A proof-of-concept CT radiomics model reported mixed results for predicting early response to radiotherapy in elderly esophageal cancer patients. The study found performance instability under fully nested validation and did not establish clear clinical benefit from combining imaging and clinical variables. The work serves as a reality check for radiomics-to-clinic translation, emphasizing that strong initial metrics can weaken once validation rigor increases. For biotech teams developing imaging AI tools, it reinforces the need for reproducible evaluation frameworks before deployment claims. Even with limitations, the approach remains relevant because response prediction tools—if validated properly—can help stratify treatment intensity and avoid ineffective regimens in vulnerable populations.