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Chan LWC, Wong SCC, Cho WCS, Huang M, Zhang F, Chui ML, Lai UNY, Chan TYK, Cheung ZHC, Cheung JCY, Tang KF, Tse ML, Wong HK, Kwok HMF, Shen X, Zhang S, Chiu KWH. Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography. Diagnostics (Basel) 2022; 13:diagnostics13010102. [PMID: 36611394 PMCID: PMC9818425 DOI: 10.3390/diagnostics13010102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 01/01/2023] Open
Abstract
This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.
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Affiliation(s)
- Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Correspondence: (L.W.C.C.); (K.W.H.C.); Tel.: +852-34008561 (L.W.C.C.)
| | - Sze Chuen Cesar Wong
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Mohan Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Fei Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man Lik Chui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Una Ngo Yin Lai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tiffany Yuen Kwan Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zoe Hoi Ching Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jerry Chun Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kin Fu Tang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man Long Tse
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hung Kit Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hugo Man Fung Kwok
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Sailong Zhang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Keith Wan Hang Chiu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
- Department of Radiology & Imaging, Queen Elizabeth Hospital, Hong Kong SAR, China
- Correspondence: (L.W.C.C.); (K.W.H.C.); Tel.: +852-34008561 (L.W.C.C.)
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Chan LWC, Ding T, Shao H, Huang M, Hui WFY, Cho WCS, Wong SCC, Tong KW, Chiu KWH, Huang L, Zhou H. Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer. Front Oncol 2022; 12:659096. [PMID: 35174074 PMCID: PMC8841850 DOI: 10.3389/fonc.2022.659096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 01/03/2022] [Indexed: 11/16/2022] Open
Abstract
Background Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited. Methods Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People’s Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO). Results The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar’s test p = 0.0003). Conclusions A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.
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Affiliation(s)
- Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Lawrence Wing-Chi Chan, ; William Chi-Shing Cho, ; Haiyu Zhou,
| | - Tong Ding
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Huiling Shao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Mohan Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - William Fuk-Yuen Hui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - William Chi-Shing Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
- *Correspondence: Lawrence Wing-Chi Chan, ; William Chi-Shing Cho, ; Haiyu Zhou,
| | - Sze-Chuen Cesar Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ka Wai Tong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Keith Wan-Hang Chiu
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Luyu Huang
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Thoracic Surgery, Jiangxi Lung Cancer Institute, Jiangxi Cancer Hospital, Nanchang, China
| | - Haiyu Zhou
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Thoracic Surgery, Jiangxi Lung Cancer Institute, Jiangxi Cancer Hospital, Nanchang, China
- *Correspondence: Lawrence Wing-Chi Chan, ; William Chi-Shing Cho, ; Haiyu Zhou,
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Messaoudi R, Mtibaa A, Vacavant A, Gargouri F, Jaziri F. Ontologies for Liver Diseases Representation: A Systematic Literature Review. J Digit Imaging 2019; 33:563-573. [PMID: 31848894 DOI: 10.1007/s10278-019-00303-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia.
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France.
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Faïez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
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Messaoudi R, Labidi T, Vacavant A, Gargouri F, Grand-Brochier M, Amouri A, Fourati H, Mtibaa A, Jaziri F. An Ontological Model for Analyzing Liver Cancer Medical Reports. INFORM SYST 2019. [DOI: 10.1007/978-3-030-11395-7_29] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Messaoudi R, Jaziri F, Mtibaa A, Grand-Brochier M, Ali HM, Amouri A, Fourati H, Chabrot P, Gargouri F, Vacavant A. Ontology-Based Approach for Liver Cancer Diagnosis and Treatment. J Digit Imaging 2018; 32:116-130. [PMID: 30066122 DOI: 10.1007/s10278-018-0115-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia. .,CRNS Laboratory, University of Sfax, Sfax, Tunisia. .,Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia.
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia.,National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Manuel Grand-Brochier
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Hawa Mohamed Ali
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Ali Amouri
- Service d'imagerie médicale, CHU Hédi Chaker, Sfax, Tunisia
| | - Hela Fourati
- Service d'imagerie médicale, CHU Hédi Chaker, Sfax, Tunisia.,Unité de recherche Neuropédiatrie UR12ES16, Sfax, Tunisia
| | - Pascal Chabrot
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Faiez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia.,Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
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