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Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning

Nature Biomedical Engineering volume 2, pages 666–674 (2018)Cite this article

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Abstract

The identification of patients with aggressive cancer who require immediate therapy is a health challenge in low- and middle-income countries. Limited pathology resources, high healthcare costs and large caseloads call for the development of advanced stand-alone diagnostics. Here, we report and validate an automated, low-cost point-of-care device for the molecular diagnosis of aggressive lymphomas. The device uses contrast-enhanced microholography and a deep learning algorithm to directly analyse percutaneously obtained fine-needle aspirates. We show the feasibility and high accuracy of the device in cells, as well as the prospective validation of the results in 40 patients clinically referred for image-guided aspiration of nodal mass lesions suspicious of lymphoma. Automated analysis of human samples with the portable device should allow for the accurate classification of patients with benign and malignant adenopathy.

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Fig. 1: Stand-alone CEM system.
Fig. 2: B cell detection using the deep learning algorithm.
Fig. 3: B cell capture and size measurement.
Fig. 4: Assay validation.
Fig. 5: CEM readouts for a single clinical (DLBCL) sample.
Fig. 6: Lymphoma diagnosis for 40 patients enroled in a prospective trial.
Fig. 7: Identifying high-risk, aggressive cases.

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Acknowledgements

The authors thank J. Min for assay optimization, K. Joyes for editing the manuscript, and all members of MGH’s Division of Interventional Radiology, Department of Pathology and Cancer Center who contributed to patient care. This work was supported in part by 5UH2CA202637 (to R.W. and B.Chabner), 4R00CA201248 (to H.I.) and a grant from the V-Foundation for Cancer Research (to R.W. and C.M.C.). H.L. was supported in part by R21-CA205322, R01-HL113156 and the MGH scholar fund. A.K. has been supported by the Mac Erlaine Scholarship from the Academic Radiology Research Trust, St. Vincents Radiology Group, Dublin, Ireland, and also by the Higher Degree Bursary from the Faculty of Radiologists at the Royal College of Surgeons in Ireland.

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Author notes

  1. These authors contributed equally: Hyungsoon Im, Divya Pathania.

  2. These authors jointly supervised this work: Cesar M. Castro, Ralph Weissleder.

Authors and Affiliations

  1. Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA

    Hyungsoon Im, Divya Pathania, Philip J. McFarland, Ismail Degani, Matthew Allen, Benjamin Coble, Aoife Kilcoyne, Seonki Hong, Lucas Rohrer, Lioubov Fexon, Misha Pivovarov, Hakho Lee, Cesar M. Castro & Ralph Weissleder

  2. Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

    Hyungsoon Im, Aoife Kilcoyne, Misha Pivovarov, Hakho Lee & Ralph Weissleder

  3. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Aliyah R. Sohani

  4. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ismail Degani

  5. Department of Engineering and Management, Massachusetts Institute of Technology, Cambridge, MA, USA

    Benjamin Coble

  6. Department of Health Sciences, Northeastern University, Boston, MA, USA

    Lucas Rohrer

  7. Massachusetts General Hospital Cancer Center, Boston, MA, USA

    Jeremy S. Abramson, Bruce Chabner & Cesar M. Castro

  8. Botswana Harvard AIDS Institute, Gaborone, Botswana

    Scott Dryden-Peterson

  9. Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA

    Scott Dryden-Peterson

  10. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Scott Dryden-Peterson

  11. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Ralph Weissleder

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  1. Hyungsoon Im
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Contributions

H.I., H.L., C.M.C. and R.W. designed the study. H.I., I.D. and B.Coble designed and fabricated the CEM device. D.P., P.J.M. and H.I. designed and optimized the experiments. H.I., D.P., P.J.M., S.H. and L.R. processed and analysed the samples. H.I., M.A., H.L., L.F. and M.P. established the computational algorithms. R.W., A.K., A.R.S., J.S.A., B.Chabner and C.M.C conducted the clinical trials. All authors reviewed the data. H.I. and R.W. wrote the paper, which was edited by all authors.

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Correspondence to Cesar M. Castro or Ralph Weissleder.

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Im, H., Pathania, D., McFarland, P.J. et al. Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nat Biomed Eng 2, 666–674 (2018). https://doi.org/10.1038/s41551-018-0265-3

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