Differentiation of tumor recurrence from radiation-induced necrosis (RN) is a critical step in the follow-up management of patients treated with stereotactic radiosurgery for brain tumor. A non-invasive method that is robust in discriminating RN from recurrent tumor using non-invasive method such as MRI is of a significant value for patients and physicians. The hypothesis of this proposed project is that advanced Machine-learning (ML) and image analysis of different MRI imaging information may hold potential for accurately detecting the difference between RN and recurrence of brain tumor is a substantial challenge in the daily practice in Neuro-Oncology. Furthermore, there is a need to study feasibility of translating our ongoing works in brain tumor volume quantitation into potential imaging device. Consequently, this Administrative Supplement propose the following Specific Aims for this study:
Aim 1 : To develop novel methods to discriminate RN from tumor recurrence in MRI.
Aim 2 : To develop and prepare a fast track SBIR proposal (Phase I and II combined) to be submitted soon after this Administrative Supplemental funding expires. If successful, robust discrimination of radiation-induced RN and tumor recurrence will make our current brain tumor and abnormal tissue volume segmentation methods and tools more robust and ready for use in the radiology and oncology practices. Furthermore, the planned SBIR project funding will lead the way to fully explore the possibility of launching a commercial software technology for brain tumor volume quantitation imaging device development.
For ongoing work in our parent RO1 grant we are developing new machine learning and image analysis methods for automatic detection and segmentation of brain tumor in MRI images, which is expected to help patients to have a much better chance of successful treatment. In this Administrative Supplement we will develop methods for discrimination of radiation-induced RN and tumor recurrence that will make our ongoing brain tumor and abnormal tissue volume segmentation methods and tools more robust and ready for use in the radiology and oncology practices. Furthermore, the planned SBIR project funding will lead the way to fully explore the possibility of launching a commercial software technology for brain tumor volume quantitation imaging device development.
| Vidyaratne, L; Alam, M; Shboul, Z et al. (2018) Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation. Proc SPIE Int Soc Opt Eng 2018: |
| Shboul, Zeina; Vidyaratne, Lasitha; Alam, Mahbubul et al. (2018) Glioblastoma and Survival Prediction. Brainlesion (2017) 10670:358-368 |
| Shboul, Zeina A; Reza, Sayed M S; Iftekharuddin, Khan M (2018) Quantitative MR Image Analysis for Brian Tumor. VipIMAGE 2017 (2017) 27:10-18 |
| Pei, Linmin; Reza, Syed M S; Li, Wei et al. (2017) Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc SPIE Int Soc Opt Eng 10134: |
| Maier, Oskar; Menze, Bjoern H; von der Gablentz, Janina et al. (2017) ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 35:250-269 |
| Reza, Syed M S; Iftekharuddin, Khan M (2016) Glioma Grading Using Cell Nuclei Morphologic Features in Digital Pathology Images. Proc SPIE Int Soc Opt Eng 9785: |
| Bron, Esther E; Smits, Marion; van der Flier, Wiesje M et al. (2015) Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111:562-79 |
| Reza, Syed M S; Mays, Randall; Iftekharuddin, Khan M (2015) Multi-fractal Detrended Texture Feature for Brain Tumor Classification. Proc SPIE Int Soc Opt Eng 9414: |
