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Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system
2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)
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Prostate tissue characteristics play an important role in predicting the recurrence of prostate cancer. ...
In the context of predicting prostate cancer recurrence, the performance of the image features is comparable to that achieved using the Gleason scoring system. ...
PROSTATE TISSUE IMAGE ANALYSIS MAGIC™'s application for prostate tissue image analysis is an automated, high throughput system capable of analyzing images and producing statistical measurements. ...
doi:10.1109/isbi.2004.1398523
fatcat:fgzlel2dr5ckbl534w4i2v7zjq
Evaluation of the prognostic significance of MSMB and CRISP3 in prostate cancer using automated image analysis
2011
Modern Pathology
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their prostate cancer tissue micro array; to Elise Nilsson for her excellent technical skills in the field of immunohistochemistry; to Dr Lene Udby for providing the CRISP3 ...
ACKNOWLEDGEMENTS We are grateful to Dr Thorsten Schlomm and Professor Guido Sauter at the Martini-Clinic, Prostate Cancer Center, University Medical Center, Hamburg, Germany for their generosity in sharing ...
Indeed, the MSMB gene is one of the most down-regulated genes in less differentiated prostate cancer cells compared to committed epithelial cells. 47 In conclusion, using an automated image analysis ...
doi:10.1038/modpathol.2010.238
pmid:21240253
fatcat:gio4hkf3qjfgnnsw5mrhjn5m4a
Prognostic Value of Akt-1 in Human Prostate Cancer: A Computerized Quantitative Assessment with Quantum Dot Technology
2009
Clinical Cancer Research
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Conclusion: High levels of Akt-1, assessed by quantum dots, deconvolution imaging, and image analysis, are associated with a higher risk of biochemical recurrence and prostate cancer-specific death. ...
Akt-1 level is also predictive of prostate cancer-specific death (P = 0.0376). ...
image deconvolution and image analysis, to improve on analytical variables. ...
doi:10.1158/1078-0432.ccr-08-0826
pmid:19417030
fatcat:ju3yiu2kcfgjxaj5eamifsrqmy
Machine learning applications in prostate cancer magnetic resonance imaging
2019
European Radiology Experimental
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extension, planning of radiation therapy); and prediction of biochemical recurrence. ...
With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). ...
[34] applied a unimodal deep learning-based system using only T2-weighted images for the automated segmentation of both the prostate and PCa lesions. ...
doi:10.1186/s41747-019-0109-2
pmid:31392526
pmcid:PMC6686027
fatcat:7x3egaxnjzcdpngl5vogww27gi
Nuclear morphometry, nucleomics and prostate cancer progression
2012
Asian Journal of Andrology
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Prostate cancer (PCa) results from a multistep process. ...
In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system ...
Figure 4 4 Automated imaging technology for the diagnosis and prognostic evaluation of prostate cancer. ...
doi:10.1038/aja.2011.148
pmid:22504875
pmcid:PMC3720156
fatcat:etprwmygkjezlo7e63ymxgssmy
Cell Orientation Entropy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays
[chapter]
2013
Lecture Notes in Computer Science
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Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence ...
are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. ...
[6] examined the role of image texture features based on co-occurrence matrices for the purpose of automated CaP grading. ...
doi:10.1007/978-3-642-40760-4_50
fatcat:36jdcybzbbgwffzrhzifkfdxbe
Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature
2021
Diagnostics
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Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. ...
The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/diagnostics11101829
pmid:34679527
fatcat:5f4mtvpe6zbj3fjifb2gn5r7nm
Subcellular localization of p27 and prostate cancer recurrence: automated digital microscopy analysis of tissue microarrays
2011
Human Pathology
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Given the cost and accuracy limitations of manual scoring, particularly of tissue microarrays (TMAs), we determined if laserbased fluorescence microscopy could provide automated analysis of p27 in both ...
Our method identified a strong relationship, independent of tumor grade, stage and PSA, between p27 expression -regardless of subcellular location -and prostate cancer recurrence. ...
Supported by the Department of Pathology at the University of Illinois at Chicago and by NIH grants R01-CA90759 and U01-CA86772. ...
doi:10.1016/j.humpath.2010.10.006
pmid:21292307
pmcid:PMC3095701
fatcat:aumqlpkutza3ff7rnwozftb5ue
Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives
2021
Diagnostics
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Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. ...
When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. ...
[76] aimed to predict the recurrence and progression of PCa based on biomarker analysis from 648 samples (424 tumors, 224 normal tissue) using tissue micro assays anti Ki-67, anti ERG (erythroblast ...
doi:10.3390/diagnostics11020354
pmid:33672608
pmcid:PMC7924061
fatcat:jqktyzjrhjh2jaxvlpk3pomube
Artificial intelligence in cancer imaging: Clinical challenges and applications
2019
Ca
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AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent ...
Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems ...
Imaging analysis shows promise in predicting the risk of developing lung cancer on initial detection of an incidental lung nodule and in distinguishing indolent from aggressive lung neoplasms. ...
doi:10.3322/caac.21552
pmid:30720861
pmcid:PMC6403009
fatcat:czsiirkbm5f7fh2ueesgnqtlhi
Front Matter: Volume 9414
2015
Medical Imaging 2015: Computer-Aided Diagnosis
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The CID Number appears on each page of the manuscript. The complete citation is used on the first page, and an abbreviated version on subsequent pages. ...
SPIE uses a six-digit CID article numbering system in which: The first four digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume using a Base ...
enhancement 9414 1U Prediction of treatment outcome in soft tissue sarcoma based on radiologically defined habitats Usefulness of histogram analysis of spatial frequency components for exploring the similarity ...
doi:10.1117/12.2194210
dblp:conf/micad/X15
fatcat:wzirgkwiwbgvba6jlaucm3azzq
A Review on Prostate Cancer Detection using Deep Learning Techniques
2020
Journal of Computational Science and Intelligent Technologies
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The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research ...
Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. ...
An imaging-based methodology in the prediction of 3-years biochemical recurrence (BCR) through a novel SVM classifier was analyzed in [32] . ...
doi:10.53409/mnaa.jcsit20201204
fatcat:ek3dwtjutvgnzkrd23sja3xdq4
Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy
2018
Translational Andrology and Urology
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"Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine ...
Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. ...
(A) An axial slice of the prostate on T2-weighted (T2w) MRI. ...
doi:10.21037/tau.2018.06.05
pmid:30050803
pmcid:PMC6043736
fatcat:22h5rn2rings7jmnh2v3fdiekm
Front Matter: Volume 10581
2018
Medical Imaging 2018: Digital Pathology
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using a Base 36 numbering system employing both numerals and letters. ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
The sessions included Emerging Trends; Machine Learning Trends; Diagnosis, Prognosis, and Predictive Analysis; Precision Medicine and Grading; and Detection and Segmentation. ...
doi:10.1117/12.2323941
fatcat:wyt7wxgl4nebxooizvt2efswwq
Quantitative Analysis of Seven New Prostate Cancer Biomarkers and the Potential Future of the 'Biomarker Laboratory'
2018
Diagnostics
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We describe, herein, an efficient and tissue-conserving pipeline that uses Tissue Microarrays in a semi-automated process that could, one day, be integrated into the hospital laboratory domain, using seven ...
putative prostate cancer biomarkers for illustration. ...
image analysis pipeline, producing an objective, replicable and rapid system of biomarker evaluation. ...
doi:10.3390/diagnostics8030049
pmid:30060509
fatcat:dc2xfjypavgppahvwplqjtgtt4
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