- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04489368
Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning (QARC)
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.
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 Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Anticipated)
Contacts and Locations
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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
Exclusion Criteria:
- 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
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Other
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Study Group
Patients undergoing NA-CCRT followed by Surgery
|
Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.
Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.
Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
---|---|
Develop models to predict pCR based on pre-neoadjuvant imaging modalities
Time Frame: August 2021
|
August 2021
|
Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy
Time Frame: January 2020
|
January 2020
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Kundan S Chufal, MD, Rajiv Gandhi Cancer Institute & Research Center
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimate)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- RGCIRC/IRB/80/2020
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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