Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset
Our study demonstrates that, even when dealing with an imbalanced EGFR T790M dataset, reasonable predictive outcomes can be achieved by employing an appropriate combination of resampling techniques and classifiers. This approach has significant potential for enhancing T790M mutation detection in NSCLC patients with brain metastasis.
Authors
- Kuan-Ming Lai
- Bai-Chuan Wang (ORCID: https://orcid.org/0000-0003-4557-3112)
- Chien‐Chin Chen (ORCID: https://orcid.org/0000-0001-9599-0884)
- Wen-Feng Wu (ORCID: https://orcid.org/0009-0001-6401-8256)
- Eugene C. Lin (ORCID: https://orcid.org/0000-0002-9780-4982)
- Yi‐Jen Chen (ORCID: https://orcid.org/0000-0002-5178-2335)
- Kaiyan Chen (ORCID: https://orcid.org/0000-0001-7755-3074)
- Chia‐Hung Chen (ORCID: https://orcid.org/0000-0003-1158-9722)
- Chia‐Wei Shen
Institutions
- National Chung Cheng University (TW)
- National Chung Hsing University (TW)
- Chia-Yi Christian Hospital (TW)
- Chia Nan University of Pharmacy and Science (TW)
- Central Taiwan University of Science and Technology (TW)
- National Cheng Kung University (TW)
Publication Details
- Journal
- Discover Oncology
- Published
- 2024-09-14
- DOI
- https://doi.org/10.1007/s12672-024-01333-1
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Ditmanson Medical Foundation Chia-Yi Christian Hospital
- National Science and Technology Council