ATTRACT: ArTificial inTelligence-based RAdiogenomics in Colon Tumors
Study Details
Study Description
Brief 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, biochemical biomarkers, and genomic data recognise tumor behaviour, aggressiveness, and prognosis, identifying a radiogenomics signature of the tumor?
Participants will
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undergo a preoperative contrast-enhanced CT examination;
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undergo surgical excision of colon cancer
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undergo adjuvant therapy if deemed necessary based on current guidelines
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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
Outcome Measures
Primary Outcome Measures
- Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors [From January 2021 to December 2023]
Secondary Outcome Measures
- Correlation of radiogenomics signature (ATTRACT AI-model) of colon cancer with clinical outcomes (DFS and RFS) [From January 2024 to December 2025]
Eligibility Criteria
Criteria
Inclusion Criteria:
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patients with pathologically proven stage II and stage III colon cancer;
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availability of a CT scan with portal-venous phase at the time of diagnosis;
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availability of immunohistochemical panel
Exclusion Criteria:
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patients with no CT images prior to surgical resection;
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patients with CT scans characterized by motion artifacts preventing radiomics analysis
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | AOU Sant'Andrea | Roma | RM | Italy | 00189 |
Sponsors and Collaborators
- University of Roma La Sapienza
- Associazione Italiana per la Ricerca sul Cancro
Investigators
- Principal Investigator: Andrea Laghi, MD, University of Roma La Sapienza
Study Documents (Full-Text)
None provided.More Information
Publications
- Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. NPJ Precis Oncol. 2019 Feb 25;3:6. doi: 10.1038/s41698-019-0078-1. eCollection 2019.
- Bettegowda 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.
- Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A. Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018 Aug 22;8(1):12611. doi: 10.1038/s41598-018-30657-6. Erratum In: Sci Rep. 2018 Nov 12;8(1):16914.
- Bosman FT. Tumor Heterogeneity: Will It Change What Pathologists Do. Pathobiology. 2018;85(1-2):18-22. doi: 10.1159/000469664. Epub 2017 May 9.
- Caruso D, Zerunian M, Ciolina M, de Santis D, Rengo M, Soomro MH, Giunta G, Conforto S, Schmid M, Neri E, Laghi A. Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study. Radiol Med. 2018 Mar;123(3):161-167. doi: 10.1007/s11547-017-0833-8. Epub 2017 Nov 8.
- Chakraborty 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.
- Chen 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.
- Cozzi L, Comito T, Fogliata A, Franzese C, Franceschini D, Bonifacio C, Tozzi A, Di Brina L, Clerici E, Tomatis S, Reggiori G, Lobefalo F, Stravato A, Mancosu P, Zerbi A, Sollini M, Kirienko M, Chiti A, Scorsetti M. Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS One. 2019 Jan 18;14(1):e0210758. doi: 10.1371/journal.pone.0210758. eCollection 2019.
- Diamant A, Chatterjee A, Vallieres M, Shenouda G, Seuntjens J. Deep learning in head & neck cancer outcome prediction. Sci Rep. 2019 Feb 26;9(1):2764. doi: 10.1038/s41598-019-39206-1.
- Dienstmann 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. Erratum In: Nat Rev Cancer. 2017 Mar 23;17 (4):268.
- Dunne 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.
- Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A, Grasso S, Mare' V, Paramatti R, Russomando A, Santacesaria R, Satta A, Solfaroli Camillocci E, Faccini R, Laghi A. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol. 2019 Sep;118:1-9. doi: 10.1016/j.ejrad.2019.06.013. Epub 2019 Jun 22.
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18.
- Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rodel C, Cervantes A, Arnold D; ESMO Guidelines Committee. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017 Jul 1;28(suppl_4):iv22-iv40. doi: 10.1093/annonc/mdx224. No abstract available. Erratum In: Ann Oncol. 2018 Oct 1;29(Suppl 4):iv263.
- Guinney 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.
- Hondermarck, H., Cancer Omics: A Special Issue to Highlight Where We Are Heading. Proteomics, 2018. 18(24): p. e180038
- Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY). 2019 Nov;44(11):3764-3774. doi: 10.1007/s00261-019-02042-y.
- Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, Sala E, Garcia-Aguilar J, Gollub MJ, Petkovska I. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy. Radiology. 2018 Jun;287(3):833-843. doi: 10.1148/radiol.2018172300. Epub 2018 Mar 7.
- 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.
- Kantarjian H, Yu PP. Artificial Intelligence, Big Data, and Cancer. JAMA Oncol. 2015 Aug;1(5):573-4. doi: 10.1001/jamaoncol.2015.1203. No abstract available.
- Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub 2017 Oct 4.
- Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol. 2019 Dec;26(12):1695-1706. doi: 10.1016/j.acra.2019.07.006. Epub 2019 Aug 10.
- Li S, Wang K, Hou Z, Yang J, Ren W, Gao S, Meng F, Wu P, Liu B, Liu J, Yan J. Use of Radiomics Combined With Machine Learning Method in the Recurrence Patterns After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma: A Preliminary Study. Front Oncol. 2018 Dec 21;8:648. doi: 10.3389/fonc.2018.00648. eCollection 2018.
- Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, Tian J, Liang C, Liu Z. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget. 2016 May 24;7(21):31401-12. doi: 10.18632/oncotarget.8919.
- Lo CM, Iqbal U, Li YJ. Cancer quantification from data mining to artificial intelligence. Comput Methods Programs Biomed. 2017 Jul;145:A1. doi: 10.1016/S0169-2607(17)30594-1. No abstract available.
- Long 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.
- Mayo RC, Leung J. Artificial intelligence and deep learning - Radiology's next frontier? Clin Imaging. 2018 May-Jun;49:87-88. doi: 10.1016/j.clinimag.2017.11.007. Epub 2017 Nov 16.
- Merker 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.
- 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.
- Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging. 2018 Dec;9(6):915-924. doi: 10.1007/s13244-018-0657-7. Epub 2018 Nov 14.
- Park BW, Kim JK, Heo C, Park KJ. Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep. 2020 Mar 2;10(1):3852. doi: 10.1038/s41598-020-60868-9.
- Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology. 2018 Jun;287(3):732-747. doi: 10.1148/radiol.2018172171.
- Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018 Nov 14;2(1):36. doi: 10.1186/s41747-018-0068-z.
- Roseweir 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.
- Shen TL, Fu XL. [Application and prospect of artificial intelligence in cancer diagnosis and treatment]. Zhonghua Zhong Liu Za Zhi. 2018 Dec 23;40(12):881-884. doi: 10.3760/cma.j.issn.0253-3766.2018.12.001. Chinese.
- Soomro MH, Coppotelli M, Conforto S, Schmid M, Giunta G, Del Secco L, Neri E, Caruso D, Rengo M, Laghi A. Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network. J Healthc Eng. 2019 Jan 31;2019:1075434. doi: 10.1155/2019/1075434. eCollection 2019.
- Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S, Hollebecque A, Scoazec JY, Marabelle A, Massard C, Soria JC, Robert C, Paragios N, Deutsch E, Ferte C. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018 Sep;19(9):1180-1191. doi: 10.1016/S1470-2045(18)30413-3. Epub 2018 Aug 14.
- Thanki 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.
- Trinh A, Trumpi K, De Sousa E Melo F, Wang X, de Jong JH, Fessler E, Kuppen PJ, Reimers MS, Swets M, Koopman M, Nagtegaal ID, Jansen M, Hooijer GK, Offerhaus GJ, Kranenburg O, Punt CJ, Medema JP, Markowetz F, Vermeulen L. Practical and Robust Identification of Molecular Subtypes in Colorectal Cancer by Immunohistochemistry. Clin Cancer Res. 2017 Jan 15;23(2):387-398. doi: 10.1158/1078-0432.CCR-16-0680. Epub 2016 Jul 26.
- Wang 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.
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