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Hepatocellular Carcinoma Treatment and Prognosis / Ferroptosis and Cancer Prognosis · Journal article
Diseases of the Colon & Rectum · August 12, 2026
Encouraging direction, but not yet definitive.
This retrospective study developed and internally validated a machine learning model (random survival forests) incorporating an oxidative stress index score to predict disease-free and overall survival in locally advanced rectal cancer patients receiving neoadjuvant therapy. The model outperformed conventional ypstage classification in both training and validation cohorts (all p<0.0001), but requires prospective external validation and multi-center confirmation before clinical implementation.
Retrospective cohort study with machine learning model development and internal validation. Locally advanced rectal cancer patients who underwent total neoadjuvant therapy (training cohort) or neoadjuvant chemoradiotherapy (validation cohort), followed by total mesorectal excision with or without adjuvant chemotherapy; treated at Sun Yat-Sen University Cancer Center.. Intervention: Random survival forests machine learning model incorporating oxidative stress index score and clinicopathological characteristics for survival prediction. Compared with: Conventional ypstage classification and four alternative machine learning models. n = 970. Sun Yat-Sen University Cancer Center (single center, location implied as China based on institution name).
Random survival forests model stratified patients into low-risk and high-risk groups with significantly higher disease-free survival and overall survival in low-risk groups across both cohorts (all p<0.0001) Random survival forests demonstrated superior predictive efficiency compared to 4 other machine learning models tested Random survival forests models significantly outperformed ypstage in prediction efficiency across both training and validation cohorts
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The random survival forests model may provide improved risk stratification beyond conventional staging, potentially enabling personalized treatment intensification and follow-up planning. However, prospective external validation across multiple centers is essential before adoption into routine clinical practice.
A retrospective, single-center machine learning study shows superior prognostic stratification versus conventional staging in locally advanced rectal cancer, but lacks prospective validation and external multi-center confirmation needed to guide clinical practice.
As stated by the source record.
Quoted from the source exactly as published.
The random survival forests model may provide improved risk stratification beyond conventional staging, potentially enabling personalized treatment intensification and follow-up planning. However, prospective external validation across multiple centers is essential before adoption into routine clinical practice.
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.
BACKGROUND: Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. OBJECTIVE: To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction models incorporating oxidative stress index score. DESIGN: A novel liver enzyme indicator - oxidative stress index score was established by integrating γ-glutamyl transferase and total bilirubin. Machine learning models were constructed based on oxidative stress index score and clinicopathological characteristics to predict survival. The predictive performance of these models was evaluated and interpreted and further validated in the validation cohort. Using these models, patients were stratified into low risk and high-risk groups for both disease-free survival and overall survival, respectively. SETTINGS: Data were collected from Sun Yat-Sen University Cancer Center between May 2007 and August 2018. PATIENTS: We enrolled locally advanced rectal cancer patients who had undergone either total neoadjuvant therapy (as the training cohort) or neoadjuvant chemoradiotherapy (as the validation cohort), followed by total mesorectal excision with or without adjuvant chemotherapy. MAIN OUTCOME MEASURES: Disease-free survival and overall survival in the training and validation cohort. RESULTS: A total of 970 locally advanced rectal cancer patients were allocated to a training cohort (n = 521) and a validation cohort (n = 449). Among 5 machine learning models, random survival forests demonstrated optimal predictive efficiency. Thus, the random survival forests model was constructed based on oxidative stress index score. The random survival forests models outperformed ypstage in prediction efficiency significantly across both cohorts. In both cohorts, low-risk groups exhibited significantly higher disease-free survival and overall survival rates compared to high-risk groups (all p < 0.000 1). A free online random survival forests calculator has been developed (https://zhuoliworkroom.shinyapps.io/new_shiny/). LIMITATIONS: Our study has inherent limitations of retrospective and single-center studies. CONCLUSIONS: A higher oxidative stress index score was significantly associated with worse prognostic outcomes. Collectively, these random survival forests models exhibit superior predictive performance compared to conventional ypstage indicators. See Video Abstract.
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