Esophageal NeoplasmQARCNCT04489368
Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning
An observational study of Esophageal Neoplasm, run by Dr Kundan Singh Chufal at 2 sites in 2 countries.
Trial summary
In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by surgery is the current standard of care and ample evidence has accumulated supporting the view that complete pathological response (pCR) is a positive prognostic marker for improved outcomes. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Esophageal Neoplasm
- What is being tested?
- Neo-Adjuvant Radiotherapy, Neo-Adjuvant Chemotherapy and Esophagectomy
- Type of treatment
- Surgery or procedure
- How many people
- 150 participants
- Where
- 2 sitesin 2 countries
- Study dates
- 16 Jan 2020to Jul 2023
- 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
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patient records in each participating research center from January 2011 to 1st May 2020
Who may be able to join
- ECOG Performance Status: 0-2
- Patients with histopathological or cytopathological confirmed malignancy of the esophagus
- Histology: Squamous Cell Carcinoma and Adenocarcinoma
- Patients should have received NeoAdjuvant Concurrent Chemoradiation (NACCRT) followed by Surgery
- All therapeutic interventions (Radiotherapy, Chemotherapy \& Surgery) delivered within participating institutions
- At least one pre-NACCRT DICOM imaging dataset (HRCT/ 18-FDG PET-CT/ Radiotherapy planning CT) for each patient
Who cannot join
- Patients with any metallic implants in the region of interest
- Patient with locally advanced disease or metastatic disease (T4 disease, Fistula, metastases)
- Patients with prior history of radiotherapy in the same region
- Patients developing a second malignancy in the esophagus
Trial locations and contacts
2 sites in 2 countries.
2 sites in 2 countries
India1 site
Rajiv Gandhi Cancer Institute & Research Center
New Delhi, National Capital Territory of Delhi, 110019
About this study
Official title: Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models
In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by surgery is the current standard of care and ample evidence has accumulated supporting the view that complete pathological response (pCR) is a positive prognostic marker for improved outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could permit modification of treatment protocols for those patients unlikely to achieve pCR.
Radiomics is a new entrant in the field of imaging where specific features are derived from the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The features thus extracted can then be used for prediction modelling similar to other -omics datasets. Preliminary investigations examining its utility have been performed and its applications have thus far focused on screening and survival prediction after treatment. Due to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence (AI) methods are ideally suited for analysing and modelling radiomic features.
Read more
Machine Learning (ML) and Deep Learning (DL)[utilising Convolutional Neural Networks (CNN)] are both part of the AI framework. In contrast to ML, DL is a new entrant and has been utilised by some medical researchers for modelling using prediction-type algorithms. Besides significantly reducing the workflow associated with Radiomics-based research, feature engineering and modelling using DL are immune to the effects of incorrect ROI delineation. However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of a CNN is not known. This has been mitigated in part by the visualisation of activation maps directly on the image dataset to prove biological plausibility of predictions. The comparative performance of both types of modelling is also not known.
Our objective is to investigate pCR probability in our study population using radiomics-based ML and AI-based modelling. We will also investigate the comparative performance of both modelling techniques. For DL based prediction modelling, we will attempt to provide biological plausibility on the basis of activation maps.
- Conditions
- Esophageal Neoplasm
- Being tested
- Neo-Adjuvant Radiotherapy, Neo-Adjuvant Chemotherapy, Esophagectomy
- Keywords
- Artificial Intelligence, Deep Learning, Neoadjuvant Chemoradiation, Pathologic Response, Radiomics, Radiotherapy
- Medical subject headings
- Esophageal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Neo-Adjuvant RadiotherapyRadiation
Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.
Neo-Adjuvant ChemotherapyDrug
Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.
EsophagectomyProcedure or surgery
Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation
| Study group | Role | What they receive |
|---|---|---|
| Study Group | Patients undergoing NA-CCRT followed by Surgery Receives: Radiation: Neo-Adjuvant Radiotherapy; Drug: Neo-Adjuvant Chemotherapy; Procedure: Esophagectomy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop models to predict pCR based on pre-neoadjuvant imaging modalities
Measured: August 2021
Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy
Measured: January 2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jul 2020
- Study start16 Jan 2020
- Primary completionJul 2023 (estimated)
- Study completionJul 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Esophageal NeoplasmStudies 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
- Other
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Dr Kundan Singh Chufal
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Dr Kundan Singh Chufal, Rajiv Gandhi Cancer Institute & Research Center, India
- Investigators
- Kundan S Chufal, MD, Principal investigator, Rajiv Gandhi Cancer Institute & Research Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04489368
- Sponsor's protocol ID
- RGCIRC/IRB/80/2020
- Acronym
- QARC
- Status last verified
- Dec 2022
- First submitted
- 23 Jul 2020
- Last updated
- 28 Dec 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models
- ClinicalTrials.gov ID
- NCT04489368
- Acronym / sponsor ID
- QARC / RGCIRC/IRB/80/2020
- Conditions
- Esophageal Neoplasm
- Keywords
- Artificial Intelligence, Deep Learning, Neoadjuvant Chemoradiation, Pathologic Response, Radiomics, Radiotherapy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Dr Kundan Singh ChufalUniversity, hospital or other
- Study start
- 16 Jan 2020 (actual)
- Primary completion
- Jul 2023 (estimated)
- Study completion
- Jul 2023 (estimated)
- First posted
- 28 Jul 2020 (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.
In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by surgery is the current standard of care and ample evidence has accumulated supporting the view that complete pathological response (pCR) is a positive prognostic marker for improved outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could permit modification of treatment protocols for those patients unlikely to achieve pCR.
Radiomics is a new entrant in the field of imaging where specific features are derived from the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The features thus extracted can then be used for prediction modelling similar to other -omics datasets. Preliminary investigations examining its utility have been performed and its applications have thus far focused on screening and survival prediction after treatment. Due to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence (AI) methods are ideally suited for analysing and modelling radiomic features.
Machine Learning (ML) and Deep Learning (DL)[utilising Convolutional Neural Networks (CNN)] are both part of the AI framework. In contrast to ML, DL is a new entrant and has been utilised by some medical researchers for modelling using prediction-type algorithms. Besides significantly reducing the workflow associated with Radiomics-based research, feature engineering and modelling using DL are immune to the effects of incorrect ROI delineation. However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of a CNN is not known. This has been mitigated in part by the visualisation of activation maps directly on the image dataset to prove biological plausibility of predictions. The comparative performance of both types of modelling is also not known.
Our objective is to investigate pCR probability in our study population using radiomics-based ML and AI-based modelling. We will also investigate the comparative performance of both modelling techniques. For DL based prediction modelling, we will attempt to provide biological plausibility on the basis of activation maps.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Study Group | – | Radiation: Neo-Adjuvant Radiotherapy, Drug: Neo-Adjuvant Chemotherapy, Procedure: EsophagectomyPatients undergoing NA-CCRT followed by Surgery |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patient records in each participating research center from January 2011 to 1st May 2020
Key inclusion criteria (6 listed)
- ECOG Performance Status: 0-2
- Patients with histopathological or cytopathological confirmed malignancy of the esophagus
- Histology: Squamous Cell Carcinoma and Adenocarcinoma
- Patients should have received NeoAdjuvant Concurrent Chemoradiation (NACCRT) followed by Surgery
- All therapeutic interventions (Radiotherapy, Chemotherapy \& Surgery) delivered within participating institutions
Key exclusion criteria (4 listed)
- Patients with any metallic implants in the region of interest
- Patient with locally advanced disease or metastatic disease (T4 disease, Fistula, metastases)
- Patients with prior history of radiotherapy in the same region
- Patients developing a second malignancy in the esophagus
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 |
|---|---|---|
| Neo-Adjuvant RadiotherapyNeo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy. | Radiation | Study Group |
| Neo-Adjuvant ChemotherapyNeo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy. | Drug | Study Group |
| EsophagectomyEsophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation | Procedure or surgery | Study Group |
Primary outcome
Develop models to predict pCR based on pre-neoadjuvant imaging modalities
Time frame: August 2021
Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy
Time frame: January 2020
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Dec 2022.
- 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.
Kundan S Chufal, MD principal investigator
Rajiv Gandhi Cancer Institute & Research Center
2 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Esophageal Neoplasm, run by Dr Kundan Singh Chufal at 2 sites in 2 countries.
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: status not confirmed, as of the sponsor's last update on 28 Dec 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Australia, India. 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?
Results are not available in the source record.
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?
Develop models to predict pCR based on pre-neoadjuvant imaging modalities (time frame: August 2021). The study lists 2 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 28 Dec 2022.