Hina Shakir1, Tariq Khan2, Haroon Rasheed1, Yiming Deng3. 1. Department of Electrical EngineeringBahria University Karachi 75620 Pakistan. 2. Department of Electrical and Power EngineeringNational University of Science and Technology Islamabad 75350 Pakistan. 3. Department of Electrical and Computer EngineeringMichigan State University East Lansing MI 48824 USA.
Abstract
OBJECTIVE: To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer. METHODS: While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer. RESULTS: The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods. CONCLUSION: The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.
OBJECTIVE: To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer. METHODS: While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer. RESULTS: The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods. CONCLUSION: The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.
Entities:
Keywords:
Bayesian inversion; cancer stage estimation; nodule classification; particle filter; radiomic features
Authors: Denise R Aberle; Christine D Berg; William C Black; Timothy R Church; Richard M Fagerstrom; Barbara Galen; Ilana F Gareen; Constantine Gatsonis; Jonathan Goldin; John K Gohagan; Bruce Hillman; Carl Jaffe; Barnett S Kramer; David Lynch; Pamela M Marcus; Mitchell Schnall; Daniel C Sullivan; Dorothy Sullivan; Carl J Zylak Journal: Radiology Date: 2010-11-02 Impact factor: 11.105
Authors: Yutong Xie; Yong Xia; Jianpeng Zhang; Yang Song; Dagan Feng; Michael Fulham; Weidong Cai Journal: IEEE Trans Med Imaging Date: 2018-10-17 Impact factor: 10.048
Authors: Wookjin Choi; Jung Hun Oh; Sadegh Riyahi; Chia-Ju Liu; Feng Jiang; Wengen Chen; Charles White; Andreas Rimner; James G Mechalakos; Joseph O Deasy; Wei Lu Journal: Med Phys Date: 2018-03-12 Impact factor: 4.071
Authors: Samuel Hawkins; Hua Wang; Ying Liu; Alberto Garcia; Olya Stringfield; Henry Krewer; Qian Li; Dmitry Cherezov; Robert A Gatenby; Yoganand Balagurunathan; Dmitry Goldgof; Matthew B Schabath; Lawrence Hall; Robert J Gillies Journal: J Thorac Oncol Date: 2016-07-13 Impact factor: 15.609
Authors: Ahmed Abbas Suleiman; Sebastian Frechen; Matthias Scheffler; Thomas Zander; Deniz Kahraman; Carsten Kobe; Jürgen Wolf; Lucia Nogova; Uwe Fuhr Journal: J Thorac Oncol Date: 2015-01 Impact factor: 15.609