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Metastatic colorectal cancer (mCRC) remains a major clinical challenge, with only a subset of patients responding to first-line chemotherapy. Predicting chemoresistance could help personalize treatment and avoid unnecessary toxicity, and AI-based models have shown promise in addressing such predictive tasks. Among the different data sources used in medical AI, histopathological images offer valuable insights, but their variability, particularly in staining, poses a significant challenge. Stain normalization techniques aim to standardize color variations, improving the consistency of AI-driven analysis. Convolutional-based methods are widely used but require selecting a target patch, a process typically performed visually or randomly. The impact of this selection on normalization performance, however, remains unclear. In this study, we investigated how target patch selection influences stain normalization and compared these methods with a generative model approach (CycleGAN), which eliminates the need for a reference patch. Our results showed that different target patches not only altered the color appearance of normalized images but also affected their structural content. While the generative model resolved the target patch selection issue, its overall performance was moderate. Furthermore, stain normalization influenced feature extraction, with the color-deconvolution-based method yielding more relevant features. In the chemotherapy response prediction of mCRC patients, the two stain normalization techniques resulted in different results, with higher performance reached when using the color-deconvolution-based stain normalization method. The best classifier achieved an average cross-validated AUC value of 0.83 and 0.73 on the training and test set, respectively, demonstrating high potential in correctly predicting mCRC response to therapy.Clinical Relevance- In this study, stain normalization influenced both feature predictivity and classifier performance, emphasizing the importance of effectively managing staining variations across patients and medical centers to ensure robust and reliable predictive modeling. Our best model achieved good results in predicting the chemotherapy response of mCRC patients, demonstrating its potential for assisting treatment decisions.