Use of an elastic-scattering spectroscopy and artificial intelligence device in the assessment of lesions suggestive of skin cancer: A comparative effectiveness study.

Background: Skin cancer is the most common form of cancer worldwide. As artificial intelligence (AI) expands its scope within dermatology, leveraging technology may aid skin cancer detection. Objective: To assess the safety and effectiveness of an elastic-scattering spectroscopy (ESS) device in evaluating lesions suggestive of skin cancer. Methods: This prospective, multicenter clinical validation study was conducted at 4 US investigational sites. Patients with skin lesions suggestive of melanoma and nonmelanoma skin cancers were clinically assessed by expert dermatologists and evaluated by a device using AI algorithms comparing current ESS lesion readings with training data sets. Statistical analyses included sensitivity, specificity, AUROC, negative predictive value (NPV), and positive predictive value (PPV). Results: Overall device sensitivity was 97.04%, with subgroup sensitivity of 96.67% for melanoma, 97.22% for basal cell carcinoma, and 97.01% for squamous cell carcinoma. No statistically significant difference was found between the device and dermatologist performance (P = .8203). Overall specificity of the device was 26.22%. Overall NPV of the device was 89.58% and PPV was 57.54%. Conclusion: The ESS device demonstrated high sensitivity in detecting skin cancer. Use of this device may assist primary care clinicians in assessing suspicious lesions, potentially reducing skin cancer morbidity and mortality through expedited and enhanced detection and intervention.

Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy.

BACKGROUND: Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology. AIMS: The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately. METHODS: We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance. RESULTS: The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5 mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83. CONCLUSIONS: Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5 mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.

Toward optical spectroscopy-guided lung biopsy: Demonstration of tissue-type classification.

The diagnostic yield of standard tissue-sampling modalities of suspected lung cancers, whether by bronchoscopy or interventional radiology, can be nonoptimal, varying with the size and location of lesions. What is needed is an insitu sensor, integrated in the biopsy tool, to objectively distinguish among tissue types in real time, not to replace biopsy with an optical diagnostic, but to verify that the sampling tool is properly located within the target lesion. We investigated the feasibility of elastic scattering spectroscopy (ESS), coupled with machine learning, to distinguish lung lesions from the various nearby tissue types, in a study with freshly-excised lung tissues from surgical resections. Optical spectra were recorded with an ESS fiberoptic probe in different areas of the resected pulmonary tissues, including benign-margin tissue sites as well as the periphery and core of the lesion. An artificial-intelligence model was used to analyze, retrospectively, 2032 measurements from excised tissues of 35 patients. With high accuracy, ESS was able to distinguish alveolar tissue from bronchi, alveolar tissue from lesions, and bronchi from lesions. This ex vivo study indicates promise for ESS fiberoptic probes to be integrated with surgical intervention tools, to improve reliability of pulmonary lesion targeting.

Real-time artificial intelligence-based histologic classification of colorectal polyps with augmented visualization.

BACKGROUND AND AIMS: Artificial intelligence (AI)-based computer-aided diagnostic (CADx) algorithms are a promising approach for real-time histology (RTH) of colonic polyps. Our aim is to present a novel in situ CADx approach that seeks to increase transparency and interpretability of results by generating an intuitive augmented visualization of the model's predicted histology over the polyp surface. METHODS: We developed a deep learning model using semantic segmentation to delineate polyp boundaries and a deep learning model to classify subregions within the segmented polyp. These subregions were classified independently and were subsequently aggregated to generate a histology map of the polyp's surface. We used 740 high-magnification narrow-band images from 607 polyps in 286 patients and over 65,000 subregions to train and validate the model. RESULTS: The model achieved a sensitivity of .96, specificity of .84, negative predictive value (NPV) of .91, and high-confidence rate (HCR) of .88, distinguishing 171 neoplastic polyps from 83 non-neoplastic polyps of all sizes. Among 93 neoplastic and 75 non-neoplastic polyps ≤5 mm, the model achieved a sensitivity of .95, specificity of .84, NPV of .91, and HCR of .86. CONCLUSIONS: The CADx model is capable of accurately distinguishing neoplastic from non-neoplastic polyps and provides a histology map of the spatial distribution of localized histologic predictions along the delineated polyp surface. This capability may improve interpretability and transparency of AI-based RTH and offer intuitive, accurate, and user-friendly guidance in real time for the clinical management and documentation of optical histology results.

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpretation. The aim of this study is to test the feasibility of a system combining the use of a low-magnification, wider field-of-view pCLE probe and a computer-assisted diagnosis (CAD) algorithm that automatically classifies colonic polyps. METHODS: This feasibility study utilized images of polyps from 26 patients who underwent colonoscopy with pCLE. The pCLE images were reviewed offline by two expert and five junior endoscopists blinded to index histopathology. A subset of images was used to train classification software based on the consensus of two GI histopathologists. Images were processed to extract image features as inputs to a linear support vector machine classifier. We compared the CAD algorithm's prediction accuracy against the classification accuracy of the endoscopists. RESULTS: We utilized 96 neoplastic and 93 non-neoplastic confocal images from 27 neoplastic and 20 non-neoplastic polyps. The CAD algorithm had sensitivity of 95%, specificity of 94%, and accuracy of 94%. The expert endoscopists had sensitivities of 98% and 95%, specificities of 98% and 96%, and accuracies of 98% and 96%, while the junior endoscopists had, on average, a sensitivity of 60%, specificity of 85%, and accuracy of 73%. CONCLUSION: The CAD algorithm showed comparable performance to offline review by expert endoscopists and improved performance when compared to junior endoscopists and may be useful for assisting clinical decision making in real time.

Optical Spectroscopy as a Method for Skin Cancer Risk Assessment.

Skin cancer is the most prevalent cancer, and its assessment remains a challenge for physicians. This study reports the application of an optical sensing method, elastic scattering spectroscopy (ESS), coupled with a classifier that was developed with machine learning, to assist in the discrimination of skin lesions that are concerning for malignancy. The method requires no special skin preparation, is non-invasive, easy to administer with minimal training, and allows rapid lesion classification. This novel approach was tested for all common forms of skin cancer. ESS spectra from a total of 1307 lesions were analyzed in a multi-center, non-randomized clinical trial. The classification algorithm was developed on a 950-lesion training dataset, and its diagnostic performance was evaluated against a 357-lesion testing dataset that was independent of the training dataset. The observed sensitivity was 100% (14/14) for melanoma and 94% (105/112) for non-melanoma skin cancer. The overall observed specificity was 36% (84/231). ESS has potential, as an adjunctive assessment tool, to assist physicians to differentiate between common benign and malignant skin lesions.

Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.

Antimicrobial drugs have an important role in controlling bacterial infectious diseases. However, the increasing resistance of bacteria to antibiotics has become a global health care problem. Rapid determination of antimicrobial susceptibility of clinical isolates is often crucial for the optimal antimicrobial therapy. The conventional methods used in medical centers for susceptibility testing are time-consuming (>2 days). Two bacterial culture steps are needed, the first is used to grow the bacteria from urine on agar plates to determine the species of the bacteria (~24 hours). The second culture is used to determine the susceptibility by growing colonies from the first culture for another 24 hours. Here, the main goal is to examine the potential of infrared microscopy combined with multivariate analysis, to reduce the time it takes to identify Escherichia coli susceptibility to antibiotics and to determine the optimum choice of antibiotic to which the bacteria will respond. E coli colonies of the first culture from patients with urinary tract infections (UTI) were examined for the bacterial susceptibility using Fourier-transform infrared (FTIR). Our results show that it is possible to determine the optimum choice of antibiotic with better than 89% sensitivity, in the time span of few minutes, following the first culture.

Detection of Extended-Spectrum ß-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.

The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria is extended-spectrum ß-lactamase-producing bacteria (ESBL-positive = ESBL+). Due to widespread and continuous evolution of ESBL-producing bacteria, they become increasingly resistant to many of the commonly used antibiotics, leading to an increase in the mortality associated with resulting infections. Timely detection of ESBL-producing bacteria and rapid determination of their susceptibility to appropriate antibiotics can reduce the spread of these bacteria and the consequent complications. Routine methods used for the detection of ESBL-producing bacteria are time-consuming, requiring at least 48 h to obtain results. In this study, we evaluated the potential of infrared spectroscopic microscopy, combined with multivariate analysis for rapid detection of ESBL-producing Escherichia coli ( E. coli) isolated from urinary-tract infection (UTI) samples. Our measurements were conducted on 837 samples of uropathogenic E. coli (UPEC), including 268 ESBL+ and 569 ESBL-negative (ESBL-) samples. All samples were obtained from bacterial colonies after 24 h culture (first culture) from midstream patients' urine. Our results revealed that it is possible to detect ESBL-producing bacteria, with a 97% success rate, 99% sensitivity, and 94% specificity for the tested samples, in a time span of few minutes following the first culture.

The color of cancer: Margin guidance for oral cancer resection using elastic scattering spectroscopy.

OBJECTIVES/HYPOTHESIS: To evaluate the usefulness of elastic scattering spectroscopy (ESS) as a diagnostic adjunct to frozen section analysis in patients with diagnosed squamous cell carcinoma of the oral cavity. STUDY DESIGN: Prospective analytic study. METHODS: Subjects for this single institution, institutional review board-approved study were recruited from among patients undergoing surgical resection for squamous cell cancer of the oral cavity. A portable ESS device with a contact fiberoptic probe was used to obtain spectral signals. Four to 10 spectral readings were obtained on each subject from various sites including gross tumor and normal-appearing mucosa in the surgical margin. Each reading was correlated with the histopathologic findings of biopsies taken from the exact location of the spectral readings. A diagnostic algorithm based on multidimensional pattern recognition/machine learning was developed. Sensitivity and specificity, error rate, and area under the curve were used as performance metrics for tests involving classification between disease and nondisease classes. RESULTS: Thirty-four (34) subjects were enrolled in the study. One hundred seventy-six spectral data point/biopsy specimen pairs were available for analysis. ESS distinguished normal from abnormal tissue, with a sensitivity ranging from 84% to 100% and specificity ranging from 71% to 89%, depending on how the cutoff between normal and abnormal tissue was defined (i.e., mild, moderate, or severe dysplasia). There were statistically significant differences in malignancy scores between histologically normal tissue and invasive cancer and between noninflamed tissue and inflamed tissue. CONCLUSIONS: This is the first study to evaluate the effectiveness of ESS in guiding mucosal resection margins in oral cavity cancer. ESS provides fast, real-time assessment of tissue without the need for pathology expertise. ESS appears to be effective in distinguishing between normal mucosa and invasive cancer and between "normal" tissue (histologically normal and mild dysplasia) and "abnormal" tissue (severe dysplasia and carcinoma in situ) that might require further margin resection. Further studies, however, are needed with a larger sample size to validate these findings and to determine the effectiveness of ESS in distinguishing visibly and histologically normal tissue from visibly normal but histologically abnormal tissue. LEVEL OF EVIDENCE: NA Laryngoscope, 127:S1-S9, 2017.

Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria.

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings and, sometimes, can save lives. Currently classical procedures require at least 48 h for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility to specific antibiotics based on the IR spectra of the bacteria. IR spectroscopy was conducted on bacterial colonies, obtained after 24 h culture from patients' samples. An IR microscope was utilized, and a computational classification method was developed to analyze the IR spectra by novel pattern-recognition and statistical tools, to determine E. coli susceptibility within a few minutes to different antibiotics, gentamicin, ceftazidime, nitrofurantoin, nalidixic acid, ofloxacin. Our results show that it was possible to classify the tested bacteria into sensitive and resistant types, with success rates as high as 85% for a number of examined antibiotics. These promising results open the potential of this technique for faster determination of bacterial susceptibility to certain antibiotics.

Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis.

Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involved in many diseases, and antibiotics have been the most effective treatment for them. It is essential to treat an infection with an antibiotic to which the infecting bacteria is sensitive; otherwise, the treatment is not effective and may lead to life-threatening progression of disease. Classical microbiology methods that are used for determination of bacterial susceptibility to antibiotics are time consuming, accounting for problematic delays in the administration of appropriate drugs. Infrared-absorption microscopy is a sensitive and rapid method, enabling the acquisition of biochemical information from cells at the molecular level. The combination of Fourier transform infrared (FTIR) microscopy with new statistical classification methods for spectral analysis has become a powerful technique, with the ability to detect structural molecular changes associated with resistivity of bacteria to antibiotics. It was possible to differentiate between isolates of Escherichia (E.) coli that were sensitive or resistant to different antibiotics with good accuracy. The objective computational classifier, based on infrared absorption spectra, is highly sensitive to the subtle infrared spectral changes that correlate with molecular changes associated with resistivity. These changes enable differentiating between the resistant and sensitive E. coli isolates within a few minutes, following the initial culture. This study provides proof-of-concept evidence for the translational potential of this spectroscopic technique in the clinical management of bacterial infections, by characterizing and classifying antibiotic resistance in a much shorter time than possible with current standard laboratory methods.

Probe-based confocal laser endomicroscopy quantitative morphometric markers associated with portal hypertension in duodenal mucosa.

BACKGROUND & AIMS: Early detection of portal hypertension (PH) may help to prevent the morbidity of late-stage cirrhosis by stratifying disease severity and enabling disease-modifying interventions in potentially reversible conditions like non-alcoholic fatty liver disease and alcoholic hepatitis. This study seeks to correlate morphometric features by confocal endomicroscopy with established surrogate clinical markers of PH. METHODS: Patients with and without PH scheduled for upper endoscopy at VA Boston participated in this IRB-approved study. Real-time probe-based confocal endomicroscopy (pCLE) was performed in the duodenum. Vascular and epithelial morphometry was performed off-line, in a blinded manner, using image-processing software. RESULTS: Morphometric analysis of pCLE images from 16 patients with PH and 15 control patients was performed. Statistically significant differences were observed among control and PH patients for average vessel diameter (AVD: 11.7 µm vs. 17.1 µm), average vessel branching (AVB: 0.11 vs. 0.31 bifurcations per image frame), and average columnar cell height (ACCH: 40.0 µm vs. 52.0 µm). Spearman correlations comparing AVD, AVB and ACCH to portal gastropathy scores (0.86, 0.44 and 0.70) and to grade of oesophageal varices (0.88, 0.41 and 0.66) were statistically significant. Similarly, Pearson correlations of AVD and ACCH to spleen size (0.72 and 0.57), platelet count (-0.69 and -0.40) and the platelet count/spleen size ratio (-0.69 and -0.41) were also found to be statistically significant. CONCLUSIONS: Duodenal pCLE reveals microvascular dilatation and altered epithelial cell volume/morphology in PH. These morphometric pCLE markers correlate with surrogate markers of PH. Additional studies will define the correlation between microscopic vascular patterns, epithelial cell volume and the hepatic venous pressure gradient.

Endoscopic histological assessment of colonic polyps by using elastic scattering spectroscopy.

BACKGROUND: Elastic-scattering spectroscopy (ESS) can assess in vivo and in real-time the scattering and absorption properties of tissue related to underlying pathologies. OBJECTIVES: To evaluate the potential of ESS for differentiating neoplastic from non-neoplastic polyps during colonoscopy. DESIGN: Pilot study, retrospective data analysis. SETTING: Academic practice. PATIENTS: A total of 83 patients undergoing screening/surveillance colonoscopy. INTERVENTIONS: ESS spectra of 218 polyps (133 non-neoplastic, 85 neoplastic) were acquired during colonoscopy. Spectral data were correlated with the classification of biopsy samples by 3 GI pathologists. High-dimensional methods were used to design diagnostic algorithms. MAIN OUTCOME MEASUREMENTS: Diagnostic performance of ESS. RESULTS: Analysis of spectra from polyps of all sizes (N = 218) resulted in a sensitivity of 91.5%, specificity of 92.2%, and accuracy of 91.9% with a high-confidence rate of 90.4%. Restricting analysis to polyps smaller than 1 cm (n = 179) resulted in a sensitivity of 87.0%, specificity of 92.1%, and accuracy of 90.6% with a high-confidence rate of 89.3%. Analysis of polyps 5 mm or smaller (n = 157) resulted in a sensitivity of 86.8%, specificity of 91.2%, and accuracy of 90.1% with a high-confidence rate of 89.8%. LIMITATIONS: Sample size, retrospective validation used to obtain performance estimates. CONCLUSION: Results indicate that ESS permits accurate, real-time classification of polyps as neoplastic or non-neoplastic. ESS is a simple, low cost, clinically robust method with minimal impact on procedure flow, especially when integrated into standard endoscopic biopsy tools. Performance on polyps 5 mm or smaller indicates that ESS may, in theory, achieve Preservation and Incorporation of Valuable Endoscopic Innovations performance thresholds. ESS may one day prove to be a useful tool used in endoscopic screening and surveillance of colorectal cancer.

Elastic scattering spectroscopy as an optical marker of inflammatory bowel disease activity and subtypes.

BACKGROUND: In 10% to 15% of individuals, inflammatory bowel disease (IBD) is difficult to classify as ulcerative colitis (UC) or Crohn's disease (CD). Previous work has demonstrated that probe-based elastic scattering spectroscopy (ESS) can produce spectra, informed by parameters like tissue ultrastructure and hemoglobin content, capable of differentiating pathologies. This study investigates whether ESS is an in vivo optical biomarker for the presence, activity, and type of IBD in the colon. METHODS: Pilot study, a retrospective data analysis. ESS spectra of endoscopically normal and inflamed colon were obtained from 48 patients with IBD and 46 non-IBD controls. Measurements from patients with IBD were categorized as CD or UC based on clinical diagnosis. Spectra were analyzed using high-dimensional methods. Leave-one-patient-out cross-validation was used to obtain diagnostic performance estimates. RESULTS: Patients with IBD were distinguishable from non-IBD controls with a sensitivity of 0.93 and specificity of 0.91 based on readings from endoscopically normal mucosa, and 0.94 and 0.93 from inflamed mucosa. In patients with IBD, histologically normal and inflamed colon were distinguishable with per-class accuracies of 0.83 and 0.89, respectively; histologically normal from inactive inflammation with accuracies of 0.73 and 0.89, respectively; and inactive from active colitis with accuracies of 0.87 and 0.84, respectively. The diagnosis of CD versus UC was made with per-class accuracies of 0.92 and 0.87 in normal and 0.87 and 0.85 in inflamed mucosa, respectively. CONCLUSIONS: ESS, a simple, low-cost clinically friendly optical biopsy modality, has the potential to enhance the endoscopic assessment of IBD and its activity in real time and may help to distinguish CD from UC.

Preoperative discrimination of benign from malignant disease in thyroid nodules with indeterminate cytology using elastic light-scattering spectroscopy.

Thyroid nodules are common and often require fine needle aspiration biopsy (FNAB) to determine the presence of malignancy to direct therapy. Unfortunately, approximately 15-30% of thyroid nodules evaluated by FNAB are not clearly benign or malignant by cytology alone. These patients require surgery for the purpose of diagnosis alone; most of these nodules ultimately prove to be benign. Elastic light scattering spectroscopy (ESS) that measures the spectral differences between benign and malignant thyroid nodules has shown promise in improving preoperative determination of benign status of thyroid nodules. We describe the results of a large, prospective, blinded study validating the ESS algorithm in patients with thyroid nodules. An ESS system was used to acquire spectra from human thyroid tissue. Spectroscopic results were compared to the histopathology of the biopsy samples. Sensitivity and specificity of the ESS system in the differentiation of benign from malignant thyroid nodules are 74% and 90% respectively, with a negative predictive value of 97%. These data suggest that ESS has the potential for use in real time diagnosis of thyroid nodules as an adjunct to FNAB cytology.

Spectral classifier design with ensemble classifiers and misclassification-rejection: application to elastic-scattering spectroscopy for detection of colonic neoplasia.

Optical spectroscopy has shown potential as a real-time, in vivo, diagnostic tool for identifying neoplasia during endoscopy. We present the development of a diagnostic algorithm to classify elastic-scattering spectroscopy (ESS) spectra as either neoplastic or non-neoplastic. The algorithm is based on pattern recognition methods, including ensemble classifiers, in which members of the ensemble are trained on different regions of the ESS spectrum, and misclassification-rejection, where the algorithm identifies and refrains from classifying samples that are at higher risk of being misclassified. These "rejected" samples can be reexamined by simply repositioning the probe to obtain additional optical readings or ultimately by sending the polyp for histopathological assessment, as per standard practice. Prospective validation using separate training and testing sets result in a baseline performance of sensitivity = .83, specificity = .79, using the standard framework of feature extraction (principal component analysis) followed by classification (with linear support vector machines). With the developed algorithm, performance improves to Se ∼ 0.90, Sp ∼ 0.90, at a cost of rejecting 20-33% of the samples. These results are on par with a panel of expert pathologists. For colonoscopic prevention of colorectal cancer, our system could reduce biopsy risk and cost, obviate retrieval of non-neoplastic polyps, decrease procedure time, and improve assessment of cancer risk.

Elastic light-scattering spectroscopy for discrimination of benign from malignant disease in thyroid nodules.

BACKGROUND: Thyroid cancer is the most common endocrine malignancy. The current standard of diagnosis, fine-needle aspiration biopsy, yields approximately 10-25% of indeterminate results leading to twice as many thyroidectomies for further diagnosis. Elastic scattering spectroscopy (ESS) is a new, minimally invasive optical-biopsy technique mediated by fiber-optic probes that is sensitive to cellular and subcellular morphological features. We assessed the diagnostic potential of ESS in the thyroid to differentiate benign from malignant thyroid nodules as determined by histology. METHODS: Under an IRB approved protocol, 36 surgical patients (n = 21 benign thyroid nodules, n = 15 malignant tumors) had collection of ESS data from their fresh ex vivo thyroidectomy specimens. Using surgical pathology as our gold standard, spectral analyses were performed using a training set; these data were used to assess the ESS diagnostic potential using the leave-one-out technique. RESULTS: Our test set was 75% sensitive and 95% specific in differentiating benign from malignant thyroid lesions, with a positive predictive value (PPV) of 0.92 and a negative predictive value (NPV) of 0.83. CONCLUSIONS: The ESS can accurately distinguish benign vs malignant thyroid lesions with high PPV and NPV. With further validation ESS could potentially be used as an in situ real-time diagnostic tool or as an adjunct to conventional cytology.

INTEGRATED OPTICAL TOOLS FOR MINIMALLY INVASIVE DIAGNOSIS AND TREATMENT AT GASTROINTESTINAL ENDOSCOPY.

Over the past two decades, the bulk of gastrointestinal (GI) endoscopic procedures has shifted away from diagnostic and therapeutic interventions for symptomatic disease toward cancer prevention in asymptomatic patients. This shift has resulted largely from a decrease in the incidence of peptic ulcer disease in the era of antisecretory medications coupled with emerging evidence for the efficacy of endoscopic detection and eradication of dysplasia, a histopathological biomarker widely accepted as a precursor to cancer. This shift has been accompanied by a drive toward minimally-invasive, in situ optical diagnostic technologies that help assess the mucosa for cellular changes that relate to dysplasia. Two competing but complementary approaches have been pursued. The first approach is based on broad-view targeting of "areas of interest" or "red flags." These broad-view technologies include standard white light endoscopy (WLE), high-definition endoscopy (HD), and "electronic" chromoendoscopy (narrow-band-type imaging). The second approach is based on multiple small area or point-source (meso/micro) measurements, which can be either machine (spectroscopy) or human-interpreted (endomicroscopy, magnification endoscopy), much as histopatholgy slides are. In this paper we present our experience with the development and testing of a set of familiar but "smarter" standard tissue-sampling tools that can be routinely employed during screening/surveillance endoscopy. These tools have been designed to incorporate fiberoptic probes that can mediate spectroscopy or endomicroscopy. We demonstrate the value of such tools by assessing their preliminary performance from several ongoing clinical studies. Our results have shown promise for a new generation of integrated optical tools for a variety of screening/surveillance applications during GI endoscopy. Integrated devices should prove invaluable for dysplasia surveillance strategies that currently result in large numbers of benign biopsies, which are of little clinical consequence, including screening for colorectal polyps and surveillance of "flat" dysplasia such as Barrett's esophagus and chronic colitis due to inflammatory bowel diseases.