[Full Picture] MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

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MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer - PMC

Source: ncbi.nlm.nih.gov

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Summary Analysis Research

Article summary:

1. This study used MRI-based clinical and radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC).

2. The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients.

3. The best accuracy was achieved when the machine learning models included radiomics parameters alongside clinical MRI-based parameters.

Article analysis:

This article is a retrospective single-centre study that uses MRI-based clinical and radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). The authors have provided detailed information about the methods used, which is helpful for assessing the trustworthiness of the article.

The article does not provide any information about potential biases or sources of bias, which could affect the results of the study. Additionally, there is no discussion of possible risks associated with using this method for predicting pCR after NAC in HER2 overexpressing breast cancer patients. Furthermore, there is no mention of any counterarguments or alternative points of view that could be considered when interpreting the results of this study.

The article also does not provide any evidence to support its claims or discuss any limitations that may have affected the results. Additionally, it does not present both sides equally; instead it focuses solely on the positive aspects of using this method for predicting pCR after NAC in HER2 overexpressing breast cancer patients without exploring any potential drawbacks or risks associated with it.

In conclusion, while this article provides useful information about using MRI-based clinical and radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC), it lacks important details such as potential biases or sources of bias, possible risks associated with using this method for predicting pCR after NAC in HER2 overexpressing breast cancer patients, counterarguments or alternative points of view that could be considered when interpreting the results of this study, evidence to support its claims,

Topics for further research:

Potential biases in machine learning for HER2 expression level prediction Risks associated with using MRI-based clinical and radiomic features for predicting pCR Counterarguments to using machine learning for HER2 expression level prediction Alternative points of view for interpreting results of machine learning for HER2 expression level prediction Evidence to support claims of machine learning for HER2 expression level prediction Limitations of machine learning for HER2 expression level prediction