2022
DOI: 10.3390/biomedicines10020340
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A Prediction Model for Tumor Recurrence in Stage II–III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling

Abstract: Background: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. Methods: Pertinent clinicopathological fe… Show more

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Cited by 8 publications
(11 citation statements)
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References 62 publications
(80 reference statements)
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“…The AUC values of these models have been shown to depend primarily on how much information is incorporated into the model, what type of information is used, and what specific outcomes are investigated. Our AUC values are consistent with the recent studies that investigated the recurrence of prostate, non-small cell lung, colorectal, and biliary cancers, with reported AUC ranging from 0.581 to 0.894 [26][27][28][29][30][31] . The incorporation of more potentially predictive features tends to improve the performance of models.…”
Section: Discussionsupporting
confidence: 90%
“…The AUC values of these models have been shown to depend primarily on how much information is incorporated into the model, what type of information is used, and what specific outcomes are investigated. Our AUC values are consistent with the recent studies that investigated the recurrence of prostate, non-small cell lung, colorectal, and biliary cancers, with reported AUC ranging from 0.581 to 0.894 [26][27][28][29][30][31] . The incorporation of more potentially predictive features tends to improve the performance of models.…”
Section: Discussionsupporting
confidence: 90%
“…Conversely, adipose tissue, debris, muscle, and normal mucosa were less correlated with survival (Additional file 1 : Table S1). Many pathological characteristics can be used to predict the prognosis of colorectal carcinoma (CRC), including tumor characteristics, lymphocytes, stroma, and mucin content [ 3 6 ]. Compatible with the clinical pathological findings, our selected four tissue types were also significant.…”
Section: Discussionmentioning
confidence: 99%
“… 9 - 12 A growing body of research tends toward the development of predictive models for CRC development and disease progression by integrating potential biomarkers and verifying them in prospective studies. 13 , 14 …”
Section: Introductionmentioning
confidence: 99%
“…[9][10][11][12] A growing body of research tends toward the development of predictive models for CRC development and disease progression by integrating potential biomarkers and verifying them in prospective studies. 13,14 According to Traditional Chinese Medicine (TCM) theory, spleen-deficiency syndrome (SDS) is one of the key syndrome types in the development of CRC. Previously, our research team published a validated TCM-SDS scale 15 that included 5 items of patient-reported SDS symptoms.…”
Section: Introductionmentioning
confidence: 99%