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Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study
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| Published in: | PeerJ |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| _version_ | 1864030191975661571 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | PeerJ |
| description | |
| discipline_display | Multidisciplinary |
| discipline_facet | Multidisciplinary |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:10682 |
| institution | FRELIP |
| journal_source_facet | PeerJ |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study Multidisciplinary General Multidisciplinary |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Multidisciplinary General Multidisciplinary Multidisciplinary General Multidisciplinary |
| subject_facet | Multidisciplinary General Multidisciplinary |
| title | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_auth | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_full | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_fullStr | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_full_unstemmed | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_short | Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| title_sort | interpretable machine learning model using ct body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study |
| topic | Multidisciplinary General Multidisciplinary |
| url | https://peerj.com/articles/21051 |