Evaluation of prognostic models to improve prediction of metastasis in patients following potentially curative treatment for primary colorectal cancer: the PROSPECT trial.

Background: 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.

Objectives: 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.

Design: Prospective multicentre cohort.

Setting: Thirteen National Health Service hospitals.

Participants: Consecutive adult patients with colorectal cancer.

Interventions: Collection of prespecified standard clinicopathological variables and more novel imaging, genetic and immunohistochemical biomarkers, followed by 3-year follow-up to identify postoperative metastasis.

Main outcome: Best multivariable prognostic model including perfusion computed tomography compared with tumour/node staging. Secondary outcomes: Additive benefit of perfusion computed tomography and other biomarkers to best baseline model comprising standard clinicopathological predictors; measurement variability between local and central review; biological relationships between perfusion computed tomography and pathology variables.

Results: Between 2011 and 2016, 448 participants were recruited; 122 (27%) were withdrawn, leaving 326 (226 male, 100 female; mean ± standard deviation 66 ± 10.7 years); 183 (56%) had rectal cancer.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.

Limitations: 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.

Conclusions: 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.

Future work: It would be desirable for other researchers to externally evaluate the baseline model.

Trial registration: This trial is registered as ISRCTN95037515.

Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 09/22/49) and is published in full in Health Technology Assessment; Vol. 29, No. 8. See the NIHR Funding and Awards website for further award information.