AUTOMATED 5-YEAR MORTALITY PREDICTION USING DEEP LEARNING AND RADIOMICS FEATURES FROM CHEST COMPUTED TOMOGRAPHY

被引:0
作者
Carneiro, Gustavo [1 ]
Oakden-Rayner, Luke [2 ]
Bradley, Andrew P. [3 ]
Nascimento, Jacinto [4 ]
Palmer, Lyle [4 ]
机构
[1] Univ Adelaide, Australian Ctr Visual Technol, Adelaide, SA, Australia
[2] Univ Adelaide, Sch Publ Hlth, Adelaide, SA, Australia
[3] Univ Queensland, Sch ITEE, Brisbane, Qld, Australia
[4] Inst Super Tecn, Inst Syst & Robot, Lisbon, Portugal
来源
2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017) | 2017年
基金
澳大利亚研究理事会;
关键词
deep learning; radiomics; feature learning; hand-designed features; computed tomography; five-year mortality; CT;
D O I
暂无
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning models extended to 3-D inputs, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection and extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning models produces a mean 5-year mortality prediction AUC in [68.8%,69.8%] and accuracy in [64.5%,66.5%], while radiomics produces a mean AUC of 64.6% and accuracy of 64.6%. The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.
引用
收藏
页码:130 / 134
页数:5
相关论文
共 50 条
[41]   Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning [J].
D'Anniballe, Vincent M. ;
Tushar, Fakrul Islam ;
Faryna, Khrystyna ;
Han, Songyue ;
Mazurowski, Maciej A. ;
Rubin, Geoffrey D. ;
Lo, Joseph Y. .
BMC MEDICAL INFORMATICS AND DECISION MAKING, 2022, 22 (01)
[42]   Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction [J].
Hong, Yuan ;
Zhang, Peng ;
Teng, Zhijun ;
Cheng, Kang ;
Zhang, Zimo ;
Cheng, Yixian ;
Cao, Guodong ;
Chen, Bo .
SCIENTIFIC REPORTS, 2025, 15 (01)
[43]   Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study [J].
Bae, Joseph ;
Kapse, Saarthak ;
Singh, Gagandeep ;
Gattu, Rishabh ;
Ali, Syed ;
Shah, Neal ;
Marshall, Colin ;
Pierce, Jonathan ;
Phatak, Tej ;
Gupta, Amit ;
Green, Jeremy ;
Madan, Nikhil ;
Prasanna, Prateek .
DIAGNOSTICS, 2021, 11 (10)
[44]   Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data [J].
Huang, Weijia ;
Wang, Congjun ;
Wang, Ye ;
Yu, Zhu ;
Wang, Shengyu ;
Yang, Jian ;
Lu, Shunzu ;
Zhou, Chunyi ;
Wu, Erlv ;
Chen, Junqiang .
CLINICAL NUTRITION, 2024, 43 (03) :881-891
[45]   Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features [J].
Wang, Haoru ;
He, Ling ;
Chen, Xin ;
Ding, Shuang ;
Xie, Mingye ;
Cai, Jinhua .
TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2024, 23
[46]   Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning [J].
Han, Jung Hoon ;
Ha, Sue Young ;
Lee, Hoyeon ;
Park, Gi-Hun ;
Hong, Hotak ;
Kim, Dongmin ;
Kim, Jae Guk ;
Kim, Joon-Tae ;
Sunwoo, Leonard ;
Kim, Chi Kyung ;
Ryu, Wi-Sun .
FRONTIERS IN NEUROLOGY, 2024, 15
[47]   Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features [J].
Pourhoseingholi, Asma ;
Vahedi, Mohsen ;
Chaibakhsh, Samira ;
Pourhoseingholi, Mohamad Amin ;
Vahedian--Azimi, Amir ;
Guest, Paul C. ;
Rahimi-Bashar, Farshid ;
Sahebkar, Amirhossein .
IDENTIFCATION OF BIOMARKERS, NEW TREATMENTS, AND VACCINES FOR COVID-19, 2021, 1327 :139-147
[48]   Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization [J].
Yeow, Ling Yun ;
Teh, Yu Xuan ;
Lu, Xinyu ;
Srinivasa, Arvind Channarayapatna ;
Tan, Eelin ;
Tan, Timothy Shao Ern ;
Tang, Phua Hwee ;
Kn, Bhanu Prakash .
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2023, 47 (05) :786-795
[49]   A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography [J].
Choi, Hyewon ;
Kim, Hyungjin ;
Jin, Kwang Nam ;
Jeong, Yeon Joo ;
Chae, Kum Ju ;
Lee, Kyung Hee ;
Yong, Hwan Seok ;
Gil, Bomi ;
Lee, Hye-Jeong ;
Lee, Ki Yeol ;
Jeon, Kyung Nyeo ;
Yi, Jaeyoun ;
Seo, Sola ;
Ahn, Chulkyun ;
Lee, Joonhyung ;
Oh, Kyuhyup ;
Goo, Jin Mo .
JOURNAL OF THORACIC IMAGING, 2022, 37 (04) :253-261
[50]   Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography [J].
Ye, Guanchao ;
Wu, Guangyao ;
Li, Kuo ;
Zhang, Chi ;
Zhuang, Yuzhou ;
Song, Enmin ;
Liu, Hong ;
Qi, Yu ;
Li, Yiying ;
Yang, Fan ;
Liao, Yongde .
ACADEMIC RADIOLOGY, 2024, 31 (04) :1686-1697