1
|
Ma D, Zhou T, Chen J, Chen J. Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. BMC Med Imaging 2024; 24:144. [PMID: 38867143 PMCID: PMC11170881 DOI: 10.1186/s12880-024-01278-5] [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: 01/26/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer. METHODS We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc. RESULTS Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant. CONCLUSION Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.
Collapse
Affiliation(s)
- Dong Ma
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Teli Zhou
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
- Yibicom Health Management, Guangzhou, Guangdong, 510700, China
| | - Jing Chen
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Jun Chen
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China.
| |
Collapse
|
2
|
Guo L, Liu A, Geng X, Zhao Z, Nie Y, Wang L, Liu D, Li Y, Li Y, Li D, Wang Q, Li Z, Liu X, Li M. The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy. Thorac Cancer 2024; 15:947-964. [PMID: 38480505 PMCID: PMC11045339 DOI: 10.1111/1759-7714.15276] [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: 02/06/2024] [Revised: 02/20/2024] [Accepted: 02/23/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)-based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity. METHODS This retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre- and delta-radiomic features were extracted from enhanced CT images. LASSO-Cox regression was used to select the radiomics signatures most associated with progression-free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C-index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune-related hematological parameters was analyzed by spearman correlation analysis. RESULTS Independent prognostic factors involved TNM stage, treatment regimen, tumor location, pre- or delta-Rad-score. The AUC of the delta-radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta-radiomic features are significantly correlated with delta-ALC (absolute lymphocyte count) and delta-NLR (neutrophil-to-lymphocyte ratio). CONCLUSIONS Spleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.
Collapse
Affiliation(s)
- Longxiang Guo
- Department of Radiation OncologyShandong Cancer Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Ao Liu
- Department of Radiation OncologyShandong Cancer Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyQilu Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
| | - Xiaotao Geng
- Department of Radiation OncologyWeifang People's HospitalWeifangChina
| | - Zongxing Zhao
- Department of Radiation OncologyLiaocheng People's Hospital, Shandong First Medical UniversityLiaochengChina
| | - Yu Nie
- Department of Tumor RadiotherapyShandong Second Provincial General HospitalJi'nanChina
| | - Lu Wang
- School of Clinical Medicine, Weifang Medical UniversityWeifangChina
| | - Defeng Liu
- Department of Radiation OncologyShandong Cancer Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of Medicine, Shandong UniversityJinanChina
| | - Yi Li
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Yuanlin Li
- School of Clinical Medicine, Weifang Medical UniversityWeifangChina
| | - Dianxing Li
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Qiankun Wang
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Zhichao Li
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Xiuli Liu
- Department of Radiation OncologyShandong Cancer Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of Medicine, Shandong UniversityJinanChina
| | - Minghuan Li
- Department of Radiation OncologyShandong Cancer Hospital, Cheeloo College of Medicine, Shandong UniversityJinanChina
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| |
Collapse
|
3
|
Xu YH, Lu P, Gao MC, Wang R, Li YY, Guo RQ, Zhang WS, Song JX. Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer. World J Clin Oncol 2024; 15:419-433. [PMID: 38576593 PMCID: PMC10989267 DOI: 10.5306/wjco.v15.i3.419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/15/2024] [Accepted: 02/06/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. AIM To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. METHODS A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. RESULTS The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. CONCLUSION This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.
Collapse
Affiliation(s)
- Yan-Han Xu
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Peng Lu
- Department of Imaging, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Ming-Cheng Gao
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rui Wang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Yang-Yang Li
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rong-Qi Guo
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Wei-Song Zhang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Jian-Xiang Song
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| |
Collapse
|
4
|
Liu L, Liao H, Zhao Y, Yin J, Wang C, Duan L, Xie P, Wei W, Xu M, Su D. CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1267596. [PMID: 38577325 PMCID: PMC10993774 DOI: 10.3389/fonc.2024.1267596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024] Open
Abstract
Objective We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC). Methods The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS). Results The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability. Conclusion The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings. Systematic Review Registration Open Science Framework platform at https://osf.io/5zcnd.
Collapse
Affiliation(s)
- Liangsen Liu
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
- Department of Nuclear Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hai Liao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yang Zhao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jiayu Yin
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
- Department of Radiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chen Wang
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Lixia Duan
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Peihan Xie
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wupeng Wei
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Meihai Xu
- Department of Radiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Danke Su
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| |
Collapse
|
5
|
Geng X, Zhang Y, Li Y, Cai Y, Liu J, Geng T, Meng X, Hao F. Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma. Br J Radiol 2024; 97:652-659. [PMID: 38268475 PMCID: PMC11027331 DOI: 10.1093/bjr/tqae009] [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: 07/14/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.
Collapse
Affiliation(s)
- Xiaotao Geng
- Shandong University Cancer Center, Shandong University, 440 Jiyan Road, Jinan, 250117, China
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yaping Zhang
- Department of Radiology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yang Li
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yuanyuan Cai
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Jie Liu
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Tianxiang Geng
- Department of Biomaterials, Faculty of Dentistry, University of Oslo, Oslo, 0455, Norway
| | - Xiangdi Meng
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Furong Hao
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| |
Collapse
|
6
|
Wu YP, Wu L, Ou J, Cao JM, Fu MY, Chen TW, Ouchi E, Hu J. Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability. Eur J Radiol 2024; 170:111197. [PMID: 37992611 DOI: 10.1016/j.ejrad.2023.111197] [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: 09/25/2023] [Revised: 10/12/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
PURPOSE To develop CT radiomics models of resectable esophageal squamous cell carcinoma (ESCC) and lymph node (LN) to preoperatively identify LN+. MATERIALS AND METHODS 299 consecutive patients with ESCC were enrolled in the study, 140 of whom were LN+ and 159 were LN-. Of the 299 patients, 249 (from the same hospital) were randomly divided into a training cohort (n = 174) and a test cohort (n = 75). The remaining 50 patients, from a second hospital, were assigned to an external validation cohort. In the training cohort, preoperative contrast-enhanced CT radiomics features of ESCC and LN were extracted, then integrated with clinical features to develop three models: ESCC, LN and combined. The performance of these models was assessed using area under receiver operating characteristic curve (AUC), and F-1 score, which were validated in both the test cohort and external validation cohort. RESULTS An ESCC model was developed for the training cohort utilizing the 8 tumor radiomics features, and an LN model was constructed using 9 nodal radiomics features. A combined model was constructed using both ESCC and LN extracted features, in addition to cT stage and LN+ distribution. This combined model had the highest predictive ability among the three models in the training cohort (AUC = 0.948, F1-score = 0.878). The predictive ability was validated in both the test and external validation cohorts (AUC = 0.885 and 0.867, F1-score = 0.816 and 0.773, respectively). CONCLUSION To preoperatively determine LN+, the combined model is superior to models of ESCC and LN alone.
Collapse
Affiliation(s)
- Yu-Ping Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Lan Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Ou
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Jin-Ming Cao
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China; Department of Radiology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, China
| | - Mao-Yong Fu
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tian-Wu Chen
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
| | - Erika Ouchi
- Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI, USA
| |
Collapse
|
7
|
Cao K, Zhu J, Lu M, Zhang J, Yang Y, Ling X, Zhang L, Qi C, Wei S, Zhang Y, Ma J. Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma. EBioMedicine 2024; 99:104920. [PMID: 38101299 PMCID: PMC10733113 DOI: 10.1016/j.ebiom.2023.104920] [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: 06/13/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Oesophageal squamous cell carcinoma (ESCC) is a lethal malignancy. Immune checkpoint inhibitors (ICIs) showed great clinical benefits for patients with ESCC. We aimed to construct a model predicting prognosis and response to ICIs by integrating diverse programmed cell death (PCD) forms. METHODS Genes related to 14 PCDs were collected to generate multi-gene signatures, including apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Bulk and single-cell RNA transcriptome datasets were used to develop and validate the model. We assessed the functions of two necroptosis-related genes in ESCC cells by Western blot, co-immunoprecipitation (Co-IP), LDH release assay, CCK-8, and migration assay, followed by immunohistochemistry (IHC) staining on samples of patients with ESCC (n = 67). FINDINGS We built and validated a 16-gene prognostic combined cell death index (CCDI) by combining immunogenic cell death (ICD) and necroptosis signatures. The CCDI could also predict response to ICIs in cancer, as shown by Tumour Immune Dysfunction and Exclusion (TIDE) analysis, confirmed in four independent ICI clinical trials. Trajectory analysis revealed that HOOK1 and CUL4A might affect ESCC cell fate. We found that HOOK1 induced necroptosis and inhibited the proliferation and migration of ESCC cells, while CUL4A exhibited the opposite effects. Co-IP assay confirmed that HOOK1 and CUL4A promoted and reduced necrosome formation in ESCC cells. Data from patients with ESCC further supported that HOOK1 and CUL4A might be a tumour suppressor and oncogene, respectively. INTERPRETATION We constructed a CCDI model with potential in predicting prognosis and response to ICIs in cancer. HOOK1 and CUL4A in the CCDI model are crucial prognostic biomarkers in ESCC. FUNDING The Natural Science Foundation of China [82172786], The National Cancer Center Climbing Fund of China [NCC201908B06], The Natural Science Foundation of Heilongjiang Province [LH2021H077].
Collapse
Affiliation(s)
- Kui Cao
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Jinhong Zhu
- Biobank, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China; Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Mengdi Lu
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Jinfeng Zhang
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Yingnan Yang
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Xiaodong Ling
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Luquan Zhang
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Cuicui Qi
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Shenshui Wei
- Biobank, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China; Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China; Key Laboratories of Tumor Immunology in Heilongjiang, Harbin, China; Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China.
| | - Jianqun Ma
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China.
| |
Collapse
|
8
|
Lu S, Wang C, Liu Y, Chu F, Jia Z, Zhang H, Wang Z, Lu Y, Wang S, Yang G, Qu J. The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma. Eur Radiol 2024; 34:485-494. [PMID: 37540319 DOI: 10.1007/s00330-023-10040-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/26/2023] [Accepted: 06/19/2023] [Indexed: 08/05/2023]
Abstract
OBJECTIVES To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT). METHODS Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model. RESULTS A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781). CONCLUSIONS MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy. CLINICAL RELEVANCE STATEMENT MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy. KEY POINTS • The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.
Collapse
Affiliation(s)
- Shuang Lu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Yun Liu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Funing Chu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Zhengyan Jia
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Hongkai Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Zhaoqi Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Yanan Lu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuting Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
| | - Jinrong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
| |
Collapse
|
9
|
Huo X, Wang Y, Ma S, Zhu S, Wang K, Ji Q, Chen F, Wang L, Wu Z, Li W. Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas. Medicine (Baltimore) 2023; 102:e36581. [PMID: 38134061 PMCID: PMC10735121 DOI: 10.1097/md.0000000000036581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/17/2023] [Indexed: 12/24/2023] Open
Abstract
The presence of TERTp mutation in isocitrate dehydrogenase-wildtype (IDHwt) histologically lower-grade glioma (LGA) has been linked to a poor prognosis. In this study, we aimed to develop and validate a radiomic nomogram based on multimodal MRI for predicting TERTp mutations in IDHwt LGA. One hundred and nine IDH wildtype glioma patients (TERTp-mutant, 78; TERTp-wildtype, 31) with clinical, radiomic, and molecular information were collected and randomly divided into training and validation set. Clinical model, fusion radiomic model, and combined radiomic nomogram were constructed for the discrimination. Radiomic features were screened with 3 algorithms (Wilcoxon rank sum test, elastic net, and the recursive feature elimination) and the clinical characteristics of combined radiomic nomogram were screened by the Akaike information criterion. Finally, receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis were utilized to assess these models. Fusion radiomic model with 4 radiomic features achieved an area under the curve value of 0.876 and 0.845 in the training and validation set. And, the combined radiomic nomogram achieved area under the curve value of 0.897 (training set) and 0.882 (validation set). Above that, calibration curve and Hosmer-Lemeshow test showed that the radiomic model and combined radiomic nomogram had good agreement between observations and predictions in the training set and the validation set. Finally, the decision curve analysis revealed that the 2 models had good clinical usefulness for the prediction of TERTp mutation status in IDHwt LGA. The combined radiomics nomogram performed great performance and high sensitivity in prediction of TERTp mutation status in IDHwt LGA, and has good clinical application.
Collapse
Affiliation(s)
- Xulei Huo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yali Wang
- Department of Neuro-oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sihan Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sipeng Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ke Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiang Ji
- Department of Neuro-oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Chen
- Department of Neuro-oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenbin Li
- Department of Neuro-oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
10
|
Li C, Pan Y, Yang X, Jing D, Chen Y, Luo C, Qiu J, Hu Y, Zhang Z, Shi L, Shen L, Zhou R, Lu S, Xiao X, Chen T. CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma. Front Oncol 2023; 13:1219106. [PMID: 37681029 PMCID: PMC10482418 DOI: 10.3389/fonc.2023.1219106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/31/2023] [Indexed: 09/09/2023] Open
Abstract
Background To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.
Collapse
Affiliation(s)
- Chao Li
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Radiation Oncology, Shenzhen People’s Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yuteng Pan
- Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Xianghui Yang
- Department of Oncology, Changsha Central Hospital, Changsha, Hunan, China
| | - Di Jing
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Chen
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Chenhua Luo
- Xiangya School of Medicine, Central South University, Hunan, Changsha, China
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center, Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China Technology, Shenzhen, Guangdong, China
| | - Yongmei Hu
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zijian Zhang
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Liting Shi
- Medical Engineering and Technology Research Center, Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China Technology, Shenzhen, Guangdong, China
| | - Liangfang Shen
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Rongrong Zhou
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shanfu Lu
- Perception Vision Medical Technologies Co. Ltd, Guangzhou, China
| | - Xiang Xiao
- Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan, Changsha, China
| | - Tingyin Chen
- Department of Network and Information Center, Xiangya Hospital, Central South University, Hunan, Changsha, China
| |
Collapse
|
11
|
Xu YH, Lu P, Gao MC, Wang R, Li YY, Song JX. Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer. World J Radiol 2023; 15:216-225. [PMID: 37545645 PMCID: PMC10401402 DOI: 10.4329/wjr.v15.i7.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/11/2023] [Accepted: 06/30/2023] [Indexed: 07/24/2023] Open
Abstract
Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.
Collapse
Affiliation(s)
- Yan-Han Xu
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Peng Lu
- Department of Imaging, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Ming-Cheng Gao
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rui Wang
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Yang-Yang Li
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Jian-Xiang Song
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| |
Collapse
|
12
|
Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
Collapse
Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
13
|
Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network. Int J Radiat Oncol Biol Phys 2023:S0360-3016(23)00002-0. [PMID: 36641040 DOI: 10.1016/j.ijrobp.2022.12.050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 12/26/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
PURPOSE This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. METHODS AND MATERIALS Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. RESULTS Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853). CONCLUSIONS Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.
Collapse
|
14
|
Zhang Y, Zhang Y, Peng L, Zhang L. Research Progress on the Predicting Factors and Coping Strategies for Postoperative Recurrence of Esophageal Cancer. Cells 2022; 12:cells12010114. [PMID: 36611908 PMCID: PMC9818463 DOI: 10.3390/cells12010114] [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: 10/15/2022] [Revised: 12/01/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Esophageal cancer is one of the malignant tumors with poor prognosis in China. Currently, the treatment of esophageal cancer is still based on surgery, especially in early and mid-stage patients, to achieve the goal of radical cure. However, esophageal cancer is a kind of tumor with a high risk of recurrence and metastasis, and locoregional recurrence and distant metastasis are the leading causes of death after surgery. Although multimodal comprehensive treatment has advanced in recent years, the prediction, prevention and treatment of postoperative recurrence and metastasis of esophageal cancer are still unsatisfactory. How to reduce recurrence and metastasis in patients after surgery remains an urgent problem to be solved. Given the clinical demand for early detection of postoperative recurrence of esophageal cancer, clinical and basic research aiming to meet this demand has been a hot topic, and progress has been observed in recent years. Therefore, this article reviews the research progress on the factors that influence and predict postoperative recurrence of esophageal cancer, hoping to provide new research directions and treatment strategies for clinical practice.
Collapse
Affiliation(s)
- Yujie Zhang
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Yuxin Zhang
- Department of Pediatric Surgery, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Lin Peng
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Li Zhang
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
- Correspondence:
| |
Collapse
|
15
|
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures. Radiat Oncol 2022; 17:212. [PMID: 36575480 PMCID: PMC9795769 DOI: 10.1186/s13014-022-02186-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. METHODS 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. RESULTS There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). CONCLUSION We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.
Collapse
|
16
|
Zhang H, Liao M, Guo Q, Chen J, Wang S, Liu S, Xiao F. Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method. Med Phys 2022; 50:2049-2060. [PMID: 36563341 DOI: 10.1002/mp.16177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/07/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. PURPOSE To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method. METHODS In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively. RESULTS Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. CONCLUSION This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.
Collapse
Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Jun Chen
- Wuhan GE Healthcare, Wuhan, China
| | - Shan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Songmei Liu
- Department of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
17
|
Zhu C, Mu F, Wang S, Qiu Q, Wang S, Wang L. Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model. Eur J Med Res 2022; 27:272. [PMID: 36463269 PMCID: PMC9719117 DOI: 10.1186/s40001-022-00877-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. METHODS A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. RESULTS Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001. CONCLUSIONS We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
Collapse
Affiliation(s)
- Chao Zhu
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China ,grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Fengchun Mu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Songping Wang
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China
| | - Qingtao Qiu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Shuai Wang
- grid.268079.20000 0004 1790 6079Department of Radiation Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261000 Shandong China
| | - Linlin Wang
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| |
Collapse
|
18
|
Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
Collapse
Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| |
Collapse
|
19
|
Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning. JOURNAL OF ONCOLOGY 2022; 2022:8534262. [PMID: 36147442 PMCID: PMC9489385 DOI: 10.1155/2022/8534262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/26/2022] [Accepted: 08/13/2022] [Indexed: 11/18/2022]
Abstract
Purpose To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.
Collapse
|
20
|
Zheng X, Xu S, Wu J. Cervical Cancer Imaging Features Associated With ADRB1 as a Risk Factor for Cerebral Neurovascular Metastases. Front Neurol 2022; 13:905761. [PMID: 35903112 PMCID: PMC9315067 DOI: 10.3389/fneur.2022.905761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Bioinformatics tools are used to create a clinical prediction model for cervical cancer metastasis and to investigate the neurovascular-related genes that are involved in brain metastasis of cervical cancer. One hundred eighteen patients with cervical cancer were divided into two groups based on the presence or absence of metastases, and the clinical data and imaging findings of the two groups were compared retrospectively. The nomogram-based model was successfully constructed by taking into account four clinical characteristics (age, stage, N, and T) as well as one imaging characteristic (original_glszm_GrayLevelVariance Rad-score). In patients with cervical cancer, headaches and vomiting were more often reported in the brain metastasis group than in the other metastasis groups. According to the TCGA data, mRNA differential gene expression analysis of patients with cervical cancer revealed an increase in the expression of neurovascular-related gene Adrenoceptor Beta 1 (ADRB1) in the brain metastasis group. An analysis of the correlation between imaging features and ADRB1 expression revealed that ADRB1 expression was significantly higher in the low Rad-score group compared with the high Rad-score group (P = 0.025). Therefore, ADRB1 expression in cervical cancer was correlated with imaging features and was associated as a risk factor for cerebral neurovascular metastases. This study developed a nomogram prediction model for cervical cancer metastasis using age, stage, N, T and original_glszm_GrayLevelVariance. As a risk factor associated with the development of cerebral neurovascular metastases of cervical cancer, ADRB1 expression was significantly higher in brain metastases from cervical cancer.
Collapse
Affiliation(s)
- Xingju Zheng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Shilin Xu
- Department of Oncology, Xichang People's Hospital, Liangshan High-Tech Tumor Hospital, Xichang, China
| | - JiaYing Wu
- Department of Gynaecology and Obstetrics, Zhejiang Xinda Hospital, Huzhou, China
- *Correspondence: JiaYing Wu
| |
Collapse
|
21
|
Chu F, Liu Y, Liu Q, Li W, Jia Z, Wang C, Wang Z, Lu S, Li P, Zhang Y, Liao Y, Xu M, Yao X, Wang S, Liu C, Zhang H, Wang S, Yan X, Kamel IR, Sun H, Yang G, Zhang Y, Qu J. Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma. Eur Radiol 2022; 32:5930-5942. [PMID: 35384460 DOI: 10.1007/s00330-022-08776-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/21/2022] [Accepted: 03/26/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To develop and validate an optimal model based on the 1-mm-isotropic-3D contrast-enhanced StarVIBE MRI sequence combined with clinical risk factors for predicting survival in patients with esophageal squamous cell carcinoma (ESCC). METHODS Patients with ESCC at our institution from 2015 to 2017 participated in this retrospective study based on prospectively acquired data, and were randomly assigned to training and validation groups at a ratio of 7:3. Random survival forest (RSF) and variable hunting methods were used to screen for radiomics features and LASSO-Cox regression analysis was used to build three models, including clinical only, radiomics only and combined clinical and radiomics models, which were evaluated by concordance index (CI) and calibration curve. Nomograms and decision curve analysis (DCA) were used to display intuitive prediction information. RESULTS Seven radiomics features were selected from 434 patients, combined with clinical features that were statistically significant to construct the predictive models of disease-free survival (DFS) and overall survival (OS). The combined model showed the highest performance in both training and validation groups for predicting DFS ([CI], 0.714, 0.729) and OS ([CI], 0.730, 0.712). DCA showed that the net benefit of the combined model and of the clinical model is significantly greater than that of the radiomics model alone at different threshold probabilities. CONCLUSIONS We demonstrated that a combined predictive model based on MR Rad-S and clinical risk factors had better predictive efficacy than the radiomics models alone for patients with ESCC. KEY POINTS • Magnetic resonance-based radiomics features combined with clinical risk factors can predict survival in patients with ESCC. • The radiomics nomogram can be used clinically to predict patient recurrence, DFS, and OS. • Magnetic resonance imaging is highly reproducible in visualizing lesions and contouring the whole tumor.
Collapse
Affiliation(s)
- Funing Chu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Yun Liu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Qiuping Liu
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, Jiangsu Province, 210029, China
| | - Weijia Li
- Henan Province Institute for Medical Equipment Testing, Zhengzhou, Henan, 450000, People's Republic of China
| | - Zhengyan Jia
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Zhaoqi Wang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuang Lu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Ping Li
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Yuanli Zhang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Yubo Liao
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Mingzhe Xu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Xiaoqiang Yao
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuting Wang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Cuicui Liu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Hongkai Zhang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Xi'an, 710065, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Ihab R Kamel
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205-2196, USA
| | - Haibo Sun
- Department of Thoracic surgery, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Yudong Zhang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, Jiangsu Province, 210029, China
| | - Jinrong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
| |
Collapse
|
22
|
Wang F, Guo R, Zhang Y, Yu B, Meng X, Kong H, Yang Y, Yang Z, Li N. Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT. Front Oncol 2022; 12:844702. [PMID: 35296000 PMCID: PMC8919030 DOI: 10.3389/fonc.2022.844702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/07/2022] [Indexed: 11/16/2022] Open
Abstract
Objectives To investigate the value of 18F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with 18F-FDG PET/CT, MRI, and CECT. Methods Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed. Results For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUVmax, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6). Conclusions 18F-FDG PET/MRI has advantages over 18F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. 18F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.
Collapse
Affiliation(s)
- Fei Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Rui Guo
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Boqi Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Xiangxi Meng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Hanjing Kong
- Beijing United Imaging Research Institute of Intelligent Imaging, UIH Group, Beijing, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, UIH Group, Beijing, China
| | - Zhi Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
- *Correspondence: Nan Li, ; Zhi Yang,
| | - Nan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
- *Correspondence: Nan Li, ; Zhi Yang,
| |
Collapse
|
23
|
Pellat A, Dohan A, Soyer P, Veziant J, Coriat R, Barret M. The Role of Magnetic Resonance Imaging in the Management of Esophageal Cancer. Cancers (Basel) 2022; 14:cancers14051141. [PMID: 35267447 PMCID: PMC8909473 DOI: 10.3390/cancers14051141] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 02/01/2023] Open
Abstract
Esophageal cancer (EC) is the eighth more frequent cancer worldwide, with a poor prognosis. Initial staging is critical to decide on the best individual treatment approach. Current modalities for the assessment of EC are irradiating techniques, such as computed tomography (CT) and positron emission tomography/CT, or invasive techniques, such as digestive endoscopy and endoscopic ultrasound. Magnetic resonance imaging (MRI) is a non-invasive and non-irradiating imaging technique that provides high degrees of soft tissue contrast, with good depiction of the esophageal wall and the esophagogastric junction. Various sequences of MRI have shown good performance in initial tumor and lymph node staging in EC. Diffusion-weighted MRI has also demonstrated capabilities in the evaluation of tumor response to chemoradiotherapy. To date, there is not enough data to consider whole body MRI as a routine investigation for the detection of initial metastases or for prediction of distant recurrence. This narrative review summarizes the current knowledge on MRI for the management of EC.
Collapse
Affiliation(s)
- Anna Pellat
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
| | - Anthony Dohan
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Radiology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Philippe Soyer
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Radiology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Julie Veziant
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Department of Digestive Surgery, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Romain Coriat
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
| | - Maximilien Barret
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint Jacques, 75014 Paris, France; (A.P.); (R.C.)
- Université de Paris, 75006 Paris, France; (A.D.); (P.S.); (J.V.)
- Correspondence:
| |
Collapse
|
24
|
Yang H, Yan S, Li J, Zheng X, Yao Q, Duan S, Zhu J, Li C, Qin J. Prediction of acute versus chronic osteoporotic vertebral fracture using radiomics-clinical model on CT. Eur J Radiol 2022; 149:110197. [DOI: 10.1016/j.ejrad.2022.110197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/26/2021] [Accepted: 01/31/2022] [Indexed: 11/28/2022]
|
25
|
Fan Y, Liu P, Li Y, Liu F, He Y, Wang L, Zhang J, Wu Z. Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model. Front Oncol 2022; 11:792521. [PMID: 35059316 PMCID: PMC8763962 DOI: 10.3389/fonc.2021.792521] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/29/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Accurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively. METHODS A total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS Six significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful. CONCLUSIONS Our clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.
Collapse
Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Panpan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yiping Li
- Department of Gastroenterology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Feng Liu
- Department of Neurosurgery, Jiangxi Provincial Children's Hospital, The Affiliated Children's Hospital of Nanchang University, Nanchang, China
| | - Yu He
- Department of Craniomaxillofacial Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
26
|
Cellini F, Manfrida S, Casà C, Romano A, Arcelli A, Zamagni A, De Luca V, Colloca GF, D’Aviero A, Fuccio L, Lancellotta V, Tagliaferri L, Boldrini L, Mattiucci GC, Gambacorta MA, Morganti AG, Valentini V. Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation. Cancers (Basel) 2022; 14:431. [PMID: 35053594 PMCID: PMC8773768 DOI: 10.3390/cancers14020431&n974851=v901586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.
Collapse
Affiliation(s)
- Francesco Cellini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Stefania Manfrida
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Calogero Casà
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Angela Romano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
- Correspondence:
| | - Alessandra Arcelli
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Alice Zamagni
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Viola De Luca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Giuseppe Ferdinando Colloca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Andrea D’Aviero
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences, IRCSS—S. Orsola-Malpighi Hospital, 40138 Bologna, Italy;
| | - Valentina Lancellotta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Tagliaferri
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Gian Carlo Mattiucci
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Maria Antonietta Gambacorta
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
- Dipartimento di Medicina Specialistica Diagnostica e Sperimentale (DIMES), Alma Mater Studiorum, Bologna University, 40126 Bologna, Italy
| | - Vincenzo Valentini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| |
Collapse
|
27
|
Cellini F, Manfrida S, Casà C, Romano A, Arcelli A, Zamagni A, De Luca V, Colloca GF, D’Aviero A, Fuccio L, Lancellotta V, Tagliaferri L, Boldrini L, Mattiucci GC, Gambacorta MA, Morganti AG, Valentini V. Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation. Cancers (Basel) 2022; 14:cancers14020431. [PMID: 35053594 PMCID: PMC8773768 DOI: 10.3390/cancers14020431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/28/2021] [Accepted: 01/11/2022] [Indexed: 02/07/2023] Open
Abstract
The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.
Collapse
Affiliation(s)
- Francesco Cellini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Stefania Manfrida
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Calogero Casà
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Angela Romano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
- Correspondence:
| | - Alessandra Arcelli
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Alice Zamagni
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Viola De Luca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Giuseppe Ferdinando Colloca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Andrea D’Aviero
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences, IRCSS—S. Orsola-Malpighi Hospital, 40138 Bologna, Italy;
| | - Valentina Lancellotta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Tagliaferri
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Gian Carlo Mattiucci
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Maria Antonietta Gambacorta
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
- Dipartimento di Medicina Specialistica Diagnostica e Sperimentale (DIMES), Alma Mater Studiorum, Bologna University, 40126 Bologna, Italy
| | - Vincenzo Valentini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| |
Collapse
|
28
|
Cellini F, Manfrida S, Casà C, Romano A, Arcelli A, Zamagni A, De Luca V, Colloca GF, D’Aviero A, Fuccio L, Lancellotta V, Tagliaferri L, Boldrini L, Mattiucci GC, Gambacorta MA, Morganti AG, Valentini V. Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation. Cancers (Basel) 2022; 14:431. [PMID: 35053594 PMCID: PMC8773768 DOI: 10.3390/cancers14020431&n923648=v907986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.
Collapse
Affiliation(s)
- Francesco Cellini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Stefania Manfrida
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Calogero Casà
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Angela Romano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
- Correspondence:
| | - Alessandra Arcelli
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Alice Zamagni
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
| | - Viola De Luca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Giuseppe Ferdinando Colloca
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Andrea D’Aviero
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences, IRCSS—S. Orsola-Malpighi Hospital, 40138 Bologna, Italy;
| | - Valentina Lancellotta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Tagliaferri
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Gian Carlo Mattiucci
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy;
| | - Maria Antonietta Gambacorta
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| | - Alessio Giuseppe Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (A.A.); (A.Z.); (A.G.M.)
- Dipartimento di Medicina Specialistica Diagnostica e Sperimentale (DIMES), Alma Mater Studiorum, Bologna University, 40126 Bologna, Italy
| | - Vincenzo Valentini
- Dipartimento Universitario Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy; (F.C.); (G.C.M.); (M.A.G.); (V.V.)
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; (S.M.); (C.C.); (V.D.L.); (G.F.C.); (V.L.); (L.T.); (L.B.)
| |
Collapse
|
29
|
Fan Y, Huo X, Li X, Wang L, Wu Z. Non-invasive preoperative imaging differential diagnosis of pineal region tumor: A novel developed and validated multiparametric MRI-based clinicoradiomic model. Radiother Oncol 2022; 167:277-284. [PMID: 35033600 DOI: 10.1016/j.radonc.2022.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/26/2021] [Accepted: 01/05/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND Preoperative differential diagnosis of pineal region tumor can greatly assist clinical decision-making and avoid economic costs and complications caused by unnecessary radiotherapy or invasive procedures. The present study was performed to pre-operatively distinguish pineal region germinoma and pinealoblastoma using a clinicoradiomic model by incorporating radiomic and clinical features. METHODS 134 pineal region tumor patients (germinoma, 69; pinealoblastoma, 65) with complete clinic-radiological and histopathological data from Tiantan hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, then the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to select the clinical features, and a clinicoradiomic model incorporating the fusion radiomic model and selected clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS Seven significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.920 and 0.880 in the training and validation sets, respectively. A clinicoradiomic model that incorporated the radiomic model and four selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.950 in the training set and 0.940 in the validation set. The analysis of the decision curve showed that the radiomic model and clinicoradiomic model were clinically useful for patients with pineal region tumor. CONCLUSIONS Our clinicoradiomic model showed great performance and high sensitivity in the differential diagnosis of germinoma and pinealoblastoma, and could contribute to non-invasive development of individualized diagnosis and treatment of patients with pineal region tumor.
Collapse
Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xulei Huo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xiaojie Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| |
Collapse
|
30
|
Qu J, Ma L, Lu Y, Wang Z, Guo J, Zhang H, Yan X, Liu H, Kamel IR, Qin J, Li H. DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer. Discov Oncol 2022; 13:3. [PMID: 35201487 PMCID: PMC8777517 DOI: 10.1007/s12672-022-00464-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/31/2021] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVES To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients. METHODS This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemotherapy, followed by surgery. Response assessment was graded from 1 to 5 according to the tumor regression grade (TRG). Patients were stratified into responders (TRG1 + 2) and non-responders (TRG3 + 4 + 5). 72 radiomics features and vascular permeability parameters were extracted from DCE-MRI. The discriminating performance was assessed with ROC. Decision curve analysis (DCA) was used for comparing three different models. RESULTS This cohort included 82 patients, and 72 tumor radiomics features and vascular permeability parameters acquired from DCE-MRI. mRMR and LASSO were performed to choose the optimized subset of radiomics features, and 3 features were selected to create the radiomics signature that were significantly associated with response (P < 0.001). AUC of combining radiomics signature and DCE-MRI performance in the training (n = 41) and validation (n = 41) cohort was 0.84 (95% CI 0.57-1) and 0.86 (95% CI 0.74-0.97), respectively. This combined model showed the best discrimination between responders and non-responders, and showed the highest positive and positive predictive value in both training set and test set. CONCLUSIONS The radiomics features are useful for nCT response prediction in EC patients.
Collapse
Affiliation(s)
- Jinrong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Ling Ma
- Advanced Application Team, GE Healthcare, Shanghai, 201203, China
| | - Yanan Lu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Zhaoqi Wang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jia Guo
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Hongkai Zhang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xu Yan
- NEA MR Collaboration, Siemens Ltd., China, Shanghai, 201318, China
| | - Hui Liu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Ihab R Kamel
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205-2196, USA
| | - Jianjun Qin
- Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, 450008, Henan, China.
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Hailiang Li
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| |
Collapse
|
31
|
Xing W, Zhao L, Zheng Y, Liu B, Liu X, Li T, Zhang Y, Ma B, Yang Y, Shang Y, Fu X, Liang G, Yuan D, Qu J, Chai X, Zhang H, Wang Z, Lin H, Liu L, Ren X, Zhang J, Gao Q. The Sequence of Chemotherapy and Toripalimab Might Influence the Efficacy of Neoadjuvant Chemoimmunotherapy in Locally Advanced Esophageal Squamous Cell Cancer—A Phase II Study. Front Immunol 2021; 12:772450. [PMID: 34938292 PMCID: PMC8685246 DOI: 10.3389/fimmu.2021.772450] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/11/2021] [Indexed: 12/26/2022] Open
Abstract
BackgroundThere is no standard neoadjuvant therapy for locally advanced esophageal cancer in China. The role of neoadjuvant chemotherapy plus immunotherapy for locally advanced esophageal cancer is still being explored.MethodsThis open-label, randomized phase II study was conducted at a single center between July 2019 and September 2020; 30 patients with locally advanced esophageal squamous cell carcinoma (ESCC) (T3, T4, or lymph-node positive) were enrolled. Patients were randomized according to the enrollment order at a 1:1 ratio to receive chemotherapy on day 1 and toripalimab on day 3 (experimental group) or chemotherapy and toripalimab on day 1 (control group). The chemotherapeutic regimen was paclitaxel and cisplatin. Surgery was performed 4 to 6 weeks after the second cycle of chemoimmunotherapy. The primary endpoint was pathological complete response (pCR) rate, and the secondary endpoint was safety and disease-free survival.ResultsThirty patients completed at least one cycle of chemoimmunotherapy; 11 in the experimental group and 13 in the control group received surgery. R0 resection was performed in all these 24 patients. Four patients (36%) in the experimental group and one (7%) in the control group achieved pCR. The experimental group showed a statistically non-significant higher pCR rate (p = 0.079). PD-L1 combined positive score (CPS) examination was performed in 14 patients; one in the control group had a PD-L1 CPS of 10, and pCR was achieved; the remaining 13 all had ≤1, and 11 of the 13 patients received surgery in which two (in the experimental group) achieved pCR. Two patients endured ≥grade 3 adverse events, and one suffered from grade 3 immune-related enteritis after one cycle of chemoimmunotherapy and dropped off the study. Another patient died from severe pulmonary infection and troponin elevation after surgery.ConclusionsAlthough the primary endpoint was not met, the initial results of this study showed that delaying toripalimab to day 3 in chemoimmunotherapy might achieve a higher pCR rate than that on the same day, and further large-sample clinical trials are needed to verify this.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT 03985670.
Collapse
Affiliation(s)
- Wenqun Xing
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Lingdi Zhao
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yan Zheng
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Baoxing Liu
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Xianben Liu
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Tiepeng Li
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yong Zhang
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Baozhen Ma
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yonghao Yang
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yiman Shang
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Xiaomin Fu
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Guanghui Liang
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Dongfeng Yuan
- Department of Thoracic Surgery, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Jinrong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Xiaofei Chai
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - He Zhang
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Zibing Wang
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Hongwei Lin
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Liang Liu
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiubao Ren
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jiangong Zhang
- Department of Cancer Epidemiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
- *Correspondence: Quanli Gao, ; Jiangong Zhang,
| | - Quanli Gao
- Department of Immunotherapy, Cancer Hospital Affiliated to Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
- *Correspondence: Quanli Gao, ; Jiangong Zhang,
| |
Collapse
|
32
|
Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20:69-75. [PMID: 34816024 PMCID: PMC8591412 DOI: 10.1016/j.phro.2021.10.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation. Textural features are less robust than morphological or statistical features. A checklist is provided including items that should be reported in a radiomic study.
Purpose Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. Methods and materials Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study. Results Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features. Conclusions Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
Collapse
Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jan Bussink
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
33
|
Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases. J Transl Med 2021; 19:377. [PMID: 34488799 PMCID: PMC8419989 DOI: 10.1186/s12967-021-03015-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
Background Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. Methods We recruited 116 CNS demyelinating patients including 78 MS, and 38 NMO. Three neuroradiologists performed visual differential diagnosis based on brain MRI for comparison purpose. A multi-level scheme was designed to harness the selection of discriminative and stable radiomics features extracted from brain while mater lesions in T1-MPRAGE, T2 sequences and clinical factors. Based on the imaging phenotype composed of the selected radiomic and clinical features, Multi-parametric Multivariate Random Forest (MM-RF) model was constructed and verified with both 10-fold cross-validation and independent testing. Result interpretation was provided to build trust in diagnostic decisions. Results Eighty-six patients were randomly selected to form the training set while the rest 30 patients for independent testing. On the training set, our MM-RF model achieved accuracy 0.849 and AUC 0.826 in 10-fold cross-validation, which were significantly higher than clinical visual analysis (0.709 and 0.683, p < 0.05). In the independent testing, the MM-RF model achieved AUC 0.902, accuracy 0.871, sensitivity 0.873, specificity 0.869, respectively. Furthermore, age, sex and EDSS were found mildly correlated with the radiomic features (p of all < 0.05). Conclusions Multi-parametric radiomic features have potential as practical quantitative imaging biomarkers for differentiating MS from NMO. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03015-w.
Collapse
|
34
|
Miao Y, Wang X, Lai Y, Lin W, Huang Y, Yin H, Hou R, Zhang F. Mitochondrial calcium uniporter promotes cell proliferation and migration in esophageal cancer. Oncol Lett 2021; 22:686. [PMID: 34434285 PMCID: PMC8335723 DOI: 10.3892/ol.2021.12947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 06/14/2021] [Indexed: 12/20/2022] Open
Abstract
Increasing evidence has suggested that mitochondrial calcium uniporter (MCU) is involved in various types of cancer. However, its functions remain unclear in esophageal cancer. The aim of the present study was to explore its abnormal expression and clinical implications in esophageal cancer. A total of 110 patients with esophageal cancer were enrolled in the study. Western blotting was performed to examine the protein expression levels of MCU in 8 pairs of esophageal cancer and adjacent normal tissues. Using immunochemistry, a total of 110 esophageal cancer specimens were analyzed to identify the association between MCU expression and clinicopathological features of patients with esophageal cancer. Furthermore, immunofluorescence of MCU was performed. Pearson's correlation analysis was performed between MCU and hypoxia inducible factor (HIF)-1α/VEGF/E-cadherin/Vimentin expression based on western blotting. After KYSE-150 and TE-1 cells were treated with the MCU agonist Spermine and a small interfering RNA against MCU (si-MCU), a series of functional assays were performed, including Cell Counting Kit-8, colony formation and Transwell assays. The results revealed that, compared with in adjacent normal tissues, MCU was highly expressed in esophageal cancer tissues. MCU expression was significantly associated with depth of invasion, lymph node metastasis, TNM stage and distant metastasis. Moreover, MCU was significantly correlated with HIF-1α/VEGF/E-cadherin/Vimentin in esophageal cancer tissues. MCU overexpression promoted VEGF, MMP2, Vimentin and N-cadherin expression, while it inhibited E-cadherin expression in KYSE-150 and TE-1 cells, and opposite results were observed after transfection with si-MCU. Furthermore, MCU overexpression accelerated the proliferation and migration of KYSE-150 and TE-1 cells. Thus, the current findings suggested that high MCU expression may participate in cell proliferation, migration and epithelial-mesenchymal transition in esophageal cancer.
Collapse
Affiliation(s)
- Yu Miao
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| | - Xiaofei Wang
- Department of Pathology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Yafang Lai
- Department of Gastroenterology, Ordos Center Hospital, Ordos, Inner Mongolia 017000, P.R. China
| | - Wan Lin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| | - Ying Huang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| | - Ruirui Hou
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| | - Feixiong Zhang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, P.R. China
| |
Collapse
|
35
|
Xie CY, Pang CL, Chan B, Wong EYY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021; 13:2469. [PMID: 34069367 PMCID: PMC8158761 DOI: 10.3390/cancers13102469] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/12/2021] [Accepted: 05/15/2021] [Indexed: 11/16/2022] Open
Abstract
Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.
Collapse
Affiliation(s)
- Chen-Yi Xie
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
| | - Chun-Lap Pang
- Department of Radiology, The Christies’ Hospital, Manchester M20 4BX, UK;
- Division of Dentistry, School of Medical Sciences, University of Manchester, Manchester M15 6FH, UK
| | - Benjamin Chan
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; (B.C.); (E.Y.-Y.W.)
| | - Emily Yuen-Yuen Wong
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; (B.C.); (E.Y.-Y.W.)
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China;
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
| |
Collapse
|
36
|
Ji W, Wang J, Zhou R, Wang M, Wang W, Pang P, Kong M, Zhou C. Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma. Front Oncol 2021; 11:604480. [PMID: 34084740 PMCID: PMC8168434 DOI: 10.3389/fonc.2021.604480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 04/21/2021] [Indexed: 12/09/2022] Open
Abstract
Background Esophageal squamous cell carcinoma (ESCC) is an aggressive type of cancer, associated with poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC. Methods This prospective analysis included patients with T1–T2 stage of ESCC, without either lymphatic or metastasis, and distant metastasis. All patients underwent surgery having received two rounds of NACRT. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were divided into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI scans. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability and texture parameters was generated to predict the response to NACRT. Results In this study, the CR and PR groups included 16 patients each. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and entropy values, as well as changes to each of these parameters, extracted from the second DCE-MRI scans, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, ve, and entropy values showed good diagnostic ability (0.813, 0.789, and 0.707, respectively). A logistic regression model combining Ktrans, ve, and entropy had significant diagnostic ability (AUC=0.977). Conclusions The use of a combined model with vascular permeability and texture parameters can improve post-NACRT prognostication in patients with ESCC.
Collapse
Affiliation(s)
- Wenbing Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Jian Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Rongzhen Zhou
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Minke Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Weizhen Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Peipei Pang
- Advanced Application Team, GE Healthcare, Shanghai, China
| | - Min Kong
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province, Taizhou, China
| | - Chao Zhou
- Department of Radiotherapy, Taizhou Hospital of Zhejiang Province, Taizhou, China
| |
Collapse
|
37
|
Xiao B, Fan Y, Zhang Z, Tan Z, Yang H, Tu W, Wu L, Shen X, Guo H, Wu Z, Zhu X. Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma. Front Oncol 2021; 11:625220. [PMID: 33937027 PMCID: PMC8082417 DOI: 10.3389/fonc.2021.625220] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background Postoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma. Objective To evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma. Methods A total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction. Results Three significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction. Conclusions By integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma.
Collapse
Affiliation(s)
- Bing Xiao
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhe Zhang
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zilong Tan
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Huan Yang
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Tu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Wu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoli Shen
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hua Guo
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xingen Zhu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
38
|
Predicting Lymph Node Metastasis Using Computed Tomography Radiomics Analysis in Patients With Resectable Esophageal Squamous Cell Carcinoma. J Comput Assist Tomogr 2021; 45:323-329. [PMID: 33512851 DOI: 10.1097/rct.0000000000001125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVES We investigated the value of radiomics data, extracted from pretreatment computed tomography images of the primary tumor (PT) and lymph node (LN) for predicting LN metastasis in esophageal squamous cell carcinoma (ESCC) patients. MATERIALS AND METHODS A total 338 ESCC patients were retrospectively assessed. Primary tumor, the largest short-axis diameter LN (LSLN), and PT and LSLN interaction term (IT) radiomic features were calculated. Subsequently, the radiomic signature was combined with clinical risk factors in multivariable logistic regression analysis to build various clinical-radiomic models. Model performance was evaluated with respect to the fit, overall performance, differentiation, and calibration. RESULTS A clinical-radiomic model, which combined clinical and PT-LSLN-IT radiomic signature, showed favorable discrimination and calibration. The area under curve value was 0.865 and 0.841 in training and test set. CONCLUSIONS A venous computed tomography radiomic model based on the PT, LSLN, and IT radiomic features represents a novel noninvasive tool for prediction LN metastasis in ESCC.
Collapse
|
39
|
Lee SL, Yadav P, Starekova J, Christensen L, Chandereng T, Chappell R, Reeder SB, Bassetti MF. Diagnostic Performance of MRI for Esophageal Carcinoma: A Systematic Review and Meta-Analysis. Radiology 2021; 299:583-594. [PMID: 33787334 DOI: 10.1148/radiol.2021202857] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background Although CT, endoscopic US, and PET are critical in determining the appropriate management of esophageal carcinoma (squamous cell carcinoma and adenocarcinoma), previous reports show that staging accuracy remains low, particularly for nodal involvement sensitivity. Purpose To perform a systematic review and meta-analysis to determine the diagnostic performance of MRI for multiple staging thresholds in patients with biopsy-proven esophageal carcinoma (differentiation of stage T0 disease from stage T1 or higher disease, differentiation of stage T2 or lower disease from stage T3 or higher disease, and differentiation of stage N0 disease from stage N1 or higher disease [where T refers to tumor stage and N refers to nodal stage]). Materials and Methods Studies of the diagnostic performance of MRI in determining the stage of esophageal carcinoma in patients before esophagectomy and pathologic staging between 2000 and 2019 were searched in PubMed, Scopus, Web of Science, and Cochrane Library by a librarian and radiation oncologist. Pooled diagnostic performance of MRI was calculated with a bivariate random effects model. Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (version 2) tool. Results Twenty studies with a total of 984 patients were included in the analysis. Pooled accuracy for stage T0 versus stage T1 or higher had a sensitivity of 92% (95% CI: 82, 96) and a specificity of 67% (95% CI: 51, 81). Pooled accuracy for stage T2 or lower versus stage T3 or higher had a sensitivity of 86% (95% CI: 76, 92) and a specificity of 86% (95% CI: 75, 93). Pooled accuracy for stage N0 versus stage N1 or higher had a sensitivity of 71% (95% CI: 60, 80) and a specificity of 72% (95% CI: 64, 79). The concern for applicability was low for the patient selection, index test, and reference test domains, except for 10% of studies (two of 20) that had unclear concern for patient selection applicability. Conclusion MRI has high sensitivity but low specificity for the detection of esophageal carcinoma, which shows promise for determining neoadjuvant therapy response and for detecting locally advanced disease for potential trimodality therapy. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Leeflang in this issue.
Collapse
Affiliation(s)
- Sangjune Laurence Lee
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Poonam Yadav
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Jitka Starekova
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Leslie Christensen
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Thevaa Chandereng
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Richard Chappell
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Scott B Reeder
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| | - Michael F Bassetti
- From the Department of Oncology, Division of Radiation Oncology, University of Calgary, 1331 29 St NW, Calgary, AB, Canada T2N 4N2 (S.L.L.); Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wis (S.L.L., P.Y., M.F.B.); Department of Radiology, University of Wisconsin-Madison, Madison, Wis (J.S., S.B.R.); Departments of Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine, University of Wisconsin-Madison, Madison, Wis (S.B.R); University of Wisconsin School of Medicine and Public Health, Madison, Wis (L.C.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wis (T.C., R.C.)
| |
Collapse
|
40
|
Ou J, Wu L, Li R, Wu CQ, Liu J, Chen TW, Zhang XM, Tang S, Wu YP, Yang LQ, Tan BG, Lu FL. CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study. Quant Imaging Med Surg 2021; 11:628-640. [PMID: 33532263 DOI: 10.21037/qims-20-241] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM. Methods This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy. Results The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively). Conclusions CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.
Collapse
Affiliation(s)
- Jing Ou
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Lan Wu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rui Li
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Chang-Qiang Wu
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jun Liu
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tian-Wu Chen
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Ming Zhang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Sun Tang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yu-Ping Wu
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li-Qin Yang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bang-Guo Tan
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Fu-Lin Lu
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| |
Collapse
|
41
|
Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| |
Collapse
|
42
|
Zhang C, Shi Z, Kalendralis P, Whybra P, Parkinson C, Berbee M, Spezi E, Roberts A, Christian A, Lewis W, Crosby T, Dekker A, Wee L, Foley KG. Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: an external validation study. Br J Radiol 2020; 94:20201042. [PMID: 33264032 DOI: 10.1259/bjr.20201042] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To improve clinical lymph node staging (cN-stage) in oesophageal adenocarcinoma by developing and externally validating three prediction models; one with clinical variables only, one with positron emission tomography (PET) radiomics only, and a combined clinical and radiomics model. METHODS Consecutive patients with fluorodeoxyglucose (FDG) avid tumours treated with neoadjuvant therapy between 2010 and 2016 in two international centres (n = 130 and n = 60, respectively) were included. Four clinical variables (age, gender, clinical T-stage and tumour regression grade) and PET radiomics from the primary tumour were used for model development. Diagnostic accuracy, area under curve (AUC), discrimination and calibration were calculated for each model. The prognostic significance was also assessed. RESULTS The incidence of lymph node metastases was 58% in both cohorts. The areas under the curve of the clinical, radiomics and combined models were 0.79, 0.69 and 0.82 in the developmental cohort, and 0.65, 0.63 and 0.69 in the external validation cohort, with good calibration demonstrated. The area under the curve of current cN-stage in development and validation cohorts was 0.60 and 0.66, respectively. For overall survival, the combined clinical and radiomics model achieved the best discrimination performance in the external validation cohort (X2 = 6.08, df = 1, p = 0.01). CONCLUSION Accurate diagnosis of lymph node metastases is crucial for prognosis and guiding treatment decisions. Despite finding improved predictive performance in the development cohort, the models using PET radiomics derived from the primary tumour were not fully replicated in an external validation cohort. ADVANCES IN KNOWLEDGE This international study attempted to externally validate a new prediction model for lymph node metastases using PET radiomics. A model combining clinical variables and PET radiomics improved discrimination of lymph node metastases, but these results were not externally replicated.
Collapse
Affiliation(s)
- Chong Zhang
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Zhenwei Shi
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Petros Kalendralis
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Phil Whybra
- School of Engineering, Cardiff University, Cardiff, UK
| | | | - Maaike Berbee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Ashley Roberts
- Department of Radiology, University Hospital of Wales, Cardiff, UK
| | - Adam Christian
- Department of Pathology, University Hospital of Wales, Cardiff, UK
| | - Wyn Lewis
- Department of Upper GI Surgery, University Hospital of Wales, Cardiff, UK
| | - Tom Crosby
- Department of Clinical Oncology, Velindre Cancer Centre, Cardiff, UK
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Kieran G Foley
- Department of Radiology, Velindre Cancer Centre, Cardiff, UK
| |
Collapse
|
43
|
Elsherif SB, Andreou S, Virarkar M, Soule E, Gopireddy DR, Bhosale PR, Lall C. Role of precision imaging in esophageal cancer. J Thorac Dis 2020; 12:5159-5176. [PMID: 33145093 PMCID: PMC7578477 DOI: 10.21037/jtd.2019.08.15] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Esophageal cancer is a major cause of morbidity and mortality worldwide. Recent advancements in the management of esophageal cancer have allowed for earlier detection, improved ability to monitor progression, and superior treatment options. These innovations allow treatment teams to formulate more customized management plans and have led to an increase in patient survival rates. For example, in order for the most effective management plan to be constructed, accurate staging must be performed to determine tumor resectability. This article reviews the multimodality imaging approach involved in making a diagnosis, staging, evaluating treatment response and detecting recurrence in esophageal cancer.
Collapse
Affiliation(s)
- Sherif B Elsherif
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA.,Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sonia Andreou
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | | | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| |
Collapse
|
44
|
Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
Collapse
Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| |
Collapse
|
45
|
Li ZX, Li XD, Liu XB, Xing WQ, Sun HB, Wang ZF, Zhang RX, Li Y. Clinical evaluation of right recurrent laryngeal nerve nodes in thoracic esophageal squamous cell carcinoma. J Thorac Dis 2020; 12:3622-3630. [PMID: 32802441 PMCID: PMC7399419 DOI: 10.21037/jtd-20-774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background The accuracy of clinical N staging of esophageal squamous cell carcinoma is suboptimal. As an important station of lymph node metastasis, station C201 (right recurrent laryngeal nerve nodes) has rarely been evaluated alone. We aimed to explore an effective way to evaluate the right recurrent laryngeal nerve nodes in thoracic esophageal squamous cell carcinoma. Methods We retrospectively analyzed 628 thoracic esophageal squamous cell carcinoma patients who underwent radical resection without neoadjuvant therapy from two Chinese cancer centers. The diameter of the short axis of the largest right recurrent laryngeal nerve node (DC201) was measured on contrast-enhanced multi-slice computed tomography (MSCT). Right recurrent laryngeal nerve nodes were examined by postoperative pathologic results. The receiver operating characteristic (ROC) curve was generated to assess the diagnostic capabilities of DC201 to determine the right recurrent laryngeal nerve nodes status. Results ROC curve analysis revealed that the optimal cut-off point of DC201 was 6 mm, with an area under curve (AUC), sensitivity, specificity, and Youden index of 0.896, 71.9%, 88.8%, and 0.607 respectively. When the cut-off point of DC201 was set to 10 mm, sensitivity, specificity and the Youden index were 14.1%, 99.6% and 0.137 respectively. Among 128 patients with right recurrent laryngeal nerve node metastasis, 71 and 108 patients had the largest right recurrent laryngeal nerve node located above the suprasternal notch level and in the tracheoesophageal groove respectively. Conclusions When DC201 ≥6.0 mm instead of DC201 ≥10 mm was used to dictate the right recurrent laryngeal nerve nodes metastasis, contrast-enhanced MSCT could evaluate the status of right recurrent laryngeal nerve nodes with high sensitivity and specificity. The largest right recurrent laryngeal nerve nodes were mainly located in the tracheoesophageal groove and/or above the suprasternal notch.
Collapse
Affiliation(s)
- Zhen-Xuan Li
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao-Dong Li
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China
| | - Xian-Ben Liu
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Wen-Qun Xing
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hai-Bo Sun
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Zong-Fei Wang
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui-Xiang Zhang
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Yin Li
- Department of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.,Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
46
|
Hou Y, Xie X, Chen J, Lv P, Jiang S, He X, Yang L, Zhao F. Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI. Eur Radiol 2020; 31:24-33. [PMID: 32789530 DOI: 10.1007/s00330-020-07110-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/04/2020] [Accepted: 07/23/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI). METHODS Fifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training (n = 42) and testing (n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student's t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis. RESULTS In the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725-0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features (p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560-0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident (p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis. CONCLUSIONS The BOF-based radiomics may be helpful for the differentiation between OAL and IOI. KEY POINTS • It is challenging to differentiate OAL from IOI due to the similar clinical and image features. • Radiomics has great potential for the noninvasive diagnosis of orbital diseases. • The BOF representation from patch to image may help the differentiation of OAL and IOI.
Collapse
Affiliation(s)
- Yuqing Hou
- School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China.,Xi'an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Xiaoyang Xie
- School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China.,Xi'an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Jixin Chen
- School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China.,Xi'an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Peng Lv
- Department of Radiology, Xi'an Fourth Hospital, Xi'an, 710004, Shaanxi, China
| | - Shijie Jiang
- Department of Radiology, Xi'an Fourth Hospital, Xi'an, 710004, Shaanxi, China
| | - Xiaowei He
- School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China.,Xi'an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710069, Shaanxi, China
| | - Lijuan Yang
- Department of Radiology, Xi'an Fourth Hospital, Xi'an, 710004, Shaanxi, China.
| | - Fengjun Zhao
- School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China. .,Xi'an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710069, Shaanxi, China.
| |
Collapse
|
47
|
Fan Y, Chai Y, Li K, Fang H, Mou A, Feng S, Feng M, Wang R. Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: a multicenter study. J Endocrinol Invest 2020; 43:755-765. [PMID: 31849000 DOI: 10.1007/s40618-019-01159-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/08/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Proliferative activity prediction is important for determining individual treatment strategies for patients with acromegaly, and tumor proliferative activity is usually measured by the expression of Ki-67. OBJECTIVE This study aimed to assess the value of a magnetic resonance imaging (MRI)-based radiomics approach in predicting the Ki-67 index of acromegaly patients. METHODS A total of 138 patients with acromegaly were retrospectively reviewed and randomly assigned to primary and validation cohorts. Radiomics features were extracted from MR images, and then the elastic net and recursive feature elimination algorithms were applied to determine critical radiomics features for constructing a radiomics signature. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomics nomogram incorporating a radiomics signature and selected clinical features was constructed for individual predictions. Twenty-five acromegaly patients were enrolled for multicenter model validation. RESULTS Seventeen radiomics features were selected to construct a radiomics signature that achieved an area under the curve (AUC) value of 0.96 and 0.89 in the primary cohort and the validation cohort, respectively. A radiomics nomogram that incorporated the radiomics signature and eight selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.94 in the primary cohort and 0.91 in the validation cohort. The radiomics signature in the multicenter validation achieved an accuracy of 88.2%. The analysis of the decision curve showed that the radiomics signature and radiomics nomogram were clinically useful for patients with acromegaly. CONCLUSIONS The radiomics signature developed in this study could aid neurosurgeons in predicting the Ki-67 index of patients with acromegaly and could contribute to non-invasive measurement of proliferative activity, affecting individual treatment strategies.
Collapse
Affiliation(s)
- Y Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - Y Chai
- Department of Neurosurgery, Yuquan Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 100040, China
| | - K Li
- School of Queen Mary, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - H Fang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - A Mou
- Department of Radiology, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, 610072, Sichuan Province, China
| | - S Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - M Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
| | - R Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
| |
Collapse
|
48
|
Gao Z, Hua B, Ge X, Liu J, Xue L, Zhen F, Luo J. Comparison Between Size and Stage of Preoperative Tumor Defined by Preoperative Magnetic Resonance Imaging and Postoperative Specimens After Radical Resection of Esophageal Cancer. Technol Cancer Res Treat 2020; 18:1533033819876263. [PMID: 31551000 PMCID: PMC6763937 DOI: 10.1177/1533033819876263] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Our objective is to explore the accuracy of magnetic resonance imaging in determining the preoperative T and N staging, pathological stage, and the length of esophageal tumor in patients with esophageal cancer. METHODS This retrospective analysis included 57 patients admitted to the Department of Thoracic Surgery of The First Affiliated Hospital of Nanjing Medical University between January 2015 and December 2016. Postoperative pathological results were used as the reference to verify the accuracy of magnetic resonance imaging in evaluating tumor T and N staging, pathological stage, and tumor length. The correlation between tumor lengths-measured using magnetic resonance imaging and the surgical specimen measurements-was evaluated. RESULTS The mean age of the patients was 64.6 ± 7.2 years, with a range of 47 to 77 years. The overall accuracy rate of magnetic resonance imaging in T staging of esophageal cancer was 63.2%; magnetic resonance imaging was generally consistent in the N staging of esophageal cancer. Magnetic resonance imaging and surgical evaluation of tumor length were in excellent agreement (κ = .82, P < .001), while that of gastroscopy and postoperative pathology was moderate (κ = .63, P < .001). CONCLUSION Magnetic resonance imaging is highly accurate in determining the preoperative T and N staging, pathologic stage, and tumor length in patients with esophageal cancer, which is important in deciding the choice of preoperative treatment and the surgical approach.
Collapse
Affiliation(s)
- Zhenzhen Gao
- Department of Clinical Oncology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Beibei Hua
- Department of Radiation Oncology, Yili Friendship Hospital, Xinjiang, China
| | - Xiaolin Ge
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinyuan Liu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xue
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fuxi Zhen
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinhua Luo
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
49
|
Sun Q, Lin X, Zhao Y, Li L, Yan K, Liang D, Sun D, Li ZC. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region. Front Oncol 2020; 10:53. [PMID: 32083007 PMCID: PMC7006026 DOI: 10.3389/fonc.2020.00053] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 01/13/2020] [Indexed: 12/12/2022] Open
Abstract
Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models. Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.
Collapse
Affiliation(s)
- Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaona Lin
- Department of Ultrasonic Imaging, Peking University Shenzhen Hospital, Shenzhen, China
| | - Yuanshen Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Kai Yan
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Dong Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Desheng Sun
- Department of Ultrasonic Imaging, Peking University Shenzhen Hospital, Shenzhen, China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| |
Collapse
|
50
|
Wu L, Yang X, Cao W, Zhao K, Li W, Ye W, Chen X, Zhou Z, Liu Z, Liang C. Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study. Front Oncol 2020; 9:1548. [PMID: 32039021 PMCID: PMC6985546 DOI: 10.3389/fonc.2019.01548] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 12/20/2019] [Indexed: 12/24/2022] Open
Abstract
Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.
Collapse
Affiliation(s)
- Lei Wu
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaojun Yang
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wuteng Cao
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ke Zhao
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wenli Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weitao Ye
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xin Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhiyang Zhou
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zaiyi Liu
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Changhong Liang
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|