Rectal CancerNCT05331040
Radiomics in Rectal Cancer
An observational study of Rectal Cancer, run by NHS Grampian at 1 site in United Kingdom.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Rectal Cancer
- What is being tested?
- MRI
- How many people
- 0 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 May 2020to 1 May 2021
- 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: As per inclusion criteria.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study
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.
Read the detailed description
Background Bowel cancer is the 4th most common type of cancer in the UK with 14,000 individuals diagnosed with rectal cancer each year. The management of rectal cancer has significantly changed in the last decade with the advent of individualised treatment tailored to the individual patient's pathology and disease burden. Both local control and overall survival in this group of patients are significantly linked with achieving a complete resection with clear circumferential resection margin (CRM).
Radiology is essential to the patient pathway with Magnetic Resonance Imaging (MRI) used in the pre-operative staging process to evaluate not only whether the CRM is potentially involved (tumour or affected lymph node within 1-2mm from CRM), but also other adverse features such as extra mural vascular invasion (EMVI).
With the increasing use of neo-adjuvant chemoradiation therapy (nCRT) the true heterogeneous nature of rectal cancer has become apparent: Rectal cancer patients with similar initial staging have significantly different responses to treatments. MRI can accurately delineate tumour burden; however, it fails to fully take account for this heterogeneity and it is still lacking in adequately evaluating CRM and lymph nodes status. It is only through imaging that the entirety of a rectal tumour is visualised prior to the commencement of treatment. An objective radiological tool that could accurately identify patients who are likely to achieve a complete response to nCRT followed by surgery would have a significant impact in clinical practice by allowing the selection of ideal candidates for organ-sparing strategies.
Recent advances in image acquisition and image analysis that produce quantitative imaging descriptors could potentially play a significant role in bridging this unmet clinical need. The emerging field of Radiomics, uses quantitative data obtained from digital medical images, to extract uncovered mixed biological information. Radiomics is a technique that utilizes all the information available in an image via image processing software. Imaging studies become more than just pictures to be interpreted, instead the wealth of non-visual information generated by computers is used for greater understanding of disease. Within radiomics the numerical data which forms the basis of the images is extracted from a region of interest and analysed producing what is known as radiomics variables. The variables produced are vast and broadly represent the inter and intra-variability between these numerical values. Through the correlation/comparison of these variables with the pathology, genetics and treatment responses the investigators hypothesize that these imaging features (radiomics variables) capture the heterogeneity of rectal cancer. Identifying distinct phenotypic differences of tumours, which are not possible to depict by standard measurements and may have a predictive power and thus clinical significance across different diseases. This technique has been successfully applied in lung and head and neck cancers showing the translational potential of radiomics into clinical practice.
The radiomics variables that can be extracted are divided into primary, second order/texture features and higher order characteristics. Primary characteristics reflect the variables related to the numerical data when assessed alone and includes mean, kurtosis and skewness, predominantly the histogram based characteristics. These do not account for the inter or intratumour heterogeneity as are not representative of the relationship between the voxels. It is secondary order characteristics that reflect how the individual pixels relate to the each other, measuring the intratumoural variability. These variables include fractional analyses and wavelets. Higher order characteristics identify and extract patterns within the region of interest in this case the rectal tumour. Higher order statistics include fractional dimensions and kaplacian transformations. These are a reflection of entire tumours characteristics and the identification of homogeneity within these higher order statistics within population subsets may reveal further information about the underlying tumours biology.
Here, the investigators aim to assess the clinical relevance of radiomics variables in rectal cancer in order to improve the tumour assessment of this group of patients. It will facilitate more informed and personalized treatment decisions as to whether the patient should receive: new treatment versus conventional treatment or vice versa. Within rectal cancer this has the potential to develop and strengthen the role of radiology in recognizing new imaging biomarkers by radiomics. To date there are only a handful of studies applying radiomics in rectal cancer. Radiomics measurements performed separately using MRI data and recently explored using [18]-FDG-PET/CT data have shown interesting and significant results. However, very small sample sizes have been used and further investigations are needed. This study aims to address this need.
- Conditions
- Rectal Cancer
- Being tested
- MRI
- Keywords
- Radiomics, Prognostic modelling
- Medical subject headings
- Rectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MRIDiagnostic test
Radiomic analysis of newly diagnosed rectal cancers
What is being measured
The primary outcome is the main question the study is designed to answer.
Model
Development of a radiomics based prognostic model to help/guide multidisciplinary team and shared care decisions in the management of rectal cancer patients.
Measured: from baseline characteristics
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Apr 2022
- Study start1 May 2020
- Primary completion1 May 2021
- Study completion1 May 2021
Why it stopped: Ill health of CI
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Rectal CancerStudies 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
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- NHS Grampian
- Sponsor type
- Other government
- Collaborators
- Industrial centre for Artificial intelligence Research, Roland Sutton Academic Trust, Innovate UK, University of Aberdeen
- Responsible party
- Sponsor
- Investigators
- Rosalind Mitchell-Hay, Principal investigator, NHS Grampian & University of Aberdeen
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05331040
- Sponsor's protocol ID
- 2018ON004
- Status last verified
- Jun 2026
- First submitted
- 8 Apr 2022
- Last updated
- 8 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Aerts 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.
PubMed 24892406Al-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.
PubMed 22271205Bang 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.
PubMed 26338180Brown 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.
PubMed 26183150Bundschuh 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.
PubMed 24752672Coroller 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.
PubMed 25746350De Cecco CN, Ciolina M, Caruso D, Rengo M, Ganeshan B, Meinel FG, Musio D, De Felice F, Tombolini V, Laghi A. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY). 2016 Sep;41(9):1728-35. doi: 10.1007/s00261-016-0733-8.
PubMed 27056748Gillies 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.
PubMed 26579733Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, Groves A, Sizer B, Arulampalam T. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis. 2017 Apr;19(4):349-362. doi: 10.1111/codi.13496.
PubMed 27538267Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magn Reson Imaging. 2012 Nov;30(9):1234-48. doi: 10.1016/j.mri.2012.06.010. Epub 2012 Aug 13.
PubMed 22898692McClelland D, Murray GI. A Comprehensive Study of Extramural Venous Invasion in Colorectal Cancer. PLoS One. 2015 Dec 15;10(12):e0144987. doi: 10.1371/journal.pone.0144987. eCollection 2015.
PubMed 26671331Parnaby CN, Scott NW, Ramsay G, MacKay C, Samuel L, Murray GI, Loudon MA. Prognostic value of lymph node ratio and extramural vascular invasion on survival for patients undergoing curative colon cancer resection. Br J Cancer. 2015 Jul 14;113(2):212-9. doi: 10.1038/bjc.2015.211. Epub 2015 Jun 16.
PubMed 26079302Quirke P, Durdey P, Dixon MF, Williams NS. Local recurrence of rectal adenocarcinoma due to inadequate surgical resection. Histopathological study of lateral tumour spread and surgical excision. Lancet. 1986 Nov 1;2(8514):996-9. doi: 10.1016/s0140-6736(86)92612-7.
PubMed 2430152Smith N, Brown G. Preoperative staging of rectal cancer. Acta Oncol. 2008;47(1):20-31. doi: 10.1080/02841860701697720.
PubMed 17957502Vignali A, De Nardi P. Multidisciplinary treatment of rectal cancer in 2014: where are we going? World J Gastroenterol. 2014 Aug 28;20(32):11249-61. doi: 10.3748/wjg.v20.i32.11249.
PubMed 25170209
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study
- ClinicalTrials.gov ID
- NCT05331040
- Acronym / sponsor ID
- 2018ON004
- Conditions
- Rectal Cancer
- Keywords
- Radiomics, Prognostic modelling
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Ill health of CI
- Enrolment
- 0 participants (actual)
- Lead sponsor
- NHS GrampianOther government
- Collaborators
- Industrial centre for Artificial intelligence Research, Roland Sutton Academic Trust, Innovate UK, University of Aberdeen
- Study start
- 1 May 2020 (actual)
- Primary completion
- 1 May 2021 (actual)
- Study completion
- 1 May 2021 (actual)
- First posted
- 15 Apr 2022 (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.
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.
Detailed description
Background Bowel cancer is the 4th most common type of cancer in the UK with 14,000 individuals diagnosed with rectal cancer each year. The management of rectal cancer has significantly changed in the last decade with the advent of individualised treatment tailored to the individual patient's pathology and disease burden. Both local control and overall survival in this group of patients are significantly linked with achieving a complete resection with clear circumferential resection margin (CRM).
Radiology is essential to the patient pathway with Magnetic Resonance Imaging (MRI) used in the pre-operative staging process to evaluate not only whether the CRM is potentially involved (tumour or affected lymph node within 1-2mm from CRM), but also other adverse features such as extra mural vascular invasion (EMVI).
With the increasing use of neo-adjuvant chemoradiation therapy (nCRT) the true heterogeneous nature of rectal cancer has become apparent: Rectal cancer patients with similar initial staging have significantly different responses to treatments. MRI can accurately delineate tumour burden; however, it fails to fully take account for this heterogeneity and it is still lacking in adequately evaluating CRM and lymph nodes status. It is only through imaging that the entirety of a rectal tumour is visualised prior to the commencement of treatment. An objective radiological tool that could accurately identify patients who are likely to achieve a complete response to nCRT followed by surgery would have a significant impact in clinical practice by allowing the selection of ideal candidates for organ-sparing strategies.
Recent advances in image acquisition and image analysis that produce quantitative imaging descriptors could potentially play a significant role in bridging this unmet clinical need. The emerging field of Radiomics, uses quantitative data obtained from digital medical images, to extract uncovered mixed biological information. Radiomics is a technique that utilizes all the information available in an image via image processing software. Imaging studies become more than just pictures to be interpreted, instead the wealth of non-visual information generated by computers is used for greater understanding of disease. Within radiomics the numerical data which forms the basis of the images is extracted from a region of interest and analysed producing what is known as radiomics variables. The variables produced are vast and broadly represent the inter and intra-variability between these numerical values. Through the correlation/comparison of these variables with the pathology, genetics and treatment responses the investigators hypothesize that these imaging features (radiomics variables) capture the heterogeneity of rectal cancer. Identifying distinct phenotypic differences of tumours, which are not possible to depict by standard measurements and may have a predictive power and thus clinical significance across different diseases. This technique has been successfully applied in lung and head and neck cancers showing the translational potential of radiomics into clinical practice.
The radiomics variables that can be extracted are divided into primary, second order/texture features and higher order characteristics. Primary characteristics reflect the variables related to the numerical data when assessed alone and includes mean, kurtosis and skewness, predominantly the histogram based characteristics. These do not account for the inter or intratumour heterogeneity as are not representative of the relationship between the voxels. It is secondary order characteristics that reflect how the individual pixels relate to the each other, measuring the intratumoural variability. These variables include fractional analyses and wavelets. Higher order characteristics identify and extract patterns within the region of interest in this case the rectal tumour. Higher order statistics include fractional dimensions and kaplacian transformations. These are a reflection of entire tumours characteristics and the identification of homogeneity within these higher order statistics within population subsets may reveal further information about the underlying tumours biology.
Here, the investigators aim to assess the clinical relevance of radiomics variables in rectal cancer in order to improve the tumour assessment of this group of patients. It will facilitate more informed and personalized treatment decisions as to whether the patient should receive: new treatment versus conventional treatment or vice versa. Within rectal cancer this has the potential to develop and strengthen the role of radiology in recognizing new imaging biomarkers by radiomics. To date there are only a handful of studies applying radiomics in rectal cancer. Radiomics measurements performed separately using MRI data and recently explored using [18]-FDG-PET/CT data have shown interesting and significant results. However, very small sample sizes have been used and further investigations are needed. This study aims to address this need.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- As per inclusion criteria.
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MRIRadiomic analysis of newly diagnosed rectal cancers | Diagnostic test | – |
Primary outcome
Model
Time frame: from baseline characteristics
Development of a radiomics based prognostic model to help/guide multidisciplinary team and shared care decisions in the management of rectal cancer patients.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jun 2026.
- Background: PubMed 24892406Aerts 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.
- Background: PubMed 22271205Al-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.
- Background: PubMed 26338180Bang 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.
- Background: PubMed 26183150Brown 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.
- Background: PubMed 24752672Bundschuh 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.
- Background: PubMed 25746350Coroller 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.
- Background: PubMed 27056748De Cecco CN, Ciolina M, Caruso D, Rengo M, Ganeshan B, Meinel FG, Musio D, De Felice F, Tombolini V, Laghi A. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY). 2016 Sep;41(9):1728-35. doi: 10.1007/s00261-016-0733-8.
- Background: PubMed 26579733Gillies 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.
- Background: PubMed 27538267Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, Groves A, Sizer B, Arulampalam T. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis. 2017 Apr;19(4):349-362. doi: 10.1111/codi.13496.
- Background: PubMed 22898692Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magn Reson Imaging. 2012 Nov;30(9):1234-48. doi: 10.1016/j.mri.2012.06.010. Epub 2012 Aug 13.
- Background: PubMed 26671331McClelland D, Murray GI. A Comprehensive Study of Extramural Venous Invasion in Colorectal Cancer. PLoS One. 2015 Dec 15;10(12):e0144987. doi: 10.1371/journal.pone.0144987. eCollection 2015.
- Background: PubMed 26079302Parnaby CN, Scott NW, Ramsay G, MacKay C, Samuel L, Murray GI, Loudon MA. Prognostic value of lymph node ratio and extramural vascular invasion on survival for patients undergoing curative colon cancer resection. Br J Cancer. 2015 Jul 14;113(2):212-9. doi: 10.1038/bjc.2015.211. Epub 2015 Jun 16.
- Individual participant dataSharing planned: Undecided
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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.
Rosalind Mitchell-Hay principal investigator
NHS Grampian & University of Aberdeen
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 Rectal Cancer, run by NHS Grampian at 1 site in United Kingdom.
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 1 inclusion and 3 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: withdrawn, as of the sponsor's last update on 8 Jun 2026. 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 United Kingdom. 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?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Model (time frame: from baseline characteristics). 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 NCT05331040, which also lists 16 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 8 Jun 2026.