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Li ZM, Liu W, Chen XL, Wu WZ, Xu XE, Chu MY, Yu SX, Li EM, Huang HC, Xu LY. Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data. Clin Res Hepatol Gastroenterol 2024; 48:102318. [PMID: 38471582 DOI: 10.1016/j.clinre.2024.102318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/05/2024] [Accepted: 03/09/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data. METHODS A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models. RESULTS Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively). CONCLUSION We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.
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Affiliation(s)
- Zhi-Mao Li
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China; Department of Radiation Oncology, Shantou Central Hospital, Shantou 515041, Guangdong, PR China
| | - Wei Liu
- College of Science, Heilongjiang Institute of Technology, Harbin 150050, Heilongjiang, PR China
| | - Xu-Li Chen
- Department of Clinical Laboratory Medicine, Shantou Central Hospital, Shantou 515041, Guangdong, PR China
| | - Wen-Zhi Wu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Xiu-E Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Man-Yu Chu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Shuai-Xia Yu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - He-Cheng Huang
- Department of Radiation Oncology, Shantou Central Hospital, Shantou 515041, Guangdong, PR China.
| | - Li-Yan Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, Guangdong, PR China.
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Cui J, Zhang D, Gao Y, Duan J, Wang L, Li L, Yuan S. CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy. Insights Imaging 2024; 15:87. [PMID: 38523188 PMCID: PMC10961297 DOI: 10.1186/s13244-024-01647-2] [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: 10/07/2023] [Accepted: 02/10/2024] [Indexed: 03/26/2024] Open
Abstract
OBJECTIVES The purpose of this study was to investigate the prognostic significance of radiomics in conjunction with hematological parameters in relation to the overall survival (OS) of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy (dCRT). METHODS In this retrospective analysis, a total of 122 patients with locally advanced ESCC were included. These patients were randomly assigned to either the training cohort (n = 85) or the validation cohort (n = 37). In the training group, the least absolute shrinkage and selection operator (LASSO) regression was utilized to choose the best radiomic features for calculating the Rad-score. To develop a nomogram model, both univariate and multivariate analyses were conducted to identify the clinical factors and hematologic parameters that could predict the OS. The performance of the predictive model was evaluated using the C-index, while the accuracy was assessed through the calibration curve. RESULTS The Rad-score was calculated by selecting 10 radiomic features through LASSO regression. OS was predicted independently by neutrophil-to-monocyte ratio (NMR) and Rad-score according to the results of multivariate analysis. Patients who had a Rad-score > 0.47 and an NMR > 9.76 were at a significant risk of mortality. A nomogram was constructed using the findings from the multivariate analysis. In the training cohort, the nomogram had a C-index of 0.619, while in the validation cohort, it was 0.573. The model's accuracy was demonstrated by the calibration curve, which was excellent. CONCLUSION A prognostic model utilizing radiomics and hematologic parameters was developed, enabling the prediction of OS in patients with ESCC following dCRT. CRITICAL RELEVANCE STATEMENT Patients with esophageal cancer who underwent definitive chemoradiotherapy may benefit from including CT radiomics in the nomogram model. KEY POINTS • Predicting the prognosis of ESCC patients before treatment is particularly important. • Patients with a Rad-score > 0.47 and neutrophil-to-monocyte ratio > 9.76 had a high risk of mortality. • CT-based radiomics nomogram model could be used to predict the survival of patients.
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Affiliation(s)
- Jinfeng Cui
- Center for Medical Integration and Practice, Shandong University, Jinan, Shandong, China
| | - Dexian Zhang
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yongsheng Gao
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jinghao Duan
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Affiliated to Shandong University, Shandong First Medical University and Shandong Academy of Medical Sciences, No. 440 Jiyan Road, Jinan, Shandong, 250117, China
| | - Lulu Wang
- Department of Oncology, The People's Hospital of Leling, Leling, Shandong, China
| | - Li Li
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Affiliated to Shandong University, Shandong First Medical University and Shandong Academy of Medical Sciences, No. 440 Jiyan Road, Jinan, Shandong, 250117, China.
| | - Shuanghu Yuan
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Affiliated to Shandong University, Shandong First Medical University and Shandong Academy of Medical Sciences, No. 440 Jiyan Road, Jinan, Shandong, 250117, China.
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong, China.
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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Chakrabarty N, Mahajan A, Prabhash K, Patil P, Chowhan M, Munmmudi N, Niyogi D, Dabkara D, Singh S, Singh A, Devarmani S, Dhull VS. Imaging Recommendations for Diagnosis, Staging, and Management of Esophageal Cancer. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1760324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
AbstractEarly staging and treatment initiation affect prognosis of patients with esophageal and esophagogastric junction cancer; hence, it is imperative to have knowledge of proper choice of imaging modality for staging of these patients, to effectively convey relevant imaging findings to the treating physician/surgeon. It is also essential to be aware of pertinent imaging findings that need to be conveyed to the treating physician/surgeon at staging, and after treatment, including post-therapy complications (if any), so as to provide timely management to such patients. In this article, we have provided imaging guidelines for diagnosis, staging, post-therapy response evaluation, follow-up, and assessment of post-therapy complications of esophageal and esophagogastric junction cancer in a systematic manner. Besides, risk factors and clinical workup have also been elucidated. We have also attached comprehensive staging and post-therapy contrast-enhanced computed tomography and fluorodeoxyglucose-positron emission tomography/computed tomography-based synoptic reporting formats “ECI-RADS” and “pECI-RADS,” respectively, for esophageal and esophagogastric junction cancer in the supplement, for effective communication of imaging findings between a radiologist and the treating physician/surgeon.
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Affiliation(s)
- Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Prachi Patil
- Department of Digestive Diseases and Clinical Nutrition, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Manoranjan Chowhan
- Department of Nuclear Medicine and PET/CT, Aditya Birla Memorial Hospital, Pune, Maharashtra, India
| | - Naveen Munmmudi
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Devayani Niyogi
- Department of Surgical Oncology, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Deepak Dabkara
- Department of Oncology, CHL Hospitals, Indore, Madhya Pradesh, India
| | - Suryaveer Singh
- Department of Radiodiagnosis, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Ajaykumar Singh
- Department of Medical Oncology, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Sanjana Devarmani
- Department of Radiodiagnosis, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Varun Singh Dhull
- Department of Nuclear Medicine and PET/CT, Aditya Birla Memorial Hospital, Pune, Maharashtra, India
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Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers. Int J Mol Sci 2023; 24:ijms24043316. [PMID: 36834728 PMCID: PMC9968115 DOI: 10.3390/ijms24043316] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Esophageal cancer (EC) is the deadliest cancer worldwide, with a 92% annual mortality rate per incidence. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are the two major types of ECs, with EAC having one of the worst prognoses in oncology. Limited screening techniques and a lack of molecular analysis of diseased tissues have led to late-stage presentation and very low survival durations. The five-year survival rate of EC is less than 20%. Thus, early diagnosis of EC may prolong survival and improve clinical outcomes. Cellular and molecular biomarkers are used for diagnosis. At present, esophageal biopsy during upper endoscopy and histopathological analysis is the standard screening modality for both ESCC and EAC. However, this is an invasive method that fails to yield a molecular profile of the diseased compartment. To decrease the invasiveness of the procedures for diagnosis, researchers are proposing non-invasive biomarkers for early diagnosis and point-of-care screening options. Liquid biopsy involves the collection of body fluids (blood, urine, and saliva) non-invasively or with minimal invasiveness. In this review, we have critically discussed various biomarkers and specimen retrieval techniques for ESCC and EAC.
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Liu W, Zeng C, Wang S, Zhan Y, Huang R, Luo T, Peng G, Wu Y, Qiu Z, Li D, Wu F, Chen C. A combined predicting model for benign esophageal stenosis after simultaneous integrated boost in esophageal squamous cell carcinoma patients (GASTO1072). Front Oncol 2022; 12:1026305. [PMID: 37078004 PMCID: PMC10107369 DOI: 10.3389/fonc.2022.1026305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
PurposeWe aimed to develop a combined predicting model for benign esophageal stenosis (BES) after simultaneous integrated boost (SIB) with concurrent chemotherapy in patients with esophageal squamous cell carcinoma (ESCC).MethodsThis study included 65 patients with EC who underwent SIB with chemotherapy. Esophageal stenosis was evaluated using esophagograms and the severity of eating disorders. Risk factors were investigated using univariate and multivariate analyses. Radiomics features were extracted based on contrast-enhanced CT (CE-CT) before treatment. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature selection and radiomics signature construction. The model’s performance was evaluated using Harrell’s concordance index and receiver operating characteristic curves.ResultsThe patients were stratified into low- and high-risk groups according to BES after SIB. The area under the curves of the clinical model, Rad-score, and the combined model were 0.751, 0.820 and 0.864, respectively. In the validation cohort, the AUCs of these three models were 0.854, 0.883 and 0.917, respectively. The Hosmer-Lemeshow test showed that there was no deviation from model fitting for the training cohort (p=0.451) and validation cohort (p=0.481). The C-indexes of the nomogram were 0.864 and 0.958 for the training and validation cohort, respectively. The model combined with Rad-score and clinical factors achieved favorable prediction ability.ConclusionDefinitive chemoradiotherapy could alleviate tumor-inducing esophageal stenosis but result in benign stenosis. We constructed and tested a combined predicting model for benign esophageal stenosis after SIB. The nomogram incorporating both radiomics signature and clinical prognostic factors showed favorable predictive accuracy for BES in ESCC patients who received SIB with chemotherapy.Trial registration number and date of registrationRegistered in www.Clinicaltrial.gov, ID: NCT01670409, August 12, 2012
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Affiliation(s)
- Weitong Liu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Jieyang People’s Hospital, Jeiyang, China
| | - Chengbing Zeng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Siyan Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yizhou Zhan
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ruihong Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ting Luo
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, China
| | - Guobo Peng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yanxuan Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Zihan Qiu
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Derui Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Fangcai Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Chuangzhen Chen, ; Fangcai Wu,
| | - Chuangzhen Chen
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Chuangzhen Chen, ; Fangcai Wu,
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Intarak S, Chongpison Y, Vimolnoch M, Oonsiri S, Kitpanit S, Prayongrat A, Kannarunimit D, Chakkabat C, Sriswasdi S, Lertbutsayanukul C, Rakvongthai Y. Tumor Prognostic Prediction of Nasopharyngeal Carcinoma Using CT-Based Radiomics in Non-Chinese Patients. Front Oncol 2022; 12:775248. [PMID: 35155228 PMCID: PMC8831248 DOI: 10.3389/fonc.2022.775248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/05/2022] [Indexed: 12/24/2022] Open
Abstract
PurposeWe aimed to construct predictive models for the overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) for nasopharyngeal carcinoma (NPC) patients by using CT-based radiomics.Materials and MethodsWe collected data from 197 NPC patients. For each patient, radiomic features were extracted from the CT image acquired at pretreatment via PyRadiomics. Feature selection was performed in two steps. First, features with high inter-observer variability based on multiple tumor delineations were excluded. Then, stratified bootstrappings were performed to identify feature combinations that most frequently achieved the highest (i) area under the receiver operating characteristic curve (AUC) for predicting 3-year OS, PFS, and DMFS or (ii) Harrell’s C-index for predicting time to event. Finally, regularized logistic regression and Cox proportional hazard models with the most frequently selected feature combinations as input were tuned using cross-validation. Additionally, we examined the robustness of the constructed model to variation in tumor delineation by simulating 100 realizations of radiomic feature values to mimic features extracted from different tumor boundaries.ResultsThe combined model that used both radiomics and clinical features yielded significantly higher AUC and Harrell’s C-index than models using either feature set alone for all outcomes (p < 0.05). The AUCs and Harrell’s C-indices of the clinical-only and radiomics-only models ranged from 0.758 ± 0.091 to 0.789 ± 0.082 and from 0.747 ± 0.062 to 0.767 ± 0.074, respectively. In comparison, the combined models achieved AUC of 0.801 ± 0.075 to 0.813 ± 0.078 and Harrell’s C-indices of 0.779 ± 0.066 to 0.796 ± 0.069. The results showed that our models were robust to variation in tumor delineation with the coefficient of variation ranging from 4.8% to 6.4% and from 6.7% to 9.3% for AUC and Harrell’s C-index, respectively.ConclusionOur results demonstrated that using CT-based radiomic features together with clinical features provided superior NPC prognostic prediction than using either clinical or radiomic features alone.
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Affiliation(s)
- Sararas Intarak
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yuda Chongpison
- Biostatistics Excellence Center, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Mananchaya Vimolnoch
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sornjarod Oonsiri
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sarin Kitpanit
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Anussara Prayongrat
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Danita Kannarunimit
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chakkapong Chakkabat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chawalit Lertbutsayanukul
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- *Correspondence: Yothin Rakvongthai,
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