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Trial details imported from ClinicalTrials.gov

For full trial details, please see the original record at https://clinicaltrials.gov/show/NCT04489368

Registration number
Ethics application status
Date submitted
Date registered
Date last updated

Titles & IDs
Public title
Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning
Scientific title
Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models
Secondary ID [1] 0 0
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Esophageal Neoplasm 0 0
Condition category
Condition code
Cancer 0 0 0 0
Oesophageal (gullet)

Study type
Patient registry
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
Treatment: Other - Neo-Adjuvant Radiotherapy
Treatment: Drugs - Neo-Adjuvant Chemotherapy
Treatment: Surgery - Esophagectomy

Study Group - Patients undergoing NA-CCRT followed by Surgery

Treatment: Other: Neo-Adjuvant Radiotherapy
Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.

Treatment: Drugs: Neo-Adjuvant Chemotherapy
Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.

Treatment: Surgery: Esophagectomy
Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation

Intervention code [1] 0 0
Treatment: Other
Intervention code [2] 0 0
Treatment: Drugs
Intervention code [3] 0 0
Treatment: Surgery
Comparator / control treatment
Control group

Primary outcome [1] 0 0
Develop models to predict pCR based on pre-neoadjuvant imaging modalities
Timepoint [1] 0 0
August 2021
Primary outcome [2] 0 0
Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy
Timepoint [2] 0 0
January 2020

Key inclusion criteria
- ECOG Performance Status: 0-2

- Patients with histopathological or cytopathological confirmed malignancy of the

- 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
Minimum age
18 Years
Maximum age
No limit
Both males and females
Can healthy volunteers participate?
Key exclusion criteria
- Patients with any metallic implants in the region of interest

- Patient with locally advanced disease or metastatic disease (T4 disease, Fistula,

- Patients with prior history of radiotherapy in the same region

- Patients developing a second malignancy in the esophagus

Study design
Statistical methods / analysis

Recruitment status
Active, not recruiting
Data analysis
Reason for early stopping/withdrawal
Other reasons
Date of first participant enrolment
Date of last participant enrolment
Date of last data collection
Sample size
Accrual to date
Recruitment in Australia
Recruitment state(s)
Recruitment hospital [1] 0 0
Illawarra Cancer Care Centre - Wollongong
Recruitment postcode(s) [1] 0 0
2500 - Wollongong
Recruitment outside Australia
Country [1] 0 0
State/province [1] 0 0

Funding & Sponsors
Primary sponsor type
Dr Kundan Singh Chufal

Ethics approval
Ethics application status

Brief 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. 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.
Trial website
Trial related presentations / publications
Public notes

Principal investigator
Name 0 0
Kundan S Chufal, MD
Address 0 0
Rajiv Gandhi Cancer Institute & Research Center
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for public queries
Name 0 0
Address 0 0
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for scientific queries

Summary results
Other publications