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To define predictors of survival time in late human immunodeficiency virus type 1 (HIV-1) disease, long- and short-duration survivors were studied after their CD4+ T cells fell to

Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers are urgently needed to enable individualized treatment thus improving patient outcome. We adapted the Parametric Response Map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole lung CT scans of 194 COPD individuals acquired at inspiration and expiration from the COPDGene Study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype, while providing detailed spatial information of disease distribution and location. PRMs ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients complementing standard clinical techniques.

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Assessment of radiation and chemotherapy efficacy for brain cancer patients is traditionally accomplished by measuring changes in tumor size several months after therapy has been administered. The ability to use noninvasive imaging during the early stages of fractionated therapy to determine whether a particular treatment will be effective would provide an opportunity to optimize individual patient management and avoid unnecessary systemic toxicity, expense, and treatment delays. We investigated whether changes in the Brownian motion of water within tumor tissue as quantified by using diffusion MRI could be used as a biomarker for early prediction of treatment response in brain cancer patients. Twenty brain tumor patients were examined by standard and diffusion MRI before initiation of treatment. Additional images were acquired 3 weeks after initiation of chemo-and͞or radiotherapy. Images were coregistered to pretreatment scans, and changes in tumor water diffusion values were calculated and displayed as a functional diffusion map (fDM) for correlation with clinical response. Of the 20 patients imaged during the course of therapy, 6 were classified as having a partial response, 6 as stable disease, and 8 as progressive disease. The fDMs were found to predict patient response at 3 weeks from the start of treatment, revealing that early changes in tumor diffusion values could be used as a prognostic indicator of subsequent volumetric tumor response. Overall, fDM analysis provided an early biomarker for predicting treatment response in brain tumor patients. diffusion MRI ͉ therapeutic response

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method – stimulated Raman histology (SRH) – which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

Purpose: Development of new therapeutic drug delivery systems is an area of significant research interest. The ability to directly target a therapeutic agent to a tumor site would minimize systemic drug exposure, thus providing the potential for increasing the therapeutic index. Experimental Design: Photodynamic therapy (PDT) involves the uptake of a sensitizer by the cancer cells followed by photoirradiation to activate the sensitizer. PDTusing Photofrin has certain disadvantages that include prolonged cutaneous photosensitization. Delivery of nanoparticles encapsulated with photodynamic agent specifically to a tumor site could potentially overcome the drawbacks of systemic therapy. In this study, we have developed a multifunctional polymeric nanoparticle consisting of a surface-localized tumor vasculature targeting F3 peptide and encapsulated PDTand imaging agents. Results:The nanoparticles specifically bound to the surface of MDA-435 cells in vitro and were internalized conferring photosensitivity to the cells. Significant magnetic resonance imaging contrast enhancement was achieved in i.c. rat 9L gliomas following i.v. nanoparticle administration. Serial magnetic resonance imaging was used for determination of pharmacokinetics and distribution of nanoparticles within the tumor. Treatment of glioma-bearing rats with targeted nanoparticles followed by PDT showed a significant improvement in survival rate when compared with animals who received PDT after administration of nontargeted nanoparticles or systemic Photofrin. Conclusions:This study reveals the versatility and efficacy of the multifunctional nanoparticle for the targeted detection and treatment of cancer.Photodynamic therapy (PDT) relies on the selective uptake of a photosensitizing molecule in a tumor relative to the surrounding normal parenchyma followed by exposure to the appropriate wavelength of light to activate the photosensitizer (1). When activated by light irradiation, the photosensitizer interacts with molecular oxygen to produce a cytotoxic, shortlived species known as singlet oxygen. PDT elicits both apoptotic and necrotic responses within treated tumors and produces microvascular injury leading to inflammation and hypoxia. Photofrin, a complex mixture of porphyrin oligomers, is one of the most efficient photosensitizers approved for PDT of cancer (2). However, Photofrin can cause prolonged skin photosensitization, where patients are required to avoid direct exposure to sunlight for a period of 4 to 6 weeks. Current strategies under development include attempts to direct the photosensitizing agent to the tumor by active targeting approaches, such as peptide conjugates and antibodies (3 -7), incorporation within liposomes (8, 9), and encapsulation within polymeric nanoparticles (10 -14) in an attempt to deliver higher local concentrations at the therapeutic site.A recent report of a sub-100 nm dynamic nanoparticle platform composed of polyacrylamide, which could be loaded with a photoactivatable agent (methylene blue) for the spe...

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin-staining of processed tissue is time-, resource-, and labor-intensive 2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed pathology workforce 4. Here, we report a parallel workflow that combines stimulated Raman histology (SRH) 5-7 , a label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 seconds, an order of magnitude faster than conventional techniques (e.g., 20-30 minutes) 2. In a multicenter, prospective clinical trial (n = 278) we demonstrated that CNN-based diagnosis of SRH images was non-inferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs. 93.9%). Our CNN learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. Additionally, we implemented a semantic segmentation method to identify tumor infiltrated, diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complimentary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.