MacAulay, Calum

MacAulay, Calum


Academic Rank(s): Clinical Associate Professor, Pathology and Laboratory Medicine, UBC | Head of Integrative Oncology Distinguished Scientist

Research and Scholarly Interests: Cancer, Molecular Pathology and Cell Biology

Clinical Interests:

Short Bio

Born in Halifax, Dr. MacAulay has lived and worked in Vancouver for over 30 years. His formal training includes a BSc in Engineering Physics (1982) from Dalhousie University, an MSc in Physics (1985) from Dalhousie University, and a PhD in Physics (1989) from the University of British Columbia. Dr. MacAulay is also currently an Associate Member of the Department of Physics and Astronomy and a Clinical Associate Professor in the Department of Pathology at the University of British Columbia. He has been awarded i) the BC Lung Association Scholar (1990-1995), ii) the Friesen-Rygiel Prize for outstanding Canadian academic discovery leading to uniquely positioned commercialization opportunities (1999), and iii) the Young Innovator Award, BC Science and Technology Award, Science Council of BC (1999).



Academic Background

  • BSc, Engineering Physics, Dalhousie University, 1982
  • MSc, Physics, Dalhousie University, 1987
  • PhD, Physics, UBC, 1989

Awards and Recognition


Selected Publications

    1. MacAulay C, Keyes M, Hayes M, Lo A, Wang G, Guillaud M, Gleave M, Fazli L, Korbelik J, Collins C, Keyes S, Palcic B. Quantification of large scale DNA organization for predicting prostate cancer recurrence. Cytometry A. 2017 Dec;91(12):1164-1174

    2. Abouei E, Lee AMD, Pahlevaninezhad H, Hohert G, Cua M, Lane P, Lam S, MacAulay C. Correction of motion artifacts in endoscopic optical coherence tomography and autogluorescence images based on azimuthal en face image registration. J Biomed Opt. 2018 Janl 23(1):1-13

    3. Guillaud M, MacAulay CE, Berean KW, Bullock M, GuggisbergK, Klieb H, Puttagunta L, Penner C, Kwan K, Rosin MP, Poh CF. Using quantitative tissue phenotype to assess the margins of surgical samples from a pan-Canadian surgery study. Head Neck. 2018 Jun;40(6):1263-1270.

    4. Harlow M, MacAulay C, Lane P, Lee AMD. Dual-beam manually actuated distortion-corrected imaging (DMDI): two dimensional scanning with a single-axis galvanometer. Opt Express. 2018 Jul 9;26(14):18758-18772.

    5. Zhang L, Lubpairee T, Laronde DM, Guillaud M, MacAulay CE, Rosin MP. Histological clonal change – A feature for dysplasia diagnosis. Arch Pathol Clin Res. 2018; 2: 020-028.

    6. Lee AMD, MacAulay C, Lane P. Depth-multiplexed optical coherence tomography dual-beam manually-actuated distortion-corrected imaging (DMDI) with a micromotor catheter. Biomed Opt Express. 2018 Oct 23;9(11):5678-5690.

    7. Enfield K, Martin S, Marshall E, S Kung C, Gallagher P, Milne K, Chen Z, Nelson B, Lam S; English J; MacAulay C, Lam W, Guillaud M. Hyperspectral Cell Sociology Reveals Spatial Tumor-Immune Cell Interactions Associated with Lung Cancer Recurrence. J Immunother Cancer. 2019 Jan 16;7(1):13.doi: 10.1186/s40425-018-0488-6.

    8. Enfield KSS, Marshall EA, Anderson C, Ng K, Rahmati S, Xu Z, Fuller M, Milne K, Lu D, Shi R, Rowbotham D, Becker-Santos D, Johnson F, English J, MacAulay C, Lam S, Lockwood W, Chari R, Karson A, Jurisica I, Lam WL. Epithelial tumor suppressor ELF3 is a lineage-specific amplified oncogene in lung adenocarcinoma. Nature Communications. 2019;10(1):5438. Published 2019 Nov 28. doi:10.1038/s41467-019-13295-y

    9. Buenconsejo AL, Hohert G, Manning M, Abouei E, Tingley R, Janzen I, McAlpine J, Miller D, Lee A, Lane P, MacAulay C. “Submillimeter diameter rotary-pullback fiber-optic endoscope for narrowband red-green-blue reflectance, optical coherence tomography, and autofluorescence in vivo imaging,” J Biomed Opt. 2019;25(3):1–7. doi:10.1117/1.JBO.25.3.032005

    10. Hwang H, Follen M, Guillaud M, Scheurer M, MacAulay C, Staerkel G, van Niekerk D, Yamal J-M. Cervical cytology reproducibility and associated clinical and demographic factors. Diagn Cytopathol. 2020;48(1):35–42. doi:10.1002/dc.24325

    11. Naso JR, Povshedna T, Wang G, Banyi N, MacAulay C, Ionescu DN, Zhou C. Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software. Pathology and Oncology Research. 2021. 27: 20.


Research Interest

  • Automated image analysis of cell preparations
  • In vivo tissue imaging
  • Quantitative microscopy with digital micromirror devices
  • Lung cancer chemoprevention
  • Bioinformatics

My research has concentrated on the early detection and treatment of cancer using quantitative imaging tools in microscopy, photon tissue interactions and understanding the genetic and molecular events driving the neoplastic process. I and the teams I have worked with have had a strong drive to translate our work into actual clinical tools and processes.

The teams I am part of have developed, demonstrated, and stewarded all the way into clinical utility an automated image analysis system for quantitatively stained cytology samples (lung, cervix, oral, prostate) which can be used to screen for early pre-invasive lesions as well as cancer and cancer progression risk. I have led the development of the features which classify the cells and samples, the development of some of the classifiers themselves as well as the algorithms which perform the image segmentation phase of the analysis and assisted in the stewardship into the clinical setting. This technology has been licensed and commercialized by a number of companies, it has gotten health Canada approval for screening for lung cancer in sputum samples and for screening oral cancer oral dysplasia in cytological brushings from the oral cavity. In addition, we have licensed the technology and software to a large microscope manufacturer in China where they are using it with the appropriate regulatory approvals, to screen for cervical cancer in China. They currently use this technology to screen 4 million women per year.

Recently we developed a hyperspectral condenser which enables high speed 7 colour imaging for spectral unmixing of highly multi-labeled IHC slides and it has also been transferred to industry. My group is also using novel deep learning networks to solve the segmentation of individual nuclei within heavily overlapping clusters of nuclei in histopathology images as well as developing tools for high dimensional tissue analysis on the data collected using our hyperspectral condenser and spectral unmixing at the individual cell level in histological samples.

We have developed novel sub mm imaging devices using light for the investigation of suspect areas of the lung periphery, and continue to do so, and currently are trying to add the ability to biopsy tissue under this optical imaging to improve the sampling of the tissue of clinical interest.

Currently our team is analyzing LDCT scans from more than 2500 subjects at elevated risk of developing lung cancer and are using radionics and deep learning to predict which LDCT detected nodules are likely to be cancer and which are not.

For my entire career I have had a strong driving focus on addressing early detection and treatment of lung cancer. As one reads the literature, attends conferences and in discussion with other researchers I find there are many interesting intriguing avenues of investigation. Personally, I sort through these with the lens of trying to identify which could potentially improve the identification, detection and delineation of early cancers with the intent of improving their removal/treatment and have the potential to get to clinical utility over a 5-7 year time frame.

Clinical Service

Current Projects In My Lab Include


Teaching Interest

Pathology 305