Early detection and prognosis of colon cancer metastasis are crucial for improving patient survival and guiding personalized treatment strategies. However, estimating the risk of metastasis progression and mortality remains a complex challenge due to the interplay of clinical, pathological, and genetic factors.

To address this, we present a machine learning–based simulator that predicts the probability of 3-year mortality in patients with colon cancer metastasis. By integrating genomic profiles, mutational data, and clinical variables, the simulator provides an individualized risk score that can support oncologists in decision-making and help researchers explore patterns associated with metastasis outcomes.

This predictive tool serves as a bridge between raw clinical/genomic data and practical healthcare applications, offering patients and specialists an accessible way to better understand prognosis in metastatic colon cancer.

Healthcare professionals can test this methodology with Neural Designer’s trial version.

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Inputs:

Type of Adenocarcinoma:
Metastasis primary site count:
Microsatellite instability score:
Microsatellite instability type:
Mutation count:
Cancer microsatellite subtype:
Tumour mutational burden:
KIT mutation count:
CARD11 mutation count:
RB1 mutation count:
WT1 mutation count:
PLCG2 mutation count:
DNMT1 mutation count:
BRD4 mutation count:
PIK3R1 mutation count:
IRS2 mutation count:
SESN1 mutation count:
NPM1 mutation count:
Liver metastasis risk:6.33%

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