Work

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

Year: 2024

Type: article

Abstract: For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations — which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity. • Quantification of impact of tumor invasion on radiogenomic modeling of Glioblastoma. • Deep network for realistic generation of tumor invasion given tumor mass and location. • Tackling limited size and tumor heterogeneity via joint estimation and context. • Show realism, diversity, and generalizability in generation via extensive analyses. • Radiologist blind tests, and cross-dataset testing to show realism, generalizability.

Source: Computerized Medical Imaging and Graphics

DOI: 10.1016/j.compmedimag.2024.102436

Authors: Padmaja Jonnalagedda, Brent Weinberg, Taejin L. Min, Shiv Bhanu, Bir Bhanu

Institutions: University of California, Riverside, Emory University, Riverside Community Hospital

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,760