Breast CancerNCT07426653
Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Machine Learning Models.
An observational study of Breast Cancer, run by Florence Nightingale Hospital, Istanbul.
Trial summary
This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Read more about this study
- Who is it for?
- Ages 18 to 90 years, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 298 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2010to 31 Dec 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 to 90 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of patients with breast cancer treated with neoadjuvant chemotherapy at a single tertiary breast care center between January 2010 and December 2025. The registry includes demographic, tumor staging, biomarker, and treatment-related clinicopathological data collected as part of routine clinical care. All patients underwent surgery following neoadjuvant chemotherapy with documented pathological response assessment.
Who may be able to join
- Histologically confirmed breast cancer
- Receipt of neoadjuvant chemotherapy
- Available clinicopathological data required for model development
- Surgical treatment performed following neoadjuvant chemotherapy
- Pathological response assessment available
- Recorded pathological details
Who cannot join
- Missing pathological response information
- Incomplete clinicopathological data required for model analysis
- Patients not treated with neoadjuvant chemotherapy
- Non-invasive breast cancer without indication for neoadjuvant treatment
About this study
Official title: Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Clinical and pathological data collected between 2010 and 2025 were used to train and evaluate multiple machine learning algorithms using cross-validation and independent holdout testing. The primary outcome was pathological complete response after neoadjuvant chemotherapy. Model performance was assessed using discrimination and classification metrics, including ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient. The resulting model is intended to support clinical decision-making by providing individualized probability estimates of treatment response.
Read the detailed description
This retrospective observational study was conducted using a breast cancer registry containing clinical and pathological data from patients who received neoadjuvant chemotherapy between January 2010 and December 2025. The objective of the study was to develop and validate a machine learning-based predictive model for pathological complete response (pCR) using routinely available clinicopathological variables.
An initial dataset consisting of 298 patients and 144 recorded variables was curated by breast oncology experts to identify clinically relevant predictors. A total of 20 established clinicopathological variables were selected, representing demographic characteristics, tumor staging, biomarker profiles, and treatment-related factors. Feature engineering techniques, including ordinal encoding, one-hot encoding, and binary mapping, were applied to prepare the dataset for model development. Missing values were handled using median imputation within a cross-validation pipeline to prevent data leakage.
Feature selection was performed using a hybrid importance framework integrating mutual information analysis, SHAP-based attribution from gradient boosting models, and L1-regularized logistic regression coefficients. Sequential feature subset evaluation identified an optimal subset of 10 predictors for model development.
Multiple machine learning algorithms-including logistic regression, random forest, gradient boosting models, support vector machines, k-nearest neighbors, and ensemble learning approaches-were trained and evaluated using 5-fold stratified cross-validation. Final performance was assessed on independent validation and holdout datasets using ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient.
The primary outcome was pathological complete response following neoadjuvant chemotherapy. Threshold optimization was performed to identify a clinically meaningful probability cutoff that balanced sensitivity and specificity for predicting treatment response. Model performance was compared against a prevalence-adjusted stochastic baseline using Monte Carlo simulation to confirm predictive validity beyond chance.
This study evaluates the feasibility of applying clinicopathology-based machine learning models to predict treatment response in breast cancer and to support individualized clinical decision-making in the neoadjuvant setting.
- Conditions
- Breast Cancer
- Keywords
- breast cancer, machine learning, neoadjuvant chemotherapy, artificial intelligence
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients with no residual invasive cancer in surgical pathology following neoadjuvant chemotherapy | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathological Complete Response (pCR)
Pathological complete response is defined as the absence of residual invasive cancer in the breast and axillary lymph nodes at the time of surgery following completion of neoadjuvant chemotherapy.
Measured: At time of surgery following completion of neoadjuvant chemotherapy (approximately 4-6 months after treatment initiation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Feb 2026
- Study start1 Jan 2010
- Primary completion31 Dec 2025
- Study completion31 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for 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
- 298 participants (actual)
Sponsor and investigators
- Lead sponsor
- Florence Nightingale Hospital, Istanbul
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Enver Özkurt, Florence Nightingale Hospital, Istanbul
- Investigators
- Enver Özkurt, Assoc. Prof., Principal investigator, Demiroğlu Bilim University, Faculty of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07426653
- Sponsor's protocol ID
- FNH2026-1
- Status last verified
- Feb 2026
- First submitted
- 15 Feb 2026
- Last updated
- 23 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Kim JY, Jeon E, Kwon S, Jung H, Joo S, Park Y, Lee SK, Lee JE, Nam SJ, Cho EY, Park YH, Ahn JS, Im YH. Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer. Breast Cancer Res Treat. 2021 Oct;189(3):747-757. doi: 10.1007/s10549-021-06310-8. Epub 2021 Jul 5.
PubMed 34224056
Study at a glance
From the registry record, last updated by the sponsor on 23 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
- ClinicalTrials.gov ID
- NCT07426653
- Acronym / sponsor ID
- FNH2026-1
- Conditions
- Breast Cancer
- Keywords
- breast cancer, machine learning, neoadjuvant chemotherapy, artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 298 participants (actual)
- Lead sponsor
- Florence Nightingale Hospital, IstanbulUniversity, hospital or other
- Study start
- 1 Jan 2010 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Dec 2025 (actual)
- First posted
- 23 Feb 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.
This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Clinical and pathological data collected between 2010 and 2025 were used to train and evaluate multiple machine learning algorithms using cross-validation and independent holdout testing. The primary outcome was pathological complete response after neoadjuvant chemotherapy. Model performance was assessed using discrimination and classification metrics, including ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient. The resulting model is intended to support clinical decision-making by providing individualized probability estimates of treatment response.
Detailed description
This retrospective observational study was conducted using a breast cancer registry containing clinical and pathological data from patients who received neoadjuvant chemotherapy between January 2010 and December 2025. The objective of the study was to develop and validate a machine learning-based predictive model for pathological complete response (pCR) using routinely available clinicopathological variables.
An initial dataset consisting of 298 patients and 144 recorded variables was curated by breast oncology experts to identify clinically relevant predictors. A total of 20 established clinicopathological variables were selected, representing demographic characteristics, tumor staging, biomarker profiles, and treatment-related factors. Feature engineering techniques, including ordinal encoding, one-hot encoding, and binary mapping, were applied to prepare the dataset for model development. Missing values were handled using median imputation within a cross-validation pipeline to prevent data leakage.
Feature selection was performed using a hybrid importance framework integrating mutual information analysis, SHAP-based attribution from gradient boosting models, and L1-regularized logistic regression coefficients. Sequential feature subset evaluation identified an optimal subset of 10 predictors for model development.
Multiple machine learning algorithms-including logistic regression, random forest, gradient boosting models, support vector machines, k-nearest neighbors, and ensemble learning approaches-were trained and evaluated using 5-fold stratified cross-validation. Final performance was assessed on independent validation and holdout datasets using ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient.
The primary outcome was pathological complete response following neoadjuvant chemotherapy. Threshold optimization was performed to identify a clinically meaningful probability cutoff that balanced sensitivity and specificity for predicting treatment response. Model performance was compared against a prevalence-adjusted stochastic baseline using Monte Carlo simulation to confirm predictive validity beyond chance.
This study evaluates the feasibility of applying clinicopathology-based machine learning models to predict treatment response in breast cancer and to support individualized clinical decision-making in the neoadjuvant setting.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with no residual invasive cancer in surgical pathology following neoadjuvant chemotherapy | – | – |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of patients with breast cancer treated with neoadjuvant chemotherapy at a single tertiary breast care center between January 2010 and December 2025. The registry includes demographic, tumor staging, biomarker, and treatment-related clinicopathological data collected as part of routine clinical care. All patients underwent surgery following neoadjuvant chemotherapy with documented pathological response assessment.
Key inclusion criteria (6 listed)
- Histologically confirmed breast cancer
- Receipt of neoadjuvant chemotherapy
- Available clinicopathological data required for model development
- Surgical treatment performed following neoadjuvant chemotherapy
- Pathological response assessment available
Key exclusion criteria (4 listed)
- Missing pathological response information
- Incomplete clinicopathological data required for model analysis
- Patients not treated with neoadjuvant chemotherapy
- Non-invasive breast cancer without indication for neoadjuvant treatment
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
Pathological Complete Response (pCR)
Time frame: At time of surgery following completion of neoadjuvant chemotherapy (approximately 4-6 months after treatment initiation)
Pathological complete response is defined as the absence of residual invasive cancer in the breast and axillary lymph nodes at the time of surgery following completion of neoadjuvant chemotherapy.
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 23 Feb 2026.
- Background: PubMed 34224056Kim JY, Jeon E, Kwon S, Jung H, Joo S, Park Y, Lee SK, Lee JE, Nam SJ, Cho EY, Park YH, Ahn JS, Im YH. Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer. Breast Cancer Res Treat. 2021 Oct;189(3):747-757. doi: 10.1007/s10549-021-06310-8. Epub 2021 Jul 5.
- 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.
Enver Özkurt, Assoc. Prof. principal investigator
Demiroğlu Bilim University, Faculty of Medicine
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Florence Nightingale Hospital, Istanbul.
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 6 inclusion and 4 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 23 Feb 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?
Pathological Complete Response (pCR) (time frame: At time of surgery following completion of neoadjuvant chemotherapy (approximately 4-6 months after treatment initiation)). 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 NCT07426653, which also lists 1 linked publication.
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 23 Feb 2026.