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Jannatdoust P, Valizadeh P, Pahlevan-Fallahy MT, Hassankhani A, Amoukhteh M, Behrouzieh S, Ghadimi DJ, Bilgin C, Gholamrezanezhad A. Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer. Clin Imaging 2024; 113:110225. [PMID: 38905878 DOI: 10.1016/j.clinimag.2024.110225] [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: 04/02/2024] [Revised: 05/26/2024] [Accepted: 06/04/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning. METHODS A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS). RESULTS Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927). CONCLUSION Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.
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Affiliation(s)
- Payam Jannatdoust
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Parya Valizadeh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Amir Hassankhani
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Melika Amoukhteh
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Sadra Behrouzieh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Delaram J Ghadimi
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Cem Bilgin
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA.
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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.
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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.
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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.
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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
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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.
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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
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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.
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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
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Chen Z, Yu Y, Liu S, Du W, Hu L, Wang C, Li J, Liu J, Zhang W, Peng X. A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma. Clin Oral Investig 2023; 28:39. [PMID: 38151672 DOI: 10.1007/s00784-023-05423-2] [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: 08/09/2023] [Accepted: 11/21/2023] [Indexed: 12/29/2023]
Abstract
OBJECTIVES In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). MATERIALS AND METHODS CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). RESULTS A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%). CONCLUSION A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC. CLINICAL RELEVANCE The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.
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Affiliation(s)
- Zhen Chen
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Yao Yu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Shuo Liu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Wen Du
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Leihao Hu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Congwei Wang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiaqi Li
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Jianbo Liu
- Huafang Hanying Medical Technology Co., Ltd, No.19, West Bridge Road, Miyun District, Beijing, 101520, People's Republic of China
| | - Wenbo Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Xin Peng
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China.
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Zhao B, Yan S, Jia ZY, Zhu HT, Shi YJ, Li XT, Qu JR, Sun YS. CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma. Quant Imaging Med Surg 2023; 13:7996-8008. [PMID: 38106287 PMCID: PMC10722054 DOI: 10.21037/qims-23-275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 10/11/2023] [Indexed: 12/19/2023]
Abstract
Background Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.
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Affiliation(s)
- Bo Zhao
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Shuo Yan
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Zheng-Yan Jia
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jin-Rong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
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Zhang S, Sun L, Cai D, Liu G, Jiang D, Yin J, Fang Y, Wang H, Shen Y, Hou Y, Shi H, Tan L. Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma. Ann Surg Oncol 2023; 30:7452-7460. [PMID: 37355519 DOI: 10.1245/s10434-023-13694-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/15/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE This study was conducted to predict the lymph node status and survival of esophageal squamous cell carcinoma before treatment by PET-CT-related parameters. METHODS From January 2013 to July 2018, patients with pathologically diagnosed ESCC at our hospital were retrospectively enrolled. Completed esophagectomy and two- or three-field lymph node dissections were conducted. Those with neoadjuvant therapy were excluded. The first 65% of patients in each year were regarded as the training set and the last 35% as the test set. Nomogram was constructed by the "rms" package. Five-year, overall survival was analyzed based on the best cutoff value of risk score determined by the "survivalROC" package. RESULTS Ultimately, 311 patients were included with 209 in the training set and 102 in the test set. The positive rate of the lymph node in the training set was 36.8% and that in the test set was 32.4%. The C-index of the training set was 0.763 and the test set was 0.766. The decision curve analysis showed that it was superior to the previous methods based on lymph node uptake or long/short axis diameter or axial ratio. Risk score > 0.20 was significantly associated with 5-year, overall survival (p = 0.0015) in all patients. CONCLUSIONS The nomogram constructed from PET-CT parameters including primary tumor metabolic length and thickness can accurately predict the risk of lymph node metastasis in ESCC. The risk score calculated by our model accurately predicts the patient's 5-year overall survival.
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Affiliation(s)
- Shaoyuan Zhang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Linyi Sun
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Danjie Cai
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jun Yin
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Yong Fang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Hao Wang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Yaxing Shen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
| | - Lijie Tan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
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Tong Y, Chen J, Sun J, Luo T, Duan S, Li K, Zhou K, Zeng J, Lu F. A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma. Front Oncol 2023; 13:1162238. [PMID: 37901318 PMCID: PMC10602760 DOI: 10.3389/fonc.2023.1162238] [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: 02/09/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Purpose To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent. Materials and methods The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusion We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.
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Affiliation(s)
- Yahan Tong
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Junyi Chen
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
| | - Jingjing Sun
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Taobo Luo
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Kai Li
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Kefeng Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jian Zeng
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Fangxiao Lu
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
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10
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Yan S, Li FP, Jian L, Zhu HT, Zhao B, Li XT, Shi YJ, Sun YS. CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy. BMC Cancer 2023; 23:477. [PMID: 37231388 DOI: 10.1186/s12885-023-10973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
OBJECTIVE To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.
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Affiliation(s)
- Shuo Yan
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Fei-Ping Li
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Bo Zhao
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
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11
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Wu P, Wu K, Li Z, Liu H, Yang K, Zhou R, Zhou Z, Xing N, Wu S. Multimodal investigation of bladder cancer data based on computed tomography, whole slide imaging, and transcriptomics. Quant Imaging Med Surg 2023; 13:1023-1035. [PMID: 36819263 PMCID: PMC9929396 DOI: 10.21037/qims-22-679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023]
Abstract
Background Multimodal analysis has shown great potential in the diagnosis and management of cancer. This study aimed to determine the multimodal data associations between radiological, pathologic, and molecular characteristics in bladder cancer. Methods A retrospective study of computed tomography (CT), pathologic slice, and RNA sequencing data from 127 consecutive adult patients in China who underwent bladder surgery and were pathologically diagnosed with bladder cancer was conducted. A total of 200 radiological and 1,029 pathologic features were extracted by radiomics and pathomics. Multimodal associations analysis and structural equation modeling were used to measure the cross-modal associations and structural relationships between CT and pathologic slice. A convolutional neural network was constructed for molecular subtyping based on multimodal imaging features. Class activation maps were used to examine the feature contribution in model decision-making. Cox regression and Kaplan-Meier survival analysis were used to explore the relevance of multimodal features to the prognosis of patients with bladder cancer. Results A total of 77 densely associated blocks of feature pairs were identified between CT and whole slide images. The largest cross-modal associated block reflected the tumor-grade properties. A significant relation was found between pathological features and molecular subtypes (β=0.396; P<0.001). High-grade bladder cancer showed heterogeneity of significance across different scales and higher disorders at the microscopic level. The fused radiological and pathologic features achieved higher accuracy (area under the curve: 0.89; 95% CI: 0.75-1.0) than the unimodal method. Thirteen prognosis-related features from CT and whole slide images were identified. Conclusions Our work demonstrated the associations between CT, pathologic slices, and molecular signatures, and the potential to use multimodal data analysis in related clinical applications. Multimodal data analysis showed the potential of cross-inference of modal data and had higher diagnostic accuracy than the unimodal method.
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Affiliation(s)
- Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China;,Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China;,Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, China
| | - Zhe Li
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China;,Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China
| | - Kai Yang
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China
| | - Rong Zhou
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China;,Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, China
| | - Ziyu Zhou
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China
| | - Nianzeng Xing
- State Key Laboratory of Molecular Oncology and Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China;,Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China
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12
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Li Z, Chen L, Song Y, Dai G, Duan L, Luo Y, Wang G, Xiao Q, Li G, Bai S. Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma. Quant Imaging Med Surg 2023; 13:224-236. [PMID: 36620140 PMCID: PMC9816734 DOI: 10.21037/qims-22-459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022]
Abstract
Background Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Methods Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Results Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Conclusions Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.
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Affiliation(s)
- Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China;,Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Li Chen
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Ying Song
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guyu Dai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Lian Duan
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Yong Luo
- Department of Head & Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Guangyu Wang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Yu K, Ying J, Zhao T, Lei L, Zhong L, Hu J, Zhou JW, Huang C, Zhang X. Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative. Quant Imaging Med Surg 2023; 13:352-369. [PMID: 36620171 PMCID: PMC9816749 DOI: 10.21037/qims-22-368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022]
Abstract
Background The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis. Methods Data used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC). Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics. Results The comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively). Conclusions Our results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.
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Affiliation(s)
- Keyan Yu
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, China;,Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jia Ying
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Tianyun Zhao
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Lan Lei
- Program in Public Health, Stony Brook Medicine, Stony Brook, NY, USA;,Department of Medicine, Northside Hospital Gwinnett, Lawrenceville, GA, USA
| | - Lijie Zhong
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, China
| | - Jiaping Hu
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, China
| | - Juin W. Zhou
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA;,Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA;,Department of Psychiatry, Stony Brook Medicine, Stony Brook, NY, USA
| | - Xiaodong Zhang
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, China
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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.
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Wang H, Xu Z, Zhang H, Huang J, Peng H, Zhang Y, Liang C, Zhao K, Liu Z. The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer. Quant Imaging Med Surg 2022; 12:4402-4413. [PMID: 36060586 PMCID: PMC9403574 DOI: 10.21037/qims-22-77] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery. METHODS The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ2 test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables. RESULTS There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601). CONCLUSIONS It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.
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Affiliation(s)
- Huihui Wang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Haochen Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Jia Huang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Haien Peng
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Dong Y, Jiang Z, Li C, Dong S, Zhang S, Lv Y, Sun F, Liu S. Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer. Quant Imaging Med Surg 2022; 12:2658-2671. [PMID: 35502390 PMCID: PMC9014164 DOI: 10.21037/qims-21-980] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/20/2022] [Indexed: 07/30/2023]
Abstract
BACKGROUND We developed and validated novel radiomics-based nomograms to identify epidermal growth factor receptor (EGFR) mutations and the Ki-67 proliferation index of non-small cell lung cancer. METHODS We enrolled 132 patients with histologically verified non-small cell lung cancer from four hospital institutions who underwent computed tomography (CT) scans. EGFR mutations and the Ki-67 proliferation index were measured from tumor tissues. A total of 1,287 radiomic features were extracted, and a three-stage feature selection method was implemented to acquire the most valuable radiomic features. Finally, the radiomic scores and nomograms of the two tasks were established and tested. Receiver operating characteristic curves, calibration curves, and decision curves were used to evaluate their prediction performance and clinical utility. RESULTS In task [1], smoking status and histological type were significantly associated with EGFR mutations. After feature selection, 10 features were used to establish radiomic score, which showed good performance [area under the curve (AUC) =0.800] in the validation cohort. The radiomic nomogram had an AUC of 0.798 (95% CI: 0.664 to 0.931) with a C-index of 0.798 in the validation cohort. In task [2], gender, smoking status, histological type, and stage showed a significant correlation with Ki-67 proliferation index expression. A total of 28 features were selected to develop a radiomic score, with an AUC of 0.820 in the validation cohort. The final nomogram showed an AUC of 0.828 (95% CI: 0.703 to 0.953) with a C-index of 0.828 in the validation cohort. CONCLUSIONS EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer can be predicted efficiently by the novel radiomic scores and nomograms.
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Affiliation(s)
- Yinjun Dong
- Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Postdoctoral Research Workstation, Liaocheng People’s Hospital, Liaocheng, China
| | - Zekun Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Chaowei Li
- Department of Clinical Drug Research, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shuai Dong
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shengdong Zhang
- Department of Radiology, Yinan Branch of Qilu Hospital of Shandong University, Yinan County People’s Hospital, Linyi, China
| | - Yunhong Lv
- Department of Mathematics and Information Technology, Xingtai University, Xingtai, China
- Department of Mathematics and Statistics, University of Windsor, Windsor, Ontario, Canada
| | - Fenghao Sun
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shuguang Liu
- Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Liu F, Li X, Liu Q, Hu B, Xu J, Huang C. A computed tomography-based clinical-radiomics model for prediction of lymph node metastasis in esophageal carcinoma. J Cancer Res Ther 2021; 17:1665-1671. [DOI: 10.4103/jcrt.jcrt_1755_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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