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Li S, Dai Y, Chen J, Yan F, Yang Y. MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges. Cancer Imaging 2024; 24:107. [PMID: 39148139 PMCID: PMC11328409 DOI: 10.1186/s40644-024-00758-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 08/07/2024] [Indexed: 08/17/2024] Open
Abstract
Extensive efforts have been dedicated to exploring the impact of tumor heterogeneity on cancer treatment at both histological and genetic levels. To accurately measure intra-tumoral heterogeneity, a non-invasive imaging technique, known as habitat imaging, was developed. The technique quantifies intra-tumoral heterogeneity by dividing complex tumors into distinct sub- regions, called habitats. This article reviews the following aspects of habitat imaging in cancer treatment, with a focus on radiotherapy: (1) Habitat imaging biomarkers for assessing tumor physiology; (2) Methods for habitat generation; (3) Efforts to combine radiomics, another imaging quantification method, with habitat imaging; (4) Technical challenges and potential solutions related to habitat imaging; (5) Pathological validation of habitat imaging and how it can be utilized to evaluate cancer treatment by predicting treatment response including survival rate, recurrence, and pathological response as well as ongoing open clinical trials.
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Affiliation(s)
- Shaolei Li
- Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai, 201800, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai, 201800, China
| | - Fuhua Yan
- Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai, 201800, China
- Department of Radiology, Ruijin Hospital, Shanghai, 201800, China
| | - Yingli Yang
- Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai, 201800, China.
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Zhang H, Ouyang Y, Zhang H, Zhang Y, Su R, Zhou B, Yang W, Lei Y, Huang B. Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas. Clin Radiol 2024; 79:e682-e691. [PMID: 38402087 DOI: 10.1016/j.crad.2024.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/16/2024] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
AIM To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set. RESULTS The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction. CONCLUSION The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.
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Affiliation(s)
- H Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 517108, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Y Ouyang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - H Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, 518035, China
| | - Y Zhang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - R Su
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - B Zhou
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 517108, China
| | - W Yang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Y Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, 518035, China.
| | - B Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China.
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Zuo W, Li J, Zuo M, Li M, Zhou S, Cai X. Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study. Technol Health Care 2024; 32:2769-2781. [PMID: 38517821 DOI: 10.3233/thc-231980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
BACKGROUND It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.
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Affiliation(s)
- Wanzhao Zuo
- College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China
- College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Jing Li
- Department of Respiratory Medicine, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang Institute of Traditional Chinese Medicine, Xiangyang, Hubei, China
- College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Mingyan Zuo
- Department of Respiratory Medicine, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang Institute of Traditional Chinese Medicine, Xiangyang, Hubei, China
- College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Li
- Department of Respiratory Medicine, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang Institute of Traditional Chinese Medicine, Xiangyang, Hubei, China
| | - Shuang Zhou
- Department of Respiratory Medicine, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang Institute of Traditional Chinese Medicine, Xiangyang, Hubei, China
| | - Xing Cai
- Department of Respiratory Medicine, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang Institute of Traditional Chinese Medicine, Xiangyang, Hubei, China
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Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers (Basel) 2023; 15:3845. [PMID: 37568660 PMCID: PMC10417709 DOI: 10.3390/cancers15153845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.
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Affiliation(s)
- Mehnaz Tabassum
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Eric Suero Molina
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Department of Neurosurgery, University Hospital of Münster, 48149 Münster, Germany
| | - Elizabeth Pan
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Sidong Liu
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
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