Rectal CancersNCT07509632
Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning
An observational study of Rectal Cancers and Pathological Complete Response, run by Peking University People's Hospital at 1 site in China.
Trial summary
This study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features. The objective is to preoperatively predict the probability of achieving a pathological complete response (pCR) in patients with locally advanced… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Rectal Cancers, Pathological Complete Response, Neoadjuvant Chemoradiotherapy
- How many people
- 320 participants
- Where
- 1 siteChina
- Study dates
- 4 Feb 2026to 10 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients diagnosed with locally advanced or metastatic rectal cancer who underwent standard radical surgery following neoadjuvant chemoradiotherapy
Who may be able to join
- Patients with histopathologically confirmed rectal adenocarcinoma;
- Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer;
- Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy;
- Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available.
Who cannot join
- Previous history of other malignant tumors;
- Incomplete clinical data;
- Underwent emergency surgery during nCRT;
- Complicated with systemic infection or hematological diseases.
Trial locations and contacts
1 site in 1 country.
China1 site
Peking University People's Hospital
Beijing, Beijing Municipality, 100044
About this study
Official title: Development and Validation of a Machine Learning Model Based on Clinical and MRI Features for Predicting Pathological Complete Response in Rectal Cancer Following Neoadjuvant Chemoradiotherapy
This study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features. The objective is to preoperatively predict the probability of achieving a pathological complete response (pCR) in patients with locally advanced rectal cancer (CRC) following neoadjuvant chemoradiotherapy (nCRT).
Read the detailed description
This study aims to develop and validate a predictive model based on pre-neoadjuvant clinical, laboratory, and magnetic resonance imaging (MRI) features to estimate the probability of pathological complete response (pCR) in rectal cancer patients after neoadjuvant chemoradiotherapy (nCRT). This retrospective study will enroll patients who received nCRT followed by radical resection at Peking University People's Hospital between December 2017 and October 2025 as the development cohort. Least Absolute Shrinkage and Selection Operator (LASSO) regression will be used for feature selection, and machine learning algorithms will be applied to construct the prediction model. Model performance will be comprehensively evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis will be performed to enhance model interpretability. The final model is expected to provide an individualized pCR prediction tool to guide clinical decision-making for rectal cancer patients.
- Conditions
- Rectal Cancers, Pathological Complete Response, Neoadjuvant Chemoradiotherapy, Machine Learning
- Being tested
- No interventions
- Medical subject headings
- Rectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionsDiagnostic test
No interventions
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
The primary endpoint is the occurrence of pCR, assessed by two independent pathologists using the AJCC/CAP Tumor Regression Grade (TRG) system. TRG 0 (no viable cancer cells, only fibrosis or mucin pools) is defined as a positive outcome (pCR). TRG 1 to 3 are combined and defined as a negative outcome (non-pCR). The predictive performance of the model will be evaluated utilizing several metrics including the Area Under the ROC Curve (AUC), Precision-Recall (PR) curve, Calibration curve, and Decision Curve Analysis (DCA).
Measured: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
Secondary outcomes (5)
Area under the receiver operating characteristic curve (AUC) of the prediction model
To evaluate the discrimination performance of the model for pCR prediction
Measured: At the completion of model development and validation
Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model
To evaluate the diagnostic accuracy of the model at the optimal cut-off value
Measured: At the completion of model development and validation
Calibration curve of the prediction model
To evaluate the consistency between the predicted pCR probability and the actual observed pCR rate
Measured: At the completion of model development and validation
Net benefit of the model quantified by decision curve analysis (DCA)
To evaluate the clinical utility of the model across different threshold probabilities
Measured: At the completion of model development and validation
Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis
To interpret the contribution of each predictor to the model prediction
Measured: At the completion of model development and validation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2026
- Study start4 Feb 2026
- Primary completion25 Apr 2026 (estimated)
- Study completion10 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Rectal CancersStudies 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 320 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hongpeng Jiang, Peking University People's Hospital
- Investigators
- Hong-Peng Jiang, docter, Study chair, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07509632
- Sponsor's protocol ID
- 2026PHB131-001
- Status last verified
- Mar 2026
- First submitted
- 29 Mar 2026
- Last updated
- 3 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Kong JC, Guerra GR, Warrier SK, Lynch AC, Michael M, Ngan SY, Phillips W, Ramsay G, Heriot AG. Prognostic value of tumour regression grade in locally advanced rectal cancer: a systematic review and meta-analysis. Colorectal Dis. 2018 Jul;20(7):574-585. doi: 10.1111/codi.14106. Epub 2018 May 8.
PubMed 29582537Maas M, Nelemans PJ, Valentini V, Das P, Rodel C, Kuo LJ, Calvo FA, Garcia-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suarez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010 Sep;11(9):835-44. doi: 10.1016/S1470-2045(10)70172-8. Epub 2010 Aug 6.
PubMed 20692872
Study at a glance
From the registry record, last updated by the sponsor on 3 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Machine Learning Model Based on Clinical and MRI Features for Predicting Pathological Complete Response in Rectal Cancer Following Neoadjuvant Chemoradiotherapy
- ClinicalTrials.gov ID
- NCT07509632
- Acronym / sponsor ID
- 2026PHB131-001
- Conditions
- Rectal Cancers, Pathological Complete Response, Neoadjuvant Chemoradiotherapy, Machine Learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 320 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 4 Feb 2026 (actual)
- Primary completion
- 25 Apr 2026 (estimated)
- Study completion
- 10 May 2026 (estimated)
- First posted
- 3 Apr 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 study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features. The objective is to preoperatively predict the probability of achieving a pathological complete response (pCR) in patients with locally advanced rectal cancer (CRC) following neoadjuvant chemoradiotherapy (nCRT).
Detailed description
This study aims to develop and validate a predictive model based on pre-neoadjuvant clinical, laboratory, and magnetic resonance imaging (MRI) features to estimate the probability of pathological complete response (pCR) in rectal cancer patients after neoadjuvant chemoradiotherapy (nCRT). This retrospective study will enroll patients who received nCRT followed by radical resection at Peking University People's Hospital between December 2017 and October 2025 as the development cohort. Least Absolute Shrinkage and Selection Operator (LASSO) regression will be used for feature selection, and machine learning algorithms will be applied to construct the prediction model. Model performance will be comprehensively evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis will be performed to enhance model interpretability. The final model is expected to provide an individualized pCR prediction tool to guide clinical decision-making for rectal cancer patients.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with locally advanced or metastatic rectal cancer who underwent standard radical surgery following neoadjuvant chemoradiotherapy
Key inclusion criteria (4 listed)
- Patients with histopathologically confirmed rectal adenocarcinoma;
- Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer;
- Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy;
- Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available.
Key exclusion criteria (4 listed)
- Previous history of other malignant tumors;
- Incomplete clinical data;
- Underwent emergency surgery during nCRT;
- Complicated with systemic infection or hematological diseases.
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 |
|---|---|---|
| No interventionsNo interventions | Diagnostic test | – |
Primary outcome
Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
Time frame: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
The primary endpoint is the occurrence of pCR, assessed by two independent pathologists using the AJCC/CAP Tumor Regression Grade (TRG) system. TRG 0 (no viable cancer cells, only fibrosis or mucin pools) is defined as a positive outcome (pCR). TRG 1 to 3 are combined and defined as a negative outcome (non-pCR). The predictive performance of the model will be evaluated utilizing several metrics including the Area Under the ROC Curve (AUC), Precision-Recall (PR) curve, Calibration curve, and Decision Curve Analysis (DCA).
5 secondary outcomes
Area under the receiver operating characteristic curve (AUC) of the prediction model
Time frame: At the completion of model development and validation
Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model
Time frame: At the completion of model development and validation
Calibration curve of the prediction model
Time frame: At the completion of model development and validation
Net benefit of the model quantified by decision curve analysis (DCA)
Time frame: At the completion of model development and validation
Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis
Time frame: At the completion of model development and validation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Apr 2026.
- Background: PubMed 29582537Kong JC, Guerra GR, Warrier SK, Lynch AC, Michael M, Ngan SY, Phillips W, Ramsay G, Heriot AG. Prognostic value of tumour regression grade in locally advanced rectal cancer: a systematic review and meta-analysis. Colorectal Dis. 2018 Jul;20(7):574-585. doi: 10.1111/codi.14106. Epub 2018 May 8.
- Background: PubMed 20692872Maas M, Nelemans PJ, Valentini V, Das P, Rodel C, Kuo LJ, Calvo FA, Garcia-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suarez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010 Sep;11(9):835-44. doi: 10.1016/S1470-2045(10)70172-8. Epub 2010 Aug 6.
- Individual participant dataSharing planned: Undecided
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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.
Hong-Peng Jiang, docter study chair
Peking University People's Hospital
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 Rectal Cancers and Pathological Complete Response, run by Peking University People's Hospital at 1 site in China.
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 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: under way, not looking for participants, as of the sponsor's last update on 3 Apr 2026. 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 China. 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?
This study is still under way. Results are not available in the source record yet.
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) defined by Tumor Regression Grade (TRG) (time frame: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).). 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 NCT07509632, which also lists 2 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 3 Apr 2026.