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Riccardo Ponzone

Researcher at University of Turin

Publications -  146
Citations -  4364

Riccardo Ponzone is an academic researcher from University of Turin. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 38, co-authored 137 publications receiving 3881 citations. Previous affiliations of Riccardo Ponzone include The Royal Marsden NHS Foundation Trust.

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Monitoring response to primary chemotherapy in breast cancer using dynamic contrast-enhanced magnetic resonance imaging.

TL;DR: Although in this study tumor volume reduction after two cycles had the strongest predictive value, DCE-MRI has the potential to provide functional parameters that could be integrated to optimize neoadjuvant chemotherapy strategies.
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Correlations between diffusion-weighted imaging and breast cancer biomarkers

TL;DR: ADC values vary significantly according to biological tumour features, suggesting that cancer heterogeneity influences imaging parameters, and DWI may identify biological heterogeneity of breast neoplasms.
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Angiopoietin-2 expression in breast cancer correlates with lymph node invasion and short survival

TL;DR: Data suggest a possible role of ANG2 as a prognostic factor for primary breast cancer and univariate and multivariate survival analysis showed significant and independent association between ANG2 mRNA level and both disease‐free and overall survival.
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Prostate-specific antigen is a new favorable prognostic indicator for women with breast cancer.

TL;DR: This study suggests that IR-PSA is an independent favorable prognostic marker for breast cancer and may be used to identify a subgroup of estrogen receptor-negative and/or node-positive patients who have good prognoses.
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miR148b is a major coordinator of breast cancer progression in a relapse-associated microRNA signature by targeting ITGA5, ROCK1, PIK3CA, NRAS, and CSF1

TL;DR: In this paper, the role of microRNAs (miRs) during malignancy was analyzed in 77 primary breast carcinomas and identified 16 relapse-associated miRs that correlate with survival and/or distinguish tumor subtypes in different datasets.