ANZCTR - Registration

Registering a new trial?

To achieve prospective registration, we recommend submitting your trial for registration at the same time as ethics submission.

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been endorsed by the ANZCTR. Before participating in a study, talk to your health care provider and refer to this information for consumers

Trial details imported from ClinicalTrials.gov

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

Registration number

NCT04489368

Ethics application status

Date submitted

23/07/2020

Date registered

28/07/2020

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]

RGCIRC/IRB/80/2020

Universal Trial Number (UTN)

Trial acronym

QARC

Linked study record

Health condition

Health condition(s) or problem(s) studied:

Esophageal Neoplasm

Condition category

Condition code

Cancer

Oesophageal (gullet)

Intervention/exposure

Study type

Observational

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]

Treatment: Other

Intervention code [2]

Treatment: Drugs

Intervention code [3]

Treatment: Surgery

Comparator / control treatment

Control group

Outcomes

Primary outcome [1]

Develop models to predict pCR based on pre-neoadjuvant imaging modalities

Timepoint [1]

August 2021

Primary outcome [2]

Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy

Timepoint [2]

January 2020

Eligibility

Key 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

Minimum age

18 Years

Maximum age

No limit

Sex

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, metastases)
* Patients with prior history of radiotherapy in the same region
* Patients developing a second malignancy in the esophagus

Study design

Purpose

Duration

Selection

Timing

Statistical methods / analysis

Recruitment

Recruitment status

Active, not recruiting

Data analysis

Reason for early stopping/withdrawal

Other reasons

Date of first participant enrolment

Anticipated

Actual

16/01/2020

Date of last participant enrolment

Anticipated

Actual

Date of last data collection

Anticipated

1/07/2023

Actual

Sample size

Target

150

Accrual to date

Final

Recruitment in Australia

Recruitment state(s)

Recruitment hospital [1]

Illawarra Cancer Care Centre - Wollongong

Recruitment postcode(s) [1]

2500 - Wollongong

Recruitment outside Australia

Country [1]

India

State/province [1]

Delhi

Funding & Sponsors

Primary sponsor type

Other

Name

Dr Kundan Singh Chufal

Address

Country

Ethics approval

Ethics application status

Summary

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

https://clinicaltrials.gov/study/NCT04489368

Trial related presentations / publications

Public notes

Contacts

Principal investigator

Name

Kundan S Chufal, MD

Address

Rajiv Gandhi Cancer Institute & Research Center

Country

Phone

Fax

Contact person for public queries

Name

Address

Country

Phone

Fax

Contact person for scientific queries

Data sharing statement

Will individual participant data (IPD) for this trial be available (including data dictionaries)?

No/undecided IPD sharing reason/comment

What supporting documents are/will be available?

No Supporting Document Provided

Results publications and other study-related documents

No documents have been uploaded by study researchers.

Results not provided in https://clinicaltrials.gov/study/NCT04489368

Register a trial

Registering a new trial?

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