ChemImage’s LightGuard sensor incorporates short-wave infrared (SWIR) hyperspectral imaging (HSI) to help address the problem of standoff detection of IEDs. The sensor technology exploits uncooled focal plane array technology in combination with novel liquid crystal optics and real-time machine vision processing methods to provide high definition imagery of IED threats in highly cluttered environments. LightGuard technology has been tested successfully at speeds up to 15 mph and at distances up to 200 m for the detection of military and homemade explosives, IED command wires, EFP camouflage and disturbed earth associated with IED emplacements. A summary of validated test results will be discussed which show LightGuard technology having unprecedented detection capability, including high sensitivity and low false alarm rates which enhance vehicle survivability.

Introduction In HF patients, clinical congestion assessment is a cardinal sign in diagnosing and adapting therapeutic intensity but is subjective and considered insensitive. Objective non-invasive assessment of congestion would provide considerable support for early HF treatment and prevention of hospitalization for acute decompensation. Methods A novel optical Molecular Chemical Imaging (MCI) device was used to collect reflected light from the skin and quantify the sub-cutaneous water content based on reflected wave-length modification using a short wave infrared (SWIR) device. To provide a highly acurate reference method we compared SWIR to MRI assessment of tissue water content using a 6-Point Dixon method (Liver Lab, Siemens Healthineers, Erlangen, Germany) in 12 hospitalized HF patients. The calve imaging region locations were co-located for SWIR and MRI using the patella as reference point and tests were performed within 4 hours. 4 independent Water Fraction (WF) values were measured within the calve area for each patient and quantified as a percentage. Accuracy was calculated by comparing predicted value of SWIR to the value measured by MRI within an MRI error tolerance (0.11±0.02). Results 12 subjects (age 72±12.8, 6 women) presented with no edema in 5 and mild edema in 7. Five of the 48 (10%) limb measurements were excluded due to inadequate imaging. In 39 of 43 coupled WF measurements the SWIR results were within the MRI error range (accuracy 90.7%). Correlation between WF values obtained by SWIR and MRI was excellent (R2 0.8), as shown in Figure 1. Despite the frequent absence of clinical edema, quantification of WF by MRI or SWIR demonstrated a broad range of congestion levels, unsuspected clinically. Conclusion In patients hospitalized for HF, SWIR assessment of subcutaneous water fraction was feasible and highly correlated with measurement by MRI. There was a broader range of congestion than was appreciated clinically. A larger experience is warranted to expand the accuracy assessment with all types of skin and broader levels of clinical edema. This early experience suggests that optical non-invasive congestion assessment has the potential to improve objective evaluation of congestion in patients with HF clinical management, an essential element in improving clinical outcomes.

Background Heart failure (HF) is a healthcare problem burdening patients, care teams, and health care systems. A relentless, downward spiral of decompensation, hospital admission, and functional deterioration is challenging to break, partly because of the lack of an objective, inexpensive tool to aid the clinician’s physical examination. We studied performance of a shortwave infrared (SWIR) molecular chemical imaging (MCI) tool to measure relative tissue congestion (TC) in HF patients’ shins, compared to MRI Dixon sequence TC measurements as ground truth.Methods Forty-seven (47) subjects underwent paired SWIR MCI and MRI measurements of their lower extremities. Thirty-six (36) subjects were hospitalized with decompensated HF while 11 healthy outpatients served as controls. A partial least squares (PLS) regression model was trained to ingest the SWIR MCI spectra and produce a CardioVerification Index (CVI) that mirrored MRI measurements.Results The SWIR MCI model reflected MRI TC measurements accurately for all subjects with a 0.743 linear correlation coefficient. Surprisingly, the MRI results identified a significant fraction of non-HF subjects with elevated TC levels, so a sub-analysis was performed on “Low TC” subjects with low and undetectable levels of extremity edema. This subpopulation’s linear correlation between MRI and CVI was 0.674. A logistic classifier model differentiated HF from non-HF subjects; the area under the receiver operating characteristic curves was 0.906 for the entire subject group and 0.821 for the Low TC subpopulation.Conclusion SWIR MCI techniques can be used in a TC-measuring tool that replicates MRI TC measurements. Clinicians could use such tools to help guide therapy for HF patients during in-hospital management of acute HF decompensation and for outpatient monitoring. SWIR MCI’s ability to identify elevated TC in otherwise normal subjects may also meet the unmet need for a novel, widely applicable HF screening tool.### Competing Interest StatementMichael Chappuis and Maurice Enriquez-Sarano are paid consultants of ChemImage. Adam Saltman, Richik Ghosh, Shawna Tazik, Heather Gomer, Matthew Nelson, and Patrick Treado are paid, full-time employees of ChemImage Corporation. Robert Schweitzer was a paid, full-time employee of ChemImage Corporation during the time the study was conducted.### Funding StatementThis study was funded entirely by ChemImage Corporation.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study was approved by the Institutional Review Board of Advarra and Allina Abbott Northwestern Hospital.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced in the present study are the property of ChemImage.

Abstract. Significance: Peripheral pitting edema is a clinician-administered measure for grading edema. Peripheral edema is graded 0, 1  +  , 2  +  , 3  +  , or 4  +  , but subjectivity is a major limitation of this technique. A pilot clinical study for short-wave infrared (SWIR) molecular chemical imaging (MCI) effectiveness as an objective, non-contact quantitative peripheral edema measure is underway. Aim: We explore if SWIR MCI can differentiate populations with and without peripheral edema. Further, we evaluate the technology for correctly stratifying subjects with peripheral edema. Approach: SWIR MCI of shins from healthy subjects and heart failure (HF) patients was performed. Partial least squares discriminant analysis (PLS-DA) was used to discriminate the two populations. PLS regression (PLSR) was applied to assess the ability of MCI to grade edema. Results: Average spectra from edema exhibited higher water absorption than non-edema spectra. SWIR MCI differentiated healthy volunteers from a population representing all pitting edema grades with 97.1% accuracy (N  =  103 shins). Additionally, SWIR MCI correctly classified shin pitting edema levels in patients with 81.6% accuracy. Conclusions: Our study successfully achieved the two primary endpoints. Application of SWIR MCI to monitor patients while actively receiving HF treatment is necessary to validate SWIR MCI as an HF monitoring technology.

Heart failure (HF) has a significant impact on patient outcomes and health care costs. Objective monitoring of pitting edema level of a HF patient may help clinicians reduce the volume of patient re-admissions. To address this, ChemImage is developing a Molecular Chemical Imaging (MCI) device for noninvasive measurement of peripheral edema level in HF patients. In an initial clinical study, edema grade was predicted in HF patients with 86% accuracy. Results from a follow-up clinical trial demonstrating the capability of MCI to monitor changes in a HF patient’s peripheral edema over time during the course of treatment will be presented.

Heart failure (HF) has a large impact on patient outcomes and health care costs. Objective monitoring of the pitting edema level of a HF patient may help clinicians reduce the amount of readmissions. ChemImage is developing a Molecular Chemical Imaging (MCI) device for monitoring HF patients that will non-invasively quantify peripheral edema. Results from a completed in-human clinical trial will be presented demonstrating ability to discriminate between healthy volunteers and HF patients with all levels of pitting edema and correct prediction of peripheral edema grade across the patient population. Follow-on clinical trials will address monitoring patients during treatment.

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD23)1 Apr 2020PD23-06 KIDNEY TUMOR DETECTION WITH HISTOLOGICAL SUBTYPE DIFFERENTIATION USING MOLECULAR CHEMICAL IMAGING: AN INNOVATIVE, NON-INVASIVE INTRAOPERATIVE IMAGING DEVICE Arash Samiei*, Shona Stewart, Ralph Miller, John Lyne, Aaron Smith, Marlena Darr, Heather Gomer, Patrick Treado, and Jeffrey Cohen Arash Samiei*Arash Samiei* More articles by this author , Shona StewartShona Stewart More articles by this author , Ralph MillerRalph Miller More articles by this author , John LyneJohn Lyne More articles by this author , Aaron SmithAaron Smith More articles by this author , Marlena DarrMarlena Darr More articles by this author , Heather GomerHeather Gomer More articles by this author , Patrick TreadoPatrick Treado More articles by this author , and Jeffrey CohenJeffrey Cohen More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000873.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Visualization and detection of tumors during extirpative surgeries can be challenging. Currently, there is no intraoperative imaging device to detect kidney tumors and determine their histological subtype with high accuracy. We are developing a non-invasive intraoperative Molecular Chemical Imaging (MCI) device for detecting critical structures, including tumors, in real-time and without the use of contrast agents. MCI is achieved by incorporating molecular spectroscopy and digital imaging. Used in conjunction with machine learning and computer vision strategies, MCI generates enhanced visualization of tissue structures against surrounding tissues. In this study, we report performance of kidney tumor differentiation from normal tissue and tumor histological subtype identification using MCI. METHODS: We studied 22 human kidney cancer specimens after radical nephrectomy. During MCI, samples were exposed to white light, and the light reflected from the tissue was analyzed by a MCI device operating in the visible & near-infrared spectral regions. Discrimination of tumor from non-tumor tissue was demonstrated using a machine learning approach, partial least squared discriminant analysis (PLS-DA). Discrimination performance of the PLS-DA model was evaluated with metrics such as sensitivity (Sn), specificity (Sp), and accuracy. Tumor subtypes from 18 specimens were further investigated. A multi-class PLS-DA model from 13 clear cell renal cell carcinomas (ccRCC), 2 papillary RCC, 2 transitional cell carcinoma (TCC), and 1 chromophobe RCC was built, and classification accuracy for each tumor subtype was generated. RESULTS: Tumor discrimination was achieved with high performance. The PLS-DA model differentiated between tumor and non-tumor tissues with 93.5% accuracy, 88.6% Sn, and 95.4% Sp. To evaluate MCI tumor subtyping capability, the multi-class PLS-DA model classified TCC with 100% accuracy, chromophobe RCC with 100% accuracy, ccRCC with 80.8% accuracy, and papillary RCC with 66.7% accuracy. A spectral peak at 975 nm, corresponding to water, was most intense for ccRCC which indicates hypervascularity, in contrast to other subtypes that showed hypovascularity. CONCLUSIONS: These positive results demonstrate the potential of MCI for augmenting a surgeon’s ability to accurately visualize kidney tumors and to identify histological subtype without the use of contrast agents. This innovative imaging modality has the capability of being applied to other forms of extirpative surgeries. Source of Funding: Internally funded by R&D of ChemImage Corporation © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e466-e466 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Arash Samiei* More articles by this author Shona Stewart More articles by this author Ralph Miller More articles by this author John Lyne More articles by this author Aaron Smith More articles by this author Marlena Darr More articles by this author Heather Gomer More articles by this author Patrick Treado More articles by this author Jeffrey Cohen More articles by this author Expand All Advertisement PDF downloadLoading ...

Explosive, chemical and narcotic materials, when in the wrong hands, pose an immediate threat to public health and safety. As the nature of these threats become more pervasive and lethal to innocent bystanders and unsuspecting military and law enforcement authorities, there is a growing demand for rapid and effective detection of materials in real-time with a high degree of autonomy and portability at safe distances. In an effort to address this need, ChemImage has been developing novel, adaptable, handheld, short-wave infrared (SWIR) molecular chemical imaging systems for real-time analysis of complex environments, including for detection of hazardous materials (e.g., explosives, chemical warfare agents, drugs of abuse). At the heart of this sensor is the Conformal Filter (CF), which is a liquid crystal based tunable filter (LCTF) that transmits multi-band waveforms that mimic the functionality of a discriminant vector for classification of target threats amongst background clutter. Real-time detection (≥10 detection fps) is achieved by operating two CFs in tandem within a dual polarization (DP) system, allowing for simultaneous acquisition of the compressed hyperspectral imaging data. This paper will focus on the development, characterization and testing results of a prototype handheld DP-CF sensor. Details of the autonomous, low size, weight and power (SWaP) sensor and applications of the technology to address realworld detection challenges including High Throughput Mail Screening (HTMS) and Chemical Warfare Agent (CWA) surveying and mapping will be discussed.

The smuggling of drug into correctional facilities through the mail is a major concern. ChemImage has developed the VeroVision (TM) mail screener system, which highlights drugs from background based on score imagery computed from selected wavelengths based on the chemical signatures. More recently, sophisticated techniques to hide drugs by dissolution into paper are being used. We introduce a combined heterogeneous anomaly detection with a deep learning classifier. Anomaly detection initially extracts suspect stain patterns. A You Only Look Once (YOLO) based classifier then classifies anomalies as drug or non-drug stain patterns. We report its first successful detection on a limited set of meth samples, with 87.4% probability of detection (PD) and 7.0% probability of false alarm (PFA). The results show that widefield, multispectral short-field infrared (SWIR) imaging can allow for dissolved concealed drug screening of mail which has benefits for mail inspection efficiency and accuracy.

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD23)1 Apr 2020Surgical Technology & Simulation: Instrumentation & Technology II (PD23) View All Author Informationhttps://doi.org/10.1097/JU.0000000000000873AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Expand All Advertisement PDF downloadLoading ...

SIGNIFICANCE:A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision.AIM:We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries.APPROACH:In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border.RESULTS:Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy.CONCLUSION:Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.

Heart failure (HF) occurs when the heart is unable to pump enough blood to meet blood and oxygen requirements and is among the most common causes for hospitalization in the United States. A retrospective analysis determined that 22% of HF patients are readmitted within 30 days of release from the hospital, and the costs for readmission are substantial. Measuring the severity of peripheral edema is one method for monitoring the treatment of a HF patient. Pitting peripheral edema is a subjective measure administered by clinicians who create an indentation mid-tibia and observe depth and time to resolve the indentation. The results are graded 0, 1, 2, 3 or 4, and this information is used in the patient treatment plan. ChemImage is engaged in a clinical study to determine whether Molecular Chemical Imaging (MCI) in the short wave infrared (SWIR) spectral region can provide an objective measure of peripheral edema in HF patients. In this paper, the performance of SWIR MCI for discriminating between healthy volunteers and HF patients with high grade pitting edema will be presented. This technology may provide a non-invasive methodology for quantitative peripheral edema measurement. As the technology matures, it is envisioned patient self-monitoring, with wireless transmission of edema levels while at home, can aid clinicians in monitoring HF patients for necessary treatment changes remotely, to improve patient outcomes, and ultimately, reduce HF hospital readmission rates.

As the nature of explosive, chemical and narcotic threats become more pervasive and lethal to innocent bystanders and unsuspecting military and law enforcement authorities, there is a growing demand for rapid and effective detection of materials in real-time with a high degree of autonomy at safe distances. In an effort to address this need, ChemImage has been developing novel, adaptable, short-wave infrared (SWIR) molecular chemical imaging systems for real-time analysis of complex environments, including for detection of hazardous materials (e.g., explosives, chemical warfare agents, drugs of abuse). At the heart of these systems is the Conformal Filter (CF), which is a liquid crystal (LC)-based tunable filter that transmits multi-band waveforms. Building on concepts of multivariate optical computing, the CF is tuned electro-optically and dynamically to mimic the functionality of a discriminant vector for classification. The resulting integrated detector response approximates the detection response of conventional hyperspectral imaging with only two discrete measurements instead of hundreds to thousands. Real-time detection is achieved by operating two CFs in tandem within a dual polarization (DP) system, which exploits the polarization sensitivity of the LC filters and allows for simultaneous acquisition of the compressed hyperspectral imagery. This paper will discuss the development, characterization, and test results of a prototype, handheld CF sensor, with a focus on its application to explosives, chemical and narcotic threat detection.

INTRODUCTION:Colorectal cancer (CRC) is the third most common cancer in the U.S. Early detection of CRC can substantially increase survival rates. Test compliance may be improved by offering a blood-based test option.METHODS:Endoscopy II trial specimens were tested for AFP, CA19-9, CEA, hs-CRP, CyFra 21-1, Ferritin, Galectin-3, and TIMP-1 levels. These biomarkers, as well as patient demographic information (e.g., age, gender), were included in algorithm development. Six statistical methods were utilized to develop algorithms that would discriminate cancer vs. noncancers. Statistical methods included logistic regression, adaptive index modeling, partial least-squares discriminant analysis, feature vector (weighted and unweighted), and random forest. The performance of these algorithms was compared against benchmark criteria established for stool-based tests.RESULTS:Using several statistical methods, the presence of CRC and high-risk adenomas was detected with an AUCs of at least 0.65-0.76, with a few of models approaching the stool-based tests benchmark performance. Further, common markers were utilized across the different statistical techniques, with model complexities ranging from 3 to 9 markers.CONCLUSIONS:Predictive models identified subjects with CRC and high-risk adenomas with the similar levels of statistical accuracy. Clinical performance differences were minimal across the statistical techniques, although the intuitive interpretations, model complexity, clinical adoption and implementation varied.

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology III (MP20)1 Apr 2019MP20-01 MOLECULAR CHEMICAL IMAGING ENDOSCOPE, AN INNOVATIVE IMAGING MODALITY FOR ENHANCING THE SURGEON’S VIEW DURING LAPAROSCOPIC PROCEDURES Arash Samiei*, Ralph Miller, John Lyne, Aaron Smith, Shona Stewart, Heather Gomer, Patrick Treado, and Jeffrey Cohen Arash Samiei*Arash Samiei* More articles by this author , Ralph MillerRalph Miller More articles by this author , John LyneJohn Lyne More articles by this author , Aaron SmithAaron Smith More articles by this author , Shona StewartShona Stewart More articles by this author , Heather GomerHeather Gomer More articles by this author , Patrick TreadoPatrick Treado More articles by this author , and Jeffrey CohenJeffrey Cohen More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555510.24601.efAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Visualization of critical structures during surgery can be challenging, and misidentification can lead to iatrogenic injuries. Augmentation of a surgeon's ability to discriminate different structures, a contrast reagent-free intraoperative device capable of detecting different anatomical structures in real-time is an unmet clinical need. We have developed a molecular chemical imaging endoscope (MCI-E) which combines molecular spectroscopy and digital imaging into a non-contact sensing strategy to achieve real-time detections of biological materials. MCI-E exploits novel machine learning and computer vision strategies to generate an enhanced visualization of tissue structures. METHODS: MCI-E exploits diffuse reflectance imaging, and is capable of collecting hyperspectral images and videos in the visible and near-infrared spectrum. A porcine surgical model was used for this experiment. During data collection, the tissue is exposed to white light, without contrast materials, and the light absorbed or reflected from the tissue is analyzed. Utilizing liquid crystal technology, MCI-E generates real-time wavelength-specific imagery targeting tissue structures, and resulting detections are overlaid onto conventional high definition video. To evaluate detection performance, the contrast between the target tissue and surrounding tissues is quantified with the signal-to-noise ratio (SNR) and area under the ROC curve(AUC). RESULTS: We demonstrate MCI-E enhanced visualization of several important tissues against the background. The ureter is visualized against background tissues with AUC of 0.90 and SNR of 2.3. Artery and vein are differentiated from their surrounding tissues with AUC of 0.97, 0.98 and SNR of 4.75 and 4.39 respectively. Lymph nodes are visualized with AUC of 0.97 and SNR of 4.8. The neurovascular bundle is visualized with AUC of 0.98 and SNR of 3.1. In addition, we showed that MCI-E is able to distinguish ischemic bowel from non-ischemic bowel with high accuracy (AUC of 0.97 and SNR of 4.3). MCI-E is capable of locating obscured objects inside lumens and in the presence of fat or blood. For this experiment, we used gallstones with different sizes and put them inside different locations of the biliary tree. The obscured stones could be detected with AUC of 0.99 and SNR of 3.8. CONCLUSIONS: Real-time, high contrast MCI-E detections in vivo demonstrate the potential of this new imaging modality for enabling a surgeon to discriminate critical structures intraoperatively and without contrast agents. Source of Funding: This research received funding from “The Western Pennsylvania Prostate Foundation”, Pittsburgh, PA, USA. Pittsburgh, PA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e282-e283 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Arash Samiei* More articles by this author Ralph Miller More articles by this author John Lyne More articles by this author Aaron Smith More articles by this author Shona Stewart More articles by this author Heather Gomer More articles by this author Patrick Treado More articles by this author Jeffrey Cohen More articles by this author Expand All Advertisement PDF downloadLoading ...

Improved patient outcomes highlight the need to avoid errors in surgery. Molecular Chemical Imaging (MCI) aids in identifying critical structures without reagents and in vivo results are presented for a range of surgical applications.

Accurate identification, precise dissection, and careful preservation of critical structures, such as nerves and blood vessels, are key to successful surgical outcomes. Unintended and/or unrecognized injuries to critical structures result in debilitating short- and long-term morbidity, avoidable mortality, and considerable socioeconomic and healthcare burdens. ChemImage has developed a Molecular Chemical Imaging endoscope (MCI-E) to be deployed as an intraoperative imaging device for real-time detection of key anatomical structures. MCI-E does not require the use of contrast agents, and employs visible-near infrared (vis-NIR) reflectance hyperspectral imaging. We tested the in vivo performance of MCI-E by collecting high quality vis-NIR signatures from several anatomical structures, including ureters, arteries, and veins in live pigs under general anesthesia. In this paper, we will present successful MCI-E detection of lymph node, ureter, vessels, nerve, bowel, and thyroid in background tissues under relevant in vivo conditions. If successful, integration of MCI-E into surgical procedures will enable real-time automated detection of anatomical structures during surgeries. The benefits of this capability include the opportunity of reduced surgery time, decreased patient risk, fewer repeat surgeries, and enhancement of surgeon training.

Abstract Background: Prostate cancer (PCa) is the most common cancer in males and the second leading cause of cancer deaths. One of the most confounding problems faced by urologists is predicting the progression of PCa. Current prognostic tools for PCa rely solely on clinical and pathologic variables that cannot always accurately predict the progression of the disease. More accurate diagnostic tools to aid clinicians and patients to decide the course of treatment, even in the early stage of cancer, have long been sought. In a prospective study, we utilized Raman molecular imaging (RMI) to identify patients at risk for biochemical recurrence following a prostatectomy. RMI is a technology that combines the molecular chemical analysis capability of Raman spectroscopy with high-definition digital image visualization, enabling analysis of the molecular environment of prostate tissue by providing highly specific molecular imaging data. Objective: The aim of this study is to evaluate RMI as a methodology for predicting the outcome of PCa patients following radical prostatectomy. Methods: PCa patients who underwent prostatectomy were prospectively enrolled with informed consent. Surgeries were performed by two practiced surgeons in a single clinical center from 2009–2011, and 60 months’ follow-up time was recorded. Pathology data were reviewed by one genitourinary pathologist. Preoperative and postoperative data (preoperation prostate-specific antigen level [PSA], Nadir PSA, Gleason scores, pathologic stage, biochemical recurrence time) were recorded, and biochemical failure was defined as 2 consecutive increases in postoperative PSA (≥0.2 ng/mL). Patient identifiers were removed. Raman molecular images were collected from unstained prostatectomy tissue sections. Raman spectral data were extracted from epithelial and stromal regions, and multivariate statistical methodologies were applied to these spectra. Partial least squares discriminant analysis (PLS-DA) was employed in conjunction with leave-one-patient-out cross-validation to generate models based on the pathology data and RMI. Results: Initial data analysis was performed on a representative population of 38 PCa patients. In this population the mean (median) age at the time of diagnosis was 60 (61) years; the pathologic stage for 57.9% of the patients was T3 and for 42.1% of the patients T2. Nine patients (23.7%) progressed to biochemical failure in 60 months. Analysis of RMI performed on prostatectomy tissues from these patients indicates that the RMI data are distinguishable between those with biochemical failure (progressors) and those with no evidence of disease (NED). A PLS-DA model comprising 9 progressors and 29 NED patients differentiated between the two tissue classes with 89% sensitivity and 90% specificity, and with an area under the ROC curve of 0.87. In this model, classification accuracy was 90% and the p–value < 0.01. Analysis revealed most notable differentiation between progressors and NEDs when evaluating the epithelium and stroma as separate histologic elements. A PLS-DA model based on epithelial cells from 9 progressors and 10 NEDs discriminated between the two populations with 89% sensitivity and 100% specificity, with AUROC of 0.92 and a p-value < 0.01. A similar model differentiated between the stroma of progressors and NEDs with 100% specificity and 100% sensitivity, and a p-value well below 0.01. Conclusion: RMI is a novel technique that shows promise for identifying patients at risk of progression by visualizing molecular information not seen using other current methods. In Gleason 7 disease, RMI indicates distinctive chemical differences in patients who had biochemical failure postoperatively. This preliminary work lays the foundation for the further study of RMI for evaluating prostate tissue and developing an assay that may impact clinicians and patients with PCa. Citation Format: Arash Samiei, Ralph Miller, Jeffrey Cohen, Heather Gomer, Shona Stewart, Patrick Treado. Prospective study on the use of Raman molecular imaging to determine postoperative oncological outcomes in patients with prostate cancer: Analysis of a single center [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B079.

The current opioid epidemic represents a significant health and security threat. This epidemic has affected correctional facilities with an increased smuggling of illicit drugs concealed in envelopes, letters, greeting cards, and business cards to inmates. Short‐wave infrared chemical imaging sensors are being successfully applied to the automated, high confidence detection of drugs concealed in prison mail. Once detected, end users have a need to confirm the detection and to identify the specific drug being detected. The challenge for identification is that the spectral signature of the concealed drug is often convolved with the spectral signature of the piece of mail (substrate) in which the drug is concealed. This paper presents a method to remove the substrate signal from the substrate/drug mixture signal followed by a set of 3 spectral identification methods. The substrate signature is estimated by a region of interest that is spatially local to the detection, and linear unmixing uses this substrate signature to calculate the residual spectra in the detection pixels. These residual spectra represent the isolated drug spectra, and they are compared with a spectral drug library via Euclidean distance, target factor analysis, and adaptive cosine estimator methods. This methodology was applied to a set of 116 positive‐ and negative‐control samples spanning a range of drugs and concealment methods with the result that 90 of the 104 positive‐control samples were identified correctly (86.5%) and 0 of the 12 negative‐control samples were incorrectly identified as a drug in the library (0%).