Intracellular Doppler spectroscopy and deep learning in clinical trials to personalize cancer chemotherapy

2021 ◽  
Author(s):  
David Nolte ◽  
Ran An ◽  
John Turek
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Cancer Chemotherapy ◽  
Doppler Spectroscopy

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

scholarly journals Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model

2020 ◽  
Author(s):  
Anusha Bompelli ◽  
Jianfu Li ◽  
Yiqi Xu ◽  
Nan Wang ◽  
Yanshan Wang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Dietary Supplements ◽  
Data Model ◽  
Common Data Model ◽  
Recruitment Process ◽  
Eligibility Criteria ◽  
Cohort Identification ◽  
Timely Fashion ◽  
Deep Learning Model

Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.

scholarly journals Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment

2015 ◽  
Vol 11 (12) ◽  
pp. 1489-1499 ◽  
Author(s):  
Vamsi K. Ithapu ◽  
Vikas Singh ◽  
Ozioma C. Okonkwo ◽  
Richard J. Chappell ◽  
N. Maritza Dowling ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithms

Myelotoxicity and Dose Intensity of Chemotherapy: Reporting Practices From Randomized Clinical Trials

2003 ◽  
Vol 1 (3) ◽  
pp. 440-454 ◽  
Author(s):  
David C. Dale ◽  
Gordon C. McCarter ◽  
Jeffrey Crawford ◽  
Gary H. Lyman
Keyword(s):  
Clinical Trials ◽  
Cancer Chemotherapy ◽  
Randomized Clinical Trials ◽  
Early Stage ◽  
Dose Intensity ◽  
Hematologic Toxicity ◽  
Intensity Data ◽  
Reporting Practices ◽  
Standard Procedures ◽  
Treatment Alternatives

Delivery of cancer chemotherapy is often limited by myelotoxicity, primarily neutropenia. As part of an effort to create a model to predict the risk of chemotherapy-induced neutropenia, we reviewed the reports of randomized clinical trials with more than 50 patients per arm in early-stage breast cancer (ESBC) and non-Hodgkin's lymphoma (NHL) published between 1990 and 2000. We observed that no hematologic toxicity data were reported in 39% and 34% of the ESBC and NHL trials, respectively. The remaining trials reported on hematologic toxicity in 16 different ways. When reported, rates of neutropenia, leukopenia, and hematotoxicity varied widely with the same and similar chemotherapy regimens. Dose-intensity data were not reported in 39% and 54% of ESBC and NHL trials, respectively. The majority of the remaining studies reported incomplete dose-intensity data such as percentages of patients completing all cycles or receiving a given percentage of planned dose intensity. Only 28% reported the mean or median relative dose intensity received by patients. Based on this review, we conclude that current practices for reporting chemotherapy treatments are inadequate for describing the risk of chemotherapy to patients or for quantitatively assessing the risk of treatment alternatives. We recommend that standard procedures for documenting and reporting hematologic toxicity and dose intensity in cancer chemotherapy trials be required for publication of chemotherapy trials.

Antioxidants and toxicity of cancer chemotherapy: A systematic review of the evidence from randomized controlled trials

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 9130-9130
Author(s):  
K. I. Block ◽  
A. C. Koch ◽  
M. N. Mead ◽  
P. Tothy ◽  
R. A. Newman ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Chemotherapy ◽  
Statistical Power ◽  
Meta Analysis ◽  
Controlled Clinical Trials ◽  
Randomized Controlled Clinical Trials ◽  
Randomized Controlled ◽  
Standardized Protocol ◽  
Concurrent Use ◽  
Relapsed Disease

9130 Background: Much debate has focused on whether or not antioxidants interfere with the efficacy of cancer chemotherapy. The objective of this study is to systematically review the medical literature to compile results from randomized, controlled clinical trials evaluating the effects of concurrent use of antioxidants with chemotherapy on toxic side effects. Methods: We performed a search of literature from 1966-December 2006 using MEDLINE, Cochrane, CinAhl, AMED, AltHealthWatch and EMBASE databases. Randomized, controlled clinical trials reporting mitigation of chemotherapy toxicity were included in the final tally. The searches were performed in duplicate following a standardized protocol. No meta-analysis was performed due to heterogeneity of cancers and chemotherapy regimens. Results: Of 848 articles considered, 32 trials met the inclusion criteria. Antioxidants evaluated were: glutathione (9), melatonin (6), vitamin A (1), an antioxidant mixture (3), N-acetylcysteine (3), vitamin E (5), selenium (1), L-carnitine (1), Co-Q10 (2) and ellagic acid (1). Subjects of most studies had advanced or relapsed disease. Conclusions: One of the 32 studies included reported evidence of significant increase in toxicity from the concurrent use of antioxidants with chemotherapy. In 18 studies, antioxidant groups experienced significantly lower toxicity than control groups. Statistical power and poor study quality were concerns with some of the studies. We have reported elsewhere that randomized trials of various antioxidants given with chemotherapy did not demonstrate an adverse effect on treatment response or survival. Well-designed studies evaluating larger populations of patients given antioxidants concurrently with chemotherapy are thus warranted. No significant financial relationships to disclose.

scholarly journals Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus

2021 ◽  
Author(s):  
Sohrab Ferdowsi ◽  
Nikolay Borissov ◽  
Julien Knafou ◽  
Poorya Amini ◽  
Douglas Teodoro
Keyword(s):  
Clinical Trials ◽  
Deep Learning
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