Clinical trial · Observational
Radiomics in Rectal Cancer
The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study
NCT05331040CI-TRIAL-00113908withdrawnClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Why stopped (as posted): Ill health of CI
Summary
Brief summary (as posted)
This retrospective study aims to investigate whether initial imaging characteristics of rectal cancer on Magnetic Resonance Imaging (MRI) correlate with the underlying tumour pathology and oncological outcomes such as response to treatment. Using radiomic features, calculated using new high throughput analysis of previously acquired imaging, a statistically robust prognostic model will be created with the overall aim of developing imaging biomarkers.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Rectal Cancer | Malignant Rectal Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| MRI | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Model
- timeFrame
- from baseline characteristics
- description
- Development of a radiomics based prognostic model to help/guide multidisciplinary team and shared care decisions in the management of rectal cancer patients.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * All patients with newly diagnosed rectal cancer within NHSG for the five year period (2010-2015) who had a pre-operative staging MRI at NHSG for whom pathology reported within NHSG. Exclusion Criteria: * Those patients whose MRI scans are degraded from artifact (such as metal artifact from hip replacements) * Patients lost to follow-up or moved out with NHS Grampian during follow-up. * Patients with incomplete clinical or pathological data.
References
Publications (16)
- BACKGROUNDAerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. PMID 24892406
- BACKGROUNDAl-Sukhni E, Milot L, Fruitman M, Beyene J, Victor JC, Schmocker S, Brown G, McLeod R, Kennedy E. Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis. Ann Surg Oncol. 2012 Jul;19(7):2212-23. doi: 10.1245/s10434-011-2210-5. Epub 2012 Jan 20. PMID 22271205
- BACKGROUNDBang JI, Ha S, Kang SB, Lee KW, Lee HS, Kim JS, Oh HK, Lee HY, Kim SE. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016 Mar;43(3):422-31. doi: 10.1007/s00259-015-3180-9. Epub 2015 Sep 4. PMID 26338180
- BACKGROUNDBrown GT, Cash B, Alnabulsi A, Samuel LM, Murray GI. The expression and prognostic significance of bcl-2-associated transcription factor 1 in rectal cancer following neoadjuvant therapy. Histopathology. 2016 Mar;68(4):556-66. doi: 10.1111/his.12780. Epub 2015 Sep 17. PMID 26183150
- BACKGROUNDBundschuh RA, Dinges J, Neumann L, Seyfried M, Zsoter N, Papp L, Rosenberg R, Becker K, Astner ST, Henninger M, Herrmann K, Ziegler SI, Schwaiger M, Essler M. Textural Parameters of Tumor Heterogeneity in (1)(8)F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer. J Nucl Med. 2014 Jun;55(6):891-7. doi: 10.2967/jnumed.113.127340. Epub 2014 Apr 21. PMID 24752672
- BACKGROUNDCoroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJ. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015 Mar;114(3):345-50. doi: 10.1016/j.radonc.2015.02.015. Epub 2015 Mar 4.