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A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
[article]
2020
arXiv
pre-print
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Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. ...
A 5-fold cross validation on the training set led to an AUC of 0.84 +/- 0.03, and the prediction on the testing dataset reached AUC of 0.73 +/- 0.02 for the outcome of 30% diameter shrinkage. ...
INTRODUCTION Immunotherapy has demonstrated significant efficacy in the treatment of non-small cell lung cancer [1, 2] . ...
arXiv:2003.02943v1
fatcat:z2gxpavqonednoctdnw7vv2tyu
The application of artificial intelligence and radiomics in lung cancer
2020
Precision Clinical Medicine
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In this study, we gave a brief review of the current application of AI and radiomics for precision medical management in lung cancer. ...
Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. ...
For example, Huang et al. performed the LASSO Cox-regression model to select the most valuable features for the prediction of prognosis of early-stage non-small cell lung cancer 64 . ...
doi:10.1093/pcmedi/pbaa028
fatcat:gpjll7iwavgppgfmcm4qiwnl5e
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
2017
Annals of Oncology
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This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice. ...
The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine ...
Acknowledgement The authors wish to thank C. Verjat for preparation of the figures.
Funding The authors of this review received no grant from any funding agency; no grant number is applicable. ...
doi:10.1093/annonc/mdx034
pmid:28168275
fatcat:son4hx7ixvb6jntxmifdypigym
Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
2022
Frontiers in Oncology
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In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation ...
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research ...
(19) identified primary mass entropy as a prognostic indicator of the overall survival in a training set for non-small cell lung cancer (NSCLC), but not reproduced in the validation cohort, thereby ...
doi:10.3389/fonc.2022.773840
pmid:35251962
pmcid:PMC8891653
fatcat:3h5tnm3aznb33k5ylkcd6tvs4e
Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review
2020
Korean Journal of Radiology
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In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues. ...
Recent advances in computer technology have generated a new area of research known as radiomics. ...
Despite the small patient number and the biased selection of features, their study indicated that a radiomics analysis of DBT images could be used to facilitate cancer detection and to acquire a better ...
doi:10.3348/kjr.2019.0855
pmid:32524780
pmcid:PMC7289696
fatcat:ka76i2d2tffubinyxu66o273nu
Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine
[chapter]
2019
Machine Learning in Medicine and Biology [Working Title]
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(i.e., potentials for decision-making support toward a practical application of precision medicine in radiation therapy) of patient care. ...
With the era of big data, the utilization of machine learning algorithms in radiation oncology research is growing fast with applications including patient diagnosis and staging of cancer, treatment simulation ...
The author also specially thanks the IntechOpen for granting this chapter a full funding for Open-Access publication. ...
doi:10.5772/intechopen.84629
fatcat:tobl67e5qvgf5mat43ex5lounq
Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors
2019
Contrast Media & Molecular Imaging
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Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component ...
The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. ...
Acknowledgments We want to thank Victor Parmar for proofreading the article. e manual segmentation masks were acquired during the project "LungStage-Computer Aided Staging of Non-Small Cell Lung Cancer ...
doi:10.1155/2019/1545747
pmid:31354393
pmcid:PMC6636561
fatcat:5vitbq73nza7vhmiadpsglhrsi
Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature
2021
Cancers
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Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. ...
In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. ...
Data Availability Statement: Primary data cited in this review are openly available in PubMed, Embase and Cochrane database.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/cancers13102469
pmid:34069367
fatcat:aydobyzvh5be7hhym5hh23wsda
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology
2021
Frontiers in Oncology
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Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, ...
Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. ...
ACKNOWLEDGMENTS We owe thanks to all the members participating in this work. ...
doi:10.3389/fonc.2020.570465
pmid:33575207
pmcid:PMC7870863
fatcat:skgv7ttfeffojft2o7fl63lgzy
Synergizing medical imaging and radiotherapy with deep learning
2020
Machine Learning: Science and Technology
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It is believed that deep learning in particular, and artificial intelligence and machine learning in general, will have a revolutionary potential to advance and synergize medical imaging and radiotherapy ...
prediction) as well as the connections between them. ...
Tseng et al employed a DRL approach to enable the adjustment of radiation dose during the course of radiotherapy for non-small cell lung cancer, maximizing tumor local control at a reduced rate of radiation ...
doi:10.1088/2632-2153/ab869f
fatcat:aibfmfelcngkrk4ilwcs25c77a
The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future
2020
Diagnostics
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The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious ...
This issue is owed to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack ...
by the enterochromaffin cells located in the small intestine. ...
doi:10.3390/diagnostics10121083
pmid:33322819
pmcid:PMC7763988
fatcat:edo3cgaeb5h4fjhq2vgswhdw4e
AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study
[chapter]
2021
Simulation Modelling [Working Title]
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In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert ...
the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. ...
Acknowledgements The author would like to express his gratitude and grateful appreciation to the Kuwait Foundation for the Advancement of Sciences (KFAS) for financially supporting this project. ...
doi:10.5772/intechopen.99442
fatcat:eniarjcqdba55csjcqrox6c7zu
Technology-driven research for radiotherapy innovation
2020
Molecular Oncology
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Although a large fraction of cancer patients receive radiotherapy, this is certainly not reflected in the worldwide budget for radiotherapy research. ...
Technology has a pivotal role in the continuous development of radiotherapy. ...
However, based on a systematic review of oligometastatic non-small cell lung cancer (NSCLC), surgical resection was almost the exclusive local treatment modality until 2003 and the predominant modality ...
doi:10.1002/1878-0261.12659
pmid:32124546
fatcat:aac5tstppbav3nyjpxoljmhoyi
CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020
2020
International Journal of Computer Assisted Radiology and Surgery
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A hybrid (analogue and digital) CARS 2020 has therefore been envisaged to take place at the University Hospital in Munich, with a balanced combination of analogue/personal and digital presentations and ...
The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants ...
Besides, use of gloves is low, so doses of hands are still high. Therefore, a master-slave robotic system for VI is necessary for minimization of the radiation exposure. ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
Proceedings of the World Molecular Imaging Congress 2021, October 5-8, 2021: General Abstracts
2022
Molecular Imaging and Biology
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In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.7 for processed MRI images. ...
The method can be applied to clinical MRI images and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. ...
was superior over the small molecular counterpart for prostate cancer therapy. ...
doi:10.1007/s11307-021-01693-y
pmid:34982365
pmcid:PMC8725635
fatcat:4sfb3isoyfdhfbiwxfr55gvqym
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