ArticleJournal of gastrointestinal oncology2018
Stage II-III colon cancer: a comparison of survival calculators.
Article in Journal of gastrointestinal oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Development and validation of nomograms for predicting overall survival and cancer-specific survival in unresected colorectal cancer patients undergoing chemotherapy.Scientific reports · 2025Article
- A narrative review of prognostic indices in the evaluation of gastrointestinal cancers.Journal of gastrointestinal oncology · 2023Review
- A Prediction Model for Tumor Recurrence in Stage II-III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling.Biomedicines · 2022Article
- Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer.Frontiers in oncology · 2022Article
- Prognostic models for stage I-III esophageal cancer: a comparison between existing calculators.Journal of gastrointestinal oncology · 2021Article
- Tumor Deposits in Stage III Colon Cancer: Correlation With Other Histopathologic Variables, Prognostic Value, and Risk Stratification-Time to Consider "N2c".American journal of clinical oncology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIndividualized postoperative survival calculators for patients with cancer can be an aid for predicting prognosis and clinical decision making, such as the use of adjuvant chemotherapy. The aim of this study was to compare existing survival calculators for colon cancer and determine their performance using an independent cohort of patients.
methodsA retrospective analysis of a multi-site institutional experience was performed on patients diagnosed with stage II-III colon cancer between January 2012 and March 2013. Patient survival rates were estimated using Roswell Park Comprehensive Cancer Center (RPCCC), Memorial Sloan Kettering Cancer Center (MSKCC), and MD Anderson Cancer Center (MDACC) calculators. These calculators vary in the number and breadth of variables that are included. The agreement between selected models was obtained through a scatter plot matrix and related intra-class correlation coefficient (ICC). Calculators' performances were compared using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) values.
resultsAfter the application of inclusion and exclusion criteria, a total of 97 patients were included in the analysis. Survival data were available for all patients. Median follow-up was 57.6 months, and the overall 5-year survival rate was 0.74 (95% CI: 0.64-0.82). Overall, the different calculators tended to predict survival similarly (ICC =0.017). However, there was variation among calculator performance with the RPCCC calculator showing the highest performance (AUC =0.913), followed by the MSKCC calculator (AUC =0.803), and the MDACC calculator (AUC =0.644).
conclusionsPrognostic models incorporating a more comprehensive amount of patient and tumor specific variables may provide a more accurate estimate of individual patient survival rates. These tools can be an actual aid in the clinical practice, allowing physicians to personalize treatment and follow-up for patients with colon cancer.
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