Real-world evaluation of imipenem/cilastatin/relebactam across US medical centres.

We assessed 160 patients who received imipenem/cilastatin/relebactam for ≥2 days. At treatment initiation, the median Charlson Comorbidity Index was 5, 45% were in the intensive care unit, and 19% required vasopressor support. The in-hospital mortality rate was 24%. These data advance our understanding of real-world indications and outcomes of imipenem/cilastatin/relebactam use.

Machine learning to identify chronic cough from administrative claims data.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database. Of the 327,423 patients who met the study criteria, 4,818 had chronic cough based on linked claims-electronic health record data. The XG Boost model showed the best performance, achieving a Receiver-Operator Characteristic Area Under the Curve (ROC-AUC) of 0.916. We selected a cutoff that favors a high positive predictive value (PPV) to minimize false positives, resulting in a sensitivity, specificity, PPV, and negative predictive value of 18.0%, 99.6%, 38.7%, and 98.8%, respectively on the held-out testing set (n = 82,262). Logistic regression and neural network models achieved slightly lower ROC-AUCs of 0.907 and 0.838, respectively. The XG Boost and logistic regression models maintained their robust performance in subgroups of individuals with higher rates of chronic cough. Machine learning algorithms are one way of identifying conditions that are not coded in medical records, and can help identify individuals with chronic cough from claims data with a high degree of classification value.

Post-operative urinary retention is impacted by neuromuscular block reversal agent choice: A retrospective cohort study in US hospital setting.

STUDY OBJECTIVE: Perioperative neuromuscular blocking agents are pharmacologically reversed to minimize complications associated with residual neuromuscular block. Neuromuscular block reversal with anticholinesterases (e.g., neostigmine) require coadministration of an anticholinergic agent (e.g., glycopyrrolate) to mitigate muscarinic activity; however, sugammadex, devoid of cholinergic activity, does not require anticholinergic coadministration. Single-institution studies have found decreased incidence of post-operative urinary retention associated with sugammadex reversal. This study used a multicenter database to better understand the association between neuromuscular block reversal technique and post-operative urinary retention. DESIGN: Retrospective cohort study utilizing large healthcare database. SETTING: Non-profit, non-governmental and community and teaching hospitals and health systems from rural and urban areas. PATIENTS: 61,898 matched adult inpatients and 95,500 matched adult outpatients. INTERVENTIONS: Neuromuscular block reversal with sugammadex or neostigmine plus glycopyrrolate. MEASUREMENTS: Incidence of post-operative urinary retention by neuromuscular block reversal agent and the independent association of neuromuscular block reversal technique and risk of post-operative urinary retention. MAIN RESULTS: The incidence of post-operative urinary retention was 2-fold greater among neostigmine with glycopyrrolate compared to sugammadex patients (5.0% vs 2.4% inpatients; 0.9% vs 0.4% outpatients; both p < 0.0001). Multivariable logistic regression identified reversal with neostigmine to be independently associated with greater risk of post-operative urinary retention (inpatients: odds ratio, 2.20; 95% confidence interval, 2.00 to 2.41; p < 0.001; outpatients: odds ratio, 2.57; 95% confidence interval, 2.13 to 3.10; p < 0.001). Post-operative urinary retention-related visits within 2 days following discharge were five-fold higher among those reversed with neostigmine than sugammadex among inpatients (0.05% vs. 0.01%, respectively; p = 0.018) and outpatients (0.5% vs. 0.1%; p < 0.0001). CONCLUSION: Though this study suggests that neuromuscular block reversal with neostigmine can increase post-operative urinary retention risk, additional studies are needed to fully understand the association.

Evaluation of a Natural Language Processing Model to Identify and Characterize Patients in the United States With High-Risk Non-Muscle-Invasive Bladder Cancer.

PURPOSE: Treatment of non-muscle-invasive bladder cancer (NMIBC) is guided by risk stratification using clinical and pathologic criteria. This study aimed to develop a natural language processing (NLP) model for identifying patients with high-risk NMIBC retrospectively from unstructured electronic medical records (EMRs) and to apply the model to describe patient and tumor characteristics. METHODS: We used three independent EMR-derived data sets including adult patients with a bladder cancer diagnosis in 2011-2020 for NLP model development and training (n = 140), validation (n = 697), and application for the retrospective cohort analysis (n = 4,402). Deep learning methods were used to train NLP recognition of medical chart terminology to identify seven high-risk NMIBC criteria; model performance was assessed using the F1 score, weighted across features. An algorithm was then used to classify each patient as high-risk NMIBC (yes/no). Manually reviewed records served as the gold standard. RESULTS: The F1 scores after model training were >0.7 for all but one uncommon feature (prostatic urethral involvement). The highest area under the receiver operating curves (AUC) was observed for Ta (0.897) and T1 (0.897); the lowest AUC was for carcinoma in situ (CIS; 0.617). For high-risk NMIBC classification, positive predictive value was 79.4%, negative predictive value was 93.2%, and false-positive rate was 8.9%. Sensitivity and specificity were 83.7% and 91.1%, respectively. Of 748 patients manually confirmed as having high-risk NMIBC, 196 (26%) had CIS (of whom 19% also had T1 and 23% also had Ta disease); 552 tumors (74%) had no associated CIS. CONCLUSION: The NLP model, combined with a rule-based algorithm, identified high-risk NMIBC with good performance and will enable future work to study real-world treatment patterns and clinical outcomes for high-risk NMIBC.

Corrigendum: Real-world treatment patterns and outcomes among individuals receiving first-line pembrolizumab therapy for recurrent/metastatic head and neck squamous cell carcinoma.

[This corrects the article DOI: 10.3389/fonc.2023.1160144.].

Real-world treatment patterns and outcomes among individuals receiving first-line pembrolizumab therapy for recurrent/metastatic head and neck squamous cell carcinoma.

Background: Pembrolizumab, a PD-1 immune checkpoint inhibitor, is approved as first-line (1L) treatment for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) as monotherapy or in combination with platinum and 5-fluorouracil chemotherapy. Limited data exist on the use of these regimens in real-world settings. Objective: Our primary objectives were to describe baseline characteristics and real-world overall survival (rwOS), time on treatment (rwToT), and time to next treatment (rwTTNT) among individuals with R/M HNSCC receiving approved 1L pembrolizumab therapies. We also aimed to identify baseline factors associated with choice of 1L pembrolizumab therapy and with rwOS. Methods: This was a retrospective cohort study of adults with R/M HNSCC receiving 1L pembrolizumab monotherapy or pembrolizumab plus chemotherapy. We used Kaplan-Meier analyses to assess real-world outcomes, logistic regression modeling to identify factors associated with choice of 1L pembrolizumab therapy, and Cox proportional hazards models to identify factors associated with rwOS. Results: The study population included 431 individuals receiving 1L pembrolizumab monotherapy and 215 receiving 1L pembrolizumab plus chemotherapy. The use of 1L pembrolizumab monotherapy was associated with higher baseline combined positive score for PD-L1 expression, older age, higher Eastern Cooperative Oncology Group performance status (ECOG PS), laryngeal tumor site, and human papillomavirus (HPV)-positive tumor status. The pembrolizumab monotherapy group had a median (95% CI) rwOS of 12.1 (9.2-15.1) months, rwToT of 4.2 (3.5-4.6) months, and rwTTNT of 6.5 (5.4-7.4) months. Among this group, HPV-positive tumor status and lower ECOG PS were associated with longer rwOS, and oral cavity tumor site with shorter rwOS. The pembrolizumab plus chemotherapy cohort had a median (95% CI) rwOS of 11.9 (9.0-16.0) months, rwToT of 4.9 (3.8-5.6) months, and rwTTNT of 6.6 (5.8-8.3) months. In this group, HPV-positive tumor status was associated with longer rwOS. Conclusions: This study adds to clinical trial data by summarizing real-world treatment outcomes with 1L pembrolizumab-containing therapies in a more heterogeneous population. Overall survival outcomes in both treatment groups were similar to those observed in the registration clinical trial. These findings support the use of pembrolizumab as standard of care for R/M HNSCC.

Real-world treatment patterns and clinical outcomes for chemotherapy-based regimens in first-line MSI-H/dMMR metastatic colorectal cancer.

MICRO ABSTRACT: This retrospective observational study assessed real-world treatment patterns and clinical outcomes among first-line MSI-H/dMMR metastatic colorectal cancer patients. Of 150 patients in the study cohort, 38.7% were treated with chemotherapy and 61.3% with chemotherapy + EGFR/VEGF inhibitor (EGFRi/VEGFi). Clinical outcomes were better among patients who received chemotherapy + EGFR/VEGF inhibitor than those who received chemotherapy. INTRODUCTION: Prior to pembrolizumab approval in first-line (1L) treatment of MSI-H/dMMR metastatic colorectal cancer (mCRC), patients were managed with chemotherapy with or without an EGFRi or VEGFi, agnostic of biomarker testing or mutation status. This study assessed real-world treatment patterns and clinical outcomes among 1L MSI-H/dMMR mCRC patients treated with standard of care (SOC). PATIENTS AND METHODS: Retrospective observational evaluation of patients ≥18 years diagnosed with stage IV MSI-H/dMMR mCRC who received community-based oncology care. Eligible patients were identified (01-Jun-2017 - 29-Feb-2020) and followed longitudinally until 31-Aug-2020/the last patient record/date of death. Descriptive statistics and Kaplan-Meier analyses were conducted. RESULTS: Of 150 1L MSI-H/dMMR mCRC patients, 38.7% were treated with chemotherapy and 61.3% with chemotherapy + EGFRi/VEGFi. Accounting for censoring, the overall median real-world time to treatment discontinuation (95% CI) was 5.3 (4.4, 5.8) months; 3.0 (2.1, 4.4) and 6.2 (5.5, 7.6) months in the chemotherapy and chemotherapy + EGFRi/VEGFi cohorts, respectively. The combined median overall survival was 27.7 (23.2, not reached [NR]) months; 25.3 (14.5, NR) and 29.8 (23.2, NR) months in the chemotherapy and chemotherapy + EGFRi/VEGFi cohorts, respectively. The overall median real-world progression-free survival was 6.8 (5.3, 7.8) months; 4.2 (2.8, 6.1) and 7.7 (6.1, 10.2) months in the chemotherapy and chemotherapy + EGFRi/VEGFi cohorts, respectively. CONCLUSIONS: 1L MSI-H/dMMR mCRC patients receiving chemotherapy with EGFRi/VEGFi had better outcomes than those receiving only chemotherapy. An unmet need and opportunity to improve outcomes exists in this population that may be addressed by newer treatments like immunotherapies.

Development of a natural language processing algorithm to detect chronic cough in electronic health records.

BACKGROUND: Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC. METHODS: This was a retrospective observational study of enrollees in Optum's Integrated Clinical + Claims Database. Participants were 18-85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC. RESULTS: The positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the 4818 individuals identified as having CC, 37% were identified using NLP-identified cough mentions in provider notes alone, 16% by diagnosis codes and/or written medication orders, and 47% through a combination of provider notes and diagnosis codes/medications. Chronic cough patients were, on average, 61.0 years and 67.0% were female. The most prevalent comorbidities were respiratory infections (75%) and other lower respiratory disease (82%). CONCLUSIONS: Our EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC research studies.

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data.

BACKGROUND: Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. RESULTS: The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. CONCLUSIONS: To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Neuromuscular Blockade and Reversal Practice Variability in the Outpatient Setting: Insights From US Utilization Patterns.

BACKGROUND: Neuromuscular blockade (NMB) is a critical part of many surgical procedures. Data on practice patterns of NMB agents (NMBAs) and NMB reversal in recent years in the US ambulatory surgical care setting are limited. METHODS: This retrospective analysis of US adult outpatients was conducted using the Premier Healthcare Database. We describe anesthesia practice trends in NMB management and assess the association of patient, procedural, and site characteristics with NMB reversal approach using multivariable logistic regression. RESULTS: Approximately 5.2 million outpatient surgical encounters involving NMB and 4.6 million involving rocuronium or vecuronium between January 2014 and June 2019 were included. Following the introduction of sugammadex to US clinical practice (~2016), there was an increased use of rocuronium or vecuronium and a decrease in succinylcholine alone. Before 2016, NMB was pharmacologically reversed with neostigmine in approximately two-thirds of outpatient encounters. Over time, active reversal increased; by 2019, 42.3% and 36.0% of encounters were reversed by neostigmine and sugammadex, respectively, with 21.7% undergoing spontaneous recovery. Choice of NMBA (rocuronium or vecuronium alone), time since 2016, obesity, peripheral vascular disease, and procedures on the digestive, ocular, and female genital systems (vs musculoskeletal procedures) were independently and positively associated with pharmacologic reversal (versus spontaneous reversal). Conversely, advanced age; Western geography; and cardiovascular, endocrine, hemic/lymphatic, respiratory, and ear, nose, and throat procedures were independently and negatively associated with pharmacologic reversal of NMB.Among pharmacologic reversals, time since 2016 was positively and independently associated with sugammadex compared with neostigmine (odds ratios [ORs], ranged from 1.8 in 2017 to 3.2, P < .0001 in 2019). Those administered rocuronium or vecuronium without succinylcholine, with increased age and history of certain comorbidities, and those undergoing ocular or respiratory procedures (compared with musculoskeletal) were positively associated with reversal with sugammadex and endocrine procedure negatively and independently associated with reversal with sugammadex. There was variability in the association of several factors with NMB reversal choices by geographic region, particularly in patients' race, ethnicity, and size of affiliated hospital. CONCLUSIONS: Overall, active pharmacological reversal of NMB increased in US adult outpatients following the introduction of sugammadex, although there remains significant practice variability. The multifactorial relationship between patient-, procedural-, and environmental-level characteristics and NMB management is rapidly evolving. Additional research on how these anesthesia practice patterns may be impacted by the shift to the ambulatory care setting and how they may impact patient outcomes and health disparities is warranted.

Rapid detection and identification of bacteria directly from whole blood with light scattering spectroscopy based biosensor.

Bacterial infections are one of the major causes of death worldwide. The identification of a bacterial species that is the source of an infection generally takes a long time, and often exceeds the treatment window for seriously ill patients. Many of these deaths are preventable if the bacterial species can be identified quickly. Here we present an optical spectroscopic method for rapid detection and identification of bacteria directly from whole blood using a light scattering spectroscopy technique. This technique was originally developed to detect pre-cancerous changes in epithelial tissues, characterize changes in tissue on the cellular scale, and characterize biological structures comparable to or smaller than a single wavelength. We demonstrate here that not only can an inexpensive light scattering spectroscopy-based biosensor rapidly detect and identify four bacteria species in the blood, responsible for the majority of death causing infections, but that species-level identification can potentially be made based on approximately one thousand bacterial cells per milliliter of blood. Observing entire colonies or performing susceptibility testing is therefore not required.

Coherent confocal light scattering spectroscopic microscopy evaluates cancer progression and aggressiveness in live cells and tissue.

The observation of biological structures in live cells beyond the diffraction limit with super-resolution fluorescence microscopy is limited by the ability of fluorescence probes to permeate live cells and the effect of these probes, which are often toxic, on cellular behavior. Here we present a coherent confocal light scattering and absorption spectroscopic microscopy that for the first time enables the use of large numerical aperture optics to characterize structures in live cells down to 10 nm spatial scales, well beyond the diffraction limit. Not only does this new capability allow high resolution microscopy with light scattering contrast, but it can also be used with almost any light scattering spectroscopic application which employs lenses. We demonstrate that the coherent light scattering contrast based technique allows continuous temporal tracking of the transition from non-cancerous to an early cancerous state in live cells, without exogenous markers. We also use the technique to sense differences in the aggressiveness of cancer in live cells and for label free identification of different grades of cancer in resected tumor tissues.

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction. METHODS: Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes). RESULTS: The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data. CONCLUSIONS: By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.

Neuromuscular Blockade and Reversal Agent Practice Variability in the US Inpatient Surgical Settings.

INTRODUCTION: The management of neuromuscular blockade (NMB) has evolved over time and remains a critical component of general anesthesia. However, NMB use varies by patient and procedural characteristics, clinical practices, protocols, and drug access. National utilization patterns are unknown. We describe changes in NMB and NMB reversal agent administration in surgical inpatients since the US introduction of sugammadex in December 2015. METHODS: In a retrospective observational study of inpatients involving NMB with rocuronium or vecuronium in the Premier Healthcare Database, we estimate associations between factors related to choice of (1) active NMB reversal versus spontaneous recovery and (2) sugammadex versus neostigmine as the reversal agent. RESULTS: Among 4.3 million adult inpatient encounters involving rocuronium or vecuronium, the most widely administered NMB agent was rocuronium alone (86%). Over time, gradual declines in both neostigmine use and spontaneous reversal were observed (64% and 36% in 2014 to 38% and 28%, respectively in the first half of 2019). Several factors were independently associated with use of active versus spontaneous NMB recovery including years since 2016, patient (age, race, comorbidities), and procedure (admission and surgery type) characteristics. Among those actively reversed, these and other factors were independently associated with choice of reversal agent administered, including size and teaching affiliation of hospital. While both impacted choices in treatment, the direction and magnitude of effect of patient comorbidities and procedure type varied in their impact on choice of mode (pharmacologic vs. spontaneous) and agent (neostigmine vs. sugammadex) of NMB reversal independent of other factors and each other. Sites which adopted sugammadex earlier were more likely to choose sugammadex over neostigmine compared with later adopters independent of other factors. CONCLUSIONS: Among US adult inpatients administered NMBs, we observed complex relationships between patient, site, procedural characteristics, and NMB management choices as NMBA choice and active reversal options among inpatient cases changed over time.

Neuromuscular blocking agents, medications that temporarily paralyze muscles, are used frequently during surgical procedures to facilitate intubation and patient immobility. Over time, muscle function can return spontaneously or through pharmacological reversal agents. This study looked at how the use of reversal agents in inpatients undergoing surgical procedures changed after a new reversal agent, sugammadex, became available for use in the USA in December 2015.Medical records of 4.3 million adult patients treated with neuromuscular blocking agents (rocuronium or vecuronium) in the USA were studied. In 2014 (before sugammadex was available), one-third of patients (36%) recovered spontaneously from a neuromuscular blocking agent and two-thirds (64%) were treated with the reversal agent neostigmine. The use of both neostigmine and spontaneous recovery reduced gradually after sugammadex became available, so that by the first half of 2019, 38% of patients were treated with neostigmine and 28% of patients recovered spontaneously.Whether or not a patient was treated with a reversal agent and what type of agent was chosen were affected by the length of time since 2016, patient characteristics, the type of surgical procedure that was performed as well as local hospital characteristics and practice differences.

Multispectral Endoscopy with Light Gating for Early Cancer Detection.

This paper reports the application of endoscopic light scattering spectroscopy (LSS) with light gating to detect malignancies in the biliary and pancreatic ducts, and also reviews the application of endoscopic LSS for differentiating cystic neoplasms in the pancreas and detecting invisible dysplasia in Barrett's esophagus. Information about tissue structure within the superficial epithelium where malignancy starts is present within the spectra of reflected light. Fortunately, this component of the reflected light is not yet randomized. However multiple scattering randomizes the signal from the underlying connective tissue which obscures the desired signal. In order to extract diagnostic information from the reflected signal the multiple scattering component related to connective tissue scattering and absorption must be removed. This is accomplished using described here spatial or polarization gating implemented with endoscopically compatible fiber optic probes.

Picoanalysis of Drugs in Biofluids with Quantitative Label-Free Surface-Enhanced Raman Spectroscopy.

The enormous increase of Raman signal in the vicinity of metal nanoparticles allows surface-enhanced Raman spectroscopy (SERS) to be employed for label-free detection of substances at extremely low concentrations. However, the ultimate potential of label-free SERS to identify pharmaceutical compounds at low concentrations, especially in relation to biofluid sensing, is far from being fully realized. Opioids are a particular challenge for rapid clinical identification because their molecular structural similarities prevent their differentiation with immunolabeling approaches. In this paper, a new method called quantitative label-free SERS (QLF-SERS) which involves the formation of halide-conjugated gold nanoclusters trapping the analyte of interest near the SERS hot spots is reported, and it is demonstrated that it yields a 105 fold improvement in the detection limit over previously reported results for the entire class of clinically relevant opioids and their metabolites. Measurements of opioid concentrations in multicomponent mixtures are also demonstrated. QLF-SERS has comparable detection limits as currently existing laboratory urine drug testing techniques but is significantly faster and inexpensive and, therefore, can be easily adapted as part of a rapid clinical laboratory routine.

Multispectral light scattering endoscopic imaging of esophageal precancer.

Esophageal adenocarcinoma is the most rapidly growing cancer in America. Although the prognosis after diagnosis is unfavorable, the chance of a successful outcome increases tremendously if detected early while the lesion is still dysplastic. Unfortunately, the present standard-of-care, endoscopic surveillance, has major limitations, since dysplasia is invisible, often focal, and systematic biopsies typically sample less than one percent of the esophageal lining and therefore easily miss malignancies. To solve this problem we developed a multispectral light scattering endoscopic imaging system. It surveys the entire esophageal lining and accurately detects subcellular dysplastic changes. The system combines light scattering spectroscopy, which detects and identifies invisible dysplastic sites by analyzing light scattered from epithelial cells, with rapid scanning of the entire esophageal lining using a collimated broadband light beam delivered by an endoscopically compatible fiber optic probe. Here we report the results of the first comprehensive multispectral imaging study, conducted as part of routine endoscopic procedures performed on patients with suspected dysplasia. In a double-blind study that characterized the system's ability to serve as a screening tool, 55 out of 57 patients were diagnosed correctly. In addition, a smaller double-blind comparison of the multispectral data in 24 patients with subsequent pathology at locations where 411 biopsies were collected yielded an accuracy of 90% in detecting individual locations of dysplasia, demonstrating the capability of this method to serve as a guide for biopsy.

Light scattering spectroscopy identifies the malignant potential of pancreatic cysts during endoscopy.

Pancreatic cancers are usually detected at an advanced stage and have poor prognosis. About one fifth of these arise from pancreatic cystic lesions. Yet not all lesions are precancerous, and imaging tools lack adequate accuracy for distinguishing precancerous from benign cysts. Therefore, decisions on surgical resection usually rely on endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). Unfortunately, cyst fluid often contains few cells, and fluid chemical analysis lacks accuracy, resulting in dire consequences, including unnecessary pancreatic surgery for benign cysts and the development of cancer. Here, we report an optical spectroscopic technique, based on a spatial gating fibre-optic probe, that predicts the malignant potential of pancreatic cystic lesions during routine diagnostic EUS-FNA procedures. In a double-blind prospective study in 25 patients, with 14 cysts measured in vivo and 13 postoperatively, the technique achieved an overall accuracy of 95%, with a 95%confidence interval of 78-99%, in cysts with definitive diagnosis.

Spectral biomarkers for chemoprevention of colonic neoplasia: a placebo-controlled double-blinded trial with aspirin.

OBJECTIVE: A major impediment to translating chemoprevention to clinical practice has been lack of intermediate biomarkers. We previously reported that rectal interrogation with low-coherence enhanced backscattering spectroscopy (LEBS) detected microarchitectural manifestations of field carcinogenesis. We now wanted to ascertain if reversion of two LEBS markers spectral slope (SPEC) and fractal dimension (FRAC) could serve as a marker for chemopreventive efficacy. DESIGN: We conducted a multicentre, prospective, randomised, double-blind placebo-controlled, clinical trial in subjects with a history of colonic neoplasia who manifested altered SPEC/FRAC in histologically normal colonic mucosa. Subjects (n=79) were randomised to 325 mg aspirin or placebo. The primary endpoint changed in FRAC and SPEC spectral markers after 3 months. Mucosal levels of prostaglandin E2 (PGE2) and UDP-glucuronosyltransferase (UGT)1A6 genotypes were planned secondary endpoints. RESULTS: At 3 months, the aspirin group manifested alterations in SPEC (48.9%, p=0.055) and FRAC (55.4%, p=0.200) with the direction towards non-neoplastic status. As a measure of aspirin's pharmacological efficacy, we assessed changes in rectal PGE2 levels and noted that it correlated with SPEC and FRAC alterations (R=-0.55, p=0.01 and R=0.57, p=0.009, respectively) whereas there was no significant correlation in placebo specimens. While UGT1A6 subgroup analysis did not achieve statistical significance, the changes in SPEC and FRAC to a less neoplastic direction occurred only in the variant consonant with epidemiological evidence of chemoprevention. CONCLUSIONS: We provide the first proof of concept, albeit somewhat underpowered, that spectral markers reversion mirrors antineoplastic efficacy providing a potential modality for titration of agent type/dose to optimise chemopreventive strategies in clinical practice. TRIAL NUMBER: NCT00468910.