Abstract
Mortality in colorectal cancer (CRC) remains high, resulting in 860,000 deaths annually. Carcinoembryonic antigen is widely used in clinics for CRC patient follow-up, despite carrying a limited prognostic value. Thus, an obvious need exists for multivariate prognostic models. We analyzed 48 biomarkers using a multiplex immunoassay panel in preoperative serum samples from 328 CRC patients who underwent surgery at Helsinki University Hospital between 1998 and 2003. We performed a multivariate prognostic forward-stepping background model based on basic clinicopathological data, and a multivariate machine-learned prognostic model based on clinicopathological data and biomarker variables, calculating the disease-free survival using the value of importance score. From the 48 analyzed biomarkers, only IL-8 emerged as a significant prognostic factor for CRC patients in univariate analysis (HR 4.88; 95% CI 2.00–11.92; p = 0.024) after correcting for multiple comparisons. We also developed a multivariate model based on all 48 biomarkers using a random survival forest analysis. Variable selection based on a minimal depth and the value of importance yielded two tentative candidate CRC prognostic markers: IL-2Ra and IL-8. A multivariate prognostic model using machine-learning technologies improves the prognostic assessment of survival among surgically treated CRC patients.
Highlights
Colorectal cancer (CRC) leads to 860,000 deaths worldwide annually[1]
We aimed to develop a multivariate prognostic colorectal cancer (CRC) model including biomarkers combined with clinical background data
Among 48 analyzed biomarkers, six biomarkers resulted in p < 0.05: interleukin 6 (IL-6), interleukin 8 (IL-8), interleukin 2 receptor alpha chain (IL-2Rα), cutaneous T-cell attracting chemokine (CTACK), macrophage migration inhibitory factor (MIF) and stromal cell-derived factor 1 alpha (SDF-1α)
Summary
Colorectal cancer (CRC) leads to 860,000 deaths worldwide annually[1]. CRC represents a global health hazard with an enormous impact on global health and welfare. Several clinicopathological methods have been developed to classify CRC and predict disease progression. Patients with distal rectal cancer receive neoadjuvant radio- or chemoradiotherapy. Some predictive models using computational intelligence methods have been developed, we lack a consensus model for CRC prognostics[5,6]. Trials to develop multivariate background models are n eeded[7]. Some researchers have assumed that models using artificial intelligence (AI) would provide a superior accuracy compared to conventional methods[8]. We aimed to develop a multivariate prognostic CRC model including biomarkers combined with clinical background data. We compare the performance of this model—that is, the study model—to a model based solely on basic conventional clinicopathological background data plus CEA. We hypothesized that the study model would perform better than the background model
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