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Cho EEL, Law M, Yu Z, Yong JN, Tan CS, Tan EY, Takahashi H, Danpanichkul P, Nah B, Soon GST, Ng CH, Tan DJH, Seko Y, Nakamura T, Morishita A, Chirapongsathorn S, Kumar R, Kow AWC, Huang DQ, Lim MC, Law JH. Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review. Dig Dis Sci 2024:10.1007/s10620-024-08747-5. [PMID: 39708260 DOI: 10.1007/s10620-024-08747-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 11/06/2024] [Indexed: 12/23/2024]
Abstract
BACKGROUND Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging. AIMS As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC. METHODS A systemic search was conducted on Medline and Embase databases from inception to 7th April 2024. Included studies generated a predictive model for TACE response and evaluated its performance by area under the curve (AUC), specificity, or sensitivity analysis. Systematic reviews, meta-analyses, case series and reports, pediatric, and animal studies were excluded. Secondary search of references of included articles ensured comprehensiveness. RESULTS 64 articles, with 13,412 TACE-treated patients, were included. AI in pre-treatment CT scans provided value in predicting the efficacy of TACE in HCC treatment. A positive association was observed for AI in pre-treatment MRI scans. Models incorporating radiomics had numerically better performance than those incorporating manual measured radiological variables. 39 studies demonstrated that combined predictive models had numerically better performance than models based solely on imaging or non-imaging features. CONCLUSION A combined predictive model incorporating clinical features, laboratory investigations, and radiological characteristics may effectively predict response to TACE treatment for HCC.
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Affiliation(s)
- Elina En Li Cho
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Michelle Law
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhenning Yu
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jie Ning Yong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Claire Shiying Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - En Ying Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | | | - Pojsakorn Danpanichkul
- Immunology Unit, Department of Microbiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Benjamin Nah
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Cheng Han Ng
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
- Department of Medicine, Division of Gastroenterology, Kurume University School of Medicine, Fukuoka, Japan
| | - Darren Jun Hao Tan
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Yuya Seko
- Department of Molecular Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine Graduate School of Medical Science, Kyoto, Japan
| | - Toru Nakamura
- Department of Medicine, Division of Gastroenterology, Kurume University School of Medicine, Fukuoka, Japan
| | - Asahiro Morishita
- Department of Gastroenterology and Neurology, Kagawa University School of Medicine, Kagawa, Japan
| | | | - Rahul Kumar
- Department of Gastroenterology, Changi General Hospital, Singapore, Singapore
| | - Alfred Wei Chieh Kow
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- National University Centre for Organ Transplantation, National University Health System, Singapore, Singapore
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital Singapore, Singapore, Singapore
| | - Daniel Q Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Mei Chin Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Diagnostic Imaging, National University Health System, Singapore, Singapore
| | - Jia Hao Law
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital Singapore, Singapore, Singapore.
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Peng K, Zhang X, Li Z, Wang Y, Sun HW, Zhao W, Pan J, Zhang XY, Wu X, Yu X, Wu C, Weng Y, Lin X, Liu D, Zhan M, Xu J, Zheng L, Zhang Y, Lu L. Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma. Front Immunol 2024; 15:1493735. [PMID: 39687612 PMCID: PMC11646988 DOI: 10.3389/fimmu.2024.1493735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 11/12/2024] [Indexed: 12/18/2024] Open
Abstract
Background The potential of preoperative CT in the assessment of myeloid immune response and its application in predicting prognosis and immune-checkpoint therapy outcomes in hepatocellular carcinoma (HCC) has not been explored. Methods A total of 165 patients with pathological slides and multi-phase CT images were included to develop a radiomics signature for predicting the imaging-based myeloid response score (iMRS). Overall survival (OS) and recurrence-free survival (RFS) were assessed according to the iMRS risk group and validated in a surgical resection cohort (n = 98). The complementary advantage of iMRS incorporating significant clinicopathologic factors was investigated by the Cox proportional hazards analysis. Additionally, the iMRS in inferring the benefits of immune checkpoint therapy was explored in an immunotherapy cohort (n = 36). Results We showed that AUCs of the optimal radiomics signature for iMRS were 0.941 [95% confidence interval (CI), 0.909-0.973] and 0.833 (0.798-0.868) in the training and test cohorts, respectively. High iMRS was associated with poor RFS and OS. The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (p < 0.05), with a 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs. progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144-4.672; p = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1. Conclusions iMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.
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Affiliation(s)
- Kangqiang Peng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Medical AI Lab, Hebei Provincial Engineering Research Center for AI-Based Cancer Treatment Decision-Making, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhongliang Li
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
| | - Yongchun Wang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Hong-Wei Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
| | - Wei Zhao
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Department of Management, School of Business, Macau University of Science and Technology, Macau, Macau SAR, China
| | - Jielin Pan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Department of Radiology, Zhuhai People’s Hospital, Jinan University, Zhuhai, China
| | - Xiao-Yang Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Xiaoling Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xiangrong Yu
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Department of Radiology, Zhuhai People’s Hospital, Jinan University, Zhuhai, China
| | - Chong Wu
- Ministry of Education (MOE) Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yulan Weng
- Ministry of Education (MOE) Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xiaowen Lin
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
| | - Dingjie Liu
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- The Department of Cerebrovascular Disease, Zhuhai People’s Hospital, Jinan University, Zhuhai, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Guangzhou First People’s Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jing Xu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Limin Zheng
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
- Ministry of Education (MOE) Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaojun Zhang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ligong Lu
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College), Jinan University, Zhuhai, China
- Guangzhou First People’s Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Vengateswaran HT, Habeeb M, You HW, Aher KB, Bhavar GB, Asane GS. Hepatocellular carcinoma imaging: Exploring traditional techniques and emerging innovations for early intervention. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2024; 24:100327. [DOI: 10.1016/j.medntd.2024.100327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2024] Open
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Nardone V, Reginelli A, Rubini D, Gagliardi F, Del Tufo S, Belfiore MP, Boldrini L, Desideri I, Cappabianca S. Delta radiomics: an updated systematic review. LA RADIOLOGIA MEDICA 2024; 129:1197-1214. [PMID: 39017760 PMCID: PMC11322237 DOI: 10.1007/s11547-024-01853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Dino Rubini
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Federico Gagliardi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Sara Del Tufo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [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: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Yang P, Shan J, Ge X, Zhou Q, Ding M, Niu T, Du J. Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study. Phys Eng Sci Med 2024; 47:295-307. [PMID: 38165634 DOI: 10.1007/s13246-023-01366-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/06/2023] [Indexed: 01/04/2024]
Abstract
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Affiliation(s)
- Pengfei Yang
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jingjing Shan
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xin Ge
- School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Qinxuan Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
| | - Tianye Niu
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
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Sheng J, Lam S, Zhang J, Zhang Y, Cai J. Multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy. Comput Biol Med 2024; 168:107684. [PMID: 38039891 DOI: 10.1016/j.compbiomed.2023.107684] [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: 08/07/2023] [Revised: 10/06/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023]
Abstract
Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.
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Affiliation(s)
- Jiabao Sheng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - SaiKit Lam
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
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Abbas E, Fanni SC, Bandini C, Francischello R, Febi M, Aghakhanyan G, Ambrosini I, Faggioni L, Cioni D, Lencioni RA, Neri E. Delta-radiomics in cancer immunotherapy response prediction: A systematic review. Eur J Radiol Open 2023; 11:100511. [PMID: 37520768 PMCID: PMC10371799 DOI: 10.1016/j.ejro.2023.100511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023] Open
Abstract
Background The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.
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Affiliation(s)
- Engy Abbas
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
| | | | - Claudio Bandini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | | | - Emanuele Neri
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
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Leung VWS, Ng CKC, Lam SK, Wong PT, Ng KY, Tam CH, Lee TC, Chow KC, Chow YK, Tam VCW, Lee SWY, Lim FMY, Wu JQ, Cai J. Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy. J Pers Med 2023; 13:1643. [PMID: 38138870 PMCID: PMC10744672 DOI: 10.3390/jpm13121643] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.
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Affiliation(s)
- Vincent W. S. Leung
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| | - Sai-Kit Lam
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China;
| | - Po-Tsz Wong
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Ka-Yan Ng
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Cheuk-Hong Tam
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Tsz-Ching Lee
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Kin-Chun Chow
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Yan-Kate Chow
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Victor C. W. Tam
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Shara W. Y. Lee
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Fiona M. Y. Lim
- Department of Oncology, Princess Margaret Hospital, Hong Kong SAR, China;
| | - Jackie Q. Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27708, USA;
| | - Jing Cai
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
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