Journal Article

FDG metabolic parameter-based models for predicting recurrence after upfront surgery in synchronous colorectal cancer liver metastasis

Park, Sohyun, Kwak, Jung-Myun, Oh, Jae Hwan, Lee, Ji Sung, Kim, Jae Seung, Kwon, Hyun Woo, Kim, Seok-ki, Lim, Seok-Byung, Kim, Jin Cheon, Hong, Yong Sang, Kim, Hyun Joo, Yu, Chang Sik, Oh, Minyoung, Oh, Ho-Suk, Kim, Sungeun, Kim, Tae-Sung, Kwak, Jae Young, Lee, Hyo Sang, Han, Sangwon, Kim, Tae Won

2023

Overview

Objective This study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM). Methods In this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong’s clinical risk score (FCRS). Results A total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS ( p < .05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68–0.84] and 0.76 [0.68–0.84] vs. 0.65 [0.57–0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator ( https://cpmodel.shinyapps.io/SCLM/ ) and nomograms. Conclusions FDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM. Key Points • In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT. • The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model. • The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Calibration / Cancer / Colorectal cancer / Colorectal carcinoma / Colorectal Neoplasms - pathology / Colorectal Neoplasms - surgery / Decision analysis / Decision making / Diagnostic Radiology / Fluorodeoxyglucose F18 / Humans / Imaging / Internal Medicine / Interventional Radiology / Liver / Liver cancer / Liver Neoplasms - diagnostic imaging / Liver Neoplasms - secondary / Liver Neoplasms - surgery / Male / Mathematical models / Medical prognosis / Medicine / Medicine & Public Health / Metabolism / Metastases / Metastasis / Neuroradiology / Nomograms / Nuclear Medicine / Parameters / Patients / Performance prediction / Positron emission tomography / Positron Emission Tomography Computed Tomography / Prediction models / Prognosis / Radiology / Regression analysis / Retrospective Studies / Risk / Surgery / Ultrasound