Ahmad A Hammoudi

Weill Cornell Medical College, New York City, New York, United States

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Publications (5)11.83 Total impact

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    ABSTRACT: Aimed at bridging imaging technology development with cancer diagnosis, this paper first presents the prevailing challenges of lung cancer detection and diagnosis, with an emphasis on imaging techniques. It then elaborates on the working principle of coherent anti-Stokes Raman scattering microscopy, along with a description of pathologic applications to show the effectiveness and potential of this novel technology for lung cancer diagnosis. As a nonlinear optical technique probing intrinsic molecular vibrations, coherent anti-Stokes Raman scattering microscopy offers an unparalleled, label-free strategy for clinical cancer diagnosis and allows differential diagnosis of fresh specimens based on cell morphology information and patterns, without any histology staining. This powerful feature promises a higher biopsy yield for early cancer detection by incorporating a real-time imaging feed with a biopsy needle. In addition, molecularly targeted therapies would also benefit from early access to surgical specimen with high accuracy but minimum tissue consumption, therefore potentially saving specimens for follow-up diagnostic tests. Finally, we also introduce the potential of a coherent anti-Stokes Raman scattering-based endoscopy system to support intraoperative applications at the cellular level.
    Archives of pathology & laboratory medicine 12/2012; 136(12):1502-1510. · 2.78 Impact Factor
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    ABSTRACT: The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type diagnosis is performed using small biopsies or cytology specimens that are often insufficient for molecular testing after morphologic analysis. Thus, the ability to rapidly recognize different cancer cell types, with minimal tissue consumption, would accelerate diagnosis and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular vibrational imaging framework enabling three-dimensional (3-D) image acquisition and quantitative analysis of cellular structures for identification of NSCLC cell types. This diagnostic imaging system employs superpixel-based 3-D nuclear segmentation for extracting such disease-related features as nuclear shape, volume, and cell-cell distance. These features are used to characterize cancer cell types using machine learning. Using fresh unstained tissue samples derived from cell lines grown in a mouse model, the platform showed greater than 97% accuracy for diagnosis of NSCLC cell types within a few minutes. As an adjunct to subsequent histology tests, our novel system would allow fast delineation of cancer cell types with minimum tissue consumption, potentially facilitating on-the-spot diagnosis, while preserving specimens for additional tests. Furthermore, 3-D measurements of cellular structure permit evaluation closer to the native state of cells, creating an alternative to traditional 2-D histology specimen evaluation, potentially increasing accuracy in diagnosing cell type of lung carcinomas.
    Journal of Biomedical Optics 06/2012; 17(6):066017. · 2.75 Impact Factor
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    ABSTRACT: We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.
    Journal of Biomedical Optics 09/2011; 16(9):096004. · 2.75 Impact Factor
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    ABSTRACT: We demonstrated an optical fiber delivered coherent anti-Stokes Raman scattering (CARS) microscopy imaging system with a polarization-based mechanism for suppression of four-wave mixing (FWM) signals in delivery fiber. Polarization maintaining fibers (PMF) were used as the delivery fiber to ensure stability of the state of polarization (SOP) of lasers. The pump and Stokes waves were coupled into PMFs at orthogonal SOPs along the slow and fast axes of PMFs, respectively, resulting in a significant reduction of FWM signals generated in the fiber. At the output end of PMFs, a dual-wavelength waveplate was used to realign the SOPs of the two waves into identical SOPs prior to their entrance into the CARS microscope. Therefore, it allows the pump and Stokes waves with identical SOPs to excite samples at highest excitation efficiency. Our experimental results showed that this polarization-based FWM-suppressing mechanism can dramatically reduce FWM signals generated in PMFs up to approximately 99%. Meanwhile, the PMF-delivered CARS microscopy system with this mechanism can still produce high-quality CARS images. Consequently, our PMF-delivered CARS microscopy imaging system with the polarization-based FWM-suppressing mechanism potentially offers a new strategy for building fiber-based CARS endoscopes with effective suppression of FWM background noises.
    Optics Express 04/2011; 19(9):7960-70. · 3.55 Impact Factor
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    ABSTRACT: Coherent anti-Stokes Raman scattering (CARS) microscopy is attracting major scientific attention because its high-resolution, label-free properties have great potential for real time cancer diagnosis during an image-guided-therapy process. In this study, we develop a nuclear segmentation technique which is essential for the automated analysis of CARS images in differential diagnosis of lung cancer subtypes. Thus far, no existing automated approaches could effectively segment CARS images due to their low signal-to-noise ratio (SNR) and uneven background. Naturally, manual delineation of cellular structures is time-consuming, subject to individual bias, and restricts the ability to process large datasets. Herein we propose a fully automated nuclear segmentation strategy by coupling superpixel context information and an artificial neural network (ANN), which is, to the best of our knowledge, the first automated nuclear segmentation approach for CARS images. The superpixel technique for local clustering divides an image into small patches by integrating the local intensity and position information. It can accurately separate nuclear pixels even when they possess subtly lower contrast with the background. The resulting patches either correspond to cell nuclei or background. To separate cell nuclei patches from background ones, we introduce the rayburst shape descriptors, and define a superpixel context index that combines information from a given superpixel and it's immediate neighbors, some of which are background superpixels with higher intensity. Finally we train an ANN to identify the nuclear superpixels from those corresponding to background. Experimental validation on three subtypes of lung cancers demonstrates that the proposed approach is fast, stable, and accurate for segmentation of CARS images, the first step in the clinical use of CARS for differential cancer analysis.
    Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings; 01/2011

Publication Stats

13 Citations
11.83 Total Impact Points

Institutions

  • 2011–2012
    • Weill Cornell Medical College
      New York City, New York, United States
    • Rice University
      • Department of Electrical and Computer Engineering
      Houston, TX, United States