast cancer samples
Measured: Through study completion, 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2026
- Study start10 Dec 1996
- Primary completion23 Jul 2026
- Study completion23 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Early Breast 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
- 3,648 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hellenic Cooperative Oncology Group
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07842068
- Sponsor's protocol ID
- HE_DL7/25
- Status last verified
- Sep 2026
- First submitted
- 21 Sep 2026
- Last updated
- 25 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Vandenberghe ME, Scott ML, Scorer PW, Soderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep. 2017 Apr 5;7:45938. doi: 10.1038/srep45938.
PubMed 28378829Soliman A, Li Z, Parwani AV. Artificial intelligence's impact on breast cancer pathology: a literature review. Diagn Pathol. 2024 Feb 22;19(1):38. doi: 10.1186/s13000-024-01453-w.
PubMed 38388367Saldanha OL, Loeffler CML, Niehues JM, van Treeck M, Seraphin TP, Hewitt KJ, Cifci D, Veldhuizen GP, Ramesh S, Pearson AT, Kather JN. Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. NPJ Precis Oncol. 2023 Mar 28;7(1):35. doi: 10.1038/s41698-023-00365-0.
PubMed 36977919Prat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012 Aug;135(1):301-6. doi: 10.1007/s10549-012-2143-0. Epub 2012 Jul 3.
PubMed 22752290Fiorin A, Lopez Pablo C, Lejeune M, Hamza Siraj A, Della Mea V. Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets. J Imaging Inform Med. 2024 Dec;37(6):2996-3008. doi: 10.1007/s10278-024-01043-8. Epub 2024 May 28.
PubMed 38806950El Nahhas OSM, van Treeck M, Wolflein G, Unger M, Ligero M, Lenz T, Wagner SJ, Hewitt KJ, Khader F, Foersch S, Truhn D, Kather JN. From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat Protoc. 2025 Jan;20(1):293-316. doi: 10.1038/s41596-024-01047-2. Epub 2024 Sep 16.
PubMed 39285224El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Bruchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun. 2024 Feb 29;15(1):1868. doi: 10.1038/s41467-024-46298-5. No abstract available.
PubMed 38424093Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
PubMed 30207593Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudecek J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD; International Immuno-Oncology Biomarker Working Group. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer. 2020 May 12;6:16. doi: 10.1038/s41523-020-0154-2. eCollection 2020.
PubMed 32411818
Study at a glance
From the registry record, last updated by the sponsor on 25 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Biomarker Prediction Through Deep Learning Approaches From Histopathological Whole Slide Images in Early Breast Cancer
- ClinicalTrials.gov ID
- NCT07842068
- Acronym / sponsor ID
- HE_DL7/25
- Conditions
- Early Breast Cancer
- Keywords
- Artificial Intelligence model, FISH images, digitalised H/E slides, digitalised IHC slides
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,648 participants (actual)
- Lead sponsor
- Hellenic Cooperative Oncology GroupUniversity, hospital or other
- Study start
- 10 Dec 1996 (actual)
- Primary completion
- 23 Jul 2026 (actual)
- Study completion
- 23 Jul 2026 (actual)
- First posted
- 25 Sep 2026 (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.
Despite the improvements in therapeutic approaches, breast cancer remains the most commonly occurring cancer and leading cause of cancer death in women worldwide. Breast cancer treatment decisions are based mainly on the presence or absence of established prognostic and predictive biomarkers, mainly the hormone receptors, estrogen (ER) and progesterone (PgR), the human epidermal growth factor receptor 2 oncoprotein (HER2) in correlation with ki67 proliferation index.
Currently, PCR-based assays, next-generation sequencing (NGS), and immunohistochemistry (IHC), followed by in situ hybridization (ISH) when needed, are used for biomarker testing. Ιn an effort to overpass technical limitations, human subjectivity which may yield different results between different observers leading to inappropriate treatment and complications that can adversely affect patient quality of life, alternative, cost-effective approaches using AI-based digital pathology analysis of histopathology images has received significant attention last years. Digital pathology uses whole-slide image scanners and combined with artificial intelligence (AI) algorithms intend to improve diagnostic accuracy, enabling rapid patient stratification and optimal treatment regimens, thereby improving patient outcomes. Identification of invasive breast tumors and lymph node metastasis, evaluation of hormonal status, breast cancer grading and mitotic count evaluation, biomarkers identification based on images of the tumor microenvironment have also been enhanced by AI quantitative analysis. AI has promising results so far. More studies are, however, needed in order to overcome obstacles and before it is validated for patient care in the clinical setting.
DL technology has been implemented in clinical pathology in an attempt to automate the way pathologists evaluate hematoxylin and eosin (H/E) stained slides, but also to attain information such as molecular background and protein expression, given that genetic variations could likely be reflected in the morphology of cancer cells, and although not detectable to the human eye, these changes can be recognized by advanced DL algorithms and then correlated with a specific molecular variation or protein expression.
For the purpose of our study, we will use FFPE tumor blocks from patients enrolled in 7 randomized and observational studies conducted by the Hellenic Cooperative Oncology Group (HeCOG) to define via NGS their underlying mutational profile. Subsequently, a DL algorithm will be trained in order to examine if any genetic alterations found in breast cancer could be detected by the DL model in H/E whole section slides, which would suggest that these variants reflect in the morphology of the cancer cells.
Accordingly, we will explore if our algorithm could be trained to identify tumor infiltrating lymphocytes (TILs) by a similar approach using the annotated by our pathologist TILs regions on the H/E slides, as despite their importance, TILs scoring seems to be differentiated among pathologists because of substantial interobserver variation. This fact raises an urgent need for the exploration of novel methods for a more accurate scoring system. Indeed, the International Immuno-Oncology Working Group suggested a computational assessment of TILs based on deep learning models. The evaluation of biomarkers related to TILs, such as CD8, in IHC-stained images has also shown significant potential when DL approaches are used, yielding promising results in no biased TILs evaluation and providing us with valuable details about their distribution and spatial relationships .
Since there are still limitations and things to enhance and more data and parameters need to be incorporated to create more accurate and powerful models, we aim to improve TILs evaluation and therapeutic response prediction in breast cancer, through our deep learning model with the use of H/E whole slide images and digitalized IHC-stained CD8 images for lymphocytes' automatic detection for better quantification of immune response.
Additionally, and apart from ER and PgR IHC-stained slides digitalization, we intend also to digitalize our archive's breast cancer IHC sections stained with HER2 ab, which have already been evaluated by our experienced pathologist, as well as HER2/TOP2A/CEN17 FISH slides. Subsequently, we intend to develop an automated deep learning (DL) - based image analysis algorithm with the ultimate goal of HER2 evaluation with a more accurate than human scoring manner, with the advantage of scoring every cell within a sample specifically to better identify patients with low-level HER2 expression given that T-DXd seems to be efficacious in this patient population . In this way, we will improve diagnostic accuracy by enabling pathologists to review high-resolution digital slides, apply advanced image analysis algorithms, and reduce inter-observer variability, for an accurate HER2 expression in breast cancers.
Detailed description
From 1996 until 2020, 5.320 eligible patients with operable breast cancer were treated, within the context of 3 randomized and 4 observational clinical studies with dds-CT including taxanes and anthracyclines Adjuvant hormonal and radiation treatment were administered, as indicated (Table 1).
All patients have signed a study-specific written informed consent before randomization, consenting for the trial and permitting the use of their biological material for future research purposes. All studies were conducted in accordance with the Declaration of Helsinki.
IHC and FiSH data from patients enrolled in the above randomized phase III trials conducted by the Hellenic Cooperative Oncology Group (HeCOG) will be retrospectively reviewed.
Briefly, tumor material is examined mainly on tissue microarrays (TMA). Although, there is a HER2 assessment from local pathology laboratories, all tumors are re-evaluated centrally for ER, PgR and HER2 according to ASCO/CAP guidelines. Tumors are subtyped with immunohistochemistry (IHC4) for ER, PgR, HER2 and Ki67. FISH is performed for the assessment of HER2 gene status. IHC for EGFR and CK5 is used for the classification of basal-like tumors; the expression of CD8 on TILs is also evaluated. Moreover, we investigate the mutational profile of Greek women with breast cancer, via the application of DNA Next Generation Sequencing (NGS) technologies, relative also to patient outcome. All H\&E and IHC-stained slides will be digitalized for the investigation of the potential of a deep learning-based system for automated biomarker prediction.
Patient demographic, clinicopathological and treatment data are available in all cases.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with operable breast cancer treated with dds-CT including taxanes and anthracyclines. Adjuvant hormonal and radiation treatment were administered, as indicated
Key inclusion criteria (4 listed)
- Age 18 and above
- Histologically confirmed BC
- All treated with adjuvant dose-dense sequential chemotherapy (dds-CT)
- Tumor tissue specimen (FFPE) availability
Key exclusion criteria (1 listed)
- not adequate, and unsuitable tissue for IHC, FISH and analysis
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Development and Evaluation of an AI-based predictive algorithm
Time frame: Through study completion, 2 years
Evaluation of the efficiency of the AI-predictive algorithm, by determining key metrics such as sensitivity and specificity
2 secondary outcomes
Development of an AI-predictive algorithm from H/E and IHC digitalized slides
Time frame: Through study completion, 2 years
Development of an AI-based algorithm from FISH images
Time frame: Through study completion, 2 years
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 25 Sep 2026.
- Background: PubMed 28378829Vandenberghe ME, Scott ML, Scorer PW, Soderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep. 2017 Apr 5;7:45938. doi: 10.1038/srep45938.
- Background: PubMed 38388367Soliman A, Li Z, Parwani AV. Artificial intelligence's impact on breast cancer pathology: a literature review. Diagn Pathol. 2024 Feb 22;19(1):38. doi: 10.1186/s13000-024-01453-w.
- Background: PubMed 36977919Saldanha OL, Loeffler CML, Niehues JM, van Treeck M, Seraphin TP, Hewitt KJ, Cifci D, Veldhuizen GP, Ramesh S, Pearson AT, Kather JN. Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. NPJ Precis Oncol. 2023 Mar 28;7(1):35. doi: 10.1038/s41698-023-00365-0.
- Background: PubMed 22752290Prat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012 Aug;135(1):301-6. doi: 10.1007/s10549-012-2143-0. Epub 2012 Jul 3.
- Background: PubMed 38806950Fiorin A, Lopez Pablo C, Lejeune M, Hamza Siraj A, Della Mea V. Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets. J Imaging Inform Med. 2024 Dec;37(6):2996-3008. doi: 10.1007/s10278-024-01043-8. Epub 2024 May 28.
- Background: PubMed 39285224El Nahhas OSM, van Treeck M, Wolflein G, Unger M, Ligero M, Lenz T, Wagner SJ, Hewitt KJ, Khader F, Foersch S, Truhn D, Kather JN. From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat Protoc. 2025 Jan;20(1):293-316. doi: 10.1038/s41596-024-01047-2. Epub 2024 Sep 16.
- Background: PubMed 38424093El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Bruchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun. 2024 Feb 29;15(1):1868. doi: 10.1038/s41467-024-46298-5. No abstract available.
- Background: PubMed 30207593Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
- Background: PubMed 32411818Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudecek J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD; International Immuno-Oncology Biomarker Working Group. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer. 2020 May 12;6:16. doi: 10.1038/s41523-020-0154-2. eCollection 2020.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Early Breast Cancer, run by Hellenic Cooperative Oncology Group.
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 25 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
Development and Evaluation of an AI-based predictive algorithm (time frame: Through study completion, 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07842068, which also lists 9 linked publications.
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 25 Sep 2026.