Principal Investigators

Ali Sadeghi-Naini, PhD

Scientist
Sunnybrook Health Sciences Centre
2075 Bayview Ave., Room M6 605
Toronto, ON
M4N 3M5

Education:

• M.Sc., 2006 artificial intelligence, Tehran Polytechnic University, Iran
• PhD, 2011, biomedical engineering, Western University, Canada
• Postdoctoral Fellowship, 2015, medical biophysics and radiation oncology, University of Toronto, Canada

Appointments and Affiliations:

Scientist, Physical Sciences, Odette Cancer Research Program, Sunnybrook Research Institute
Scientist, department of radiation oncology, Sunnybrook Health Sciences Centre
Assistant professor, department of medical biophysics, U of T

Research Summary:

The focus of Dr. Sadeghi-Naini’s research is on developing computer-aided image-guided technologies to improve personalized cancer therapeutics. In particular, he is interested in developing integrated imaging and computational frameworks to detect and characterize cancer, to facilitate cancer-targeting interventions and to evaluate response to treatment.

In this context, he is investigating novel methods of multimodal cancer imaging to explore different stages during cancer development and decay from various structural and functional perspectives.

Specifically, he is developing integrated frameworks to adapt complementary aspects of quantitative ultrasound imaging, optical spectroscopy, elastography, computed tomography, and magnetic resonance imaging to characterize a tumour in terms of its micro-structure, physiology, perfusion, metabolism and biomechanical properties.Further, he is investigating alterations in such tumour characteristics to develop sensitive biomarkers of cancer response to treatment.

Dr. Sadeghi-Naini is also transforming multimodal imaging within computational frameworks to facilitate the planning and navigation of interventional procedures such as biopsy, brachytherapy and radiation therapy.

A particular area of interest is in developing ad hoc models for quantification of spatial heterogeneity in cancer imaging. Alterations within a tumour during its formation or degeneration are frequently inhomogeneous. Therefore, quantifying intra-tumour heterogeneity can provide further insights into tumour characteristics or rapidly flag a change in tumour state within its life cycle. In order to quantify intra-tumour heterogeneity noninvasively, Dr. Sadeghi-Naini is developing novel image-processing techniques to model and analyze the texture within tumour images. He is adapting machine learning techniques to determine how to correspond these textural features to specific tumour characteristics, or to a change that indicates tumour response to treatment.

 

Publications

2024

  1. Saednia K, Tran WT, Sadeghi-Naini A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies Med Phys. 2023 Dec; 50(12): 7852-7864. PMID: 37403567.

2023

  1. Kheirkhah N, Kornecki A, Czarnota GJ, Samani A, Sadeghi-Naini A. Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Phys Med. 2023 Aug;112:102619. PMID: 37343438.
  2. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. Automatic assessment of stereotactic radiation therapy outcome in brain metastasis using longitudinal tumour segmentation on serial MRI. IEEE J. Biomed Health Inform. 2023 Jun;27(6):2681-2692. PMID: 37018589.
  3. Kheirkhah N, Dempsey S, Sadeghi-Naini A, Samani A. A novel tissue mechanics-based method for improving tissue displacement and strain estimation in ultrasound elastography. Med Phy. 2023 Apr;50(4): 2176-2194. PMID: 36398744.
  4. Ferre R, Elst J, Senthilnathan S, Lagree A, Tabbarah A, Lu FI, Sadeghi-Naini A, Tran WT, Curpen B. Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes. Breast Dis. 2023 Mar;42(1):59-66.
  5. Saednia K, Tran WT, Sadeghi-Naini A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies. Medical Physics. 2023; Dec;50(12): 7852-7864. PMID: 37403567
  6. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. Automatic assessment of stereotactic radiation therapy outcome in brain metastasis using longitudinal tumour segmentation on serial MRI. 2023 Jun;27(6): 2681-2692. PMID: 37018589.
  7. Jalalifar A, Soliman A, Sahgal A, Sadeghi-Naini A. A self-attention-guided 3D deep residual network with big transfer to predict local failure in brain metastasis after radiotherapy using multi-channel MRI. IEEE Journal of Translational Engineering in Health and Medicine. 2023; 11: 13-22. PMID: 36478770.

2022

  1. Taleghamar H, Jalalifar SA, Czarnota GJ, Sadeghi-Naini A. Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy Sci Rep. 2022 Feb 10;12(1):2244. PMID: 35145158
  2. Saednia K, Lagree A, Alera MA, Fleshner L, Shiner A, Law E, Law B, Dodington DW, Lu FI, Tran WT, Sadeghi-Naini A.Saednia K, et al. Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Sci Rep. 2022 Jun 11;12(1):9690. doi: 10.1038/s41598-022-13917-4.
  3. Jalalifar SA, Soliman H, Sahgal A, Sadeghi-Naini A. A_Self-Attention-Guided_3D_Deep_Residual_Network_With_Big_Transfer_to_Predict_Local_Failure_in_Brain_Metastasis_After_Radiotherapy_Using_Multi-Channel_MRI IEEE Journal of Translational Engineering in Health and Medicine. 2022 Nov 4;11:13-22. doi: 10.1109/JTEHM.2022.3219625. 2022; 11: 13-22. PMID: 36478770
  4. Jalalifar SA, Soliman H, Sahgal A, Sadeghi-Naini A. A. Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features. Medical Physics. 2022; 49(11): 7167-7178. PMID: 35727568
  5.  Jalalifar, H. Soliman, A. Sahgal, A. Sadeghi-Naini. Impact of tumour segmentation accuracy on efficacy of quantitative MRI biomarkers of radiotherapy outcome in brain metastasis. Cancers. 2022; 14(20): 5133. PMID: 36291917
  6. Saednia K, Tran WT, Sadeghi-Naini A .A cascaded deep learning framework for segmentation of nuclei in digital histology images. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). July 2022; Glasgow, Scotland, UK. pp. 4764-4767. PMID: 36086360
  7. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. A self-attention-guided 3D deep residual network with big transfer to predict local failure in brain metastasis after radiotherapy using multi-channel MRI. IEEE J Transl Eng Health Med. 2022 Nov 4:11: 13-22. PMID: 36478770.
  8. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. A. Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features. Med Phys. 2022 Nov;49(11): 7167-7178. PMID 35727568.
  9. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. Impact of tumour segmentation accuracy on efficacy of quantitative MRI biomarkers of radiotherapy outcome in brain metastasis. Cancers (Basel). 2022 Oct 20;14(20): 5133. PMID 36297917.

2021

  1. Meti N, Sadeghi-Naini A, Tran WT. Reply to A. Pfob et al. JCO Clin Cancer Inform. 2021 Jun;5:656-657. doi: 10.1200/CCI.21.00059. PMID: 34110932.
  2. Jaberipour M, Soliman H, Sahgal A, Sadeghi-Naini A.Jaberipour M, et al A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning. Sci Rep. 2021 Nov 3;11(1):21620. doi: 10.1038/s41598-021-01024-9.Sci Rep. 2021. PMID: 34732781
  3. Moghadas-Dastjerdi H, Rahman SH, Sannachi L, Wright FC, Gandhi S, Trudeau ME, Sadeghi-Naini A, Czarnota GJ. Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning. Transl Oncol. 2021 Oct;14(10):101183. doi: 10.1016/j.tranon.2021.101183. Epub 2021 Jul 19. PMID: 34293685; PMCID: PMC8319580.
  4. Taleghamar H, Moghadas-Dastjerdi H, Czarnota GJ, Sadeghi-Naini A. Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment. Sci Rep. 2021 Jul 21;11(1):14865. doi: 10.1038/s41598-021-94004-y. PMID: 34290259; PMCID: PMC8295369.
  5. Kolios C, Sannachi L, Dasgupta A, Suraweera H, DiCenzo D, Stanisz G, Sahgal A, Wright F, Look-Hong N, Curpen B, Sadeghi-Naini A, Trudeau M, Gandhi S, Kolios MC, Czarnota GJ. MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Oncotarget. 2021 Jul 6;12(14):1354-1365. doi: 10.18632/oncotarget.28002. PMID: 34262646; PMCID: PMC8274727.
  6. Lagree A, Mohebpour M, Meti N, Saednia K, Lu FI, Slodkowska E, Gandhi S, Rakovitch E, Shenfield A, Sadeghi-Naini A, Tran WT. AA review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Sci Rep. 2021 Apr 13;11(1):8025. doi: 10.1038/s41598-021-87496-1. PMID: 33850222; PMCID: PMC8044238.
  7. Dodington DW, Lagree A, Tabbarah S, Mohebpour M, Sadeghi-Naini A, Tran WT, Lu FI. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res Treat. 2021 Apr;186(2):379-389. doi: 10.1007/s10549-020-06093-4. Epub 2021 Jan 23. PMID: 33486639.
  8. Meti N, Saednia K, Lagree A, Tabbarah S, Mohebpour M, Kiss A, Lu FI, Slodkowska E, Gandhi S, Jerzak KJ, Fleshner L, Law E, Sadeghi-Naini A, Tran WT. Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features. JCO Clin Cancer Inform. 2021 Jan;5:66-80. doi: 10.1200/CCI.20.00078. PMID: 33439725.

2020

  1. Kheirkhah N, Dempsey SCH, Rivaz H, Samani A, Sadeghi-Naini A. A Tissue Mechanics Based Method to Improve Tissue Displacement Estimation in Ultrasound Elastography. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2051-2054. doi: 10.1109/EMBC44109.2020.9175869. PMID: 33018408.
  2. Jaberipour M, Sahgal A, Soliman H, Sadeghi-Naini A. Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1323-1326. doi: 10.1109/EMBC44109.2020.9175746. PMID: 33018232.
  3. Saednia K, Jalalifar A, Ebrahimi S, Sadeghi-Naini A. An Attention-Guided Deep Neural Network for Annotating Abnormalities in Chest X-ray Images: Visualization of Network Decision Basis.  Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1258-1261. doi: 10.1109/EMBC44109.2020.9175378. PMID: 33018216.
  4. Jalalifar A, Soliman H, Ruschin M, Sahgal A, Sadeghi-Naini A. A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1067-1070. doi: 10.1109/EMBC44109.2020.9176263. PMID: 33018170.
  5. Jalalifar A, Soliman H, Sahgal A, Sadeghi-Naini A. A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1063-1066. doi: 10.1109/EMBC44109.2020.9175489. PMID: 33018169.
  6. Sannachi L, Gangeh M, Naini AS, Bhargava P, Jain A, Tran WT, Czarnota GJ. Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners. Ultrasound Med Biol. 2020 May;46(5):1142-1157. doi: 10.1016/j.ultrasmedbio.2020.01.022. Epub 2020 Feb 25. PubMed PMID: 32111456.
  7. Saednia K, Tabbarah S, Lagree A, Wu T, Klein J, Garcia E, Hall M, Chow E, Rakovitch E, Childs C, Sadeghi-Naini A, Tran WT. Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning. Int J Radiat Oncol Biol Phys. 2020 Apr 1;106(5):1071-1083. doi: 10.1016/j.ijrobp.2019.12.032. Epub 2020 Jan 23. PubMed PMID: 31982495.
  8. Moghadas-Dastjerdi H, Sha-E-Tallat HR, Sannachi L, Sadeghi-Naini A, Czarnota GJ. A priori prediction of tumour response to neroadjuvant chemotherapy in breat cancer patients using quantitative CT and machine learning Sci Rep. 2020 Jul 2;10(1):10936. doi: 10.1038/s41598-020-67823-8.PMID: 32616912

2019

  1. Jafari P, Hoover DA, Yaremko BP, Parraga G, Samani A, Sadeghi-Naini A. Incorporating Pathology-Induced Heterogeneities in a Patient-Specific Biomechanical Model of the Lung for Accurate Tumor Motion Estimation. Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:6964-6967. doi: 10.1109/EMBC.2019.8856707. PubMed PMID: 31947441.
  2. Tran WT, Suraweera H, Quaioit K, Cardenas D, Leong KX, Karam I, Poon I, Jang D, Sannachi L, Gangeh M, Tabbarah S, Lagree A, Sadeghi-Naini A, Czarnota GJ. Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer. Future Sci OA. 2019 Nov 26;6(1):FSO433. doi: 10.2144/fsoa-2019-0048. PubMed PMID: 31915534; PubMed Central PMCID: PMC6920736.
  3. Karami E, Soliman H, Ruschin M, Sahgal A, Myrehaug S, Tseng CL, Czarnota GJ, Jabehdar-Maralani P, Chugh B, Lau A, Stanisz GJ, Sadeghi-Naini A. Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis. Sci Rep. 2019 Dec 27;9(1):19830. doi: 10.1038/s41598-019-56185-5. PubMed PMID: 31882597; PubMed Central PMCID: PMC6934477.
  4.  Sannachi L, Gangeh M, Tadayyon H, Gandhi S, Wright FC, Slodkowska E, Curpen B, Sadeghi-Naini A, Tran W, Czarnota GJ. Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models. Transl Oncol. 2019 Oct;12(10):1271-1281. doi: 10.1016/j.tranon.2019.06.004. Epub 2019 Jul 17. PubMed PMID: 31325763; PubMed Central PMCID: PMC6639683.
  5. Fernandes J, Sannachi L, Tran WT, Koven A, Watkins E, Hadizad F, Gandhi S, Wright F, Curpen B, El Kaffas A, Faltyn J, Sadeghi-Naini A, Czarnota G. Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography. Transl Oncol. 2019 Sep;12(9):1177-1184. doi: 10.1016/j.tranon.2019.05.004. Epub 2019 Jun 18. PubMed PMID: 31226518; PubMed Central PMCID: PMC6586920.
  6. Tran WT, Jerzak K, Lu FI, Klein J, Tabbarah S, Lagree A, Wu T, Rosado-Mendez I, Law E, Saednia K, Sadeghi-Naini A. Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. J Med Imaging Radiat Sci. 2019 Dec;50(4S2):S32-S41. doi: 10.1016/j.jmir.2019.07.010. Epub 2019 Aug 22. PubMed PMID: 31447230.
  7. Karami E, Ruschin M, Soliman H, Sahgal A, Stanisz GJ, Sadeghi-Naini A. An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis. Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:1022-1025. doi: 10.1109/EMBC.2019.8856558. PubMed PMID: 31946067
  8. Karami E, Jalalifar A, Ruschin M, Soliman H, Sahgal A, Stanisz GJ, Sadeghi-Naini A. An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy(.). Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:463-466. doi: 10.1109/EMBC.2019.8856858. PubMed PMID: 31945938.
  9.  Karami E, Soliman H, Ruschin M, Sahgal A, Myrehaug S, Tseng CL, Czarnota GJ, Jabehdar-Maralani P, Chugh B, Lau A, Stanisz GJ, Sadeghi-Naini A. Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis. Sci Rep. 2019 Dec 27;9(1):19830. doi: 10.1038/s41598-019-56185-5. PubMed PMID: 31882597; PubMed Central PMCID: PMC6934477.

2018

  1.  Sannachi L, Gangeh M, Tadayyon H, Sadeghi-Naini A, Gandhi S, Wright FC, Slodkowska E, Curpen B, Tran W, Czarnota GJ. Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features. PLoS One. 2018 Jan 3;13(1):e0189634. doi: 10.1371/journal.pone.0189634. eCollection 2018. PubMed PMID: 29298305; PubMed Central PMCID: PMC5751990.

2017

  1. Sadeghi-Naini A, Sannachi L, Tadayyon H, Tran WT, Slodkowska E, Trudeau M, Gandhi S, Pritchard K, Kolios MC, Czarnota GJ. Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities. Sci Rep. 2017 Sep 4;7(1):10352. doi: 10.1038/s41598-017-09678-0. PubMed PMID: 28871171; PubMed Central PMCID: PMC5583340
  2. Sadeghi-Naini A, Suraweera H, Tran WT, Hadizad F, Bruni G, Rastegar RF, Curpen B, Czarnota GJ. Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps. Sci Rep. 2017 Oct 20;7(1):13638. doi: 10.1038/s41598-017-13977-x. PubMed PMID: 29057899; PubMed Central PMCID: PMC5651882.
  3. Mousavi SR, Rivaz H, Czarnota GJ, Samani A, Sadeghi-Naini A. Ultrasound Elastography of the Prostate Using an Unconstrained Modulus Reconstruction Technique: A Pilot Clinical Study. Transl Oncol. 2017 Oct;10(5):744-751. doi: 10.1016/j.tranon.2017.06.006. Epub 2017 Jul 20. Review. PubMed PMID: 28735201; PubMed Central PMCID: PMC5522957.
  4. Tadayyon H, Sannachi L, Gangeh M, Sadeghi-Naini A, Tran W, Trudeau ME, Pritchard K, Ghandi S, Verma S, Czarnota GJ. Correction: Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach. Oncotarget. 2017 May 23;8(21):35481. doi: 10.18632/oncotarget.18068. PubMed PMID: 28545221; PubMed Central PMCID: PMC5471072.
  5. Tran WT, Gangeh MJ, Sannachi L, Chin L, Watkins E, Bruni SG, Rastegar RF, Curpen B, Trudeau M, Gandhi S, Yaffe M, Slodkowska E, Childs C, Sadeghi-Naini A, Czarnota GJ. Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis. Br J Cancer. 2017 May 9;116(10):1329-1339. doi: 10.1038/bjc.2017.97. Epub 2017 Apr 18. PubMed PMID: 28419079; PubMed Central PMCID: PMC5482739.
  6. Tadayyon H, Sannachi L, Gangeh MJ, Kim C, Ghandi S, Trudeau M, Pritchard K, Tran WT, Slodkowska E, Sadeghi-Naini A, Czarnota GJ. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound. Sci Rep. 2017 Apr 12;7:45733. doi: 10.1038/srep45733. PubMed PMID: 28401902; PubMed Central PMCID: PMC5388850.

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