The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): a population based study from the SEER database
Problem
Determining whether elderly early triple-negative breast cancer patients benefit from chemotherapy and predicting their survival.
Why it matters
Chemotherapy decisions for patients over 70 with early TNBC directly affect survival outcomes.
What the AI did
Nine machine learning models were built on 4,696 SEER patients to predict 5-year overall and breast cancer-specific survival for chemotherapy patients.
Results
Chemotherapy group showed better survival than matched non-chemotherapy patients; stage II and III patients benefited; model performance metrics not stated in source
Impact
Findings may inform chemotherapy decisions for elderly TNBC patients; clinical validation not stated.
Record
| Venue | BMC Geriatrics |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Kaiyan Huang, Jie Zhang, Yushuai Yu, Yuxiang Lin, Chuangui Song |
| Citations | 37 (OpenAlex, at import time) |
| References | 26 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s12877-022-02936-5 |
| Open access | gold |
| Keywords | breast cancer, survival prediction, machine learning, SEER, chemotherapy, elderly |
Importance rating
real but narrower advance
Chemotherapy decisions directly affect survival of elderly early TNBC patients; 4,696 SEER patients analyzed, a real but narrow clinical subpopulation.