1
|
Xie SH, Zhang WF, Wu Y, Tang ZL, Yang LT, Xue YJ, Lin JB, Kang MQ. Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study. Transl Oncol 2024; 47:102050. [PMID: 38981245 PMCID: PMC11292555 DOI: 10.1016/j.tranon.2024.102050] [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: 12/09/2023] [Revised: 02/24/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024] Open
Abstract
PURPOSE Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment. METHODS The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity. RESULTS A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1-2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort. CONCLUSION The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.
Collapse
Affiliation(s)
- Shu-Han Xie
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Key Laboratory of Cardio-Thoracic Surgery(Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China; Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, Fujian, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, Fujian, China
| | - Wan-Fei Zhang
- Department of Thoracic Surgery, Quanzhou First Hospital, Quanzhou, Fujian, China; Department of Thoracic Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China
| | - Yue Wu
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; The School of Medical Imaging, Fujian Medical University, Fuzhou, Fujian, China
| | - Zi-Lu Tang
- Department of Thoracic Surgery, Quanzhou First Hospital, Quanzhou, Fujian, China; Department of Thoracic Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China
| | - Li-Tao Yang
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Department of Thoracic Surgery, Baoji Traditional Chinese Medicine Hospital, Baoji, Shaanxi, China
| | - Yun-Jing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Jiang-Bo Lin
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Key Laboratory of Cardio-Thoracic Surgery(Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China; Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, Fujian, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, Fujian, China
| | - Ming-Qiang Kang
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Key Laboratory of Cardio-Thoracic Surgery(Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China; Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, Fujian, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, Fujian, China.
| |
Collapse
|
2
|
Jia J, Liu Z, Wang F, Bai G. Consensus Clustering Analysis Based on Enhanced-CT Radiomic Features: Esophageal Squamous Cell Carcinoma patients' 3-Year Progression-Free Survival. Acad Radiol 2024; 31:2807-2817. [PMID: 38199900 DOI: 10.1016/j.acra.2023.12.025] [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: 11/15/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
RATIONALE AND OBJECTIVES To assess the efficacy of consensus cluster analysis based on CT radiomics in stratifying risk and predicting postoperative progression-free survival (PFS) in patients diagnosed with esophageal squamous cell carcinoma (ESC). MATERIALS AND METHODS We conducted a retrospective study involving 546 patients diagnosed with ESC between January 2016 and March 2021. All patients underwent preoperative enhanced CT examinations. From the enhanced CT images, radiomics features were extracted, and a consensus clustering algorithm was applied to group the patients based on these features. Statistical analysis was performed to examine the relationship between the clustering results and gene protein expression, histopathological features, and patients' 3-year PFS. We applied the Kruskal-Wallis test for continuous data, chi-square or Fisher's exact tests for categorical data, and the log-rank test for PFS. RESULTS This study identified four groups: Cluster 1 (n = 100, 18.3%), Cluster 2 (n = 197, 36.1%), Cluster 3 (n = 205, 37.5%), and Cluster 4 (n = 44, 8.1%). The cancer gene Breast Cancer Susceptibility Gene 1 (BRCA1) was most highly expressed in Cluster 4 (75%), showing significant differences between the four subtypes with a P-value of 0.035. The expression of programmed death-1 (PD-1) was highest in Cluster 1 (51%), with a P-value of 0.022. Vascular invasion occurred most frequently in Cluster 2 (28.9%), with a P-value of 0.022. The majority of patients with stage T3-4 were in Cluster 2 (67%), with a P-value of 0.003. Kaplan-Meier survival analysis revealed significant differences in PFS between the four groups (P = 0.013). Among them, patients in Cluster 1 had the best prognosis, while those in Cluster 2 had the worst. CONCLUSION This study highlights the effectiveness of consensus clustering analysis based on enhanced CT radiomics features in identifying associations between radiomics features, histopathological characteristics, and prognosis in different clusters. These findings provide valuable insights for clinicians in accurately and effectively evaluating the prognosis of esophageal cancer.
Collapse
Affiliation(s)
- Jianye Jia
- The Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, Jiangsu, PR China
| | - Ziyan Liu
- The Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, Jiangsu, PR China
| | - Fen Wang
- The Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, Jiangsu, PR China
| | - Genji Bai
- The Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, Jiangsu, PR China.
| |
Collapse
|
3
|
Liu L, Liao H, Zhao Y, Yin J, Wang C, Duan L, Xie P, Wei W, Xu M, Su D. CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1267596. [PMID: 38577325 PMCID: PMC10993774 DOI: 10.3389/fonc.2024.1267596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024] Open
Abstract
Objective We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC). Methods The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS). Results The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability. Conclusion The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings. Systematic Review Registration Open Science Framework platform at https://osf.io/5zcnd.
Collapse
Affiliation(s)
- Liangsen Liu
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
- Department of Nuclear Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hai Liao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yang Zhao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jiayu Yin
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
- Department of Radiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chen Wang
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Lixia Duan
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Peihan Xie
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wupeng Wei
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Meihai Xu
- Department of Radiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Danke Su
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, China
| |
Collapse
|
4
|
Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
Collapse
Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| |
Collapse
|