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-income and middle-income countries. Limited pathology resources, high healthcare costs and large-case loads call for the development of advanced standalone 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 for lymphoma. Automated analysis of human samples with the portable device should allow for the accurate classification of patients with benign and malignant adenopathy.

Keywords: adenopathy; artificial intelligence; cancer; deep learning; diagnostics; holography; low-middle income countries; lymphoma.

Fig. 1. Stand-alone CEM system

a, A photograph of the CEM device. The CEM device consists of an imaging component (an LED and a CMOS image sensor), microcomputer (Raspberry Pi 3 with wireless and bluetooth unit), 7-inch touch screen and sample tray. See Figure S1 for inner components. The case was fabricated by 3D printing. The overall size is 205 mm (L) x 120 mm (W) x 175 mm (H). b, CEM assay. Cells were labeled by antibodies and introduced into a disposable sample cartridge. B-cells were specifically captured on the bottom glass substrate and subsequently stained for kappa light chain, lambda light chain or Ki67. Hologram patterns of stained and unstained B-cells were imaged by the CEM device. c, A deep learning algorithm based on a convolutional neural network identified B-cells directly from holograms. Scale bars: 200 μm.

Fig. 2. B-cell detection using the deep learning algorithm

a, The deep learning algorithm identified B-cells and their locations. The images of found cells were further processed to measure the nuclear size and marker expression, depicted here by different-sized circles and their colors. The entire field of view (FOV) was ~ 25 mm², about 100 times bigger than the FOV of conventional bright-field microscope with a 20× objective. b, Comparison between bright-field microscope and CEM images. Stained color intensity correlated with marker expression. Scale bars: 50 μm. c, CEM signal comparison between the original image reconstruction method and the new deep learning algorithm. The error bars represent mean ± s.d. obtained from ~800 individual cells. d, The deep learning network was trained by >5,000 cellular and non-cellular hologram patterns and showed 99% accuracy in an independent test set. e, Using the deep learning algorithm, overall calculation time was 5 times shorter, and the entire image processing can be completed in 5 minutes. When using a central server with a graphic processing unit, the computation time is significantly improved and can be as short as 12 seconds.

Fig. 3. B-cell capture and size measurement

a, B-cell capture efficiency was tested using two B-cell lymphoma (Daudi and DB) and T-cell leukemia (Jurkat) cell lines. For Daudi and DB, the device showed > 90% capture efficiency, while non-specific T-cell binding was < 5%. The bars represent mean ± s.d. from quadruplicate measurements. b, Correlation between expected and measured captured cell counts for Daudi and Jurkat cell mixtures with varying cell proportions (Pearson correlation coefficient r = 0.97; P = 0.0013). c, Size calibration with size-standard microspheres (3, 6, 8, and 16 μm) showing a linear correlation over the range tested (Pearson correlation coefficient r = 0.998; P = 0.0020). The dots represent mean ± s.d. from more than 10 measurements.

Fig. 4. Assay validation

a, Different chromogenic substrates were tested to reveal Ki67 in lymphoma cell lines. Among those tested, the ImmPACT VIP substrate showed the greatest contrast between stained and unstained control samples. b, The CEM measured marker expressions of kappa light chain, lambda light chain and Ki67 for three different cell lines (Daudi, DB and Jurkat) and compared to marker expressions measured by gold-standard flow cytometry. Note the congruency. c, Ki67 Antibodies were lyophilized and stored at different temperatures. After rehydration, the antibodies and the CEM assay showed good reproducibility with a standard variation of < 5%. At least 200 individual cells were analyzed for each condition and the data are displayed as mean ± s.d. Dashed lines indicate ± 5% of the mean value.

Fig. 5. CEM readouts for a single clinical sample (DLBCL example)

Each clinical sample obtained by fine-needle aspirates is tested for a number of parameters including total cell count, B-cell count (positive for CD19/20), B-cell counts positive for kappa light chain, lambda light chain and Ki67 and nuclear size. a, A representative example shows high lambda light chain, and Ki67 counts. b, Corresponding histogram graphs. An average of 77 B-cells were analyzed (59 – 98 B-cells) in each channel. Detailed numbers are summarized in Table S1. Scale bars: 250 μm.

Fig. 6. Lymphoma diagnosis for 40 patients enrolled in a prospective trial

a, The CEM diagnostic algorithm for detecting B-cell lymphoma. b, 2D scatter plot of B-cell population vs clonality for lymphoma detection. Each dot represents a patient. c, Comparison of CEM diagnosis and flow cytometry. Green cells indicate correct diagnosis, blue cells are false positives, red means false negatives. Grey are non-diagnostic due to insufficient number of cells for diagnosis. DLBCL, diffuse large B-cell lymphoma; MCL, mantle cell lymphoma; FL, follicular lymphoma; SBCL, small B-cell lymphoma; DF, disease free.

Fig. 7. Identifying high-risk, aggressive cases

a–d, B-cell counts (a), clonality (b), Ki67 level (c) and percentile of large cells (> 15 μm) (d) between aggressive (n = 6) and low-grade lymphoma cases (n = 15). The bars represent mean ± s.d.