TY - JOUR
T1 - Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence
T2 - the PROSPeCT trial
AU - Goh, Vicky
AU - Mallett, Susan
AU - Boulter, Victor
AU - Glynne-Jones, Robert
AU - Khan, Saif
AU - Lessels, Sarah
AU - Patel, Dominic
AU - Prezzi, Davide
AU - Rodriguez-Justo, Manuel
AU - Taylor, Stuart A.
AU - Beable, Richard
AU - Betts, Margaret
AU - Breen, David J.
AU - Britton, Ingrid
AU - Brush, John
AU - Correa, Peter
AU - Dodds, Nicholas
AU - Dunlop, Joanna
AU - Gourtsoyianni, Sofia
AU - Griffin, Nyree
AU - Higginson, Antony
AU - Lowe, Andrew
AU - Slater, Andrew
AU - Strugnell, Madeline
AU - Tolan, Damian
AU - Zealley, Ian
AU - Halligan, Steve
AU - PROSPECT investigators
AU - Thomas, Biju
AU - Oliver, Colin
AU - Mohammad, Amjad
AU - McDermaid, Michelle
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11
Y1 - 2024/11
N2 - Objective: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence. Material and methods: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure. Results: Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58–0.68; specificity, 0.75–0.76). Conclusion: A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation. Clinical relevance statement: A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone. Key Points: Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.
AB - Objective: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence. Material and methods: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure. Results: Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58–0.68; specificity, 0.75–0.76). Conclusion: A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation. Clinical relevance statement: A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone. Key Points: Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.
KW - Angiogenesis
KW - CT-perfusion
KW - Large bowel
KW - Prognostic model, Neoplasms/primary
UR - http://www.scopus.com/inward/record.url?scp=85206876104&partnerID=8YFLogxK
U2 - 10.1007/s00330-024-10803-7
DO - 10.1007/s00330-024-10803-7
M3 - Article
C2 - 38836939
AN - SCOPUS:85206876104
SN - 0938-7994
VL - 34
SP - 6922
EP - 7001
JO - European Radiology
JF - European Radiology
ER -