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Zhang Y, Li L, Wang J, Yang X, Zhou H, He J, Xie Y, Jiang Y, Sun W, Zhang X, Zhou G, Zhang Z. Texture-preserving diffusion model for CBCT-to-CT synthesis. Med Image Anal 2025; 99:103362. [PMID: 39393132 DOI: 10.1016/j.media.2024.103362] [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: 12/19/2023] [Revised: 08/07/2024] [Accepted: 09/29/2024] [Indexed: 10/13/2024]
Abstract
Cone beam computed tomography (CBCT) serves as a vital imaging modality in diverse clinical applications, but is constrained by inherent limitations such as reduced image quality and increased noise. In contrast, computed tomography (CT) offers superior resolution and tissue contrast. Bridging the gap between these modalities through CBCT-to-CT synthesis becomes imperative. Deep learning techniques have enhanced this synthesis, yet challenges with generative adversarial networks persist. Denoising Diffusion Probabilistic Models have emerged as a promising alternative in image synthesis. In this study, we propose a novel texture-preserving diffusion model for CBCT-to-CT synthesis that incorporates adaptive high-frequency optimization and a dual-mode feature fusion module. Our method aims to enhance high-frequency details, effectively fuse cross-modality features, and preserve fine image structures. Extensive validation demonstrates superior performance over existing methods, showcasing better generalization. The proposed model offers a transformative pathway to augment diagnostic accuracy and refine treatment planning across various clinical settings. This work represents a pivotal step toward non-invasive, safer, and high-quality CBCT-to-CT synthesis, advancing personalized diagnostic imaging practices.
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Affiliation(s)
| | - Li Li
- JancsiLab, JancsiTech, Hong Kong, China
| | - Jie Wang
- JancsiLab, JancsiTech, Hong Kong, China
| | | | | | - Jiahui He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, 518055, China; School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Zhejiang 315100, China
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, 518055, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Wei Sun
- University of Science and Technology of China, Anhui, 230026, China
| | - Xinyuan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangdong, China
| | - Guanqun Zhou
- JancsiLab, JancsiTech, Hong Kong, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, 518055, China.
| | - Zhicheng Zhang
- JancsiLab, JancsiTech, Hong Kong, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, 518055, China.
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2
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Wang R, Liu Q, You W, Chen Y. A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1. Int Immunopharmacol 2024; 142:113099. [PMID: 39265355 DOI: 10.1016/j.intimp.2024.113099] [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: 05/08/2024] [Revised: 07/26/2024] [Accepted: 09/03/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehensively utilizing the ICI-related biomarkers, we aim to conduct multidimensional feature selection and deep learning model construction. METHODS We used statistical and machine learning methods to map features of different levels to next-generation sequencing gene expression. We integrated genes from different sources into the feature input of a deep learning model, by means of self-attention mechanism. RESULTS We performed feature selection at the single-cell sequencing level, PD-L1 (CD274) analysis level, tumor mutational burden (TMB)/mismatch repair (MMR) level, and somatic copy number alteration (SCNA) level, obtaining 96 feature genes. Based on the pan-cancer dataset, we trained a multi-task deep learning model. We tested the model in the bladder urothelial carcinoma testing set 1 (AUC = 0.62, n = 298), bladder urothelial carcinoma testing set 2 (AUC = 0.66, n = 89), non-small cell lung cancer testing set (AUC = 0.85, n = 27), and skin cutaneous melanoma testing set (AUC = 0.71, n = 27). CONCLUSION Our study demonstrates the potential of the deep learning model for integrating multidimensional features in predicting the outcome of ICI. Our study also provides a potential methodological case for medical scenarios requiring the integration of multiple levels of features.
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Affiliation(s)
- Ren Wang
- The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Qiumei Liu
- The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Wenhua You
- The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Chen
- The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
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Kaczmarzyk JR, Saltz JH, Koo PK. Explainable AI for computational pathology identifies model limitations and tissue biomarkers. ARXIV 2024:arXiv:2409.03080v2. [PMID: 39279830 PMCID: PMC11398542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
INTRODUCTION Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability. METHODS We developed HIPPO, an explainable AI framework that systematically modifies tissue regions in whole slide images to generate image counterfactuals, enabling quantitative hypothesis testing, bias detection, and model evaluation beyond traditional performance metrics. HIPPO was applied to a variety of clinically important tasks, including breast metastasis detection in axillary lymph nodes, prognostication in breast cancer and melanoma, and IDH mutation classification in gliomas. In computational experiments, HIPPO was compared against traditional metrics and attention-based approaches to assess its ability to identify key tissue elements driving model predictions. RESULTS In metastasis detection, HIPPO uncovered critical model limitations that were undetectable by standard performance metrics or attention-based methods. For prognostic prediction, HIPPO outperformed attention by providing more nuanced insights into tissue elements influencing outcomes. In a proof-of-concept study, HIPPO facilitated hypothesis generation for identifying melanoma patients who may benefit from immunotherapy. In IDH mutation classification, HIPPO more robustly identified the pathology regions responsible for false negatives compared to attention, suggesting its potential to outperform attention in explaining model decisions. CONCLUSIONS HIPPO expands the explainable AI toolkit for computational pathology by enabling deeper insights into model behavior. This framework supports the trustworthy development, deployment, and regulation of weakly-supervised models in clinical and research settings, promoting their broader adoption in digital pathology.
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Mostafa G, Mahmoud H, Abd El-Hafeez T, E ElAraby M. The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review. BMC Med Inform Decis Mak 2024; 24:287. [PMID: 39367397 PMCID: PMC11452940 DOI: 10.1186/s12911-024-02682-1] [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: 11/21/2023] [Accepted: 09/13/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process. OBJECTIVE Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC. DESIGN The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development. RESULTS The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features. CONCLUSIONS We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.
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Affiliation(s)
- Ghada Mostafa
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
| | - Hamdi Mahmoud
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef National University, Beni-Suef, Egypt.
| | - Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
| | - Mohamed E ElAraby
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
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Deasy JO. Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making. Semin Radiat Oncol 2024; 34:379-394. [PMID: 39271273 DOI: 10.1016/j.semradonc.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.
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Affiliation(s)
- Joseph O Deasy
- Department of Medical Physics, Attending Physicist, Chief, Service for Predictive Informatics, Chair, Memorial Sloan Kettering Cancer Center, New York, NY..
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Xiao Y, Li Y, Zhao H. Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective. Mol Cancer 2024; 23:202. [PMID: 39294747 PMCID: PMC11409752 DOI: 10.1186/s12943-024-02113-9] [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: 07/03/2024] [Accepted: 09/05/2024] [Indexed: 09/21/2024] Open
Abstract
Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.
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Affiliation(s)
- Yang Xiao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China
| | - Yongsheng Li
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Huakan Zhao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
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Jiang Y, Li W, Zhang J, Liu K, Wu Y, Wang Z. NFS1 as a Candidate Prognostic Biomarker for Gastric Cancer Correlated with Immune Infiltrates. Int J Gen Med 2024; 17:3855-3868. [PMID: 39253726 PMCID: PMC11382660 DOI: 10.2147/ijgm.s444443] [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: 07/12/2024] [Accepted: 09/01/2024] [Indexed: 09/11/2024] Open
Abstract
Background Cysteine desulfurase (NFS1) is closely associated with the occurrence and development of human tumors, but its relationship with the prognosis and immunity of gastric cancer (GC) patients remains unclear. Methods To study the relationship between NFS1 and GC, GC-related data of TCGA were downloaded and analyzed. At the same time, Tumor Immune Estimation Resource (TIMER) and Kaplan‒Meier Plotter were used for relevant online analysis. Clinical samples were collected for immunohistochemical testing to validate the results. Results The mRNA and protein levels of NFS1 in GC tissues were significantly higher than those in normal tissues. In terms of the operating characteristic curve (ROC), the area under the curve (AUC) was 0.793, indicating that NFS1 had a high diagnostic value for GC. Further analysis showed that NFS1 expression was highly correlated with the depth of tumor invasion, lymph node metastasis, and tumor stage. Survival analysis showed that patients with high expression of NFS1 had a poorer prognosis, and NFS1 was an independent risk factor. Enrichment analysis by GO, KEGG, and GSEA showed that NFS1 was enriched in immune-related pathways. The expression of NFS1 was significantly positively correlated with the proportion of macrophages M0 and plasma cells but negatively correlated with the proportion of B cells memory, monocytes, and mast cells resting. In addition, NFS1 expression was significantly correlated with TMB levels and responses to immunotherapy. Conclusion Our results suggest that NFS1 may be a potential biomarker for the diagnosis and prediction of prognosis and immunotherapy efficacy in GC.
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Affiliation(s)
- You Jiang
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
- Department of General Surgery, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
| | - Wenbo Li
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
- Department of General Surgery, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
| | - Jun Zhang
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
| | - Kun Liu
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
| | - Yuee Wu
- Department of Electrocardiogram Diagnosis, Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230060, People's Republic of China
| | - Zhengguang Wang
- Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230011, People's Republic of China
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He W, Huang W, Zhang L, Wu X, Zhang S, Zhang B. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm (Beijing) 2024; 5:e722. [PMID: 39252824 PMCID: PMC11381657 DOI: 10.1002/mco2.722] [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: 03/23/2024] [Revised: 08/06/2024] [Accepted: 08/18/2024] [Indexed: 09/11/2024] Open
Abstract
Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.
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Affiliation(s)
- Wenle He
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Wenhui Huang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Lu Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Xuewei Wu
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Shuixing Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Bin Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
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Wu X, Zhang S, Zhang Z, He Z, Xu Z, Wang W, Jin Z, You J, Guo Y, Zhang L, Huang W, Wang F, Liu X, Yan D, Cheng J, Yan J, Zhang S, Zhang B. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. NPJ Precis Oncol 2024; 8:181. [PMID: 39152182 PMCID: PMC11329669 DOI: 10.1038/s41698-024-00670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024] Open
Abstract
Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.
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Affiliation(s)
- Xuewei Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shuaitong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zicong He
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zexin Xu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yang Guo
- Department of Neurosurgery, The Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Wenhui Huang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongming Yan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
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Zhou Z, Wang J, Wang J, Yang S, Wang R, Zhang G, Li Z, Shi R, Wang Z, Lu Q. Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond. Mol Cancer 2024; 23:131. [PMID: 38918817 PMCID: PMC11201788 DOI: 10.1186/s12943-024-02047-2] [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: 03/25/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024] Open
Abstract
Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.
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Affiliation(s)
- Zhaokai Zhou
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Jiahui Wang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Nephrology, Union Medical College Hospital, Chinese Academy of Medical Sciences, PekingBeijing, 100730, China
| | - Jiaojiao Wang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Shuai Yang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ruizhi Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Zhengrui Li
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Run Shi
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Qiong Lu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
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Cui Y, Zhao K, Meng X, Mao Y, Han C, Shi Z, Yang X, Tong T, Wu L, Liu Z. A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study. Int J Surg 2024; 110:2845-2854. [PMID: 38348900 PMCID: PMC11093466 DOI: 10.1097/js9.0000000000001161] [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: 10/17/2023] [Accepted: 01/26/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). MATERIALS AND METHODS In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy. RESULTS The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not. CONCLUSION The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
| | - Xiaochun Meng
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
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12
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Chen Z, Wang X, Jin Z, Li B, Jiang D, Wang Y, Jiang M, Zhang D, Yuan P, Zhao Y, Feng F, Lin Y, Jiang L, Wang C, Meng W, Ye W, Wang J, Qiu W, Liu H, Huang D, Hou Y, Wang X, Jiao Y, Ying J, Liu Z, Liu Y. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. NPJ Precis Oncol 2024; 8:73. [PMID: 38519580 PMCID: PMC10959936 DOI: 10.1038/s41698-024-00579-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.
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Affiliation(s)
- Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Xiaobing Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zelin Jin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Bosen Li
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanqiu Wang
- Departments of Pathology, International Peace Maternity and Child Health Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengping Jiang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Dandan Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Pei Yuan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yahui Zhao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feiyue Feng
- Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Liping Jiang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenxi Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wenjing Ye
- Division of Rheumatology and Immunology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Wang
- Departments of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wenqing Qiu
- Shanghai Xuhui Central Hospital, Shanghai, China
| | - Houbao Liu
- Shanghai Xuhui Central Hospital, Shanghai, China
- Department of General Surgery/Biliary Tract Disease Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xuefei Wang
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of General Surgery, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, China
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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13
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Ligero M, Gielen B, Navarro V, Cresta Morgado P, Prior O, Dienstmann R, Nuciforo P, Trebeschi S, Beets-Tan R, Sala E, Garralda E, Perez-Lopez R. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? NPJ Precis Oncol 2024; 8:42. [PMID: 38383736 PMCID: PMC10881558 DOI: 10.1038/s41698-024-00534-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Bente Gielen
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Victor Navarro
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Pablo Cresta Morgado
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
- Prostate Cancer Translational Research Group, Institute of Oncology (VHIO), Vall d'Hebron University Hospital, Barcelona, Spain
| | - Olivia Prior
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Evis Sala
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy
| | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
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14
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Laubenbacher R, Adler F, An G, Castiglione F, Eubank S, Fonseca LL, Glazier J, Helikar T, Jett-Tilton M, Kirschner D, Macklin P, Mehrad B, Moore B, Pasour V, Shmulevich I, Smith A, Voigt I, Yankeelov TE, Ziemssen T. Forum on immune digital twins: a meeting report. NPJ Syst Biol Appl 2024; 10:19. [PMID: 38365857 PMCID: PMC10873299 DOI: 10.1038/s41540-024-00345-5] [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: 10/16/2023] [Accepted: 01/25/2024] [Indexed: 02/18/2024] Open
Abstract
Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.
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Affiliation(s)
| | - Fred Adler
- Department of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | - Gary An
- Department of Surgery, University of Vermont, Burlington, VT, USA
| | - Filippo Castiglione
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, UAE
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Luis L Fonseca
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - James Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Tomas Helikar
- Department of Biochemistry, University of Nebraska, Lincoln, NE, USA
| | | | - Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Borna Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Beth Moore
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Virginia Pasour
- U.S. Army Research Office, Research Triangle Park, Raleigh, NC, USA
| | | | - Amber Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Isabel Voigt
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
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