Radiomics in Rectal Cancer

Rectal Cancer Clinical Trial

Official title:

The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05331040
• Other study ID #: 2018ON004
• Status: Active, not recruiting
• Start date: May 1, 2020
• Est. completion date: May 2025

Study information

• Verified date: April 2022
• Source: NHS Grampian
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 350
• Est. completion date: May 2025
• Est. primary completion date: March 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Rectal Cancer
• Rectal Neoplasms

Intervention

Diagnostic Test:
• MRI
Radiomic analysis of newly diagnosed rectal cancers

Locations

Country	Name	City	State
United Kingdom	NHS Grampian	Aberdeen	Aberdeenshire

Sponsors (5)

Lead Sponsor	Collaborator
NHS Grampian	Industrial centre for Artificial intelligence Research, Innovate UK, Roland Sutton Academic Trust, University of Aberdeen

Country where clinical trial is conducted

United Kingdom, 

References & Publications (16)

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. Erratum in: Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara]. — View Citation

Al-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. — View Citation

Bang 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. — View Citation

Brown 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. — View Citation

Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér 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 ¹8F-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. — View Citation

Coroller 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. — View Citation

De 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. — View Citation

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. — View Citation

Jalil 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. — View Citation

Kumar 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. Review. — View Citation

McClelland 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. — View Citation

Parnaby 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. — View Citation

Quirke 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. — View Citation

Smith N, Brown G. Preoperative staging of rectal cancer. Acta Oncol. 2008;47(1):20-31. Review. — View Citation

Vignali 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. Review. — View Citation

Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016 Jul 7;61(13):R150-66. doi: 10.1088/0031-9155/61/13/R150. Epub 2016 Jun 8. Review. — View Citation

* Note: There are 16 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Model	Development of a radiomics based prognostic model to help/guide multidisciplinary team and shared care decisions in the management of rectal cancer patients.	from baseline characteristics