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Deng Z, Liu X, Wu R, Yan H, Gou L, Hu W, Wan J, Song C, Chen J, Ma D, Zhou H, Tian D. Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study. BMC Cancer 2024; 24:536. [PMID: 38678211 PMCID: PMC11055367 DOI: 10.1186/s12885-024-12306-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC. METHODS This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively. RESULTS In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968). CONCLUSIONS The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.
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Affiliation(s)
- Zhiqiang Deng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Xiaoling Liu
- Department of Ultrasound, Nanchong Central Hospital, Nanchong, China
| | - Renmei Wu
- Department of Ultrasound, Suining Central Hospital, Suining, China
| | - Haoji Yan
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Lingyun Gou
- Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wenlong Hu
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jiaxin Wan
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Chenwanqiu Song
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Jing Chen
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Daiyuan Ma
- Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
| | - Haining Zhou
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China.
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
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Al Moussawy M, Lakkis ZS, Ansari ZA, Cherukuri AR, Abou-Daya KI. The transformative potential of artificial intelligence in solid organ transplantation. FRONTIERS IN TRANSPLANTATION 2024; 3:1361491. [PMID: 38993779 PMCID: PMC11235281 DOI: 10.3389/frtra.2024.1361491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 07/13/2024]
Abstract
Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.
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Affiliation(s)
- Mouhamad Al Moussawy
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zoe S Lakkis
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zuhayr A Ansari
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aravind R Cherukuri
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Khodor I Abou-Daya
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
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Tian D, Yan HJ, Huang H, Zuo YJ, Liu MZ, Zhao J, Wu B, Shi LZ, Chen JY. Machine Learning-Based Prognostic Model for Patients After Lung Transplantation. JAMA Netw Open 2023; 6:e2312022. [PMID: 37145595 PMCID: PMC10163387 DOI: 10.1001/jamanetworkopen.2023.12022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/23/2023] [Indexed: 05/06/2023] Open
Abstract
Importance Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable. Objective To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm. Design, Setting, and Participants This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019. Main Outcomes And Measures Overall survival in patients after LTx. Results A total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P < .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P < .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P < .001), respectively. Conclusions and relevance In this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.
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Affiliation(s)
- Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, China
| | - Hao-Ji Yan
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Heng Huang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yu-Jie Zuo
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Ming-Zhao Liu
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, China
| | - Jin Zhao
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, China
| | - Bo Wu
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, China
| | - Ling-Zhi Shi
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, China
| | - Jing-Yu Chen
- Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, 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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Li DL, Zhang L, Yan HJ, Zheng YB, Guo XG, Tang SJ, Hu HY, Yan H, Qin C, Zhang J, Guo HY, Zhou HN, Tian D. Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma. Front Oncol 2022; 12:986358. [PMID: 36158684 PMCID: PMC9496653 DOI: 10.3389/fonc.2022.986358] [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: 07/05/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022] Open
Abstract
Background For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC. Methods Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model’s generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied. Results Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701). Conclusions ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.
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Affiliation(s)
- Dong-lin Li
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Lin Zhang
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Hao-ji Yan
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Academician (Expert) Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yin-bin Zheng
- Department of Thoracic Surgery, Nanchong Central Hospital, Nanchong, China
| | - Xiao-guang Guo
- Department of Pathology, Nanchong Central Hospital, Nanchong, China
| | - Sheng-jie Tang
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Hai-yang Hu
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Hang Yan
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Chao Qin
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Jun Zhang
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Hai-yang Guo
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
| | - Hai-ning Zhou
- Department of Thoracic Surgery, Suining Central Hospital, Sunning, China
- *Correspondence: Hai-ning Zhou, ; Dong Tian,
| | - Dong Tian
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Academician (Expert) Workstation, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- *Correspondence: Hai-ning Zhou, ; Dong Tian,
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