ArTificial inTelligence-based RAdiogenomics in Colon Tumors (ATTRACT)

October 24, 2023 updated by: Andrea Laghi, University of Roma La Sapienza

ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors

The goal of this clinical trial is to develop an artificial intelligence-based model to assess radiogenomics signature of colon tumor in patients with stage II-III colon cancer. The main question it aims to answer is:

• Can artificial intelligence-based algorithm of radiomics features combined with clinical factors, biochemical biomarkers, and genomic data recognise tumor behaviour, aggressiveness, and prognosis, identifying a radiogenomics signature of the tumor?

Participants will

  • undergo a preoperative contrast-enhanced CT examination;
  • undergo surgical excision of colon cancer
  • undergo adjuvant therapy if deemed necessary based on current guidelines

Study Overview

Status

Recruiting

Conditions

Detailed Description

The ATTRACT trial consist of a retrospective and a prospective part. In the retrospective part of the trial, radiology, pathology, and genomics data of 300 patients with stage II-III colon cancer will be used to identify the genetic and radiomics features of colon tumors and the clinical endpoints as the outcomes of the predictive model.

Tumors will be manually segmented on CT images and used for the AI (artificial intelligence)-model generation. Pathological annotations will be associated to the corresponding anonymised profiles. Immunohistochemistry will be used to classify the samples in the 4 molecular subtypes according.RNA-seq profiles will be also generated from tissue samples through targeted transcriptomics using custom NGS (next-generation sequencing) panels specifically designed to evaluate gene expression and assess Tumor Mutational Burden (TMB). Raw data will be processed and modelled using Topological Pathway Analysis to stratify patients according to the relevant molecular features and define molecular annotations that will be used to train the model for the identification of specific clinically relevant groups. Raw data together with radiological data will be used to generate and train the AI-models for the automated segmentation and the extraction of the radiogenomics signature.

Radiomics features will be extracted from manually segmented tumors. Standard PyRadiomics tools as well custom-made tools will be used. Feature robustness will be guaranteed by selecting only those with high inter-observer statistical correlation. Two families of AI models will be generated, one family dedicated to segmentation, and the other dedicated to radiogenomics-based phenotyping according to the clinical, molecular biology and pathological data available. The two families will be fused for the creation of the ATTRACT AI-model. For the generation of these models, specific convolutional neural network (CNN) architectures based on deep learning (DL) and Artificial Intelligence like UNet and MaskNet will be applied. The training will be performed using the manual ROI (region of interest) segmentations as ground truth. For the generation of radiogenomics analysis models, radiomics and genomic features will be combined using different multivariate algorithms. The classificatory will be trained to recognise the cancer subtypes and clinical endpoints.

In the prospective part of the trial, patients with stage II-III colon cancer will be recruited and will undergo a preoperative contrast-enhanced CT examination. The recruitment rate will be 70 patients per year, for a total of 210 patients. After pre-operative CT, surgery will be performed according to international standard protocols. Eventually adjuvant therapy will be considered following current guidelines. Pathological sample of the prospective enrollment will be analyzed. First, with RNA-seq data, TMB (of coding genes) and clinical data, patients will be clustered by making use of two different techniques Markov Cluster algorithm (MCL) and t-SNE (t-distributed stochastic neighbor embedding), Multi-Layer Network clustering. Patients will be represented as node of a network, edges between nodes will be weighted and thresholded according to the Jaccard Similarity. The similarity will be computed on top of Gene Expression, TMB, and perturbation information coming from Topological Pathway Analysis. Results of clustering will be matched with those coming from Immunohistochemistry. Clinical follow-up data (i.e. outcome of the therapy etc...) will be, once available, also plugged into the workflow to enforce the learning. Extracted knowledge will be used to annotate the dataset used to train and validate the radiomics classification. Gross specimen will be analyzed in order to extract different transcriptomics molecular subtypes (CMS1, CMS2, CMS3, CMS4) in accordance to the Colorectal Cancer (CRC) Subtyping Consortium (CRCSC) assessing the presence or absence of core subtype patterns among existing gene expression-based CRC subtyping algorithms. The accordance between pathological molecular profile and ctDNA analysis during protocol will be related to radiomics classification in order to provide a new whole-diagnostic model of approach in CRC treatment and surveillance.

Prospective data will be used to validate the AI models. For the segmentation models, the Dice Coefficient will be used as an indicator to measure the degree of overlap between the automated and the expert segmentation. For the radiogenomics model, performances will be evaluated using accuracy, integral under the receiver operator curve (ROC-AUC) and clinical decision curve. The investigators will also take into consideration, in order to select the best AI models, the response to the variation of the input characteristics and will produce saliency maps where the features of the input image that mostly contributed to the classification are highlighted.

Clinical evaluation with will be performed every 6 months for 2 years, including regular serum CEA (carcinoembryonic antigen) tests and Whole-Body CT every 6-12 months in patients who are at higher risk of recurrence in the first 3 years following ESMO (European Society for Medical Oncology) guidelines .Disease-free survival (DFS) and relapse-free survival (RFS) will be calculated.

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Italy
    • RM
      • Roma, RM, Italy, 00189

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

The training phase will consist of a retrospective selection of three-hundred patients with pathologically proven stage II and III colon cancer from an institutional database.

Test and validation will consist of a prospective data collection form a new patient population. Patients' recruitment will be performed by the Oncologic Unit and Unit of Surgery of Sant'Andrea Hospital - Sapienza University of Rome. Pathology and genomic will be extracted from the sample obtained during surgery and blood samples for ctDNA analysis will be collected before and during chemotherapy/follow up visits (within 30 day after colorectal surgery, after 3 and then every 6 months)

Description

Inclusion Criteria:

  • patients with pathologically proven stage II and stage III colon cancer;
  • availability of a CT scan with portal-venous phase at the time of diagnosis;
  • availability of immunohistochemical panel

Exclusion Criteria:

  • patients with no CT images prior to surgical resection;
  • patients with CT scans characterized by motion artifacts preventing radiomics analysis

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors
Time Frame: From January 2021 to December 2023
From January 2021 to December 2023

Secondary Outcome Measures

Outcome Measure
Time Frame
Correlation of radiogenomics signature (ATTRACT AI-model) of colon cancer with clinical outcomes (DFS and RFS)
Time Frame: From January 2024 to December 2025
From January 2024 to December 2025

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Roma La Sapienza

Collaborators

Associazione Italiana per la Ricerca sul Cancro

Investigators

  • Principal Investigator: Andrea Laghi, MD, University of Roma La Sapienza

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 2, 2021

Primary Completion (Estimated)

December 31, 2024

Study Completion (Estimated)

December 31, 2025

Study Registration Dates

First Submitted

July 15, 2023

First Submitted That Met QC Criteria

October 24, 2023

First Posted (Actual)

October 31, 2023

Study Record Updates

Last Update Posted (Actual)

October 31, 2023

Last Update Submitted That Met QC Criteria

October 24, 2023

Last Verified

October 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IG24974

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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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