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Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands

Lou M. Maas et al.

Expert Review of Pharmacoeconomics & Outcomes Research2026https://doi.org/10.1080/14737167.2026.2615683article
ABDC C
Weight
0.37

What the paper says

Findings of this study present the economic impact of a hypothetical AI-based approach to treatment response prediction in Stage II-III LARC patients who receive nCRT and are eligible for consecutive surgery. The results of this study highlight the complexity of healthcare decision-making in tools that could be cost-saving yet yield lower effectiveness when parameters are uncertain.

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https://doi.org/https://doi.org/10.1080/14737167.2026.2615683

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@article{lou2026,
  title        = {{Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands}},
  author       = {Lou M. Maas et al.},
  journal      = {Expert Review of Pharmacoeconomics & Outcomes Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/14737167.2026.2615683},
}

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Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.