In Reply: Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan
We thank Ayub et al1 for their thoughtful letter in response to our recent article, “Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions”. Their contribution provides a valuable perspective from Pakistan and highlights the realities of implementing radiomics in low-resource healthcare systems, especially for urological cancers.2
Authors
- William Ying Khee Hwang (ORCID: https://orcid.org/0000-0003-0143-6263)
- Min Dong (ORCID: https://orcid.org/0000-0001-7758-7856)
- Ignace Yao
Institutions
- SingHealth (SG)
- Hwa Chong Institution (SG)
- National Cancer Centre Singapore (SG)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100263
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00