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Magnesium in Health and Disease / Esophageal Cancer Research and Treatment · Journal article
Frontiers in Oncology · August 14, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a single-centre retrospective machine learning study that develops a random forest model for predicting 1-year and 5-year survival in esophageal cancer patients using preoperative laboratory and clinical markers. The model achieved AUC 0.69 in the test population but lacks external validation, comparison to standard prognostic approaches, and prospective clinical evaluation needed to assess whether it changes clinical practice.
Retrospective single-centre cohort study with machine learning model development and test-set evaluation. Esophageal cancer patients who underwent surgery at Sichuan Cancer Hospital; baseline examinations and preoperative biochemical blood tests available.. Intervention: Random forest machine learning model trained on preoperative baseline clinical and laboratory markers. n = 3,204. Sichuan Cancer Hospital (single centre, China).
Random forest model achieved 0.74 accuracy and 0.72 F1 score at 1-year prediction Area Under the Curve (AUC) adjusted to 0.69 in test population Gender and blood magnesium levels were among the six most important features affecting 1-year and 5-year survival rates
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If externally validated and compared to existing prognostic scores, this model could support risk stratification and individualised treatment decisions. Current evidence is insufficient to recommend clinical adoption without prospective validation and comparison to standard approaches.
Single-centre retrospective registry study developing a machine learning prediction model with moderate discrimination (AUC 0.69) on a private dataset; lacks external validation, comparison to standard prognostic tools, and prospective clinical outcome assessment.
As stated by the source record.
Quoted from the source exactly as published.
If externally validated and compared to existing prognostic scores, this model could support risk stratification and individualised treatment decisions. Current evidence is insufficient to recommend clinical adoption without prospective validation and comparison to standard approaches.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
To create an interpretable machine learning model based on non-invasive biomarkers for the early diagnosis and improved prognostic value of esophageal cancer. We gathered a private dataset at Sichuan Cancer Hospital, comprising 3204 esophageal cancer patients who underwent surgery. Baseline markers and preoperative biochemical blood tests were thoroughly reviewed. The necessary factors were identified using an elastic net, and 27 machine learning methods were used to construct prediction models. The random forest model was chosen for its high performance, and additional optimization was performed using the White Shark Optimizer (WSO) algorithm. To improve the model’s interpretability, the Shapley Additive exPlanations (SHAP) technique was used. We proposed an interpretable machine learning framework for exploratory early risk stratification of postoperative esophageal squamous cell carcinoma patients using routinely available preoperative baseline examinations. The random forest (RF) model achieved 0.74 accuracy and 0.72 F1 score after 1 year. It was adjusted to 0.69 in the Area Under the Curve (AUC). Gender and blood magnesium levels were the best six characteristics that affected the 1-year and 5-year survival rates. The model with the highest discriminative ability accurately predicted the prognosis of esophageal cancer patients in the test population. Our research led to the creation of a machine learning model that accurately predicts the prognosis of esophageal cancer and identifies early indicators of adverse outcomes. SHAP-improved interpretability facilitates physician-patient interaction and trust and promotes individualized therapy.
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