ArticleBioMed research international2022
Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis.Asian Pacific journal of cancer prevention : APJCP · 2023Pooled it
- DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer.Discover oncology · 2025Article
- Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients.Frontiers in medicine · 2024Article
- Oleuropein impact on colorectal cancer.Future science OA · 2024Review
- Secure medical image transmission using deep neural network in e-health applications.Healthcare technology letters · 2023Article
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- Retracted: Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.BioMed research international · 2023Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Human Subject Welfare · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).
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Registered trials
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