NDLI: Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques

Content Provider	IEEE Xplore Digital Library
Author	Tong Lin Cervino, L. Xiaoli Tang Vasconcelos, N. Jiang, S.B.
Copyright Year	2008
Description	Author affiliation: Dept. of Radiat. Oncology, Univ. of California San Diego, La Jolla, CA (Tong Lin; Cervino, L.; Xiaoli Tang; Vasconcelos, N.; Jiang, S.B.)
Abstract	Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating and tracking. Gating is considered as the current state of the art for precise lung cancer radiotherapy, which irradiates the tumor when it moves into a predefined gating window. Tracking seems to be a next-generation technique, and it operates in a more aggressive fashion by following the tumor position with radiation beam in real time. Existing methods for gating and tracking often rely on observed motion patterns of external surrogates or implanted fiducial markers. However, external surrogates suffer from certain degrees of inaccuracy, and implanted fiducial markers are in limited uses due to the risk of pneumothorax. Therefore, direct tumor targeting techniques without implanting fiducial markers are desired. Previous studies in fluoroscopic markerless targeting are mainly based on template matching methods, which may fail when tumor boundary is unclear in fluoroscopic images. In this paper, we propose a novel framework of markerless gating and tracking based on machine learning algorithms. Specifically, gating is treated as a two-class classification problem, which is solved by principal component analysis (PCA) and artificial neural network (ANN). Further, we formulate the tracking problem as a regression task, which employs the correlation between the tumor position and nearby surrogate anatomic features in the image. Four regression methods were tested in this study: 1-degree and 2-degree linear regression, artificial neural network (ANN), and support vector machine (SVM). Finally, we demonstrate the superb performance of the proposed markerless gating and tracking algorithms on 10 fluoroscopic image sequences of 9 patients. For gating, the target coverage (the precision) ranges from 90% to 99%, with mean of 96.5%. For tracking, the mean localization error is about 2.1 pixels and the maximum error at 95% confidence level is about 4.6 pixels (pixel size is about 0.5 mm).
Starting Page	533
Ending Page	538
File Size	161135
Page Count	6
File Format	PDF
ISBN	9780769534954
DOI	10.1109/ICMLA.2008.143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Machine learning algorithms Linear regression Artificial neural networks Support vector machines lung cancer tumor motion Machine learning radiotherapy Lung neoplasms Cancer Principal component analysis Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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