Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning
NCT ID: NCT04489368
Last Updated: 2022-12-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
150 participants
OBSERVATIONAL
2020-01-16
2023-07-31
Brief Summary
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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.
Detailed Description
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Conditions
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Keywords
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Study Design
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COHORT
OTHER
Study Groups
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Study Group
Patients undergoing NA-CCRT followed by Surgery
Neo-Adjuvant Radiotherapy
Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.
Neo-Adjuvant Chemotherapy
Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.
Esophagectomy
Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation
Interventions
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Neo-Adjuvant Radiotherapy
Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.
Neo-Adjuvant Chemotherapy
Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.
Esophagectomy
Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation
Eligibility Criteria
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Inclusion Criteria
* 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
* 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
18 Years
ALL
No
Sponsors
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Dr Kundan Singh Chufal
OTHER
Responsible Party
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Dr Kundan Singh Chufal
Senior Consultant & Chief of Thoracic Radiation Oncology, Department of Radiation Oncology
Principal Investigators
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Kundan S Chufal, MD
Role: PRINCIPAL_INVESTIGATOR
Rajiv Gandhi Cancer Institute & Research Center
Locations
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Illawarra Cancer Care Centre
Wollongong, , Australia
Rajiv Gandhi Cancer Institute & Research Center
New Delhi, National Capital Territory of Delhi, India
Countries
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Other Identifiers
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RGCIRC/IRB/80/2020
Identifier Type: -
Identifier Source: org_study_id