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8,942 results for “prostate”
177Lu-PSMA-617 vs. Androgen Receptor-Directed Therapy in the Treatment of Progressive Metastatic Castrate Resistant Prostate Cancer
ClinicalTrials.gov study NCT04689828. IPD Sharing: YES. Countries: 14. Publications: 2.
Enhancing Survivorship Care Planning for Patients With Localized Prostate Cancer Using A Couple-focused Web-based Tailored Symptom Self-management Program
ClinicalTrials.gov study NCT04350788. IPD Sharing: YES. Countries: 1. Publications: 1.
The Effect of Deep Versus Moderate Muscle Relaxants in Men During and After Robotic Surgery for Prostate Cancer
ClinicalTrials.gov study NCT03808077. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study of Rucaparib in Patients With Metastatic Castration-resistant Prostate Cancer and Homologous Recombination Gene Deficiency
ClinicalTrials.gov study NCT02952534. IPD Sharing: YES. Countries: 12. Publications: 4.
Study of 177Lu-PSMA-617 In Metastatic Castrate-Resistant Prostate Cancer
ClinicalTrials.gov study NCT03511664. IPD Sharing: YES. Countries: 10. Publications: 12.
Targeted Radiotherapy in Androgen-suppressed Prostate Cancer Patients.
ClinicalTrials.gov study NCT03644303. IPD Sharing: YES. Countries: 1. Publications: 1.
ETV4 mediates dosage-dependent prostate tumor initiation and cooperates with p53 loss to generate prostate cancer
Open the record for dataset details and reuse information.
Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
Open the record for dataset details and reuse information.
miR-145-5p mimic inhibits bone metastasis of prostate cancer via the regulation of epithelial mesenchymal transition
<p><strong>Background.</strong> The bone is the most common site of distant metastasis in prostate cancer. However, treatments for the bone metastasis of prostate cancer remain unsatisfactory. MicroRNAs (miRNAs) are small noncoding RNAs that play a variety of critical roles in tumor development and progression. Studies have confirmed that miRNA mimics could regulate the response to therapy in many cancers. <strong>Methods.</strong> In this study, a set of forty-four miRNAs were reduced in prostate cancer patients with bone metastases by high-throughput sequencing analysis. Wound healing and transwell assays and western blotting analysis were used to explore the role of miRNA mimic in prostate cancer bone metastasis.<strong> Results.</strong> These mimics of down-regulated miRNAs may be able to cure prostate cancer bone metastasis, including hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-133a-3p, hsa-miR-222-5p, hsa-miR-204-3p, hsa-miR-145-5p, hsa-miR-3681-5p, hsa-miR-184, hsa-miR-144-3p, hsa-miR-204-5p, and hsa-miR-221-5p. To further investigate the role of these miRNA mimics on prostate cancer bone metastasis, miR-145-5p was randomly selected for validation. Bioinformatics analysis showed that miR-145-5p target genes significantly affected TGF-beta signaling pathway. Wound healing and transwell assays and western blotting analysis revealed that miR-145-5p mimic inhibited proliferation, migration and invasion. Importantly, miR-145-5p mimic increased the expression of E-cadherin and reduced the expression of matrix metalloproteinase 2 and 9. These results revealed that miR-145-5p mimic mediated epithelial mesenchymal transition. Meanwhile, miR-145-5p mimic enhanced the level of caspase 9, which is an important promoter of apoptosis. These results indicate that miR-145-5p mimic could inhibit the progress of prostate cancer bone metastasis via regulation of epithelial mesenchymal transition. In addition, miR-145-5p mimic could induce the apoptosis of prostate cancer cells with bone metastases. In summary, the miR-145-5p mimic is expected to become a novel strategy for the treatment of tumor metastasis.</p>
Prostate specific antigen density values among patients with symptomatic prostatic enlargement in Nigeria
<p><strong>Background</strong></p> <p>This study aims to estimate the prostate specific antigen density(PSAD) cut off level for detecting prostate cancer( CAP) in Nigerian men with “grey zone PSA’’(4-10ng/ml) and normal digital rectal examination findings . We addressed this research question: Is the international PSAD cut off of 0.15 ideal for detecting CAP in our symptomatic patients with “grey zone PSA’’?</p> <p><strong> Methods</strong></p> <p>Aim: To estimate the prostate specific antigen density(PSAD) cut off level for detecting prostate cancer( CAP) in Nigerian men with “grey zone PSA’’(4-10ng/ml) and normal digital rectal examination findings .</p> <p>Design: prospective</p> <p>Setting: A tertiary medical center in Enugu Nigeria</p> <p> Participants: Two hundred and fifty four men with either benign prostatic hyperplasia (BPH) or prostate cancer (CAP) were recruited. </p> <p> Intervention: Patients with PSA above 4ng/ml or abnormal digital rectal examination or hypoecoic lesion in the prostate were biopsied.</p> <p>Outcome measures: PSAD and histology report of BPH or CAP</p> <p><strong>Results</strong></p> <p>Ninety seven patients had cancer of the prostate (CAP) while 157 has benign prostatic hyperplasia (BPH). Seventy two patients had their serum PSA value within the range of 4.0 and 10ng/ml . PSAD cut off level to detect CAP was 0.04(sensitivity 95.88 %; specificity 28.7 %).</p> <p> </p> <p><strong>CONCLUSION</strong> </p> <p>The PSAD cut off level generated for Nigerian men in this study is 0.04 which is relatively different from international consensus. This PSAD cut off level has a positive correlation with histology and could detect patients with CAP who have “grey zone PSA’’.</p>
Filtered and annotated SNV and indel variants in the PC3 and LNCaP human prostate cancer cell lines
<p>150bp paired-end reads (insert size 350bp) were obtained using the Illumina HiSeqX sequencer. Samtools v1.3.1 mpileup and bcftools were used to interrogate indexed BAM files, from whole-genome reads aligned to human reference genome GRCh38 build 82, and generate a VCF (Variant Call Format) file of single nucleotide variants (SNVs) and short indel variants. Variants private, or unique to a particular cell line, or shared by both were next identified. Variants (likely to be common germline variants) present in HapMap, 1000 genomes phase 3 (2,504 human genomes), and the National Heart Lung and Blood Institute’s Exome Sequencing Project (ESP) (bundled variant data file available at https://goo.gl/mEogvD) were excluded. Variant files (VCF) were filtered using SnpSift with the following parameters: 'QUAL \textgreater= 200 \&\& DP \textgreater= 30', where QUAL denotes minimum variance confidence and DP total depth threshold. Filtered variants were annotated using SnpEff v4.3g. Please see https://github.com/sciseim/PCaWGS for associated scripts.</p> <p> </p>
de novo genome assembly of the LNCaP human prostate cancer cell line
<p>Whole-genome sequencing reads from the LNCaP human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p>
de novo genome assembly of the PC3 human prostate cancer cell line
<p>Whole-genome sequencing reads from the PC3 human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p> <p> </p> <p> </p> <p> </p>
DICOM converted annotations for the Prostate-MRI-US-Biopsy collection
<p>This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (<a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy</a>). Prostate-MRI-US-Biopsy collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations.</p><p>This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.</p><p>Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (<a href="https://www.pixelmed.com/dicomtoolkit.html">https://www.pixelmed.com/dicomtoolkit.html</a>). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (<a href="https://www.slicer.org/">https://www.slicer.org/</a>) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (<a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a>) [5]. Resulting objects were validated using dicom3tools dciodvfy (<a href="https://www.dclunie.com/dicom3tools.html">https://www.dclunie.com/dicom3tools.html</a>). Details describing the conversion process as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: <a href="https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion">https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion</a>.</p><p>Specific files included in the record are:</p><ol><li><strong>Prostate-MRI-US-Biopsy-DICOM-Annotations.zip</strong>: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm</li><li><strong>referenced_images_sorted-idc_file_manifest.s5cmd</strong>: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (<a href="https://github.com/peak/s5cmd">https://github.com/peak/s5cmd</a>), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download.</li></ol><p><strong>References</strong></p><p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. <i>Cancer Res.</i> <strong>81,</strong> 4188–4193 (2021). doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p><p>[2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">10.7937/TCIA.2020.A61IOC1A</a></p><p>[3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: <a href="https://doi.org/10.1016/j.juro.2012.08.095">10.1016/j.juro.2012.08.095</a> </p><p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: <a href="https://doi.org/10.1007/s10278-013-9622-7">10.1007/s10278-013-9622-7</a></p><p>[5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. <i>Cancer Res. </i><strong>77,</strong> e87–e90 (2017). DOI: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-17-0336">10.1158/0008-5472.CAN-17-0336</a>.</p>
Automated bio-AFM generation of large mechanome data set and their analysis by machine learning to classify prostatic cell lines_Training base 100 PC3-GFP
Open the record for dataset details and reuse information.
Supplementary material: CYB561 supports the neuroendocrine phenotype in castration-resistant prostate cancer
<p>Castration-resistant prostate cancer (CRPC) is associated with resistance to androgen deprivation therapy, and an increase in the population of neuroendocrine (NE) differentiated cells. It is hypothesized that NE differentiated cells secrete neuropeptides that support androgen-independent tumor growth and induce aggressiveness of adjacent proliferating tumor cells through a paracrine mechanism. The cytochrome b561 (<em>CYB561</em>) gene, which codes for a secretory vesicle transmembrane protein, is constitutively expressed in NE cells and highly expressed in CRPC. CYB561 is involved in the α-amidation-dependent activation of neuropeptides and contributes to regulating iron metabolism which is often dysregulated in cancer. These findings led us to hypothesize that CYB561 may be a key player in the NE differentiation process that drives the progression and maintenance of the highly aggressive NE phenotype in CRPC. In our study, we found that <em>CYB561</em> expression is upregulated in metastatic and NE prostate cancer (NEPC) tumors and cell lines compared to normal prostate epithelia and that its expression is independent of androgen regulation. Knockdown of <em>CYB561</em> in androgen-deprived LNCaP cells dampened NE differentiation potential and transdifferentiation-induced increase in iron levels. In NEPC PC-3 cells, depletion of CYB561 reduced the secretion of growth-promoting factors, lowered intracellular ferrous iron concentration, and mitigated the highly aggressive nature of these cells in complementary assays for cancer hallmarks. These findings demonstrate the role of CYB561 in facilitating transdifferentiation and maintenance of NE phenotype in CRPC through its involvement in neuropeptide biosynthesis and iron metabolism pathways.</p>
Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets
<p>Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.<strong> </strong></p> <p>Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate are available for ProstateX collection, whole prostate gland and PZ for QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection.</p> <p>We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12]. </p> <p>This dataset is organized in three parts: </p> <p>AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern:</p> <p>PatientID/<br> └───Modality-%StudyInstanceUID/<br> └───%SeriesInstanceUID-%SeriesDescription.dcm.</p> <p>AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects.</p> <p>Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here:<br><a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Additional Notes</h2> <p><strong> </strong>This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.<br>https://portal.imaging.datacommons.cancer.gov/</p> <p>nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> <br>Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a><br>Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> <br>Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> <br>DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> <br>Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Related information</h2> <p>ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a><br><br>QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a><br><br>PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a></p> <h2>References </h2> <p>[1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.</p> <p>[2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11.</p> <p>[3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.</p> <p>[4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817.</p> <p>[5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[6] Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND</p> <p>[7] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris</p> <p>[9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57.</p> <p>[10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9.</p> <p>[11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90.</p> <p>[12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.</p> <p> </p>
ProstateZones - Segmentations of the prostatic zones and urethra for the PROSTATEx dataset
<p>Segmentations of the prostatic zones and urethra for 200 patients from the publicly avaliable PROSTATEx image dataset [1-3]. The dataset is intended for research purposes in the field of medical imaging and to enable training and evaluation of autmatic segmentation methods.</p> <p>The prostatic zones and urethra were manually segmented slice by slice on axial T2w MRIs by two experienced radiologists in collaboration with three junior colleagues. All delineations by junior colleagues has been checked, and if necessary, corrected by one of the more experienced radiologists. The two most experienced radiologists have independently delineated duplicate segmentations for 40 patients, for a total of 240 segmentations. This is intended as a test set where automatic methods can compare their performance with the inter-reader variability of the two radiologists.</p> <p>To ease the use of the dataset, help with structuring the data can be found in the linked GitHub repository.</p> <p> </p> <p>For more information about the dataset, see the dataset publication:</p> <p>Holmlund, W., Simkó, A., Söderkvist, K. <em>et al.</em> ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset. <em>Sci Data</em> <strong>11</strong>, 1097 (2024). https://doi.org/10.1038/s41597-024-03945-2</p> <p> </p> <p> </p> <div> <h4>PROSTATEx</h4> </div> <p>[1] Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman. "ProstateX Challenge data", The Cancer Imaging Archive (2017). DOI: 10.7937/K9TCIA.2017.MURS5CL</p> <p>[2] Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092. DOI: 10.1109/TMI.2014.2303821</p> <p>[3] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7</p>
Papers & Videos Prostate Cancer Studies
<p>El dataset incluye artículos y videos relacionados con investigaciones sobre el cáncer de próstata, publicados en diversas revistas científicas. Este conjunto de datos tiene como objetivo analizar la frecuencia y el enfoque de los estudios realizados en esta área, así como corroborar la relevancia que la comunidad científica otorga al estudio del cáncer de próstata.</p> <p>La data se obtuvo de un website especializado en la publicación de revistas cientificas, revisadas por pares de acceso abierto: http://www.dovepress.com</p>
Metascape Results for Prostate Cancer Multiomics Data
<p><strong>ABSTRACT </strong></p> <p>Large <em>p</em> small <em>n</em> problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100. The proposed TD based unsupervised FE outperformed other conventional supervised feature selection methods, such as random forest, categorical regression (also known as analysis of variance, ANOVA), and penalized linear discriminant analysis when they are applied to not only multiomics datasets but also synthetic datasets. Genes selected by TD based unsupervised FE were biologically reliable. TD based unsupervised FE turned out to be not only the superior feature selection method but also the method that can select biologically reliable genes. </p> <p><strong>Instructions: </strong></p> <p>This is a supplementary file of paper submitted to bigdata2020</p> <p> </p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Y-h. Taguchi, July 17, 2020, "Metascape results for Prostate cancer multiomics data", IEEE Dataport, doi: https://dx.doi.org/10.21227/rdmb-jm40.</p> <p>https://ieee-dataport.org/documents/metascape-results-prostate-cancer-multiomics-data</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.