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Payal Kapur

Researcher at University of Texas Southwestern Medical Center

Publications -  244
Citations -  11224

Payal Kapur is an academic researcher from University of Texas Southwestern Medical Center. The author has contributed to research in topics: Clear cell renal cell carcinoma & Cancer. The author has an hindex of 42, co-authored 223 publications receiving 8743 citations. Previous affiliations of Payal Kapur include University of Texas at Dallas & University of California, Berkeley.

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Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type

TL;DR: This work developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type, and discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models.
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Loss of DAB2IP in RCC cells enhances their growth and resistance to mTOR-targeted therapies

TL;DR: DAB2IP appears to be a new prognostic/predictive marker for mRCC patients, and its function provides a new insight into the molecular mechanisms of drug resistance to mTOR inhibitors, which also can be used to develop new strategies to overcome drug-resistant mR CC.
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Primary Mucinous Adenocarcinoma of the Thymus: A Case Report and Review of the Literature

TL;DR: A third case of primary mucinous adenocarcinoma of the thymus in a 41-year-old man who presented with an anterior mediastinal mass with subsequent metastasis to the lung is described.
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Prospective comparison of molecular signatures in urothelial cancer of the bladder and the upper urinary tract--is there evidence for discordant biology?

TL;DR: The results demonstrate the molecular similarity of upper tract urothelial carcinoma and bladder cancer in terms of cell cycle and proliferative tissue markers and support the further extrapolation of treatment paradigms established in bladder cancer to upper tract Urothelia carcinoma.
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Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model.

TL;DR: In this paper, a new multi-classifier multi-objective (MCMO) radiogenomics predictive model was proposed to obtain more reliable prediction results, where similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training.