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.
ABSTRACT
Low-cost, rapid and accurate detection technologies are key requisites to cope with the growing global cancer challenges. The need is particularly pronounced in resource-limited settings where treatment opportunities are often missed due to the absence of timely diagnoses. We herein describe a Holographic Assessment of Lymphoma Tissue (HALT) system that adopts a smartphone as the basis for molecular cancer diagnostics. The system detects malignant lymphoma cells labeled with marker-specific microbeads that produce unique holographic signatures. Importantly, we optimized HALT to detect lymphomas in fine-needle aspirates from superficial lymph nodes, procedures that align with the minimally invasive biopsy needs of resource-constrained regions. We equipped the platform to directly address the practical needs of employing novel technologies for "real world" use. The HALT assay generated readouts in <1.5 h and demonstrated good agreement with standard cytology and surgical pathology.
Subject(s)
Holography/methods , Lymphoma/diagnosis , Molecular Diagnostic Techniques/methods , Optical Imaging/methods , Smartphone , Staining and Labeling/methods , Biopsy, Fine-Needle , Lymph Nodes/pathology , Lymphoma/pathology , Microspheres , Time FactorsABSTRACT
The widespread distribution of smartphones, with their integrated sensors and communication capabilities, makes them an ideal platform for point-of-care (POC) diagnosis, especially in resource-limited settings. Molecular diagnostics, however, have been difficult to implement in smartphones. We herein report a diffraction-based approach that enables molecular and cellular diagnostics. The D3 (digital diffraction diagnosis) system uses microbeads to generate unique diffraction patterns which can be acquired by smartphones and processed by a remote server. We applied the D3 platform to screen for precancerous or cancerous cells in cervical specimens and to detect human papillomavirus (HPV) DNA. The D3 assay generated readouts within 45 min and showed excellent agreement with gold-standard pathology or HPV testing, respectively. This approach could have favorable global health applications where medical access is limited or when pathology bottlenecks challenge prompt diagnostic readouts.
Subject(s)
Cell Phone , Human Papillomavirus DNA Tests/methods , Papillomavirus Infections/diagnosis , Precancerous Conditions/diagnosis , Uterine Cervical Neoplasms/diagnosis , Alphapapillomavirus/genetics , Alphapapillomavirus/physiology , Cost-Benefit Analysis , Female , Host-Pathogen Interactions , Humans , Image Processing, Computer-Assisted/economics , Image Processing, Computer-Assisted/instrumentation , Image Processing, Computer-Assisted/methods , Papillomavirus Infections/virology , Precancerous Conditions/virology , Reproducibility of Results , Sensitivity and Specificity , Telemedicine/economics , Telemedicine/instrumentation , Telemedicine/methods , Time Factors , Uterine Cervical Neoplasms/virologyABSTRACT
Current intraoperative imaging systems are typically not able to provide 'sharp' images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix.
Subject(s)
Image Enhancement/methods , Optical Imaging/methods , Signal-To-Noise Ratio , Surgery, Computer-Assisted/methods , Algorithms , Animals , Intraoperative Period , Male , Mice , Phantoms, Imaging , Urogenital System/surgeryABSTRACT
Fusion imaging of radionuclide-based molecular (PET) and structural data [x-ray computed tomography (CT)] has been firmly established. Here we show that optical measurements [fluorescence-mediated tomography (FMT)] show exquisite congruence to radionuclide measurements and that information can be seamlessly integrated and visualized. Using biocompatible nanoparticles as a generic platform (containing a (18)F isotope and a far red fluorochrome), we show good correlations between FMT and PET in probe concentration (r(2) > 0.99) and spatial signal distribution (r(2) > 0.85). Using a mouse model of cancer and different imaging probes to measure tumoral proteases, macrophage content and integrin expression simultaneously, we demonstrate the distinct tumoral locations of probes in multiple channels in vivo. The findings also suggest that FMT can serve as a surrogate modality for the screening and development of radionuclide-based imaging agents.
