Colon Cancer Stage IIATTRACTNCT06108310
ArTificial inTelligence-based RAdiogenomics in Colon Tumors
An observational study of Colon Cancer Stage II and Colon Cancer Stage III, run by University of Roma La Sapienza at 1 site in Italy.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colon Cancer Stage II, Colon Cancer Stage III
- How many people
- 500 participants
- Where
- 1 siteItaly
- Study dates
- 2 Jan 2021to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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)
Who may be able to join
- 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
Who cannot join
- patients with no CT images prior to surgical resection;
- patients with CT scans characterized by motion artifacts preventing radiomics analysis
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
AOU Sant'Andrea
Roma, RM, 00189
Andrea Laghi, MD, +390633775285, andrea.laghi@uniroma1.it
About this study
Official title: 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
Read the 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.
- Conditions
- Colon Cancer Stage II, Colon Cancer Stage III
- Keywords
- colon cancer, artificial intelligence, segmentation, computed tomography
- Medical subject headings
- Colonic Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors
Measured: From January 2021 to December 2023
Secondary outcomes (1)
Correlation of radiogenomics signature (ATTRACT AI-model) of colon cancer with clinical outcomes (DFS and RFS)
Measured: From January 2024 to December 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Oct 2023
- Study start2 Jan 2021
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (41)Papers the sponsor has linked to this study.
- Other trials for Colon Cancer Stage IIStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Roma La Sapienza
- Sponsor type
- University, hospital or other
- Collaborators
- Associazione Italiana per la Ricerca sul Cancro
- Responsible party
- Principal investigator, Andrea Laghi, University of Roma La Sapienza
- Investigators
- Andrea Laghi, MD, Principal investigator, University of Roma La Sapienza
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06108310
- Sponsor's protocol ID
- IG24974
- Acronym
- ATTRACT
- Status last verified
- Oct 2023
- First submitted
- 15 Jul 2023
- Last updated
- 31 Oct 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016 Jul;66(4):271-89. doi: 10.3322/caac.21349. Epub 2016 Jun 2.
PubMed 27253694Bosman FT. Tumor Heterogeneity: Will It Change What Pathologists Do. Pathobiology. 2018;85(1-2):18-22. doi: 10.1159/000469664. Epub 2017 May 9.
PubMed 28482346Chakraborty S, Hosen MI, Ahmed M, Shekhar HU. Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. Biomed Res Int. 2018 Oct 3;2018:9836256. doi: 10.1155/2018/9836256. eCollection 2018.
PubMed 30402498Chen S, Zhang N, Shao J, Wang T, Wang X. Multi-omics Perspective on the Tumor Microenvironment based on PD-L1 and CD8 T-Cell Infiltration in Urothelial Cancer. J Cancer. 2019 Jan 1;10(3):697-707. doi: 10.7150/jca.28494. eCollection 2019.
PubMed 30719168Hondermarck, H., Cancer Omics: A Special Issue to Highlight Where We Are Heading. Proteomics, 2018. 18(24): p. e180038
Huang L, Brunell D, Stephan C, Mancuso J, Yu X, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics. 2019 Oct 1;35(19):3709-3717. doi: 10.1093/bioinformatics/btz109.
PubMed 30768150Long NP, Jung KH, Anh NH, Yan HH, Nghi TD, Park S, Yoon SJ, Min JE, Kim HM, Lim JH, Kim JM, Lim J, Lee S, Hong SS, Kwon SW. An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers (Basel). 2019 Jan 29;11(2):155. doi: 10.3390/cancers11020155.
PubMed 30700038Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P, Bot BM, Morris JS, Simon IM, Gerster S, Fessler E, De Sousa E Melo F, Missiaglia E, Ramay H, Barras D, Homicsko K, Maru D, Manyam GC, Broom B, Boige V, Perez-Villamil B, Laderas T, Salazar R, Gray JW, Hanahan D, Tabernero J, Bernards R, Friend SH, Laurent-Puig P, Medema JP, Sadanandam A, Wessels L, Delorenzi M, Kopetz S, Vermeulen L, Tejpar S. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038/nm.3967. Epub 2015 Oct 12.
PubMed 26457759Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer. 2017 Feb;17(2):79-92. doi: 10.1038/nrc.2016.126. Epub 2017 Jan 4.
PubMed 28050011Dunne PD, O'Reilly PG, Coleman HG, Gray RT, Longley DB, Johnston PG, Salto-Tellez M, Lawler M, McArt DG. Stratified analysis reveals chemokine-like factor (CKLF) as a potential prognostic marker in the MSI-immune consensus molecular subtype CMS1 of colorectal cancer. Oncotarget. 2016 Jun 14;7(24):36632-36644. doi: 10.18632/oncotarget.9126.
PubMed 27153559Roseweir AK, McMillan DC, Horgan PG, Edwards J. Colorectal cancer subtypes: Translation to routine clinical pathology. Cancer Treat Rev. 2017 Jun;57:1-7. doi: 10.1016/j.ctrv.2017.04.006. Epub 2017 May 4.
PubMed 28505475Thanki K, Nicholls ME, Gajjar A, Senagore AJ, Qiu S, Szabo C, Hellmich MR, Chao C. Consensus Molecular Subtypes of Colorectal Cancer and their Clinical Implications. Int Biol Biomed J. 2017 Summer;3(3):105-111. Epub 2017 Jun 13.
PubMed 28825047Wang W, Kandimalla R, Huang H, Zhu L, Li Y, Gao F, Goel A, Wang X. Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities. Semin Cancer Biol. 2019 Apr;55:37-52. doi: 10.1016/j.semcancer.2018.05.002. Epub 2018 May 18.
PubMed 29775690Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM, Antonarakis ES, Azad NS, Bardelli A, Brem H, Cameron JL, Lee CC, Fecher LA, Gallia GL, Gibbs P, Le D, Giuntoli RL, Goggins M, Hogarty MD, Holdhoff M, Hong SM, Jiao Y, Juhl HH, Kim JJ, Siravegna G, Laheru DA, Lauricella C, Lim M, Lipson EJ, Marie SK, Netto GJ, Oliner KS, Olivi A, Olsson L, Riggins GJ, Sartore-Bianchi A, Schmidt K, Shih lM, Oba-Shinjo SM, Siena S, Theodorescu D, Tie J, Harkins TT, Veronese S, Wang TL, Weingart JD, Wolfgang CL, Wood LD, Xing D, Hruban RH, Wu J, Allen PJ, Schmidt CM, Choti MA, Velculescu VE, Kinzler KW, Vogelstein B, Papadopoulos N, Diaz LA Jr. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014 Feb 19;6(224):224ra24. doi: 10.1126/scitranslmed.3007094.
PubMed 24553385Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, Lindeman N, Lockwood CM, Rai AJ, Schilsky RL, Tsimberidou AM, Vasalos P, Billman BL, Oliver TK, Bruinooge SS, Hayes DF, Turner NC. Circulating Tumor DNA Analysis in Patients With Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J Clin Oncol. 2018 Jun 1;36(16):1631-1641. doi: 10.1200/JCO.2017.76.8671. Epub 2018 Mar 5.
PubMed 29504847
Study at a glance
From the registry record, last updated by the sponsor on 31 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
- ClinicalTrials.gov ID
- NCT06108310
- Acronym / sponsor ID
- ATTRACT / IG24974
- Conditions
- Colon Cancer Stage II, Colon Cancer Stage III
- Keywords
- colon cancer, artificial intelligence, segmentation, computed tomography
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- University of Roma La SapienzaUniversity, hospital or other
- Collaborators
- Associazione Italiana per la Ricerca sul Cancro
- Study start
- 2 Jan 2021 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 31 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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)
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- patients with no CT images prior to surgical resection;
- patients with CT scans characterized by motion artifacts preventing radiomics analysis
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors
Time frame: From January 2021 to December 2023
1 secondary outcome
Correlation of radiogenomics signature (ATTRACT AI-model) of colon cancer with clinical outcomes (DFS and RFS)
Time frame: From January 2024 to December 2025
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Oct 2023.
- Background: PubMed 27253694Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016 Jul;66(4):271-89. doi: 10.3322/caac.21349. Epub 2016 Jun 2.
- Background: PubMed 28482346Bosman FT. Tumor Heterogeneity: Will It Change What Pathologists Do. Pathobiology. 2018;85(1-2):18-22. doi: 10.1159/000469664. Epub 2017 May 9.
- Background: PubMed 30402498Chakraborty S, Hosen MI, Ahmed M, Shekhar HU. Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. Biomed Res Int. 2018 Oct 3;2018:9836256. doi: 10.1155/2018/9836256. eCollection 2018.
- Background: PubMed 30719168Chen S, Zhang N, Shao J, Wang T, Wang X. Multi-omics Perspective on the Tumor Microenvironment based on PD-L1 and CD8 T-Cell Infiltration in Urothelial Cancer. J Cancer. 2019 Jan 1;10(3):697-707. doi: 10.7150/jca.28494. eCollection 2019.
- BackgroundHondermarck, H., Cancer Omics: A Special Issue to Highlight Where We Are Heading. Proteomics, 2018. 18(24): p. e180038
- Background: PubMed 30768150Huang L, Brunell D, Stephan C, Mancuso J, Yu X, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics. 2019 Oct 1;35(19):3709-3717. doi: 10.1093/bioinformatics/btz109.
- Background: PubMed 30700038Long NP, Jung KH, Anh NH, Yan HH, Nghi TD, Park S, Yoon SJ, Min JE, Kim HM, Lim JH, Kim JM, Lim J, Lee S, Hong SS, Kwon SW. An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers (Basel). 2019 Jan 29;11(2):155. doi: 10.3390/cancers11020155.
- Background: PubMed 26457759Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P, Bot BM, Morris JS, Simon IM, Gerster S, Fessler E, De Sousa E Melo F, Missiaglia E, Ramay H, Barras D, Homicsko K, Maru D, Manyam GC, Broom B, Boige V, Perez-Villamil B, Laderas T, Salazar R, Gray JW, Hanahan D, Tabernero J, Bernards R, Friend SH, Laurent-Puig P, Medema JP, Sadanandam A, Wessels L, Delorenzi M, Kopetz S, Vermeulen L, Tejpar S. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038/nm.3967. Epub 2015 Oct 12.
- Background: PubMed 28050011Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer. 2017 Feb;17(2):79-92. doi: 10.1038/nrc.2016.126. Epub 2017 Jan 4.
- Background: PubMed 27153559Dunne PD, O'Reilly PG, Coleman HG, Gray RT, Longley DB, Johnston PG, Salto-Tellez M, Lawler M, McArt DG. Stratified analysis reveals chemokine-like factor (CKLF) as a potential prognostic marker in the MSI-immune consensus molecular subtype CMS1 of colorectal cancer. Oncotarget. 2016 Jun 14;7(24):36632-36644. doi: 10.18632/oncotarget.9126.
- Background: PubMed 28505475Roseweir AK, McMillan DC, Horgan PG, Edwards J. Colorectal cancer subtypes: Translation to routine clinical pathology. Cancer Treat Rev. 2017 Jun;57:1-7. doi: 10.1016/j.ctrv.2017.04.006. Epub 2017 May 4.
- Background: PubMed 28825047Thanki K, Nicholls ME, Gajjar A, Senagore AJ, Qiu S, Szabo C, Hellmich MR, Chao C. Consensus Molecular Subtypes of Colorectal Cancer and their Clinical Implications. Int Biol Biomed J. 2017 Summer;3(3):105-111. Epub 2017 Jun 13.
Related trials for Colon Cancer Stage II
Active, not recruitingPhase 2
Infliximab for Treatment of Immune Checkpoint Inhibitor Colitis
Melanoma Stage III, Melanoma Stage IV
Not yet recruitingPhase 2
Personalized Ultrafractionated Stereotactic Adaptive Radiotherapy Combined With Penpulimab and Fruquintinib for Advanced Colorectal Cancer
Colo-rectal Cancer
RecruitingPhase 1/2
Beamion BCGC-1: A Study to Find a Suitable Dose of Zongertinib Used Alone and in Combination With Other Treatments to Test Whether it Helps People With Different Types of HER2+ Cancer That Has Spread
Metastatic Breast Cancer, Metastatic Gastric Adenocarcinoma
RecruitingPhase 2
SBRT of Metastases Following Neo-adjuvant Treatment for Colorectal Cancer With Synchronous Liver Metastases
Colorectal Cancer, Liver Metastasis Colon Cancer
RecruitingPhase 2
Ivarmacitinib Combined With Camrelizumab and Fruquintinib for Patients With Metastatic Colorectal Cancer After Failure of Second-line or Later Treatment
Refractory Metastatic Colorectal Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Andrea Laghi, MD principal investigator
University of Roma La Sapienza
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colon Cancer Stage II and Colon Cancer Stage III, run by University of Roma La Sapienza at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 31 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Andrea Laghi, MD (+390633775285, andrea.laghi@uniroma1.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors (time frame: From January 2021 to December 2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06108310, which also lists 41 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Oct 2023.