Optimal Decision Making in Radiotherapy Using Panomics Analytics

Funding resource: NIH/NCI R01 CA233487

The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data.

Publications:

  1. Yi Luo, Shruti Jolly, David Palma, Theodore S. Lawrence, Huan-Hsin Tseng, Gilmer Valdes, Daniel McShan, Randall K. Ten Haken, Issam EI Naqa: A Human-in-the-Loop Bayesian Networks Approach for Accurate and Credible Personalized Adaptive Radiotherapy Outcomes Prediction in Non-Small-Cell Lung Cancer Patients, 2021
  2. Balagurunathan Y, Mitchell R, El Naqa I, Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-78. PMID: 33721700
  3. S Cui, R Ten Haken, I El Naqa: Integrating Multi-Omics Information in Deep Learning Architecture for Joint Actuarial Outcome Prediction in Non-Small-Cell Lung Cancer Patients After Radiation Therapy, Int J Radiat Oncol Biol Phys. Feb, 2021. PMID: 33539966.
  4. I El Naqa an S Das: The Role of Machine and Deep Learning in Modern Medical Physics, Medical Physics, 2020. PMID: 32418342
  5. Kirby J, Prior F, Petrick N, Hadjiski L, Farahani K, Drukker K, Kalpathy-Cramer J, Glide-Hurst C, El Naqa I: Introduction to Special Issue on Datasets hosted in The Cancer Imaging Archive (TCIA). Med Phys, 2020. PMID: 33202038
  6. Moiseenko V, Marks LB, Grimm J, Jackson A, Milano MT, Hattangadi-Gluth JA, Huynh-Le MP, Pettersson N, Yorke E, El Naqa I, A Primer on Dose-Response Data Modeling in Radiation Therapy. Int J Radiat Oncol Biol Phys., 2020. PMID: 33358230
  7. Lise Wei, Dawn Owen, Benjamin Rosen, Xinzhou Guo, Kyle Cuneo, Theodore S Lawrence, Randall Ten Haken, Issam El Naqa: A deep survival interpretable radiomics model of hepatocellular carcinoma patients, Phys Med. 2021 Mar 10;82:295-305. PMID: 33714190.
  8. Farha M, Jairath NK, Lawrence TS, El Naqa I: Characterization of the Tumor Immune Microenvironment Identifies M0 Macrophage-Enriched Cluster as a Poor Prognostic Factor in Hepatocellular Carcinoma. JCO Clin Cancer Inform 4: 1002-1013, 2020. PMID: 33136432
  9. Wei L, Cui C, Xu J, Kaza R, El Naqa I, Dewaraja YK: Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics. EJNMMI Phys 7(1): 74, 2020. PMID: 33296050 PMCID: PMC7726084
  10. Jiali Yu, Michael D. Green, Sara Journey, Jae Eu Choi, Syed Monem Rizvi, Angel Qin, Jessica Waninger, Xueting Lang, Zoey Chopra, Issam El Naqa, Jiajia Zhou, Shasha Li, Shuang Wei, Wojciech Szeliga, Linda Vatan, Charles Mayo, Meredith Morgan, Arul Chinnaiyan, Caitlin Schonewolf, Kyle Cuneo, Ilona Kryczek, Theodore S. Lawrence, Nithya Ramnath, Fei Wen, Marcin Cieslik, Ajjai Alva, and Weiping Zou: Liver metastasis restrains immunotherapy efficacy via myeloid-mediated T-cell elimination: Nature Medicine, PMID: 33398162
  11. Pakela JM, Tseng HH, Matuszak MM, Ten Haken RK, McShan DL, El Naqa I: Quantum-inspired algorithm for radiotherapy planning optimization. Med Phys: 2020. 020 Jan;47(1):5-18. doi: 10.1002/mp.13840. PMID: 31574176 PMCID: PMC6980234
  12. El Naqa I, Haider MA, Giger ML, Ten Haken RK: Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 93(1106): 20190855, 2020. PMID: 31965813. PMCID: PMC7055429
  13. Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallieres, Arvind Rao, Olivier Morin, Sarah Mattonen, Issam El Naqa: Machine and Deep Learning Methods for Radiomics, Medical Physics: 2020. (In Press)
  14. Sunan Cui, Huan-Hsin Tseng, Julia Pakela, Randall K. Ten Haken, and Issam El Naqa: Introduction to Machine and Deep Learning for Medical Physicists, Medical Physics, 2020. (In Press)
  15. Veit-Haibach P, El Naqa I, Visvikis D.: Radiomics in nuclear medicine and hybrid imaging: current standings on clinical applicability, Q J Nucl Med Mol Imaging. 2019 Dec;63(4):321-322. doi: 10.23736/S1824-4785.19.03222-9. PMID: 31560184.
  16. Waninger JJ, Green MD, Cheze Le Rest C, Rosen B, El Naqa I.: Integrating radiomics into clinical trial design, Q J Nucl Med Mol Imaging. 2019 Dec;63(4):339-346. doi: 10.23736/S1824-4785.19.03217-5. PMID: 31527581.
  17. Wei L, Osman S, Hatt M, El Naqa I.: Machine learning for radiomics-based multimodality and multiparametric modeling, Q J Nucl Med Mol Imaging. 2019 Dec;63(4):323-338. doi: 10.23736/S1824-4785.19.03213-8. PMID: 31527580.

 

Combined Radiation Acoustics and Ultrasound Imaging for Real-Time Guidance in Radiotherapy

Funding resource: NIH/NCI R37 CA222215

To develop an evaluated an integrated tomographic feedback system that uses X-ray acoustics (XACT) and advanced ultrasound (US) images to monitor a patient’s present status during radiotherapy delivery.

Publications:

  1. Ba Sunbul N, Oraiqat I, Rosen B, Miller C, Meert C, Matuszak MM, Clarke S, Pozzi S, Moran JM, El Naqa I. “Application of radiochromic gel dosimetry to commissioning of a megavoltage research linear accelerator for small-field animal irradiation studies.” Med Phys. 2021a Mar;48(3):1404-1416. doi: 10.1002/mp.14685. PMID: 33378092
  2. Oraiqat I, Zhang W, Litzenberg D, Lam K, Ba Sunbul N, Moran J, Cuneo K, Carson P, Wang X, El Naqa I (2020a). An Ionizing Radiation Acoustic Imaging (iRAI) Technique for Real-Time Deep Tissue Dosimetric Measurements for FLASH Radiotherapy. Medical Physics
  3. Zhang W, Oraiqat I, Lei H, Carson P, EI Naqa I, Wang X. (2020). Dual-modality x-ray induced radiation acoustic and ultrasound imaging for real-time monitoring of radiotherapy Radiation acoustic for radiotherapy monitoring. Science BME Frontier (accepted; https://spj.sciencemag.org/bmef/aip/9853609/).
  4. Ba Sunbul N, Zhang W, Oraiqat I, Litzenberg D, Lam K, Cuneo K, Moran JM, Matuszak MM, Carson P, Wang X, Clarke S, Pozzi S, , El Naqa I. “Feasibility of Ionizing Radiation Acoustic Imaging (iRAI) as a Real-Time Dosimetric 1 Technique for FLASH Radiotherapy: A simulation Analysis.” Medical Physics, 2021b, accepted.

 

Cerenkov Multi-Spectral Imaging (CMSI) for Adaptation and Real-Time Imaging in Radiotherapy

Funding resource: NIH/NCI R41 CA243722

Sponsor: Endectra LLC

In this STTR proposal, Endectra will work with oncology researchers at Moffitt to develop and evaluate a novel Cerenkov Multi-Spectral Imaging (CMSI) technique using new solid state on-body probes to conduct routine optical measurements of radiation dose and molecular imaging during cancer radiotherapy delivery. This approach is expected to provide more accurate tumor physiological representation and dose adaptation during treatment, reduce overall patient exposure to radiation, and allow for ongoing assessment of tumor physiological parameters. If successful, Endectra will develop CMSI as an alternative cost saving and effective molecular imaging/targeting modality for routine radiotherapy applications, greatly improving radiotherapy outcomes and yielding a major impact on public health.

Publications:

  1. Ibrahim Oraiqat, Samuel DeBruin, Robin Pearce, Christopher Como, Justin Mikell, Charles Taylor, John Way, Manuel Suarez, Alnawaz Rehemtulla, Roy Clarke, Issam El Naqa: Silicon Photomultipliers for Cherenkov Emission Detection During External Beam Radiotherapy IEEE Photonics Journal DOI 10.1109 /JPHOT.2019.2931845: 1-1, 2019.
  2. Ibrahim Malek Oraiqat*, Essam Al-Snayyan, Andrew Calcaterra, Roy Clarke, Alnawaz Rehemtullaand Issam El Naqa: Measuring Tumor Microenvironment pH during Radiotherapy Using a Novel Cerenkov Emission Multispectral Optical Probe based on Silicon Photomultipliers Frontiers in Physics, 2021.

 

Medical Imaging and Data Resource Center (MIDRC) for Rapid Response to COVID-19 Pandemic

Funding Resource: University of Chicago (Prime: NIH/NBIB 75N92020D00018/75N92020F0001)

To develop machine learning tools for image-based modeling and development of a clinical decision support tool for Covid19 patient management.

Publications:

  1. Issam El Naqa, Hui Li, Jordan Fuhrman, Qiyuan Hub, Naveena Gorre, Weijie Chen, Maryellen L. Giger, “Lessons Learned in Transitioning to AI in the Medical Imaging of COVID-19,” under review
  2. Jordan D Fuhrman, Naveena Gorre, Qiyuan Hu, Hui Li, Issam El Naqa, Maryellen L Giger, “A Review of Explainable and Interpretable AI with applications in COVID-19 imaging, under review