Work

In Reply: Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan

Year: 2025

Type: letter

Abstract: 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

Source: Mayo Clinic Proceedings Digital Health

DOI: 10.1016/j.mcpdig.2025.100263

Authors: Isaiah Z. Yao, Min Dong, William Y.K. Hwang

Institutions: Hwa Chong Institution, SingHealth, Singapore General Hospital, National Cancer Centre Singapore

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,500

OpenAlex ID: W4414217042

Cites: 2

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