Establishment of a prediction model of postoperative infection complications in patients with gastric cancer and its impact on prognosis

Zheng, Dan; Li, Gang; Tu, Langping; Su, Xiaobao; Yu, Yu

doi:10.21037/jgo-23-231

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…This helps to determine the survival risk of patients and provides a basis for developing personalized treatment plans more accurately. Several studies have shown that radiology has a good predictive ability in subtype classification ( 10 , 11 ), staging evaluation ( 12 ), clinical outcome ( 13 - 16 ), and treatment response of tumor patients ( 17 , 18 ). Moreover, machine learning has unique advantages in processing high-dimensional data and finding feature variables ( 19 - 21 ).…”

Section: Introductionmentioning

confidence: 99%

Radiomics based on machine learning algorithms could predict prognosis and postoperative chemotherapy benefits of patients with gastric cancer: a retrospective cohort study

Xiang,

Hu,

Chen

et al. 2023

J Gastrointest Oncol

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Background Traditional clinical characteristics have certain limitations in evaluating cancer prognosis. The radiomics features provide information on tumor morphology, tissue texture, and hemodynamics, which can accurately reflect personalized predictions. This study investigated the clinical value of radiomics features on contrast-enhanced computed tomography (CT) images in predicting prognosis and postoperative chemotherapy benefits for patients with gastric cancer (GC). Methods For this study, 171 GC patients who underwent radical gastrectomy and pathology confirmation of the malignancy at the First Affiliated Hospital of Wenzhou Medical University were retrospectively enrolled. The general information, pathological characteristics, and postoperative chemotherapy information were collected. Patients were also monitored through telephone interviews or outpatient treatment. GC patients were randomly divided into the developing cohort (n=120) and validation cohort (n=51). The intra-tumor areas of interest inside the tumors were delineated, and 1,218 radiomics features were extracted. The optimal radiomics risk score (RRS) was constructed using 8 machine learning algorithms and 29 algorithm combinations. Furthermore, a radiomics nomogram that included clinicopathological characteristics was constructed and validated through univariate and multivariate Cox analyses. Results Eleven prognosis-related features were selected, and an RRS was constructed. Kaplan-Meier curve analysis showed that the RRS had a high prognostic ability in the developing and validation cohorts (log-rank P<0.01). The RRS was higher in patients with a larger tumor size (≥3 cm), higher Charlson score (≥2), and higher clinical stage (Stages III and IV) (all P<0.001). Furthermore, GC patients with a higher RRS significantly benefited from postoperative chemotherapy. The results of univariate and multivariate Cox regression analyses demonstrated that the RRS was an independent risk factor for overall survival (OS) and disease-free survival (DFS) (P<0.001). A visual nomogram was established based on the significant factors in multivariate Cox analysis (P<0.05). The C-index was 0.835 (0.793–0.877) for OS and 0.733 (0.677–0.789) for DFS in the developing cohort. The calibration curve also showed that the nomogram had good agreement. Conclusions A nomogram that combines the RRS and clinicopathological characteristics could serve as a novel noninvasive preoperative prediction model with the potential to accurately predict the prognosis and chemotherapy benefits of GC patients.

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