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This software/dataset accompany the data as described in Chitalia, R., Miliotis, M., Jahani, N. et al. Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy. Commun Med 3, 46 (2023). (https://doi.org/10.1038/s43856-023-00273-1) and can be used to recreate the tables and figures provided in the publication.
breast cancer, neoadjuvant chemotherapy, image recognition, genomic signatures, radiogenomics, biomarker, survival
breast cancer, neoadjuvant chemotherapy, image recognition, genomic signatures, radiogenomics, biomarker, survival
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 35 | |
| downloads | 89 |

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Downloads provided by UsageCounts