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Song J, Zhang J, Liu G, Guo Z, Liao H, Feng W, Lin W, Li L, Zhang Y, Yang Y, Liu B, Luo R, Chen H, Wang S, Liu JH. PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma. Insights Imaging 2024; 15:161. [PMID: 38913225 PMCID: PMC11196479 DOI: 10.1186/s13244-024-01737-1] [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: 01/06/2024] [Accepted: 06/02/2024] [Indexed: 06/25/2024] Open
Abstract
OBJECTIVES The clinical decision-making regarding choosing surgery alone (SA) or surgery followed by postoperative adjuvant chemotherapy (SPOCT) in esophageal squamous cell carcinoma (ESCC) remains controversial. We aim to propose a pre-therapy PET/CT image-based deep learning approach to improve the survival benefit and clinical management of ESCC patients. METHODS This retrospective multicenter study included 837 ESCC patients from three institutions. Prognostic biomarkers integrating six networks were developed to build an ESCC prognosis (ESCCPro) model and predict the survival probability of ESCC patients treated with SA and SPOCT. Patients who did not undergo surgical resection were in a control group. Overall survival (OS) was the primary end-point event. The expected improvement in survival prognosis with the application of ESCCPro to assign treatment protocols was estimated by comparing the survival of patients in each subgroup. Seven clinicians with varying experience evaluated how ESCCPro performed in assisting clinical decision-making. RESULTS In this retrospective multicenter study, patients receiving SA had a median OS 9.2 months longer than controls. No significant differences in survival were found between SA patients with predicted poor outcomes and the controls (p > 0.05). It was estimated that if ESCCPro was used to determine SA and SPOCT eligibility, the median OS in the ESCCPro-recommended SA group and SPOCT group would have been 15.3 months and 24.9 months longer, respectively. In addition, ESCCPro also significantly improved prognosis accuracy, certainty, and the efficiency of clinical experts. CONCLUSION ESCCPro assistance improved the survival benefit of ESCC patients and the clinical decision-making among the two treatment approaches. CRITICAL RELEVANCE STATEMENT The ESCCPro model for treatment decision-making is promising to improve overall survival in ESCC patients undergoing surgical resection and patients undergoing surgery followed by postoperative adjuvant chemotherapy. KEY POINTS ESCC is associated with a poor prognosis and unclear ideal treatments. ESCCPro predicts the survival of patients with ESCC and the expected benefit from SA. ESCCPro improves clinicians' stratification of patients' prognoses.
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Affiliation(s)
- Jiangdian Song
- School of Health Management, China Medical University, Shenyang, China.
| | - Jie Zhang
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Guichao Liu
- The Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong, China
| | - Zhexu Guo
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors China Medical University, Ministry of Education, Shenyang, China
- Department of VIP In-Patient Ward, The First Hospital of China Medical University, Shenyang, China
| | - Hongxian Liao
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Wenhui Feng
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Wenxiang Lin
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Lei Li
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Yi Zhang
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Yuxiang Yang
- Department of Radiology, The Second People's Hospital of Xiangzhou, Zhuhai, China
| | - Bin Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Hao Chen
- Department of Gastroenterology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Siyun Wang
- Department of Oncology, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Jian-Hua Liu
- Department of Oncology, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
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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.
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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
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Guo H, Tang HT, Hu WL, Wang JJ, Liu PZ, Yang JJ, Hou SL, Zuo YJ, Deng ZQ, Zheng XY, Yan HJ, Jiang KY, Huang H, Zhou HN, Tian D. The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy. Front Oncol 2023; 13:1082960. [PMID: 37091180 PMCID: PMC10117779 DOI: 10.3389/fonc.2023.1082960] [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: 11/01/2022] [Accepted: 03/27/2023] [Indexed: 04/25/2023] Open
Abstract
Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.
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Affiliation(s)
- Hai Guo
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Thoracic Surgery, Sichuan Tianfu New Area People’s Hospital, Chengdu, China
| | - Hong-Tao Tang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Wen-Long Hu
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Wang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Pei-Zhi Liu
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Yang
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Sen-Lin Hou
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Yu-Jie Zuo
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Zhi-Qiang Deng
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Xiang-Yun Zheng
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Hao-Ji Yan
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kai-Yuan Jiang
- Department of Surgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Heng Huang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hai-Ning Zhou
- Department of Thoracic Surgery, Suining Central Hospital, Suining, China
- *Correspondence: Dong Tian, ; Hai-Ning Zhou,
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Dong Tian, ; Hai-Ning Zhou,
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Zhang AQ, Zhao HP, Li F, Liang P, Gao JB, Cheng M. Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer. Front Oncol 2022; 12:969707. [PMID: 36212443 PMCID: PMC9537615 DOI: 10.3389/fonc.2022.969707] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/05/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose Preoperative evaluation of lymph node metastasis (LNM) is the basis of personalized treatment of locally advanced gastric cancer (LAGC). We aim to develop and evaluate CT-based model using deep learning features to preoperatively predict LNM in LAGC. Methods A combined size of 523 patients who had pathologically confirmed LAGC were retrospectively collected between August 2012 and July 2019 from our hospital. Five pre-trained convolutional neural networks were exploited to extract deep learning features from pretreatment CT images. And the support vector machine (SVM) was employed as the classifier. We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model was built with clinical factors only for baseline comparison. Results The optimal model with features extracted from ResNet yielded better performance with AUC of 0.796 [95% confidence interval (95% CI), 0.715-0.865] and accuracy of 75.2% (95% CI, 67.2%-81.5%) in the testing cohort, compared with 0.704 (0.625-0.783) and 61.8% (54.5%-69.9%) for the radiomics model. The predictive performance of all the radiological models were significantly better than the clinical model. Conclusion The novel and noninvasive deep learning approach could provide efficient and accurate prediction of lymph node metastasis in LAGC, and benefit clinical decision making of therapeutic strategy.
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Affiliation(s)
- An-qi Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-ping Zhao
- Department of Radiology, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Fei Li
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Gu L, Liu Y, Guo X, Tian Y, Ye H, Zhou S, Gao F. Computed tomography-based radiomic analysis for prediction of treatment response to salvage chemoradiotherapy for locoregional lymph node recurrence after curative esophagectomy. J Appl Clin Med Phys 2021; 22:71-79. [PMID: 34614265 PMCID: PMC8598151 DOI: 10.1002/acm2.13434] [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: 07/12/2021] [Revised: 08/15/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Objective To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the locoregional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy. Methods This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast‐enhanced CT images. The least absolute shrinkage and selection operator with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan–Meier method was used to determine the local recurrence time of cancer. Results The radiomic model suggested seven features that could be used to predict treatment response. The AUCs in training and validated cohorts were 0.777 (95% CI: 0.667–0.878) and 0.765 (95% CI: 0.556–0.975), respectively. A significant difference in the radiomic scores (Rad‐scores) between response and nonresponse was observed in the two cohorts (p < 0.001, 0.034, respectively). Two features were identified for classifying whether there will be relapse in 2 years. AUC was 0.857 (95% CI: 0.780–0.935) in the training cohort. The local control time of the high Rad‐score group was higher than the low group in both cohorts (p < 0.001 and 0.025, respectively). As inferred from the Cox regression analysis, the low Rad‐score was a high‐risk factor for local recurrence within 2 years. Conclusions The radiomic approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients.
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Affiliation(s)
- Liang Gu
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China.,Department of Radiation Oncology, The Second Affiliated Hospital of Soochow University, Su Zhou, Jiangsu Province, China
| | - Yangchen Liu
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Xinwei Guo
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Ye Tian
- Department of Radiation Oncology, The Second Affiliated Hospital of Soochow University, Su Zhou, Jiangsu Province, China
| | - Hongxun Ye
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Shaobin Zhou
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Fei Gao
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
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