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NYU Dataset
Physician Decision-Making During Active Surveillance of Prostate Cancer
- Authors
- Stacy LoebCaitlin E. CurnynAngela FagerlinR. Scott Braithwaite4 more author(s)...
- Description
This dataset includes structured, in-depth interviews with 24 physicians from diverse clinical backgrounds and geographic areas within the United States. These physicians were identified with purposive sampling strategies. Physicians who reported currently caring for patients through active surveillance were invited to participate. An interview guide was developed based on a literature review and previous...
- Subject
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CancerHealth Care System
- Timeframe
- 2015
- Access Rights
- Application RequiredAuthor Approval Required
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NYU Dataset
fastMRI
- Authors
- Florian KnollPatricia M. JohnsonDaniel K. SodicksonMichael P. Recht1 more author(s)...
- Description
This deidentified imaging dataset is comprised of raw k-space data in several sub-dataset groups. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMRD format and the RSNA Clinical Trial Processor, respectively. Manual inspection of each DICOM image was also performed to check for the presence of any unexpected protected health information (PHI), with spot checking...
- Subject
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AnatomyCancer
- Access Rights
- Free to AllApplication Required
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Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial Datasets
- Alternate Title(s)
- PLCO
- Description
The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was a large randomized controlled trial that assessed whether certain screening practices decreased deaths from prostate, lung, colorectal and ovarian cancer. Approximately 155,000 men and women aged 55 to 74 years old were enrolled from 10 screening centers across the United States between November 1993 and July 2001. Participants...
- Subject
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CancerChronic DiseaseHealth Care System
- Access Rights
- Application Required
- Local Expert
- Richard B. Hayes
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NYU Dataset
RNA Immunoprecipitation Sequencing Reveals ORF1p Association with Prostate Cancer
- Authors
- Erica M. BriggsWilson McKerrowPaolo MitaJef D. Boeke2 more author(s)...
- Description
Long interspersed element-1 (LINE-1) is an autonomous retroelement that utilizes copy and paste mechanism to propagate themselves throughout the genome through a process called retrotransposition. The LINE-1 bicistronic mRNA codes for two proteins, ORF1p and ORF2. While LINE-1 transcription is usually repressed in most healthy somatic cells, ORF1p expression has been observed in tumors, including prostate...
- Subject
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CancerGenomics
- Access Rights
- Free to All
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NYU Dataset
PIM1 Phosphorylation Regulates Gene Transcription in Prostate Cancer
- Authors
- Sophie E. RuffNikita VasilyevEvgeny NudlerSusan K. Logan1 more author(s)...
- Description
Previous study showed that PIM1 phosphorylates the androgen receptor (AR), the primary therapeutic target in prostate cancer. This study examined the mechanism how PIM1 phosphorylation of AR alters its transcriptional activity. They identified the AR co-activator, 14-3-3 ζ, as an endogenous PIM1 substrate in LNCaP cells. Rapid immunoprecipitation and mass spectrometry of endogenous proteins on chromatin...
- Subject
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CancerGenomicsProteomics
- Access Rights
- Free to All
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NYU Dataset
Identification of PIM1 Substrates Reveals a Role for NDRG1 Phosphorylation in Prostate Cancer
- Authors
- Russell J. LedetSophie E. RuffYu WangShruti Nayak4 more author(s)...
- Description
This study performed a chemical genetic screen to identify PIM1 substrates in prostate cancer cells. They identified 25 previously unknown PIM1 substrates and their phosphorylation sites. Among the identified PIM1 substrates, N-Myc Downstream-Regulated Gene 1 (NDRG1) was shown to be as an inhibitor of metastasis. The study demonstrated that PIM1 phosphorylation of NDRG1 reduced its stability, nuclear...
- Subject
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CancerProteomics
- Access Rights
- Free to All
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NYU Dataset
Mapping the Landscape of Histomorphological Cancer Phenotypes Using Self-Supervised Learning on Unannotated Pathology Slides
- Authors
- Adalberto Claudio QuirosNicolas CoudrayAnna H. YeatonXinyu Yang12 more author(s)...
- Description
Histomorphological Phenotype Learning (HPL) was developed as a self-supervised methodology to discriminatory features in microscopy images to aid in cancer diagnosis and management. The methodology partitions whole slide images (WSIs) into meaningful Histomorphological Phenotype Clusters (HPCs) that can be used to define and quantify morphological phenotypes which recur within and between cases. HPL...
- Subject
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CancerChronic Disease
- Access Rights
- Free to AllAuthor Approval Required