Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection

Yang, Xiaoyan; Yu, Wei; Yang, Feimin; Cai, Xiujun

doi:10.3389/fsurg.2022.1049933

Cited by 2 publications

(1 citation statement)

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“…The predictive performance of the LR remained outstanding, with an AUC of 0.96 (95% CI: 0.75-0.97) for DFS and 0.92 (95% CI: 0.66-0.92) for OS. Given these compelling results, we concluded that single clinicopathological classifiers exhibit robust efficacy (38,39). To further validate our predictive model, we applied it to an external validation cohort.…”

Section: /42mentioning

confidence: 97%

Application of machine learning in the prognosis prediction of malignant large bowel obstruction: a two-cohort study

Chen,

Zhang,

et al. 2024

Preprint

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Background The Colon and Rectal NCCN Clinical Practice Guidelines currently identify obstructions as risk factors rather than as specific types. A personalized and intelligent prognostic evaluation system for malignant large bowel obstruction (MLBO) is urgently needed. Methods We conducted a retrospective study on 170 MLBO patients who underwent radical excision at two centers. The training and validation sets were randomly derived from the combined data of each center at a 7:3 ratio. We employed machine learning methods, including the logistic regression classifier (LR), linear discriminant analysis classifier (LDA), extreme gradient boosting classifier (XGB), AdaBoost classifier (AB), and light gradient boosting machine classifier (LGBM). These classifiers were based on clinical features (clinical model), radiological features (radiomics model), and their combination (merged model). The best model was identified through the area under the operating characteristic curve (AUC). Results Using clinicopathologic parameters, clinicopathologic models XGB achieved an impressive AUC of 0.97 for DFS, and LDA maintained strong performance with an AUC of 0.92 for OS, rather than radio-omics and dual-omics models. Using the Qingdao Center(QD) dataset as a single validation set, the model performance was not ideal due to demographic differences, with AUC values of 0.42 and 0.50 for DFS and OS, respectively. Finally, when cross-training and validating clinicopathological features from two centers were conducted, LDA exhibited exceptional performance for both DFS and OS, with AUCs of 0.96 and 0.95, respectively. Regardless of DFS or OS, the worse prognosis group had higher levels of the following metrics compared to the better prognosis group. [For DFS: pT(p < 0.001), pN(p < 0.006), pM(p < 0.001), monocyte count(0.64 vs. 0.52, p = 0.038), and carbohydrate antigen 199(CA199) (27.59 vs. 15.14, p = 0. 006); For OS: pT(p = 0.002), pN(p = 0.002) and pM(p < 0.001), as well as LVI (p = 0.037), monocyte count(0.68 vs. 0.51, p = 0.005) and CA199 (31.78 vs. 15.88, p = 0.006)]. Conclusions High-efficacy models for the prognosis prediction of MLBO via clinicopathological features across two centers was constructed. We recommend heightened vigilance for MLBO patients with a high TNM stage, lymphovascular invasion occurrence, elevated CA199 levels, and high monocyte count.

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Section: /42mentioning

confidence: 97%