NDLI: Leveraging hybrid biomarkers in clinical endpoint prediction

Content Provider	Springer Nature : BioMed Central
Author	Saad, Maliazurina Lee, Ik Hyun
Abstract	Background Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60 and 80%. An accurate prognosis assessment is required for making effective clinical decisions. Methods We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). Results The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77 to 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002–0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6–2.2 years longer (mean survival) and 0.5–2.0 years longer (median survival), than those with less dense composition (non-solid dominant). Conclusion The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-020-01262-3.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-020-01262-3
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2020-10-07
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Predictive models Clinical decision- making Biomarker Imaging Endpoint
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improved preventive care clinical decision-making efficiency: leveraging a point-of-care clinical decision support system

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care

Automated extraction of Biomarker information from pathology reports

Unsatisfactory colposcopy: clinical decision-making in conditions of uncertainty

BMC Medical Informatics and Decision Making

Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients

Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support

Context factors in clinical decision-making: a scoping review

Leveraging hybrid biomarkers in clinical endpoint prediction

Similar Documents

Improved preventive care clinical decision-making efficiency: leveraging a point-of-care clinical decision support system

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care

Automated extraction of Biomarker information from pathology reports

Unsatisfactory colposcopy: clinical decision-making in conditions of uncertainty

BMC Medical Informatics and Decision Making

Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients

Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support

Context factors in clinical decision-making: a scoping review

Leveraging hybrid biomarkers in clinical endpoint prediction