Periodic Reporting for period 2 - OncoViroMRI (Brain Cancer Therapy Monitoring using a Novel Quantitative and Rapid Magnetic Resonance Imaging-based Method)
Periodo di rendicontazione: 2021-11-01 al 2022-10-31
Glioblastoma multiforme (GBM) is the most common type of brain tumor found in adults and is fatal in all cases. A very promising therapeutic approach for GBM is the use of oncolytic viruses (OVs) that selectively infect, replicate in, and destroy tumor cells while sparing the surrounding normal cells. Nevertheless, to achieve successful oncolytic virotherapy, frequent non-invasive monitoring of the process must be performed. This is crucial for gaining a better understanding of the interactions between the virus and its tumor-host and predicting therapeutic response. Thus, the development of a non-invasive method capable of accurately quantifying the location and extent of the viral spread in the tumor is highly required and is of great importance. Accordingly, the main research goal of this action is to develop a magnetic resonance imaging (MRI)- based method for accurate, quantitative, and rapid imaging of OVs delivery and spread in clinically relevant tumor models. The specific research objectives include developing a method for the detection and imaging of OV treatment response, increasing the specificity and sensitivity of the method using quantitative MRI techniques and machine learning, quantifying and validating the results using mouse tumor models, translating of the MRI protocols to clinical scanners, and examination of the method for additional applications. The envisioned technology could be expanded to various additional clinical conditions, and its dissemination could improve patient care. Following the project results and analysis, we conclude that a new method for noninvasive MRI of brain tumor treatment response was developed and validated (mostly in preclinical settings), demonstrating a rapid and quantitative assessment of tissue state.
The following results were obtained during the project:
1) A novel MRI-based method for quantitative and fast molecular imaging was developed and validated using numerical simulations and imaging of tissue-mimicking models.
2) The ability to detect reporter genes using molecular MR imaging was improved and validated using tumor-bearing mice.
3) A deep learning approach was designed and combined with biophysical MRI models.
4) The deep learning framework was initially validated using tissue-mimicking models (phantoms), and later using an extensive tumor-bearing mice imaging study, successfully allowing the early detection of tumor-treatment response to virus-based therapy. The resulting quantitative molecular images were in good agreement with histology.
5) The technological progress was translated to clinical MRI scanners and examined on healthy human volunteers and a small cohort of brain tumor patients. A good agreement was demonstrated between the quantitative biophysical parameter maps of the healthy brain obtained by the proposed method and the literature.
6) Two additional methods for accelerating the image acquisition and reconstruction time using machine-learning approaches were developed.
The results were disseminated in 8 scientific journal publications (6 original research and two review papers), 13 presentations/posters at scientific conferences, and presented at several scientific/general public gatherings/events.
1) A novel MRI-based method for quantitative and fast molecular imaging was developed and validated using numerical simulations and imaging of tissue-mimicking models.
2) The ability to detect reporter genes using molecular MR imaging was improved and validated using tumor-bearing mice.
3) A deep learning approach was designed and combined with biophysical MRI models.
4) The deep learning framework was initially validated using tissue-mimicking models (phantoms), and later using an extensive tumor-bearing mice imaging study, successfully allowing the early detection of tumor-treatment response to virus-based therapy. The resulting quantitative molecular images were in good agreement with histology.
5) The technological progress was translated to clinical MRI scanners and examined on healthy human volunteers and a small cohort of brain tumor patients. A good agreement was demonstrated between the quantitative biophysical parameter maps of the healthy brain obtained by the proposed method and the literature.
6) Two additional methods for accelerating the image acquisition and reconstruction time using machine-learning approaches were developed.
The results were disseminated in 8 scientific journal publications (6 original research and two review papers), 13 presentations/posters at scientific conferences, and presented at several scientific/general public gatherings/events.
We have demonstrated that magnetic resonance imaging (MRI) and artificial intelligence (AI) can be used to detect early signs of tumor cell death in response to a novel virus-based cancer therapy. Particularly, we have used quantitative molecular MRI images to measure multiple tissue properties, including tissue pH and protein concentration, that are altered with cell death. This method allows therapeutic response monitoring much earlier than with previous techniques. The treatment responses were visible just 48 hours after viral-therapy, long before any changes in tumor volume were observed. The MRI molecular fingerprinting method was validated in a mouse brain tumor study where the tumors were treated with a novel virus-based therapy that selectively killed cancer cells.
Our work describes a new method for detecting tumor cell death non-invasively using MRI. The capacity to do this could be useful for non-invasive monitoring of cancer treatment, potentially improving patient care and tailoring the treatment to an individual patient. The same approach might also be beneficial for detecting and characterizing other medical conditions where elevated cell death occurs, such as stroke and liver disease. While the study was mainly validated using a mouse brain tumor model, we have also customized and further optimized the image-acquisition protocol and reconstruction method for clinical MR scanners. This allows for future examinations and validation of the developed approach in humans (beyond the proof-of-concept human studies performed in the project).
Our work describes a new method for detecting tumor cell death non-invasively using MRI. The capacity to do this could be useful for non-invasive monitoring of cancer treatment, potentially improving patient care and tailoring the treatment to an individual patient. The same approach might also be beneficial for detecting and characterizing other medical conditions where elevated cell death occurs, such as stroke and liver disease. While the study was mainly validated using a mouse brain tumor model, we have also customized and further optimized the image-acquisition protocol and reconstruction method for clinical MR scanners. This allows for future examinations and validation of the developed approach in humans (beyond the proof-of-concept human studies performed in the project).