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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

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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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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