Special Session Call for Papers

 

Special Session

on

Machine Learning Methods for Modeling Treatment Outcomes in Cancer and Radiation Therapy

http://www.icmla-conference.org/icmla09/

 


AIMS AND SCOPE

Cancer is a leading cause of death worldwide and can affect people at all ages.  It is responsible for about 13% of all deaths in the world.  Cancer treatment methods are based on different modalities such as surgery, chemotherapy, radiotherapy, targeted gene therapy or combinations of these. The majority of cancer patients receive chemotherapy and radiotherapy as part of their care. Patients tend to respond differently to standard care. This is due to complex interactions between the treatment modality, disease, and patient’s bio-profile.  Machine learning (ML) methods could offer unique opportunities to understanding relationships between treatment regimens and patient’s response. This involves the ability to navigate complex molecular pathways, different treatment techniques parameters, clinical factors, and patient-specific factors.

Radiation therapy is one of most important treatments for cancer patients, which involves the use of high-energy radiation beams to kill cancer cells while sparing nearby normal tissues. Computational techniques such as Machine Learning (ML) have been increasingly used in radiotherapy to help accurately localize the tumors in images, precisely target the radiation to the tumors, analyze treatment outcomes, and improve treatment quality and patient safety. This session aims to provide a platform to present and discuss recent advancements in the application of ML methods in the radiotherapy field

 

Topics:

 

This session would solicit original research papers on modeling treatment outcomes (radiotherapy, chemotherapy, etc.) of cancer patients and general application in radiotherapy including but not limited to the following:

­    Treatment outcomes modeling using linear and nonlinear kernel-based models

­    Extraction of cancer prognostic factors from clinically and biologically relevant data

­    Analysis of high through-put biotechnology data (genomics and proteomics) related to treatment response

­    Treatment metrics and biomarkers methods for predicting outcomes

­    Imaging patterns as predictor of response

­    Image guided radiotherapy

­    Medical image segmentation and analysis

­    Machine learning to support diagnosis

­    Analysis and prediction of anatomical motion

­    Gating of respiratory motion

­    Treatment outcome analysis

­    Treatment quality assurance

 

 

 

IMORTANT DATES

Paper Submission Deadline:                                     August 1, 2009
Notification of acceptance:                                     September 7, 2009
Camera-ready papers & Pre-registration:                   October 1, 2009
The ICMLA Conference:                                          December 13-15, 2009
 

 

The special session will be held as a part of the ICMLA’09 conference. The authors would submit papers through the main conference submission website. Papers must correspond to the requirements detailed in the instructions to authors. Accepted papers must be presented by one of the authors to be published in the conference proceeding. If you have any questions, do not hesitate to direct your questions to elnaqa@wustl.edu or sbjiang@ucsd.edu.

 
All paper submissions will be handled electronically. Detailed instructions for submitting the papers are provided on the conference home page at 

http://www.icmla-conference.org/icmla09/

 

 

Special Session Chairs:

·         Dr. Issam El Naqa, Washington University in St. Louis

·         Dr. Steve Jiang, University of California, San Diego

 

Program Committee Members:

·         Dr. Issam El Naqa, Washington University in St. Louis

·         Dr. Steve Jiang, University of California, San Diego

·         Dr. Joseph Deasy, Washington University in St. Louis

·         Dr. Shiva K Das, Duke University

·         Dr. Moyed Miften, University of Colorado Denver

·         Dr. Yongyi Yang, Illinois Institute of Technology

·         Dr. Mustapha Lebbah, University of Paris

·         Dr. Lei Xing, Stanford University School of Medicine

·         Dr. Ross Berbeco, Brigham and Women's Hospital

·         Dr. Warren D'Souza, University of Maryland School of Medicine

·         Dr. Martin J. Murphy, Virginia Commonwealth University

·         Dr. Richard Radke, Rensselaer Polytechnic Institute