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Article summary:
1. This study developed a combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.
2. The model was able to accurately predict overall survival (OS) and disease free survival (DFS) with an area under curve (AUC) of 0.860 for OS prediction and 0.875 for DFS prediction.
3. The combined nomogram showed higher clinical decision utility than pathomics signature, radiomics signature and immunoscore alone in predicting OS and DFS.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information on the development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. The authors provide evidence for their claims by citing relevant studies in the literature, as well as providing data from their own study which supports their findings.
The article does not appear to be biased or one-sided in its reporting, as it presents both sides of the argument equally and objectively. It also does not contain any promotional content or partiality towards any particular point of view or opinion.
The article does not appear to have any unsupported claims or missing points of consideration, as all claims are supported by evidence from relevant studies in the literature or data from the authors' own study. Furthermore, all potential risks associated with the use of this model are noted in the article.
The only potential issue with this article is that it does not explore any counterarguments or alternative points of view regarding its findings, which could have provided a more comprehensive overview of the topic at hand.