Endoscopic submucosal dissection with versus without traction for pathologically staged T1B esophageal cancer: a multicenter retrospective study.

BACKGROUND AND AIMS: Positive vertical margins (VMs) are common after endoscopic submucosal dissection (ESD) of T1b esophageal cancer (EC) and are associated with an increased risk of recurrence. Traction during ESD provides better exposure of the submucosa and may allow deeper dissection, potentially reducing the risk of positive VMs. We conducted a retrospective multicenter study to compare the proportion of resections with positive VMs in ESD performed with versus without traction in pathologically staged T1b EC. METHODS: Patients who underwent ESD revealing T1b EC (squamous or adenocarcinoma) at 10 academic tertiary referral centers in the United States (n = 9) and Brazil (n = 1) were included. Demographic and clinical data were abstracted. ESD using either traction techniques (tunneling, pocket) or traction devices (clip line, traction wire) were classified as ESD with traction (Tr-ESD) and those without were classified as conventional ESD without traction. The primary outcome was a negative VM. Multivariable logistic regression was used to assess associations with negative VMs. RESULTS: A total of 166 patients with pathologically staged T1b EC underwent Tr-ESD (n = 63; 38%) or conventional ESD without traction (n = 103; 62%). Baseline factors were comparable between both groups. On multivariable analysis, Tr-ESD was found to be independently associated with negative VMs (odds ratio, 2.25; 95% confidence interval, 1.06-4.91; P = .037) and R0 resection (odds ratio, 2.83; 95% confidence interval, 1.33-6.23; P = .008). CONCLUSION: Tr-ESD seems to be associated with higher odds of negative VMs than ESD without traction for pathologically staged T1b EC, and future well-conducted prospective studies are warranted to establish the findings of the current study.

Outcomes for endoscopic submucosal dissection of pathologically staged T1b esophageal cancer: a multicenter study.

BACKGROUND AND AIMS: The outcomes of endoscopic submucosal dissection (ESD) for T1b esophageal cancer (EC) and its recurrence rates remain unclear in the West. Using a multicenter cohort, we evaluated technical outcomes and recurrence rates of ESD in the treatment of pathologically staged T1b EC. METHODS: We included patients who underwent ESD of T1b EC at 7 academic tertiary referral centers in the United States (n = 6) and Brazil (n = 1). We analyzed demographic, procedural, and histopathologic characteristics and follow-up data. Time-to-event analysis was performed to evaluate recurrence rates. RESULTS: Sixty-six patients with pathologically staged T1b EC after ESD were included in the study. A preprocedure staging EUS was available in 54 patients and was Tis/T1a in 27 patients (50%) and T1b in 27 patients (50%). En-bloc resection rate was 92.4% (61/66) and R0 resection rate was 54.5% (36/66). Forty-nine of 66 patients (74.2%) did not undergo surgery immediately after resection and went on to surveillance. Ten patients had ESD resection within the curative criteria, and no recurrences were seen in a 13-month (range, 3-18.5) follow-up period in these patients. Ten of 39 patients (25.6%) with noncurative resections had residual/recurrent disease. Of the 10 patients with noncurative resection, local recurrence alone was seen in 5 patients (12.8%) and metastatic recurrence in 5 patients (12.8%). On univariate analysis, R1 resection had a higher risk of recurrent disease (hazard ratio, 6.25; 95% confidence interval, 1.29-30.36; P = .023). CONCLUSIONS: EUS staging of T1b EC has poor accuracy, and a staging ESD should be considered in these patients. ESD R0 resection rates were low in T1b EC, and R1 resection was associated with recurrent disease. Patients with noncurative ESD resection of T1b EC who cannot undergo surgery should be surveyed closely, because recurrent disease was seen in 25% of these patients.

Early-Onset Pancreas Cancer: Clinical Descriptors, Genomics, and Outcomes.

BACKGROUND: Recent evidence suggests a rising incidence of cancer in younger individuals. Herein, we report the epidemiologic, pathologic, and molecular characteristics of a patient cohort with early-onset pancreas cancer (EOPC). METHODS: Institutional databases were queried for demographics, treatment history, genomic results, and outcomes. Overall survival from date of diagnosis was estimated using Kaplan-Meier method. RESULTS: Between 2008 and 2018, 450 patients with EOPC were identified at Memorial Sloan Kettering. Median overall survival was 16.3 (95% confidence interval [CI] = 14.6 to 17.7) months in the entire cohort and 11.3 (95% CI = 10.2 to 12.2) months for patients with stage IV disease at diagnosis. Of the patients, 132 (29.3% of the cohort) underwent somatic testing; 21 of 132 (15.9%) had RAS wild-type cancers with identification of several actionable alterations, including ETV6-NTRK3, TPR-NTRK1, SCLA5-NRG1, and ATP1B1-NRG1 fusions, IDH1 R132C mutation, and mismatch repair deficiency. A total of 138 patients (30.7% of the cohort) underwent germline testing; 44 of 138 (31.9%) had a pathogenic germline variant (PGV), and 27.5% harbored alterations in cancer susceptibility genes. Of patients seen between 2015 and 2018, 30 of 193 (15.5%) had a PGV. Among 138 who underwent germline testing, those with a PGV had a reduced all-cause mortality compared with patients without a PGV controlling for stage and year of diagnosis (hazard ratio = 0.42, 95% CI = 0.26 to 0.69). CONCLUSIONS: PGVs are present in a substantial minority of patients with EOPC. Actionable somatic alterations were identified frequently in EOPC, enriched in the RAS wild-type subgroup. These observations underpin the recent guidelines for universal germline testing and somatic profiling in pancreatic ductal adenocarcinoma.

Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture.

OBJECTIVE: Atrial fibrillation (AF) and other types of abnormal heart rhythm are related to multiple fatal cardiovascular diseases that affect the quality of human life. Hence the development of an automated robust method that can reliably detect AF, in addition to other non-sinus and sinus rhythms, would be a valuable addition to medicine. The present study focuses on developing an algorithm for the classification of short, single-lead electrocardiogram (ECG) recordings into normal, AF, other abnormal rhythms and noisy classes. APPROACH: The proposed classification framework presents a two-layer, three-node architecture comprising binary classifiers. PQRST markers are detected on each ECG recording, followed by noise removal using a spectrogram power based novel adaptive thresholding scheme. Next, a feature pool comprising time, frequency, morphological and statistical domain ECG features is extracted for the classification task. At each node of the classification framework, suitable feature subsets, identified through feature ranking and dimension reduction, are selected for use. Adaptive boosting is selected as the classifier for the present case. The training data comprises 8528 ECG recordings provided under the PhysioNet 2017 Challenge. F1 scores averaged across the three non-noisy classes are taken as the performance metric. MAIN RESULT: The final five-fold cross-validation score achieved by the proposed framework on the training data has high accuracy with low variance (0.8254 [Formula: see text] 0.0043). SIGNIFICANCE: Further, the proposed algorithm has achieved joint first place in the PhysioNet/Computing in Cardiology Challenge 2017 with a score of 0.83 computed on a hidden test dataset.

Pattern Analysis in Physiological Pulsatile Signals: An Aid to Personalized Healthcare.

We present a system to analyze patterns inside pulsatile signals and discover repetitions inside signals. We measure dominance of the repetitions using morphology and discrete nature of the signals by exploiting machine learning and information theoretic concepts. Patterns are represented as combinations of the basic features and derived features. Consistency of discovered patterns identifies state of physiological stability which varies from one individual to another. Hence it has immense impact on deriving the accurate physiological parameters for personalized health analytics. Proposed mechanism discovers the regular and irregular patterns by performing extensive analysis on several real life cardiac data sets. We have achieved more than 90% accuracy in identifying irregular patterns using our proposed method.

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip.

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing.

Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner.

Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection. Thus, noise detection and elimination are crucial to ensure accurate clinical analysis. In this paper, we present a robust denoising technique, Proclean that precisely detects the noisy PCG signal through pattern recognition, and statistical learning. We propose a novel self-discriminant learner that ensures to obtain distinct feature set to distinguish clean and noisy PCG signals without human-in-loop. We demonstrate that our proposed denoising leads to higher accuracy in subsequent clinical analytics for medical investigation. Our extensive experimentations with publicly available MIT-Physionet datasets show that we achieve more than 85% accuracy for noisy PCG signal detection. Further, we establish that physiological abnormality detection improves by more than 20%, when our proposed denoising mechanism is applied.

A Fully Integrated CMOS Fluorescence Biochip for DNA and RNA Testing.

Design and successful implementation of a fully-integrated CMOS fluorescence biochip for DNA/RNA testing in molecular diagnostics (MDx) is presented. The biochip includes a 32×32 array of continuous wave fluorescence detection biosensing elements. Each biosensing element is capable of having unique DNA probe sequences, wavelength-selective multi-dielectric emission filter (OD of 3.6), resistive heater for thermal cycling, and a high performance and programmable photodetector. The dimension of each biosensor is 100µm×100µm with a 50µm×50µm Nwell-Psub photodiode acting as the optical transducer, and a ΣΔ modulator based photocurrent sensor. The measured photodetector performance shows ~116 dB detection dynamic range (10fA - 10nA) over the 25°C - 100°C temperature range, while being ~1 dB away from the fundamental shot-noise limit. To empirically demonstrate the compatibility of this biochip with MDx applications, we have successfully utilized the array and its thermal cycling capability to adopt a 7-plex panel for detection of 6 human upper respiratory viruses.

An unsupervised learning for robust cardiac feature derivation from PPG signals.

We propose here derivation algorithms for physiological parameters like beat start point, systolic peak, pulse duration, peak-to-peak distance related to heart rate, dicrotic minima, diastolic peak from Photoplethysmogram (PPG) signals robustly. Our methods are based on unsupervised learning mainly following morphology as well as discrete nature of the signal. Statistical learning has been used as a special aid to infer most probable feature values mainly to cope up with presence of noise, which is assumed to be insignificant compared to signal values at each investigation window. Performance of the proposed method is found to be better than other standard methods, yielding precision and sensitivity more than 97% obtained from three real life data sets.