1
|
Garbarino GM, Polici M, Caruso D, Laghi A, Mercantini P, Pilozzi E, van Berge Henegouwen MI, Gisbertz SS, van Grieken NCT, Berardi E, Costa G. Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience. Cancers (Basel) 2024; 16:2664. [PMID: 39123392 PMCID: PMC11311587 DOI: 10.3390/cancers16152664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/20/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. METHODS The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. RESULTS Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. CONCLUSIONS Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.
Collapse
Affiliation(s)
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Paolo Mercantini
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Emanuela Pilozzi
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Mark I. van Berge Henegouwen
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Suzanne S. Gisbertz
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Nicole C. T. van Grieken
- Department of Pathology, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Biology and Immunology, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Eva Berardi
- Department of Radiology, San Camillo Hospital, ASL RM 1, 00152 Rome, Italy
| | - Gianluca Costa
- Department of Life Science, Health and Health Professions, Link Campus University, 00165 Rome, Italy
| |
Collapse
|
2
|
Ruby L, Jayaprakasam VS, Fernandes MC, Paroder V. Advances in the Imaging of Esophageal and Gastroesophageal Junction Malignancies. Hematol Oncol Clin North Am 2024; 38:711-730. [PMID: 38575457 DOI: 10.1016/j.hoc.2024.02.003] [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: 04/06/2024]
Abstract
Accurate imaging is key for the diagnosis and treatment of esophageal and gastroesophageal junction cancers . Current imaging modalities, such as computed tomography (CT) and 18F-FDG (2-deoxy-2-[18F]fluoro-D-glucose) positron emission tomography (PET)/CT, have limitations in accurately staging these cancers. MRI shows promise for T staging and residual disease assessment. Novel PET tracers, like FAPI, FLT, and hypoxia markers, offer potential improvements in diagnostic accuracy. 18F-FDG PET/MRI combines metabolic and anatomic information, enhancing disease evaluation. Radiomics and artificial intelligence hold promise for early detection, treatment planning, and response assessment. Theranostic nanoparticles and personalized medicine approaches offer new avenues for cancer therapy.
Collapse
Affiliation(s)
- Lisa Ruby
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Vetri Sudar Jayaprakasam
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| |
Collapse
|
3
|
Li Z, Wang F, Zhang H, Xie S, Peng L, Xu H, Wang Y. A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108052. [PMID: 38447320 DOI: 10.1016/j.ejso.2024.108052] [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: 10/28/2023] [Revised: 01/24/2024] [Accepted: 02/21/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans. METHODS Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set. RESULTS Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749. CONCLUSION Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.
Collapse
Affiliation(s)
- Zhiyang Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Fuqiang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Shenglong Xie
- Department of Thoracic Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Peng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, China.
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China.
| |
Collapse
|
4
|
Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
Collapse
Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
5
|
Guo H, Tang HT, Hu WL, Wang JJ, Liu PZ, Yang JJ, Hou SL, Zuo YJ, Deng ZQ, Zheng XY, Yan HJ, Jiang KY, Huang H, Zhou HN, Tian D. The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy. Front Oncol 2023; 13:1082960. [PMID: 37091180 PMCID: PMC10117779 DOI: 10.3389/fonc.2023.1082960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/27/2023] [Indexed: 04/25/2023] Open
Abstract
Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.
Collapse
Affiliation(s)
- Hai Guo
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Thoracic Surgery, Sichuan Tianfu New Area People’s Hospital, Chengdu, China
| | - Hong-Tao Tang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Wen-Long Hu
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Wang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Pei-Zhi Liu
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Yang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Sen-Lin Hou
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Yu-Jie Zuo
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Zhi-Qiang Deng
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Xiang-Yun Zheng
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Hao-Ji Yan
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kai-Yuan Jiang
- Department of Surgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Heng Huang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hai-Ning Zhou
- Department of Thoracic Surgery, Suining Central Hospital, Suining, China
- *Correspondence: Dong Tian, ; Hai-Ning Zhou,
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Dong Tian, ; Hai-Ning Zhou,
| |
Collapse
|
6
|
Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
Collapse
Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| |
Collapse
|
7
|
Liu X, Hu X, Yu X, Li P, Gu C, Liu G, Wu Y, Li D, Wang P, Cai J. Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature. Front Oncol 2022; 12:965773. [PMID: 36176388 PMCID: PMC9513237 DOI: 10.3389/fonc.2022.965773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To illustrate the knowledge hotspots and cutting-edge research trends of 18F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed. Methods Studies related to 18F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of 18F-FDG PET/CT radiomics in this field utilizes a citation burst detection method. Results Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. 18F-FDG PET/CT radiomics–related literature was frequently published with high citation in European Journal of Nuclear Medicine and Molecular Imaging (IF9.236, 2020), Frontiers in Oncology (IF6.244, 2020), and Cancers (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in 18F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of 18F-FDG PET/CT radiomics features. Conclusion First, this bibliometric study provides a new perspective on 18F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of 18F-FDG PET/CT. Second, the international research frontiers lie in applying 18F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of 18F-FDG PET/CT radiomics features.
Collapse
Affiliation(s)
- Xinghai Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Xianwen Hu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xiao Yu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Pujiao Li
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Cheng Gu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Guosheng Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Yan Wu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Dandan Li
- Department of Obstetrics, Zunyi Hospital of Traditional Chinese Medicine, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Pan Wang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Jiong Cai
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| |
Collapse
|
8
|
Could [18F]FDG PET/CT or PET/MRI Be Useful in Patients with Skull Base Osteomyelitis? Diagnostics (Basel) 2022; 12:diagnostics12092035. [PMID: 36140437 PMCID: PMC9497608 DOI: 10.3390/diagnostics12092035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 11/24/2022] Open
|