MLM 2013 - Special Session on Machine Learning Methods in Cancer Diagnosis and Treatment

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. It is known that early diagnosis improves cancer treatment outcomes. Additionally, there are different treatment 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 method, disease, and patient’s bio-profile. Moreover, radiotherapy involves the use of external high-energy radiation beams or radioactive sources to kill cancer cells, while sparing nearby normal tissues.
Computational techniques, such as Machine Learning (ML) techniques, have been increasingly used in diagnosis and prognosis of cancer, by helping to accurately detect and localize the tumors in images, precisely target the therapy 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 cancer field.
Topics:
This session would solicit original research papers on cancer detection and modeling
treatment outcomes (radiotherapy, chemotherapy, etc.) of cancer patients as well as on general application of ML in cancer, including but not limited to the following topics:
- Detection of cancer lesions in diagnostic images
- Computer-aided diagnosis of cancer
- 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 cancer diagnosis and prognosis
- Treatment metrics and biomarkers methods for predicting outcomes
- Imaging patterns as predictor of diagnosis and response
- Image guided and adaptive 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