ReviewComputational and mathematical methods in medicine2021
Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art.
Review in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Ensemble deep learning model based on CT scans: differentiating and subtype-classifying pancreatic inflammations and tumors, and predicting pancreatic lesion invasiveness.Quantitative imaging in medicine and surgery · 2026Article
- Survival rate in patients with pancreatic cancer in Guilan province, Iran.Caspian journal of internal medicine · 2026Article
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- Thermosensitive Liposomes for Gemcitabine Delivery to Pancreatic Ductal Adenocarcinoma.Cancers · 2024Article
- CSTF2T facilitates pancreatic adenocarcinoma growth and metastasis by elevating H3K4Me1 methylation of CALB2 via ASH2L.Cancer biology & therapy · 2023Article
- Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis.Journal of clinical medicine · 2023Review
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis.World journal of gastroenterology · 2023Review
- Review
- Pancreatic cancer, radiomics and artificial intelligence.The British journal of radiology · 2022Review
- The Association between TRP Channels Expression and Clinicopathological Characteristics of Patients with Pancreatic Adenocarcinoma.International journal of molecular sciences · 2022Article
- Clinical Study of Anti-PD-1 Immunotherapy Combined with Gemcitabine Chemotherapy in Multiline Treatment of Advanced Pancreatic Cancer.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer, are challenging to diagnose or detect early, and the stages have a fast progression rate. This paper presents the state-of-the-art techniques used in cancer survival prediction, suggesting how these techniques can be implemented in predicting the overall survival of pancreatic ductal adenocarcinoma cancer (pdac) patients. Because of bewildering and high volumes of data, the recent studies highlight the importance of machine learning (ML) algorithms like support vector machines and convolutional neural networks. Studies predict pancreatic ductal adenocarcinoma cancer (pdac) survival is within the limits of 41.7% at one year, 8.7% at three years, and 1.9% at five years. There is no significant correlation found between the disease stages and the overall survival rate. The implementation of ML algorithms can improve our understanding of cancer progression. ML methods need an appropriate level of validation to be considered in everyday clinical practice. The objective of these techniques is to perform classification, prediction, and estimation. Accurate predictions give pathologists information on the patient's state, surgical treatment to be done, optimal use of resources, individualized therapy, drugs to prescribe, and better patient management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.