Narendra N Khanna1, Ankush D Jamthikar2, Tadashi Araki3, Deep Gupta2, Matteo Piga4, Luca Saba5, Carlo Carcassi6, Andrew Nicolaides7,8, John R Laird9, Harman S Suri10, Ajay Gupta11, Sophie Mavrogeni12, George D Kitas13,14, Jasjit S Suri15. 1. Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India. 2. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India. 3. Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan. 4. Department of Rheumatology, University Clinic and AOU of Cagliari, Cagliari, Italy. 5. Department of Radiology, University of Cagliari, Cagliari, Italy. 6. Department of Genetics, University of Cagliari, Cagliari, Italy. 7. Department of Vascular Surgery, Imperial College, London, UK. 8. Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus. 9. Heart and Vascular Institute, Adventist Health St. Helena, St Helena, California. 10. Brown University, Providence, Rhode Island. 11. Department of Radiology and Feil Family Brain and Mind Research Institute, Weill Cornell Medical Center, New York, New York. 12. Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece. 13. Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester, UK. 14. Director of Research & Development-Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK. 15. Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, California.
Abstract
MOTIVATION: This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY: Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.
MOTIVATION: This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY: Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.
Authors: Ankush Jamthikar; Deep Gupta; Narendra N Khanna; Tadashi Araki; Luca Saba; Andrew Nicolaides; Aditya Sharma; Tomaz Omerzu; Harman S Suri; Ajay Gupta; Sophie Mavrogeni; Monika Turk; John R Laird; Athanasios Protogerou; Petros P Sfikakis; George D Kitas; Vijay Viswanathan; Gyan Pareek; Martin Miner; Jasjit S Suri Journal: Curr Atheroscler Rep Date: 2019-05-01 Impact factor: 5.113
Authors: Ankush D Jamthikar; Deep Gupta; Laura E Mantella; Luca Saba; John R Laird; Amer M Johri; Jasjit S Suri Journal: Int J Cardiovasc Imaging Date: 2020-11-12 Impact factor: 2.357
Authors: Ankush Jamthikar; Deep Gupta; Luca Saba; Narendra N Khanna; Tadashi Araki; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Vijay Viswanathan; Aditya Sharma; Andrew Nicolaides; George D Kitas; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2020-08
Authors: Ankush Jamthikar; Deep Gupta; Elisa Cuadrado-Godia; Anudeep Puvvula; Narendra N Khanna; Luca Saba; Klaudija Viskovic; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Chithra Shankar; Andrew Nicolaides; Vijay Viswanathan; Aditya Sharma; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2020-08
Authors: Ankush Jamthikar; Deep Gupta; Narendra N Khanna; Luca Saba; Tadashi Araki; Klaudija Viskovic; Harman S Suri; Ajay Gupta; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Vijay Viswanathan; Andrew Nicolaides; Deepak L Bhatt; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2019-10
Authors: Mainak Biswas; Luca Saba; Tomaž Omerzu; Amer M Johri; Narendra N Khanna; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Antonella Balestrieri; Petros P Sfikakis; Athanasios Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Aditya Sharma; Vijay Viswanathan; Zoltan Ruzsa; Andrew Nicolaides; Jasjit S Suri Journal: J Digit Imaging Date: 2021-06-02 Impact factor: 4.903
Authors: Jasjit S Suri; Anudeep Puvvula; Mainak Biswas; Misha Majhail; Luca Saba; Gavino Faa; Inder M Singh; Ronald Oberleitner; Monika Turk; Paramjit S Chadha; Amer M Johri; J Miguel Sanches; Narendra N Khanna; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanasios Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Puneet Ahluwalia; Raghu Kolluri; Jagjit Teji; Mustafa Al Maini; Ann Agbakoba; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Janet N A Ajuluchukwu; Mostafa Fatemi; Azra Alizad; Vijay Viswanathan; Pudukode R Krishnan; Subbaram Naidu Journal: Comput Biol Med Date: 2020-08-14 Impact factor: 4.589