Esophageal Neoplasm Clinical Trial
— QARCOfficial title:
Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models
Verified date | December 2022 |
Source | Rajiv Gandhi Cancer Institute & Research Center, India |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Active, not recruiting |
Enrollment | 150 |
Est. completion date | July 2023 |
Est. primary completion date | July 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
Australia | Illawarra Cancer Care Centre | Wollongong | |
India | Rajiv Gandhi Cancer Institute & Research Center | New Delhi | Delhi |
Lead Sponsor | Collaborator |
---|---|
Dr Kundan Singh Chufal |
Australia, India,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Develop models to predict pCR based on pre-neoadjuvant imaging modalities | August 2021 | ||
Primary | Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy | January 2020 |
Status | Clinical Trial | Phase | |
---|---|---|---|
Recruiting |
NCT05176002 -
Camrelizumab in Combination With Radiotherapy for Neoadjuvant Esophageal Carcinoma.
|
Phase 1/Phase 2 | |
Not yet recruiting |
NCT05406024 -
Feasibility Study CORPPS
|
||
Terminated |
NCT02601079 -
Endodrill vs. Conventional Biopsy. Diagnostics and Genetic Analysis
|
N/A | |
Active, not recruiting |
NCT01745107 -
Efficacy of Intensity Modulated Radiation Therapy After Surgery in Early Stage of Esophageal Carcinoma;
|
Phase 3 | |
Active, not recruiting |
NCT02969473 -
Definitive Concurrent Chemoradiotherapy With Docetaxel Plus Cisplatin Versus 5-fluorouracil Plus Cisplatin in Patients With Esophageal Squamous Cell Carcinoma
|
Phase 2 | |
Recruiting |
NCT04481100 -
CCRT With Itraconazole in Locally Advanced Squamous Esophageal Cancer
|
Phase 2 | |
Terminated |
NCT03108885 -
Measuring Cell Free DNA During the Course of Treatment for Esophageal Cancer as a Marker of Response and Recurrence
|
||
Recruiting |
NCT00288119 -
Genetic Determinants of Barrett's Esophagus and Esophageal Adenocarcinoma
|
||
Terminated |
NCT01870791 -
Study of Additive Omega-3 Fish Oil to Palliative Chemotherapy to Treat Oesophagogastric Cancer
|
Phase 2 | |
Active, not recruiting |
NCT00431756 -
Novel Biomarkers in the Neoplastic Progression of Barrett's Esophagus
|
||
Not yet recruiting |
NCT05736705 -
Monopolar and Bipolar in Esophageal ESD
|
N/A | |
Completed |
NCT02395705 -
Neoadjuvant Chemotherapy Versus Surgery Alone for Esophageal Squamous Cell Carcinoma
|
Phase 3 | |
Completed |
NCT02033213 -
Volume Restriction in Esophageal Carcinoma Surgery: Randomized Clinical Trial
|
N/A | |
Terminated |
NCT00653107 -
Palliation Dysphagia Cancer Oesophagus Stent+Brachytherapy Versus Brachytherapy Only
|
Phase 3 | |
Completed |
NCT01927016 -
Outcomes After Esophageal Cancer Surgery
|
N/A | |
Completed |
NCT02558504 -
Radiofrequency in the Treatment of Barrett's Oesophagus
|
Phase 4 | |
Active, not recruiting |
NCT02636088 -
Definitive Chemoradiotherapy and Cetuximab in the Treatment of Locally Advanced Esophageal Cancer
|
Phase 2 | |
Terminated |
NCT00423150 -
Phase 2 Study of Temozolomide in Pre-Selected Advanced Aerodigestive Tract Cancers (Study P04273AM2)(TERMINATED)
|
Phase 2 | |
Completed |
NCT00318903 -
Irinotecan and Taxotere With Radiotherapy as Preoperative Treatment in Resectable Esophageal Cancer
|
Phase 2 | |
Recruiting |
NCT02583087 -
ESD for the Treatment of Early Barrett's Neoplasia
|
N/A |