A Simplified Diagnostic Classification Scheme of Chemotherapy-Induced Peripheral Neuropathy

Background and Objective. The main purpose of this study was to develop a simple automatic diagnostic classification scheme for chemotherapy-induced peripheral neuropathy. Methods. This was a prospective cohort study that enrolled patients with colorectal or gynecologic cancer post chemotherapy for more than 1 year. The patients underwent laboratory examinations (nerve conduction studies and quantitative sensory tests), and a questionnaire about the quality of life. An unsupervised classification algorithm was used to classify the patients into groups using a small number of variables derived from the laboratory tests. A panel of five neurologists also diagnosed the types of neuropathies according to the laboratory tests. The results by the unsupervised classification algorithm and the neurologists were compared. Results. The neurologists’ diagnoses showed much higher rates of entrapment syndromes (66.1%) and radiculopathies (55.1%) than polyneuropathy (motor/sensory: 33.1%/29.7%). A multivariate analysis showed that the questionnaire was not significantly correlated with the results of quantitative sensory tests (r=0.27) or the neurologists’ diagnoses (r=0.2). All of the patients were classified into four groups by the unsupervised classification algorithm. The classification corresponded to the severity of neuropathy and correlated well with the neurologists’ diagnoses and the scales of neurological examinations. The overall correct rate of classification by the unsupervised classification algorithm was 78.8% (95% confidence interval: 73.1%-88.3%). Conclusion. The results of our unsupervised classification algorithm based on three variables of laboratory tests correlated well with the neurologists’ diagnoses.

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Measurement Error of a Simplified Protocol for Quantitative Sensory Tests in Chronic Pain Patients

Regional Anesthesia and Pain Medicine ◽

10.1097/aap.0000000000000640 ◽

2017 ◽

Vol 42 (5) ◽

pp. 660-668 ◽

Cited By ~ 1

Author(s):

Monika Müller ◽

José Alberto Biurrun Manresa ◽

Andreas Limacher ◽

Konrad Streitberger ◽

Peter Jüni ◽

...

Keyword(s):

Chronic Pain ◽

Measurement Error ◽

Chronic Pain Patients ◽

Quantitative Sensory Tests ◽

Pain Patients ◽

Sensory Tests

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Buprenorphine blood concentrations: A biomarker for analgesia

Journal of Opioid Management ◽

10.5055/jom.2021.0638 ◽

2021 ◽

Vol 17 (7) ◽

pp. 15-20

Author(s):

Michael Guarnieri, PhD, MPH

Keyword(s):

Clinical Evidence ◽

Acute Postoperative Pain ◽

Blood Concentrations ◽

Patient Reports ◽

Drug Concentrations ◽

Subjective Pain ◽

Nonopioid Analgesics ◽

Pain Reports ◽

Quantitative Sensory Tests ◽

Sensory Tests

Opioids, the frontline drugs for postsurgical analgesia, have been linked to diversion and abuse with lethal consequences. The search for safe analgesics with less harm potential has been decades long. However, clinical trials for safe opioid and nonopioid analgesics have relied on subjective pain reports, which are biased by placebo effects that increase the complexity of trials to develop new therapies to manage pain.Research in opioid naïve animals and humans demonstrates that blood concentrations of opioids that effectively saturate the morphine opioid receptor are tightly linked with patient reports and quantitative sensory tests for analgesia. Opioid drug concentrations can predict clinical responses.This report reviews preclinical and clinical evidence correlating buprenorphine pharmacokinetics with analgesia. More than 30 years of data confirm buprenorphine blood concentrations can be an objective biomarker of analgesia for moderate to severe acute postoperative pain.

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Chemotherapy-induced peripheral neuropathy in gynecologic cancer patients: risk factors and functional impact

Journal of Pain ◽

10.1016/j.jpain.2017.12.231 ◽

2018 ◽

Vol 19 (3) ◽

pp. S101-S102

Author(s):

H. Bulls ◽

A. Hoogland ◽

N. Chahal ◽

B. Small ◽

B. Gonzalez ◽

...

Keyword(s):

Risk Factors ◽

Peripheral Neuropathy ◽

Cancer Patients ◽

Gynecologic Cancer ◽

Functional Impact

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A feature reduction and unsupervised classification algorithm for multispectral data

Pattern Recognition ◽

10.1016/0031-3203(84)90020-7 ◽

1984 ◽

Vol 17 (6) ◽

pp. 667-676 ◽

Cited By ~ 14

Author(s):

K. Chidananda Gowda

Keyword(s):

Unsupervised Classification ◽

Classification Algorithm ◽

Feature Reduction ◽

Multispectral Data

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Unsupervised classification of snowflake images using a generative adversarial network and <i>K</i>-medoids classification

Atmospheric Measurement Techniques ◽

10.5194/amt-13-2949-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 2949-2964

Author(s):

Jussi Leinonen ◽

Alexis Berne

Keyword(s):

Classification Scheme ◽

Feature Space ◽

Unsupervised Classification ◽

Automated Classification ◽

Generative Adversarial Network ◽

Microphysical Properties ◽

Adversarial Network ◽

Computationally Intensive ◽

Comparison Of The Results ◽

Supervised Classification Methods

Abstract. The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.

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Repeatability of quantitative sensory testing in healthy cats in a clinical setting with comparison to cats with osteoarthritis

Journal of Feline Medicine and Surgery ◽

10.1177/1098612x17690653 ◽

2017 ◽

Vol 19 (12) ◽

pp. 1274-1282 ◽

Cited By ~ 12

Author(s):

Elena S Addison ◽

Dylan N Clements

Keyword(s):

Clinical Setting ◽

Thermal Sensitivity ◽

Sensory Threshold ◽

Vertical Force ◽

Sensory Testing ◽

Sensitivity Testing ◽

Threshold Testing ◽

Quantitative Sensory Tests ◽

Sensory Tests ◽

Plate System

Objectives The aim of this study was to evaluate the repeatability of quantitative sensory tests (QSTs) in a group of healthy untrained cats (n = 14) and to compare the results with those from cats with osteoarthritis (n = 7). Methods Peak vertical force (PVF) and vertical impulse were measured on a pressure plate system. Thermal sensitivity was assessed using a temperature-controlled plate at 7°C and 40°C. Individual paw lifts and overall duration of paw lifts were counted and measured for each limb. Paw withdrawal thresholds were measured using manual and electronic von Frey monofilaments (MVF and EVF, respectively) applied to the metacarpal or metatarsal pads. All measurements were repeated twice to assess repeatability of the tests. Results In healthy cats all tests were moderately repeatable. When compared with cats with osteoarthritis the PVF was significantly higher in healthy hindlimbs in repeat 1 but not in repeat 2. Cats with osteoarthritis of the forelimbs showed a decrease in the frequency of paw lifts on the 7°C plate compared with cats with healthy forelimbs, and the duration of paw lifts was significantly less than healthy forelimbs in the first repeat but not in the second repeat. Osteoarthritic limbs had significantly lower paw withdrawal thresholds with both MVF and EVF than healthy limbs. Conclusions and relevance QSTs are moderately repeatable in untrained cats. Kinetic gait analysis did not permit differentiation between healthy limbs and those with osteoarthritis, but thermal sensitivity testing (cold) does. Sensory threshold testing can differentiate osteoarthritic and healthy limbs, and may be useful in the diagnosis and monitoring of this condition in cats in the clinical setting.

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