1
|
Zhao K, Seeliger E, Niendorf T, Liu Z. Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope? J Magn Reson Imaging 2024; 59:1494-1513. [PMID: 37675919 DOI: 10.1002/jmri.29000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Kaixuan Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Erdmann Seeliger
- Institute of Translational Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|
2
|
Sun X, Tan X, Zhang Q, He S, Wang S, Zhou Y, Huang Q, Jiang L. 11C-CFT PET brain imaging in Parkinson's disease using a total-body PET/CT scanner. EJNMMI Phys 2024; 11:40. [PMID: 38662044 DOI: 10.1186/s40658-024-00640-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
PURPOSE This study aimed to evaluate the feasibility of 11C-CFT PET brain imaging in Parkinson's Disease using a total-body PET/CT scanner and explore the optimal scan duration to guide the clinical practice. METHODS Thirty-two patients with Parkinson's disease (PD) performing 11C-CFT PET/CT brain imaging using a total-body PET/CT scanner were retrospectively enrolled. The PET data acquired over a period of 900 s were reconstructed into groups of different durations: 900-s, 720-s, 600-s, 480-s, 300-s, 180-s, 120-s, 60-s, and 30-s (G900 to G30). The subjective image quality analysis was performed using 5-point scales. Semi-quantitative measurements were analyzed by SUVmean and dopamine transporter (DAT) binding of key brain regions implicated in PD, including the caudate nucleus and putamen. The full-time images (G900) were served as reference. RESULTS The overall G900, G720, and G600 image quality scores were 5.0 ± 0.0, 5.0 ± 0.0, and 4.9 ± 0.3 points, respectively, and there was no significant difference among these groups (P > 0.05). A significant decrease in these scores at durations shorter than 600 s was observed when compared to G900 images (P < 0.05). However, all G300 image quality was clinically acceptable (≥ 3 points). As the scan duration reduced, the SUVmean and DAT binding of caudate nucleus and putamen decreased progressively, while there were no statistically significant variations in the SUVmean of the background among the different groups. Moreover, the changes in the lesion DAT binding (ΔDAT-binding) between the full-time reference G900 image and other reconstructed group G720 to G30 images generally increased along with the reduced scan time. CONCLUSION Sufficient image quality and lesion conspicuity could be achieved at 600-s scan duration for 11C-CFT PET brain imaging in PD assessment using a total-body PET/CT scanner, while the image quality of G300 was acceptable to meet clinical diagnosis, contributing to improve patient compliance and throughput of PET brain imaging.
Collapse
Affiliation(s)
- Xiaolin Sun
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Xiaoyue Tan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Qing Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Shanzhen He
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Siyun Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Yongrong Zhou
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 Wuzhongdong Road, 200030, Shanghai, China.
| | - Lei Jiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Road, 510080, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
3
|
Wang S, Wu R, Jia S, Diakite A, Li C, Liu Q, Zheng H, Ying L. Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning. Magn Reson Med 2024. [PMID: 38624162 DOI: 10.1002/mrm.30105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/17/2024]
Abstract
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.
Collapse
Affiliation(s)
- Shanshan Wang
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ruoyou Wu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Sen Jia
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Alou Diakite
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Cheng Li
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Hairong Zheng
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Leslie Ying
- Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, New York, USA
| |
Collapse
|
4
|
Yang X, Chu XP, Huang S, Xiao Y, Li D, Su X, Qi YF, Qiu ZB, Wang Y, Tang WF, Wu YL, Zhu Q, Liang H, Zhong WZ. A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign. Eur Radiol 2024; 34:2048-2061. [PMID: 37658883 DOI: 10.1007/s00330-023-10026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN. METHODS Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks. RESULTS In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients. CONCLUSION This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes. CLINICAL RELEVANCE STATEMENT In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent. KEY POINTS • Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.
Collapse
Affiliation(s)
- Xiongwen Yang
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Xiang-Peng Chu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Shaohong Huang
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yi Xiao
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Dantong Li
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Xiaoyang Su
- Department of Thoracic Surgery, Maoming City People's Hospital, Maoming, China
| | - Yi-Fan Qi
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Zhen-Bin Qiu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Yanqing Wang
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wen-Fang Tang
- Department of Cardio-Thoracic Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Qikui Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Wen-Zhao Zhong
- School of Medicine, South China University of Technology, Guangzhou, China.
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China.
| |
Collapse
|
5
|
Tan X, Sun X, Chen Y, Wang F, Shang Y, Zhang Q, Yuan H, Jiang L. Implications of Sarcopenia and Glucometabolism Parameters of Muscle Derived From Baseline and End-of-Treatment 18F-FDG PET/CT in Diffuse Large B-Cell Lymphoma. Korean J Radiol 2024; 25:277-288. [PMID: 38413112 PMCID: PMC10912500 DOI: 10.3348/kjr.2023.0949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/08/2023] [Accepted: 11/30/2023] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVE We previously found that the incidence of sarcopenia increased with declining glucose metabolism of muscle in patients with treatment-naïve diffuse large B-cell lymphoma (DLBCL). This study aimed to investigate the relationship between sarcopenia and muscle glucometabolism using 18F-FDG PET/CT at baseline and end-of-treatment, analyze the changes in these parameters through treatment, and assess their prognostic values. MATERIALS AND METHODS The records of 103 patients with DLBCL (median 54 years [range, 21-76]; male:female, 50:53) were retrospectively reviewed. Skeletal muscle area at the third lumbar vertebral (L3) level was measured, and skeletal muscle index (SMI) was calculated to determine sarcopenia, defined as SMI < 44.77 cm²/m² and < 32.50 cm²/m² for male and female, respectively. Glucometabolic parameters of the psoas major muscle, including maximum standardized uptake value (SUVmax) and mean standardized uptake value (SUVmean), were measured at L3 as well. Their changes across treatment were also calculated as ΔSMI, ΔSUVmax, and ΔSUVmean; Δbody mass index was also calculated. Associations between SMI and the metabolic parameters were analyzed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified. RESULTS The incidence of sarcopenia was 29.1% and 36.9% before and after treatment, respectively. SMI (P = 0.004) was lower, and sarcopenia was more frequent (P = 0.011) at end-of-treatment than at baseline. The SUVmax and SUVmean of muscle were lower (P < 0.001) in sarcopenia than in non-sarcopenia at both baseline and end-of-treatment. ΔSMI was positively correlated with ΔSUVmax of muscle (P = 0.022). Multivariable Cox regression analysis showed that sarcopenia at end-of-treatment was independently negatively associated with PFS (adjusted hazard ratio [95% confidence interval], 2.469 [1.022-5.965]), while sarcopenia at baseline was independently negatively associated with OS (5.051 [1.453-17.562]). CONCLUSION Sarcopenic patients had lower muscle glucometabolism, and the muscular and metabolic changes across treatment were positively correlated. Sarcopenia at baseline and end-of-treatment was negatively associated with the prognosis of DLBCL.
Collapse
Affiliation(s)
- Xiaoyue Tan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaolin Sun
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yang Chen
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Fanghu Wang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuxiang Shang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Qing Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hui Yuan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Lei Jiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
6
|
Chen Z, He C, Zhang P, Cai X, Li X, Huang W, Huang S, Cai M, Wang L, Zhan P, Zhang Y. Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease. Brain Imaging Behav 2024:10.1007/s11682-024-00862-1. [PMID: 38337128 DOI: 10.1007/s11682-024-00862-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.
Collapse
Affiliation(s)
- Zhenzhen Chen
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Department of Neurology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, 430014, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Chentao He
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Xin Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Xiaohong Li
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Wenlin Huang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Sifei Huang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Mengfei Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lijuan Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Peiyan Zhan
- Department of Neurology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| |
Collapse
|
7
|
Han X, Guo Y, Ye H, Chen Z, Hu Q, Wei X, Liu Z, Liang C. Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response. Breast Cancer Res 2024; 26:18. [PMID: 38287356 PMCID: PMC10823720 DOI: 10.1186/s13058-024-01776-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 01/20/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUNDS Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed. METHODS In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes. RESULTS The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response. CONCLUSIONS Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.
Collapse
Affiliation(s)
- Xiaorui Han
- School of Medicine South, China University of Technology, Guangzhou, 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Huifen Ye
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, China
| | - Zhihong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
| | - Qingru Hu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
| | - Zaiyi Liu
- School of Medicine South, China University of Technology, Guangzhou, 510006, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Changhong Liang
- School of Medicine South, China University of Technology, Guangzhou, 510006, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| |
Collapse
|
8
|
Yuan H, Zhang G, Sun T, Ren J, Zhang Q, Xiang Z, Liu E, Jiang L. Kinetic modeling and parametric imaging of 18 F-PSMA-11: An evaluation based on total-body dynamic positron emission tomography scans. Med Phys 2024; 51:156-166. [PMID: 38043120 DOI: 10.1002/mp.16876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND The prostate-specific membrane antigen (PSMA) targeted positron-emitting tomography (PET) tracers are increasingly used in clinical practice, with novel tracers constantly being developed. Recently, 18 F-PSMA-11 has been gaining growing interest for several merits; however, direct in vivo visualization of its kinetic features in humans remains lacking. PURPOSE To visualize the kinetic features of 18 F-PSMA-11 in healthy subjects and patients with prostate cancer derived from the total-body dynamic PET scans. METHODS A total of 8 healthy volunteers (7 males; 1 female) and 3 patients with prostate cancer underwent total-body PET/CT imaging at 1 and 2 h post injection (p.i.) of 18 F-PSMA-11, of which 7 healthy subjects and 3 patients underwent total-body dynamic PET scans lasting 30 min. Reversible two-tissue compartments (2TC) and Patlak models were fitted based on the voxel-based time activity curves (TACs), with the parametric images generated subsequently. Additionally, semi-automated segmentation of multiple organs was performed in the dynamic images to measure the SUVmean at different time points and in the parametric images to estimate the mean value of the kinetic parameters of these organs. RESULTS 18 F-PSMA-11 showed quick accumulation within prostate cancer, as early as 45 s after tracer injection. It was rapidly cleared from blood circulation and predominantly excreted through the urinary system. High and rapid radiotracer accumulation was observed in the liver, spleen, lacrimal glands, and salivary glands, whereas gradual accumulation was observed in the skeleton. Prostate cancer tissue is visualized in all parametric images, and best seen in DV and Patlak Ki images. Patlak Ki showed a good correlation with 2TC Ki values (r = 0.858, p < 0.05) but less noise than 2TC images. A scanning time point of 30-35 min p.i. was then suggested for satisfactory tumor to background ratio. CONCLUSION Prostate cancer tissue is visible in most parametric images, and is better shown by Patlak Ki and 2TC DV images. Patlak Ki is consistent with, and thus is preferred over, 2TC Ki images for substantially quicker calculation. Based on the dynamic imaging analysis, a shorter uptake time (30-35 min) might be preferred for a better balance of tumor to background ratio.
Collapse
Affiliation(s)
- Hui Yuan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Guojin Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Taotao Sun
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Jingyun Ren
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Qing Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zeyin Xiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Entao Liu
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Lei Jiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| |
Collapse
|
9
|
Osman YBM, Li C, Huang W, Wang S. Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation. J Magn Reson Imaging 2023. [PMID: 38156427 DOI: 10.1002/jmri.29194] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. PURPOSE To build a deep learning method exploring sparse annotations, namely only a single two-dimensional slice label for each 3D training MR image. STUDY TYPE Retrospective. POPULATION Three-dimensional MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1377 image slices) are for prostate segmentation. The other 100 (8800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T; axial T2-weighted and late gadolinium-enhanced, 3D respiratory navigated, inversion recovery prepared gradient echo pulse sequence. ASSESSMENT A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled noncentral slices. Segmentation performance on testing set was reported quantitatively and qualitatively. STATISTICAL TESTS Quantitative evaluation metrics including boundary intersection-over-union (B-IoU), Dice similarity coefficient, average symmetric surface distance, and relative absolute volume difference were calculated. Paired t test was performed, and P < 0.05 was considered statistically significant. RESULTS Compared to fully supervised training with only the labeled central slice, mean teacher, uncertainty-aware mean teacher, deep co-training, interpolation consistency training (ICT), and ambiguity-consensus mean teacher, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% ± 7.6% vs. ICT B-IoU: 60.3% ± 11.2%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% ± 6.8% vs. ICT B-IoU: 60.1% ± 7.1%). DATA CONCLUSIONS A collaborative learning method trained using sparse annotations can segment prostate and left atrium with high accuracy. LEVEL OF EVIDENCE 0 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Yousuf Babiker M Osman
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weijian Huang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
- Peng Cheng Laboratory, Shenzhen, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
| |
Collapse
|
10
|
Sun X, Yao X, Zeng B, Zhu L, Shang Y, Zhang Q, He L, Jiang L. Association of mismatch repair deficiency in endometrial cancer with 18F-FDG PET/CT and clinicopathological features and their prognostic value. Ann Nucl Med 2023; 37:655-664. [PMID: 37743402 DOI: 10.1007/s12149-023-01869-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023]
Abstract
PURPOSE Identification of the mismatch repair (MMR) deficiency in endometrial cancer (EC) may aid in the screening of patients who may benefit from immunotherapy. Our goal was to investigate the relationship between MMR status and 18F-FDG PET/CT metabolic parameters and clinicopathological features in patients with EC, as well as to explore their prognostic value. METHODS This retrospective study included 106 EC patients who were classified as MMR deficient (dMMR) or MMR proficient (pMMR) group based on MMR protein expression status evaluated by immunohistochemistry. Clinicopathological characteristics and PET metabolic parameters were compared between the dMMR and pMMR groups, and their relationships with MMR status and prognosis were evaluated. RESULTS Of 106 EC patients, 30 patients (28.1%) had dMMR, while 76 (71.7%) had pMMR. Compared with the pMMR group, the dMMR group showed a lower prevalence of overweight (BMI ≥ 25) (17.2% vs. 43.9%, P = 0.019) and more lymph vascular space invasion (43.3% vs. 21.1%, P = 0.029). Although no relationship between glucometabolism parameters and MMR status was observed in all enrolled patients, higher SUVmax was observed in the endometrioid type of EC with MMR deficiency (P = 0.047). Additionally, SUVmax related to MMR status was found in EC patients with advanced FIGO stage (P = 0.026) or deep myometrial invasion (P = 0.026). Multivariate Cox regression analysis revealed that lymph node metastasis was independently predictive of PFS, while advanced FIGO stage was an independent predictor of OS. No significant association between MMR status and prognosis was found in EC. CONCLUSION Higher SUVmax was associated with MMR deficiency in EC patients with endometrioid type, advanced stage, or deep myometrial invasion, which may be useful for predicting the MMR status and thus aiding in determination of immunotherapy for patients with EC.
Collapse
Affiliation(s)
- Xiaolin Sun
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Xinchao Yao
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Baozhen Zeng
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Linbo Zhu
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yuxiang Shang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Qing Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Li He
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
| | - Lei Jiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
11
|
Duan J, Zhao Y, Sun Q, Liang D, Liu Z, Chen X, Li Z. Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer. Cancer Med 2023; 12:21256-21269. [PMID: 37962087 PMCID: PMC10726892 DOI: 10.1002/cam4.6704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/08/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.
Collapse
Affiliation(s)
- Jingxian Duan
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yuanshen Zhao
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Qiuchang Sun
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Dong Liang
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Zaiyi Liu
- Department of RadiologyGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of MedicineSouth China University of TechnologyGuangzhouChina
| | - Zhi‐Cheng Li
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| |
Collapse
|
12
|
Li S, Li Z, Wang L, Wu M, Chen X, He C, Xu Y, Dong M, Liang Y, Chen X, Liu Z. CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer. Eur Radiol 2023; 33:6861-6871. [PMID: 37171490 DOI: 10.1007/s00330-023-09688-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC). METHODS A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index. RESULTS LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89. CONCLUSIONS There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection. KEY POINTS • LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.
Collapse
Affiliation(s)
- Suyun Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Li Wang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - Mimi Wu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Xiaobo Chen
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Chutong He
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China
| | - Yao Xu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Mengyi Dong
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- School of Medicine, South China University of Technology, Guangzhou, 510006, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| |
Collapse
|
13
|
Chen Y, Chen Z, Tan X, Zhang Q, Zhou Y, Yuan H, Jiang L. Role of body composition and metabolic parameters extracted from baseline 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma. Ann Hematol 2023; 102:2779-2789. [PMID: 37530853 DOI: 10.1007/s00277-023-05379-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023]
Abstract
This study aimed to clarify the clinical and prognostic role of body composition and metabolic parameters extracted from baseline 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively collected the clinicopathological and 18F-FDG PET/CT parameters of 181 DLBCL patients. The indexes of skeletal muscle, subcutaneous adipose tissue, and visceral adipose tissue were calculated using the area measured at the 3rd lumbar level normalized for height. Additionally, the metabolic activity of corresponding muscle and adipose tissue, and maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of all lesions were measured. Survival endpoints included progression-free survival (PFS) and overall survival (OS). We identified 75 (41.4%) patients with low skeletal muscle index (sarcopenia), presenting risk factors including male, high β2-microglobulin, low BMI, high visceral adipose tissue index, low SUVmax of skeletal muscle, and high SUVmax of visceral adipose tissue. Male, low BMI, low visceral adipose tissue index, and high SUVmax of subcutaneous adipose tissue were risk factors for low subcutaneous adipose tissue index diagnosed in 105 (58.0%) patients. In total, 132 (79.2%) patients represented low visceral adipose tissue index, associated with younger age, B symptoms, and low BMI. Eastern Cooperative Oncology Group (ECOG) status, sarcopenia, and visceral adipose tissue index were found independently predictive of PFS and OS, while β2-microglobulin was independently predictive of OS. In conclusion, body composition indexes were correlated with both clinical characteristics and 18F-FDG PET/CT metabolic parameters, significantly impacting survival, such that sarcopenia and high visceral adipose tissue index were powerful predictors of poor DLBCL outcomes.
Collapse
Affiliation(s)
- Yang Chen
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Zhijian Chen
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Xiaoyue Tan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Qing Zhang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yongrong Zhou
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Hui Yuan
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
| | - Lei Jiang
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
14
|
Chen Q, Cai M, Fan X, Liu W, Fang G, Yao S, Xu Y, Li Q, Zhao Y, Zhao K, Liu Z, Chen Z. An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer. BMC Cancer 2023; 23:763. [PMID: 37592224 PMCID: PMC10433587 DOI: 10.1186/s12885-023-11289-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 08/11/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVE In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.
Collapse
Affiliation(s)
- Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ming Cai
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xinjuan Fan
- Department of Pathology, Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wenbin Liu
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Gang Fang
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yao Xu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Qian Li
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yingnan Zhao
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ke Zhao
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Zaiyi Liu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China.
| |
Collapse
|
15
|
Li H, Cai S, Deng L, Xiao Z, Guo Q, Qiang J, Gong J, Gu Y, Liu Z. Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram. Eur Radiol 2023; 33:5298-5308. [PMID: 36995415 DOI: 10.1007/s00330-023-09552-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.
Collapse
Affiliation(s)
- Haiming Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
| | - Lin Deng
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Zebin Xiao
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qinhao Guo
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| |
Collapse
|
16
|
Liu E, Lyu Z, Yang Y, Lv Y, Zhao Y, Zhang X, Sun T, Jiang L, Liu Z. Sub-minute acquisition with deep learning-based image filter in the diagnosis of colorectal cancers using total-body 18F-FDG PET/CT. EJNMMI Res 2023; 13:66. [PMID: 37428417 DOI: 10.1186/s13550-023-01015-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/29/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND This study aimed to retrospectively evaluate the feasibility of total-body 18F-FDG PET/CT ultrafast acquisition combined with a deep learning (DL) image filter in the diagnosis of colorectal cancers (CRCs). METHODS The clinical and preoperative imaging data of patients with CRCs were collected. All patients underwent a 300-s list-mode total-body 18F-FDG PET/CT scan. The dataset was divided into groups with acquisition durations of 10, 20, 30, 60, and 120 s. PET images were reconstructed using ordered subset expectation maximisation, and post-processing filters, including a Gaussian smoothing filter with 3 mm full width at half maximum (3 mm FWHM) and a DL image filter. The effects of the Gaussian and DL image filters on image quality, detection rate, and uptake value of primary and liver metastases of CRCs at different acquisition durations were compared using a 5-point Likert scale and semi-quantitative analysis, with the 300-s image with a Gaussian filter as the standard. RESULTS All 34 recruited patients with CRCs had single colorectal lesions, and the diagnosis was verified pathologically. Of the total patients, 11 had liver metastases, and 113 liver metastases were detected. The 10-s dataset could not be evaluated due to high noise, regardless of whether it was filtered by Gaussian or DL image filters. The signal-to-noise ratio (SNR) of the liver and mediastinal blood pool in the images acquired for 10, 20, 30, and 60 s with a Gaussian filter was lower than that of the 300-s images (P < 0.01). The DL filter significantly improved the SNR and visual image quality score compared to the Gaussian filter (P < 0.01). There was no statistical difference in the SNR of the liver and mediastinal blood pool, SUVmax and TBR of CRCs and liver metastases, and the number of detectable liver metastases between the 20- and 30-s DL image filter and 300-s images with the Gaussian filter (P > 0.05). CONCLUSIONS The DL filter can significantly improve the image quality of total-body 18F-FDG PET/CT ultrafast acquisition. Deep learning-based image filtering methods can significantly reduce the noise of ultrafast acquisition, making them suitable for clinical diagnosis possible.
Collapse
Affiliation(s)
- Entao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Zejian Lyu
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuelong Yang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Room 201, 2/F, WeiLun Building of Guangdong Provincial People's Hospital, 106 Zhongshan Er Road, Guangzhou, 510080, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Yang Lv
- United Imaging Healthcare, Shanghai, China
| | - Yumo Zhao
- United Imaging Healthcare, Shanghai, China
| | - Xiaochun Zhang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Taotao Sun
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Lei Jiang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Room 201, 2/F, WeiLun Building of Guangdong Provincial People's Hospital, 106 Zhongshan Er Road, Guangzhou, 510080, Guangdong, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
17
|
Ye H, Wang Y, Yao S, Liu Z, Liang C, Zhu Y, Cui Y, Zhao K. Necrosis score as a prognostic factor in stage I-III colorectal cancer: a retrospective multicenter study. Discov Oncol 2023; 14:61. [PMID: 37155090 PMCID: PMC10167085 DOI: 10.1007/s12672-023-00655-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/12/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Tumor necrosis results from failure to meet the requirement for rapid proliferation of tumor, related to unfavorable prognosis in colorectal cancer (CRC). However, previous studies used traditional microscopes to evaluate necrosis on slides, lacking a simultaneous phase and panoramic view for assessment. Therefore, we proposed a whole-slide images (WSIs)-based method to develop a necrosis score and validated its prognostic value in multicenter cohorts. METHODS Necrosis score was defined as the proportion of necrosis in the tumor area, semi-quantitatively classified into 3-level score groups by the cut-off of 10% and 30% on HE-stained WSIs. 768 patients from two centers were enrolled in this study, divided into a discovery (N = 445) and a validation (N = 323) cohort. The prognostic value of necrosis score was evaluated by Kaplan-Meier curves and the Cox model. RESULT Necrosis score was associated with overall survival, with hazard ratio for high vs. low in discovery and validation cohorts being 2.62 (95% confidence interval 1.59-4.32) and 2.51 (1.39-4.52), respectively. The 3-year disease free survival rates of necrosis-low, middle, and high were 83.6%, 80.2%, and 59.8% in discovery cohort, and 86.5%, 84.2%, and 66.5% in validation cohort. In necrosis middle plus high subgroup, there was a trend but no significant difference in overall survival between surgery alone and adjuvant chemotherapy group in stage II CRC (P = .075). CONCLUSION As a stable prognostic factor, high-level necrosis evaluated by the proposed method on WSIs was associated with unfavorable outcomes. Additionally, adjuvant chemotherapy provide survival benefits for patients with high necrosis in stage II CRC.
Collapse
Affiliation(s)
- Huifen Ye
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yiting Wang
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, 26 Yuan Cun 2 Cross Road, TianHe District, Guangzhou, 510655, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yaxi Zhu
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, 26 Yuan Cun 2 Cross Road, TianHe District, Guangzhou, 510655, China.
| | - Yanfen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, No.3, Xinjie West Alley, Taiyuan, 030013, China.
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| |
Collapse
|
18
|
Zou J, Li C, Jia S, Wu R, Pei T, Zheng H, Wang S. SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging. Bioengineering (Basel) 2022; 9:650. [PMID: 36354561 PMCID: PMC9687509 DOI: 10.3390/bioengineering9110650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 08/22/2023] Open
Abstract
Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging.
Collapse
Affiliation(s)
- Juan Zou
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruoyou Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tingrui Pei
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
- College of Information Science and Technology, Jinan University, Guangzhou 510631, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medicial Image Analysis and Application, Shenzhen 518055, China
| |
Collapse
|
19
|
Chen M, Xu Z, Zhu C, Liu Y, Ye Y, Liu C, Liu Z, Liang C, Liu C. Multiple-parameter MRI after neoadjuvant systemic therapy combining clinicopathologic features in evaluating axillary pathologic complete response in patients with clinically node-positive breast cancer. Br J Radiol 2022; 95:20220533. [PMID: 36000676 PMCID: PMC9793477 DOI: 10.1259/bjr.20220533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/04/2022] [Accepted: 08/17/2022] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE This study aimed to evaluate axillary pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer (BC) patients based on post-NST multiple-parameter MRI and clinicopathological characteristics. METHODS In this retrospective study, females with clinically node-positive BC who received NST and followed by surgery between January 2017 and September 2021 were included. All axillary lymph nodes (ALNs) on MRI were matched with pathology by ALN markers or sizes. MRI morphological parameters, signal intensity curve (TIC) patterns and apparent diffusion coefficient (ADC) values of post-NST ALNs were measured. The clinicopathological characteristics was also collected and analyzed. Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of axillary pCR. RESULTS Pathologically confirmed 137 non-pCR ALNs in 71 patients and 87 pCR ALNs in 87 patients were included in this study. Cortical thickness, fatty hilum, and TIC patterns of ALNs, hormone receptor, and human epidermal growth factor receptor 2 (HER2) status were significantly different between the two groups (all, p < 0.05). There was no significant difference for ADC values (p = 0.875). On multivariable analysis, TIC patterns (odds ratio [OR], 2.67, 95% confidence interval [CI]: 1.33, 5.34, p = 0.006), fatty hilum (OR, 2.88, 95% CI:1.39, 5.98, p = 0.004), hormone receptor (OR, 8.40, 95% CI: 2.48, 28.38, p = 0.001) and HER2 status (OR, 8.57, 95% CI: 3.85, 19.08, p < 0.001) were identified as independent predictors associated with axillary pCR. The area under the curve of the multivariate analysis using these predictors was 0.85 (95% CI: 0.79, 0.91). CONCLUSION Combining post-NST multiple-parameter MRI and clinicopathological characteristics allowed more accurate identification of BC patients who had received axillary pCR after NST. ADVANCES IN KNOWLEDGE A combined model incorporated multiple-parameter MRI and clinicopathologic features demonstrated good performance in evaluating axillary pCR preoperatively and non-invasively.
Collapse
Affiliation(s)
- Minglei Chen
- Shantou University Medical College, Shantou, China
| | | | | | | | | | | | | | | | - Chunling Liu
- Shantou University Medical College, Shantou, China
| |
Collapse
|