Summary of Predicting Breast Cancer Survival: a Survival Analysis Approach Using Log Odds and Clinical Variables, by Opeyemi Sheu Alamu et al.
Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables
by Opeyemi Sheu Alamu, Bismar Jorge Gutierrez Choque, Syed Wajeeh Abbs Rizvi, Samah Badr Hammed, Isameldin Elamin Medani, Md Kamrul Siam, Waqar Ahmad Tahir
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the use of survival analysis techniques to improve the prediction of patient outcomes for breast cancer patients. Specifically, it employs Cox proportional hazards and parametric survival models to analyze clinical variables such as tumor size, hormone receptor status, HER2 status, age, and treatment history to predict the log odds of survival. The study uses a publicly available dataset from Nigeria containing 1557 breast cancer patients and finds that older age, larger tumor size, and HER2-positive status are significantly associated with increased mortality risk. In contrast, estrogen receptor positivity and breast-conserving surgery were linked to better survival outcomes. The findings suggest that integrating these clinical variables into predictive models can improve the accuracy of survival predictions, helping identify high-risk patients who may benefit from more aggressive interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to create a better way to predict how well breast cancer patients will do after treatment. They used special math tools called survival analysis techniques to look at things like tumor size and whether the cancer has certain proteins on it. The researchers took data from over 1,500 women in Nigeria who had breast cancer and found that older age, bigger tumors, and certain protein types are linked to a higher risk of death. On the other hand, having estrogen receptors and doing breast-conserving surgery were good signs for survival. This study shows how important it is to use this type of analysis to help doctors make better treatment plans. |
