ArticleEJNMMI research2024
Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.
Article in EJNMMI research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.Scientific reports · 2026Article
- Novel molecular imaging approaches in oncology: towards a more accurate estimation of tumour response.Current opinion in oncology · 2025Review
- Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025Review
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9 authors.
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Abstract
backgroundThe aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).
methodsOne hundred and twenty-eight BC patients underwent a
resultsIn the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively).
conclusionBaseline clinical data combined with global and texture tumour metabolism parameters assessed by
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