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Spectral Machine Learning for Pancreatic Mass Imaging Classification [article]

Yiming Liu, Ying Chen, Guangming Pan, Weichung Wang, Wei-Chih Liao, Yee Liang Thian, Cheng E. Chee, Constantinos P. Anastassiades
2021 arXiv   pre-print
We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging.  ...  state-of-the-art machine learning classification, gradient boosting and random forest.  ...  We present a novel spectral machine learning (SML) method for the diagnosis of pancreatic masses on CT scan. 1) Specifically, for each patient, it extracts the first two spike eigenvectors of the covariance  ... 
arXiv:2105.00728v1 fatcat:kyz4sn3kc5e2xmvrcqvaitcaaq

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

Wen Y Chung, Elon Correa, Kentaro Yoshimura, Ming-Chu Chang, Ashley Dennison, Sen Takeda, Yu-Ting Chang
2020 American journal of translational research  
The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC.  ...  The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed.  ...  Acknowledgements We would like to thank Ayumi Iizuka, Setsuko Fukuta and Neil Loftus for the help of PESI-MS analysis and Taiwan Pancreas Foundation's support for the international collaborative project  ... 
pmid:32051746 pmcid:PMC7013221 fatcat:zkhc3xaobvbfdiqtcojqk2k2hm

New advances in CT imaging of pancreas diseases: a narrative review

Andrea Agostini, Alessandra Borgheresi, Federico Bruno, Raffaele Natella, Chiara Floridi, Marina Carotti, Andrea Giovagnoni
2020 Gland surgery  
Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis  ...  Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.  ...  The typical tasks the machine learning algorithms are trained for include the segmentation, detection or classification of tumor lesions (17, 48, 65) .  ... 
doi:10.21037/gs-20-551 pmid:33447580 pmcid:PMC7804533 fatcat:qnlnd3qmcjadnkuyopoijqg7ja

Pancreatic cancer detection and characterization—state of the art cross-sectional imaging and imaging data analysis

Georgios Kaissis, Rickmer Braren
2019 Translational Gastroenterology and Hepatology  
State of the art radiologic high-resolution cross-sectional imaging by computed tomography (CT) and magnetic resonance imaging (MRI) represent advanced techniques for early lesion detection, pre-therapeutic  ...  In light of molecular taxonomies currently under development, the implementation of advanced imaging data post-processing pipelines and the integration of imaging and clinical data for the development  ...  for Selection, Monitoring and Individualization of Cancer Therapies" (SFB824, project C6).  ... 
doi:10.21037/tgh.2019.05.04 pmid:31231702 pmcid:PMC6556687 fatcat:3dxxjnax4vhablnwr7ocfwmefa

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  features and classification methods for Barrett's cancer detection using VLE imaging 10134 0E Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography 10134 0F Fully  ...  images [10134-30] 10134 0X Quantification of CT images for the classification of high-and low-risk pancreatic cysts [10134-31] 10134 0Y Automated liver elasticity calculation for 3D MRE [10134-  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging

Juliana Pereira Lopes Gonçalves, Christine Bollwein, Anna Melissa Schlitter, Benedikt Martin, Bruno Märkl, Kirsten Utpatel, Wilko Weichert, Kristina Schwamborn
2021 Metabolites  
Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums.  ...  Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is  ...  This applies to all fields of machine learning including image analysis.  ... 
doi:10.3390/metabo11110752 pmid:34822410 pmcid:PMC8624953 fatcat:g7hhjsat3rbvtay5la2kdlu7am

Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis [article]

Maximilian Dietrich
2021 arXiv   pre-print
Automated machine learning-based diagnosis of sepsis based on HSI data, however, has not been explored to date.  ...  HSI-based tissue classification.  ...  The present contribution is further supported by the Helmholtz Association under the joint research school "HIDSS Health -Helmholtz Information and Data Science School for Health" and the Helmholtz Imaging  ... 
arXiv:2106.08445v1 fatcat:devokwfubvcafk56goag24o5qq

Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue ex Vivo, a Pilot Study

Joseph Peller, Cobey L. McGinnis, Kyle J. Thompson, Imran Siddiqui, John Martinie, David A. Iannitti, Susan R. Trammell
2021 Open Journal of Medical Imaging  
SVM Analysis of Reflectance Images of Samples Containing both Healthy and Malignant Pancreatic Tissue SVM is a kernel-based machine learning  ...  Based remains the primary treatment for most solid mass tumors.  ... 
doi:10.4236/ojmi.2021.114011 fatcat:4d6x2ed6rfcztfwjdx4gus5hwa

Machine Learning Characterization of Cancer Patients-Derived Extracellular Vesicles using Vibrational Spectroscopies [article]

Abicumaran Uthamacumaran, Samir Elouatik, Mohamed Abdouh, Michael Berteau-Rainville, Zu-hua Gao, Goffredo Arena
2022 arXiv   pre-print
a classification accuracy of above 90 percent when reduced to a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50 training: testing split.  ...  Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients  ...  The differential methylation patterns and somatic mutations of cfDNA can be used to train machine learning classification algorithms like Deep Learning networks for cancer diagnosis.  ... 
arXiv:2107.10332v8 fatcat:kbalvtcwazgvheerwectpxbm4a

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

Georgios Kaissis, Sebastian Ziegelmayer, Fabian Lohöfer, Hana Algül, Matthias Eiber, Wilko Weichert, Roland Schmid, Helmut Friess, Ernst Rummeny, Donna Ankerst, Jens Siveke, Rickmer Braren
2019 European Radiology Experimental  
To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic  ...  Acknowledgements The authors wish to thank Irina Heid, Alexander Muckenhuber, Katja Steiger, Hsi-Yu Yen, and Marcus Schwaiger for their contributions to the conception and implementation of the techniques  ...  (DOCX 403 kb) Abbreviations ADC: Apparent diffusion coefficient; AUC: Area under the curve; DWI: Diffusion-weighted imaging; ML: Machine learning; MRI: Magnetic resonance imaging; OS: Overall survival;  ... 
doi:10.1186/s41747-019-0119-0 pmid:31624935 pmcid:PMC6797674 fatcat:e5wrt2suyrgm3opadlo55w7pcq

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks [article]

Zhifei Zhang, Wanling Gao, Fan Zhang, Yunyou Huang, Shaopeng Dai, Fanda Fan, Jianfeng Zhan, Mengjia Du, Silin Yin, Longxin Xiong, Juan Du, Yumei Cheng, Xiexuan Zhou, Rui Ren (+2 others)
2019 arXiv   pre-print
Fourth, are there any benchmarks for measuring algorithms and systems for big medical data?  ...  Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together?  ...  His research interests include data mining, and machine learning.  ... 
arXiv:1901.00642v1 fatcat:fak46q7bgzesll6y4h7i6mcysi

Proteomic Analysis (GeLC–MS/MS) of ePFT-Collected Pancreatic Fluid in Chronic Pancreatitis

Joao A. Paulo, Vivek Kadiyala, Linda S. Lee, Peter A. Banks, Darwin L. Conwell, Hanno Steen
2012 Journal of Proteome Research  
Our workflow provided a mass spectrometry-based approach for the further study of the pancreatic fluid proteome which may lead to the discovery potential biomarkers of chronic pancreatitis.  ...  Spectral counting and statistical analysis thereof revealed an additional 38 and 77 proteins that were up-or down-regulated, respectively, in the pancreatic fluid from individuals with chronic pancreatitis  ...  ExPlain applied an upstream analysis approach, based on the implementation of machine learning and graph topological analysis algorithms, to identify causality key-nodes in the network of pancreatic disease  ... 
doi:10.1021/pr2011022 pmid:22243521 pmcid:PMC3294251 fatcat:d6y3kf3d7rditn43zg3sqp4tje

Applications of Raman spectroscopy in cancer diagnosis

Gregory W. Auner, S. Kiran Koya, Changhe Huang, Brandy Broadbent, Micaela Trexler, Zachary Auner, Angela Elias, Katlyn Curtin Mehne, Michelle A. Brusatori
2018 Cancer Metastasis Review  
detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells.  ...  This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including  ...  Funding for the Wayne State University projects outlined in this paper were provided by the Paul Strauss Endowed Chair, Henry Ford Health Systems, and Wayne State University SSIM Program.  ... 
doi:10.1007/s10555-018-9770-9 fatcat:hdznc3biibfjxfod6vsooriboq

Pancreatic Cancer Surgical Resection Margins: Molecular Assessment by Mass Spectrometry Imaging

Livia S. Eberlin, Katherine Margulis, Ivette Planell-Mendez, Richard N. Zare, Robert Tibshirani, Teri A. Longacre, Moe Jalali, Jeffrey A. Norton, George A. Poultsides, Andrew H. Beck
2016 PLoS Medicine  
Ambient mass spectrometry imaging has emerged as a powerful technique for chemical imaging and real-time diagnosis of tissue samples.  ...  Surgical resection with microscopically negative margins remains the main curative option for pancreatic cancer; however, in practice intraoperative delineation of resection margins is challenging.  ...  Acknowledgments We thank the Stanford Cancer Institute Tissue Procurement Facility for the services provided. Author Contributions  ... 
doi:10.1371/journal.pmed.1002108 pmid:27575375 pmcid:PMC5019340 fatcat:e5hj7zuyprbflg46uuy3vo7ahy

Quantitative phase microscopy spatial signatures of cancer cells

Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal, Hayit Greenspan, Lisa L. Satterwhite, Natan T. Shaked
2017 Cytometry Part A  
Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and  ...  The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers.  ...  We thank Ruth Gottlieb for significant biological support and advising, Dr. Ksawery Kalinowski for useful technical discussions, and Dr. Itay Barnea for useful biological discussions. *** *** *** ***  ... 
doi:10.1002/cyto.a.23100 pmid:28426133 fatcat:af7webxqpvc6xaqfhu6japdsoy
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