Authors' objectives: Despite apparently curative treatment, many patients with colorectal cancer develop subsequent metastatic disease. Current prognostic models are criticised because they are based on standard staging and omit novel biomarkers. Improved prognostication is an unmet need. To improve prognostication for colorectal cancer by developing a baseline multivariable model of standard clinicopathological predictors, and to then improve prediction via addition of promising novel imaging, genetic and immunohistochemical biomarkers. Colorectal cancer accounts for 12% of all new UK cancers, with over 42,000 new patients diagnosed each year. Despite treatment with curative intent, up to 50% of colorectal cancer patients will develop subsequent recurrent disease, normally metastasis. Chemotherapy aims to combat metastasis but identification of who will and will not develop subsequent metastasis (i.e. who does and does not merit chemotherapy) is difficult. Currently, ‘at-risk’ patients are identified by tumour and nodal (TN) staging from diagnosis and surgery (when performed) but more accurate prognostication remains an unmet need. Multivariable models promise to improve prediction by combining multiple weighted predictor factors measured from the patient in question but are not used widely. A frequent criticism is that such models ignore ‘cutting-edge’ promising biomarkers, which are currently the subject of intense research and which appear to offer an opportunity to improve risk stratification at diagnosis. Also, the move in recent years from offering chemotherapy in the postoperative (adjuvant) to preoperative (neoadjuvant) setting has shifted the need for identification of high-risk patients from the post-surgery setting (i.e. by using pathological samples from the resected specimen) to the preoperative setting (which depends on imaging and biopsy samples of the primary tumour). Our primary objective was to improve prediction of outcomes from colorectal cancer by developing a multivariable prognostic model of disease-free survival. We aimed to develop a best baseline model using standard clinicopathological variables and to then improve its prediction significantly by incorporating cutting-edge, novel imaging [perfusion computed tomography (CT)], immunohistochemical and genetic biomarkers. Our primary outcome was prediction of the baseline model incorporating CT perfusion when compared with standard TN staging. Secondary outcomes included baseline model prediction when incorporating immunohistochemical or genetic biomarkers; assessment of measurement variability between local sites and central review; and to investigate the biological relationships between perfusion CT and pathology variables.

Authors' results and conclusions: Between 2011 and 2016, 448 participants were recruited; 122 (27%) were withdrawn, leaving 326 (226 male, 100 female; mean ± standard deviation 66 ± 10.7 years); 183 (56%) had rectal cancer. Most cancers were locally advanced [≥ T3 stage, 227 (70%)]; 151 (46%) were node-positive (≥ N1 stage); 306 (94%) had surgery; 79 (24%) had neoadjuvant therapy. The resection margin was positive in 15 (5%); 93 (28%) had venous invasion; 125 (38%) had postoperative adjuvant chemotherapy; 81 (25%, 57 male) developed recurrent disease. Prediction of recurrent disease by the baseline clinicopathological time-to-event Weibull multivariable model (age, sex, tumour/node stage, tumour size and location, treatment, venous invasion) was superior to tumour/node staging: sensitivity: 0.57 (95% confidence interval 0.45 to 0.68), specificity 0.74 (95% confidence interval 0.68 to 0.79) versus sensitivity 0.56 (95% confidence interval 0.44 to 0.67), specificity 0.58 (95% confidence interval 0.51 to 0.64), respectively. Addition of perfusion computed tomography variables did not improve prediction significantly: c-statistic: 0.77 (95% confidence interval 0.71 to 0.83) versus 0.76 (95% confidence interval 0.70 to 0.82). Perfusion computed tomography parameters did not differ significantly between patients with and without recurrence (e.g. mean ± standard deviation blood flow of 60.3 ± 24.2 vs. 61.7 ± 34.2 ml/minute/100 ml). Furthermore, baseline model prediction was not improved significantly by the addition of any novel genetic or immunohistochemical biomarkers. We observed variation between local and central computed tomography measurements but neither improved model prediction significantly. We found no clear association between perfusion computed tomography variables and any immunohistochemical measurement or genetic expression. A prognostic model of standard clinicopathological variables outperformed tumour/node staging, but novel biomarkers did not improve prediction significantly. Biomarkers that appear promising in small single-centre studies may contribute nothing substantial to prognostication when evaluated rigorously. Between 2011 and 2016, we recruited 448 participants; 122 (27%) were withdrawn (mostly due to additional cancer), leaving 326 for analysis [226 male, 100 female; mean ± standard deviation (SD) 66 ± 10.7 years]; a total of 183 (56%) had rectal cancer. Most cancers were locally advanced [≥ T3 stage, 227 (70%); 151 (46%) were node-positive (≥ N1 stage)]. Surgery was performed in 306 (94%). The resection margin was positive in 15 (5%). Venous invasion was present in 93 (28%). Neoadjuvant therapy was undertaken in 79 (24%) and adjuvant therapy in 125 (38%) participants. Eighty-one (25%, 57 male) developed recurrent disease over the 3-year follow-up period. Perfusion CT measurements were available from local sites in 303 (93%) participants. Perfusion CT parameters did not differ between patients with and without positive local nodes (e.g. mean ± SD blood flow: 64.5 ± 25.2 vs. 75.0 ± 44.1 ml/minute/100 ml) or with and without recurrence (e.g. mean ± SD blood flow: 60.3 ± 24.2 vs. 61.7 ± 34.2 ml/minute/100 ml). Central review was undertaken in 291 (96%). Variability assessed by Bland–Altman plots was considerable between many local and central review perfusion CT measurements, most evident for permeability surface area product, where disagreement was greatest at higher permeability values. Although there were differences regarding where the region of interest was placed when local and central reviews were compared, this was not a major contributor to disagreement for vascular parameter values. Similarly, the individual CT scanner manufacturer did not impact substantially on disagreement, because all common manufacturers displayed large differences over all vascular parameters. There was no clear relationship between perfusion CT variables and immunohistochemical markers of angiogenesis (CD105, vascular endothelial growth factor) or hypoxia (hypoxia-inducible factor-1, glucose transporter-1) in the primary tumour, suggesting that CT does not reflect angiogenesis precisely. There was no difference between perfusion CT variables and MMR deficient/MMR proficient tumours. Prediction for the baseline clinicopathological model improved over standard TN staging due to significantly improved specificity: sensitivity 0.57 [95% confidence interval (CI) 0.45 to 0.68] and specificity 0.74 (95% CI 0.68 to 0.79) versus sensitivity 0.56 (95% CI 0.44 to 0.67) and specificity 0.58 (95% CI 0.51 to 0.64), respectively. The addition of perfusion CT variables to the baseline clinicopathological model did not improve prediction significantly: c-statistic 0.77 (95% CI 0.71 to 0.83) versus 0.76 (95% CI 0.70 to 0.82), respectively. Similarly, the addition of more novel histopathological variables (i.e. markers of angiogenesis, hypoxia, rat sarcoma virus, BRAF and MMR mutation status) to the baseline clinicopathological model did not improve model prediction significantly: c-statistic: 0.78 (95% CI 0.72 to 0.84) versus 0.76 (95% CI 0.70 to 0.82), respectively. We developed a prognostic model to predict development of metastatic disease following apparently curative treatment for colorectal cancer. The best baseline model comprising prospectively collected prespecified clinicopathological variables improved over standard TN staging prediction significantly. However, the addition of perfusion CT, immunohistochemical or genetic variables was not able to improve prediction significantly.

Authors' recommendations: Model prediction should be externally evaluated in an NHS setting, preferably by authors unrelated to model development. In addition to an external evaluation of its predictive accuracy, an evaluation should be made of the clinical utility to clinicians of the model in an NHS setting, including within neoadjuvant chemotherapy trials. Venous invasion on pathological evaluation was a strong prognostic factor within the standard model; further research into preoperative imaging assessment of venous invasion on CT for colon cancer and magnetic resonance imaging for rectal cancer is warranted. The fact that CT, immunohistochemistry and genetic markers of angiogenesis did not improve model prediction suggests that prior small, single-centre, retrospective studies including a benefit to these biomarkers are overoptimistic. This finding should be considered when contemplating funding future studies of such markers. Rather, our data suggest that future prognostic research should focus on standard clinicopathological variables.

Authors' methods: Prospective multicentre cohort. Thirteen National Health Service hospitals. Consecutive adult patients with colorectal cancer. Collection of prespecified standard clinicopathological variables and more novel imaging, genetic and immunohistochemical biomarkers, followed by 3-year follow-up to identify postoperative metastasis. The number of patients developing metastasis was lower than expected from historical data. Our findings should not be overinterpreted. While the baseline model was superior to tumour/node staging, any clinical utility needs definition in daily practice. We conducted a prospective multicentre cohort trial. Participants were recruited from 13 representative NHS teaching and district general hospitals in England and Scotland. Participants were eligible if they had histologically proven or suspected primary colorectal cancer (mass suspicious on endoscopy or imaging). Exclusions included polyp cancers, unequivocal metastases at staging, patients aged

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/09/22/49

Year Published: 2025

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/BTMT7049

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/BTMT7049

Organisation Name: NIHR Health Technology Assessment programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk