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Zhu X, Mo M, Zheng S, Han K, Li G, Zhao F. Comparing the prognosis of esophageal adenocarcinoma with bone and liver metastases: A competing risk analysis. PLoS One 2024; 19:e0303842. [PMID: 39321194 PMCID: PMC11423978 DOI: 10.1371/journal.pone.0303842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/30/2024] [Indexed: 09/27/2024] Open
Abstract
BACKGROUND About half of the patients with esophageal cancer are presenting with metastasis at initial diagnosis. However, few studies have concerned on the prognostic factors of metastatic esophageal adenocarcinoma (mEAC). This research aimed to investigate the effects of single bone metastasis (BM) and single liver metastasis (LM) on prognosis of mEAC patients. METHODS Data were obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program database. We compared the effects of LM and BM on overall survival (OS), EAC-specific survival (CSS), and EAC-specific death (EASD) by multivariate Cox regression, Kaplan-Meier analysis, and competing risk regression models. RESULTS A total of 1,278 EAC patients were recruited in this study. Of which 78.95% (1009/1278) were EASD, and 12.68% (162/1278) were non-EAC-specific death (non-EASD). In multivariate Cox regression analysis, surgery, chemotherapy, and AJCC.T2 (vs. T1) were identified as protective factors for OS&CSS, while divorced/separated, single/unmarried (vs. married), grade III-IV (vs. grade I-II) and BM (vs. LM) were identified as risk factors. Competing risk regression analysis further confirmed that surgery and chemotherapy were beneficial to the patients with mEAC, and BM (vs. LM) was a risk factor for mEAC patients when considering the existence of the competitive risk events. CONCLUSION Our study indicated that mEAC patients with BM face a worse prognosis compared to those with LM. Additionally, surgery and chemotherapy emerge as protective factors for mEAC patients. These findings offer evidence-based insights for clinical management and contribute to the field.
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Affiliation(s)
- Xinglian Zhu
- Department of Respiratory, Panyu Hexian Memorial Hospital of Guangzhou, China
| | - Mingxing Mo
- The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shaojun Zheng
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, China
| | - Kunning Han
- Department of Neurology, Shenzhen People’s Hospital, Shenzhen, China
| | - Guoyang Li
- Department of Respiratory, Panyu Hexian Memorial Hospital of Guangzhou, China
| | - Fang Zhao
- Food Inspection and Quarantine Technology Center of Shenzhen Customs District, Shenzhen, China
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Fang Y, Wan J, Zeng Y. Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study. J Cancer Res Clin Oncol 2024; 150:420. [PMID: 39283330 PMCID: PMC11405433 DOI: 10.1007/s00432-024-05937-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 08/30/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND This study aims to establish a predictive model for assessing the risk of esophageal cancer lung metastasis using machine learning techniques. METHODS Data on esophageal cancer patients from 2010 to 2020 were extracted from the surveillance, epidemiology, and end results (SEER) database. Through univariate and multivariate logistic regression analyses, eight indicators related to the risk of lung metastasis were selected. These indicators were incorporated into six machine learning classifiers to develop corresponding predictive models. The performance of these models was evaluated and compared using metrics such as The area under curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS A total of 20,249 confirmed cases of esophageal cancer were included in this study. Among them, 14,174 cases (70%) were assigned to the training set while 6075 cases (30%) constituted the internal test set. Primary site location, tumor histology, tumor grade classification system T staging criteria N staging criteria brain metastasis bone metastasis liver metastasis emerged as independent risk factors for esophageal cancer with lung metastasis. Amongst the six constructed models, the GBM algorithm-based machine learning model demonstrated superior performance during internal dataset validation. AUC, accuracy, sensitivity, and specificity values achieved by this model stood at respectively at 0.803, 0.849, 0.604, and 0.867. CONCLUSION We have developed an online calculator based on the GBM model ( https://lvgrkyxcgdvo7ugoyxyywe.streamlit.app/)to aid clinical decision-making and treatment planning.
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Affiliation(s)
- Ying Fang
- Department of Joint Surgery, Hangzhou Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
| | - Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, No.26, Chuyuan Road, Jingzhou, Hubei, China.
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai Street, Changchun, Jilin, China.
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Wan J, Zeng Y. Prediction of hepatic metastasis in esophageal cancer based on machine learning. Sci Rep 2024; 14:14507. [PMID: 38914571 PMCID: PMC11196737 DOI: 10.1038/s41598-024-63213-6] [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: 03/09/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
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Affiliation(s)
- Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, jingzhou, China
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai street, Changchun, Jilin, China.
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He W, Yu Y, Yan Z, Luo N, Yang W, Li F, Jin H, Zhang Y, Ma X, Ma M. Nomograms for Predicting Risk and Survival of Esophageal Cancer Lung Metastases: a SEER Analysis. J Cancer 2024; 15:3370-3380. [PMID: 38817873 PMCID: PMC11134440 DOI: 10.7150/jca.92389] [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: 11/19/2023] [Accepted: 04/07/2024] [Indexed: 06/01/2024] Open
Abstract
Background: The overall survival rate is notably low for esophageal cancer patients with lung metastases (LM), presenting significant challenges in their treatment. Methods: Through the Surveillance, Epidemiology, and End Results (SEER) program, individuals diagnosed with esophageal cancer between 2010 and 2015 were enrolled. Based on whether esophageal cancer metastasized to the lungs, we used propensity score matching (PSM) to balance correlated variables. Propensity score matching was a critical step in our study that helped to minimize the impact of possible confounders on the study results. We balanced variables related to lung metastases using the PSM method to ensure more accurate comparisons between the study and control groups. Specifically, we performed PSM in the following steps. First, we performed a univariate logistic regression analysis to screen for variables associated with lung metastasis. For each patient, we calculated their propensity scores using a logistic regression model, taking into account several factors, including gender, T-stage, N-stage, surgical history, radiotherapy history, chemotherapy history, and bone/brain/liver metastases. We used a 1:1 matching ratio based on the propensity score to ensure more balanced baseline characteristics between the study and control groups after matching. After matching, we validated the balance of baseline characteristics to ensure that the effect of confounders was minimized. We used logistic regression to identify risk variables for LM, while Cox regression was used to find independent prognostic factors. We then created nomograms and assessed their accuracy using the calibration curve, receiver operating curves (ROC), and C index. Results: In the post-PSM cohort, individuals diagnosed with LM experienced a median overall survival (OS) of 5.0 months (95% confidence interval [CI] 4.3-5.7), which was significantly lower than those without LM (P<0.001). LM has been associated to sex, T stage, N stage, surgery, radiation, chemotherapy, and bone/brain/liver metastases. LM survival was affected by radiation, chemotherapy, and bone/liver metastases. The nomograms' predictive power was proved using the ROC curve, C-index, and validation curve. Conclusion: Patients with LM have a worse chance of surviving esophageal cancer. The nomograms can effectively predict the risk and prognosis of lung metastases from esophageal cancer.
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Affiliation(s)
- Wenhui He
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Youzhen Yu
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Ziting Yan
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Na Luo
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Wenwen Yang
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Fanfan Li
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Hongying Jin
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Yimei Zhang
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Xiaoli Ma
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Minjie Ma
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China
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Muran AC, White PB, Goodman H. Atypical Presentation of Metastatic Carcinoma Causing Patellar Destruction and Synovial Carcinomatosis: A Case Report. J Orthop Case Rep 2024; 14:59-64. [PMID: 38420244 PMCID: PMC10898687 DOI: 10.13107/jocr.2024.v14.i02.4218] [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: 11/29/2023] [Revised: 12/19/2023] [Indexed: 03/02/2024] Open
Abstract
Introduction This case report describes the third documented example of primary esophageal carcinoma metastasizing to the patella and the first documented example of esophageal carcinoma metastasizing to synovium. Case Report A 67-year-old man with a history of metastatic esophageal carcinoma presents with right knee pain and an aggressive, destructive lesion involving the superior patella. Biopsy revealed esophageal carcinoma. After ineffective radiation, he underwent resection of the tumor-filled bone and quadricep advancement. Two months later, a recurrent tumor involving the entire patella and significant knee synovitis was observed. He underwent a total patellectomy with a radical anterior synovectomy. Further assessment showed that the entire synovium was replaced with metastatic carcinoma. Conclusion This report describes an atypical presentation of metastasis with patella and synovium involvement.
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Affiliation(s)
- Andrew C Muran
- Zucker School of Medicine at Hofstra-Northwell Health, 500 Hofstra University, Hempstead, New York, 11549, USA
| | - Peter B White
- Department of Orthopaedic Surgery, North Shore-Long Island Jewish Medical Center, Northwell Health, New York, 11040, USA
| | - Howard Goodman
- Department of Orthopaedic Surgery, North Shore-Long Island Jewish Medical Center, Northwell Health, New York, 11040, USA
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Luo P, Wu J, Chen X, Yang Y, Zhang R, Qi X, Li Y. A population-based investigation: How to identify high-risk T1-2N0 esophageal cancer patients? Front Surg 2023; 9:1003487. [PMID: 36733675 PMCID: PMC9888256 DOI: 10.3389/fsurg.2022.1003487] [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: 07/26/2022] [Accepted: 10/31/2022] [Indexed: 01/09/2023] Open
Abstract
Purpose Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients. Methods A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model. Results Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver (n = 156, 47.3%), followed by lung (n = 89, 27.0%), bone (n = 70, 21.2%) and brain (n = 15, 4.5%). Variables independently associated with DM included age at diagnosis, gender, tumor grade, primary site, tumor size and T stage. A nomogram based on the variables had a good predictive accuracy (area under the curve: 0.750). Independent risk factors for bone metastases (BoM), brain metastases (BrM), liver metastases (LiM) and lung metastases (LuM) were identified, respectively. Conclusions We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.
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Affiliation(s)
- Peng Luo
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Wu
- 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
| | - Xiankai Chen
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yafan Yang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ruixiang Zhang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuzhu Qi
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yin Li
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Correspondence: Yin Li
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Zhang XY, Lv QY, Zou CL. A nomogram model to individually predict prognosis for esophageal cancer with synchronous pulmonary metastasis. Front Oncol 2023; 12:956738. [PMID: 36686804 PMCID: PMC9848734 DOI: 10.3389/fonc.2022.956738] [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: 05/30/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023] Open
Abstract
Background Esophageal cancer (EC) is a life-threatening disease worldwide. The prognosis of EC patients with synchronous pulmonary metastasis (PM) is unfavorable, but few tools are available to predict the clinical outcomes and prognosis of these patients. This study aimed to construct a nomogram model for the prognosis of EC patients with synchronous PM. Methods From the Surveillance, Epidemiology, and End Results database, we selected 431 EC patients diagnosed with synchronous PM. These cases were randomized into a training cohort (303 patients) and a validation cohort (128 patients). Univariate and multivariate Cox regression analyses, along with the Kaplan-Meier method, were used to estimate the prognosis and cancer-specific survival (CSS) among two cohorts. Relative factors of prognosis in the training cohort were selected to develop a nomogram model which was verified on both cohorts by plotting the receiver operating characteristic (ROC) curves as well as the calibration curves. A risk classification assessment was completed to evaluate the CSS of different groups using the Kaplan-Meier method. Results The nomogram model contained four risk factors, including T stage, bone metastasis, liver metastasis, and chemotherapy. The 6-, 12- and 18-month CSS were 55.1%, 26.7%, and 5.9% and the areas under the ROC curve (AUC) were 0.818, 0.781, and 0.762 in the training cohort. Likewise, the AUC values were 0.731, 0.764, and 0.746 in the validation cohort. The calibration curves showed excellent agreement both in the training and validation cohorts. There was a substantial difference in the CSS between the high-risk and low-risk groups (P<0.01). Conclusion The nomogram model serves as a predictive tool for EC patients with synchronous PM, which would be utilized to estimate the individualized CSS and guide therapeutic decisions.
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Affiliation(s)
- Xin-yao Zhang
- Department of Pediatrics, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qi-yuan Lv
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Chang-lin Zou
- Department of Radiotherapy, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China,*Correspondence: Chang-lin Zou,
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Islam MM, Poly TN, Walther BA, Yeh CY, Seyed-Abdul S, Li YC(J, Lin MC. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14235996. [PMID: 36497480 PMCID: PMC9736434 DOI: 10.3390/cancers14235996] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Bruno Andreas Walther
- Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany
| | - Chih-Yang Yeh
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Seyed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence:
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Luo P, Wei X, Liu C, Chen X, Yang Y, Zhang R, Kang X, Qin J, Qi X, Li Y. The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study. Thorac Cancer 2022; 13:2960-2969. [PMID: 36168908 PMCID: PMC9626357 DOI: 10.1111/1759-7714.14642] [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: 07/13/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND This retrospective study aimed to explore risk factors for liver metastases (LiM) in patients with esophageal cancer (EC) and to identify prognostic factors in patients initially diagnosed with LiM. METHODS A total of 28 654 EC patients were retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. A multivariate logistic regression model was utilized to identify risk factors for LiM. A Cox regression model was used to identify prognostic factors for patients with LiM. RESULTS Of 28 654 EC patients, 4062 (14.2%) had LiM at diagnosis. The median overall survival (OS) for patients with and without LiM was 6.00 (95% CI: 5.70-6.30) months and 15.00 (95% CI: 14.64-15.36) months, respectively. Variables significantly associated with LiM included gender, age, tumor site, histology, tumor grade, tumor size, clinical T stage, clinical N stage, bone metastases (BoM), brain metastases (BrM) and lung metastases (LuM). Variables independently predicting survival for EC patients with LiM were age, histology, tumor grade, BoM, BrM, LuM, and chemotherapy. A risk prediction model and two survival prediction models were then constructed revealing satisfactory predictive accuracy. CONCLUSIONS Based on the largest known cohort of EC, independent predictors of LiM and prognostic indicators of survival for patients with LiM were identified. Two models for predicting survival as well as a risk prediction model were developed with robust predictive accuracy.
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Affiliation(s)
- Peng Luo
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiufeng Wei
- Department of Thoracic Surgery, Beijing Chuiyangliu HospitalChuiyangliu Hospital Affiliated to Tsinghua UniversityBeijingChina
| | - Chen Liu
- Department of Ophthalmology, Shanghai Changhai HospitalNaval Military Medical UniversityShanghaiChina
| | - Xiankai Chen
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yafan Yang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Ruixiang Zhang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiaozheng Kang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianjun Qin
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiuzhu Qi
- Department of UltrasoundFudan University Shanghai Cancer CenterShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yin Li
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Matsui K, Kawakubo H, Matsuda S, Hirata Y, Irino T, Fukuda K, Nakamura R, Kitagawa Y. Clinical Features of Recurrence Pattern with Lung Metastasis After Radical Esophagectomy for Thoracic Esophageal Cancer. World J Surg 2022; 46:2270-2279. [PMID: 35708753 DOI: 10.1007/s00268-022-06608-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND One of the difficulties in the treatment of esophageal cancer surgery is the high rate of postoperative recurrence. After esophagectomy, distant metastatic recurrence frequently occurs in the lung. This study aimed to determine the clinical features of a recurrence pattern with lung metastasis. METHODS The current study analyzed data from 138 patients who had postoperative recurrence of esophageal cancer after a radical esophagectomy. According to the recurrence pattern at the time of initial diagnosis, the patients were classified into two groups as follows: those with lung metastasis and those without. RESULTS Twenty-three of the 138 investigated patients had a recurrence pattern with lung metastasis. Salvage surgery and postoperative pneumonia (p = 0.041 and 0.030, respectively) were identified as risk factors for recurrence pattern with lung metastasis in multivariate analysis. When we compared the sites of primary esophageal tumors, we found that the frequencies of distant metastases, such as lung and liver metastases, as well as pleural/peritoneal dissemination, were higher in the mid and distal esophageal tumors. Patients with a recurrence pattern showing lung metastasis alone had a better overall and post-recurrence survival than those with other recurrence patterns (p < 0.001 and p < 0.001). CONCLUSIONS In patients who had postoperative recurrence after esophagectomy for thoracic esophageal cancer, salvage surgery, and postoperative pneumonia were significantly related to recurrence pattern with lung metastasis. Postoperative recurrence with lung metastasis alone had a better prognosis than other recurrence patterns; therefore, when pulmonary recurrence is suspected, performing intensive examinations for early diagnosis is critical.
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Affiliation(s)
- Kazuaki Matsui
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Hirofumi Kawakubo
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan.
| | - Satoru Matsuda
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Yuki Hirata
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Tomoyuki Irino
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Kazumasa Fukuda
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Rieko Nakamura
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, 35-banchi, Shinano-machi, Shinjuku-ku, Tokyo, Japan
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Zhang W, Ji L, Zhong X, Zhu S, Zhang Y, Ge M, Kang Y, Bi Q. Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study. Front Public Health 2022; 10:884349. [PMID: 35712294 PMCID: PMC9194823 DOI: 10.3389/fpubh.2022.884349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
Background Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM. Methods PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. Results A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. Conclusion We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.
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Affiliation(s)
- Wei Zhang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Lichen Ji
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xugang Zhong
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Senbo Zhu
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Zhang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Department of Hepatobiliary and Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Meng Ge
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Graduate Department, Bengbu Medical College, Bengbu, China
| | - Yao Kang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, Hangzhou Medical College People's Hospital, Hangzhou, China
| | - Qing Bi
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China
- Department of Orthopedics, Hangzhou Medical College People's Hospital, Hangzhou, China
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12
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Zhou H, Yang S, Xie T, Wang L, Zhong S, Sheng T, Fan G, Liao X, Xu Y. Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study. Front Surg 2022; 9:877653. [PMID: 35433803 PMCID: PMC9011336 DOI: 10.3389/fsurg.2022.877653] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/10/2022] [Indexed: 01/18/2023] Open
Abstract
Background This study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability. Methods The clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis. Results Our study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance. Conclusions Several factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.
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Affiliation(s)
- Hongmin Zhou
- Department of urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Sheng Yang
- Department of Orthopedics, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Spinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China
| | - Tiancheng Xie
- Department of urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Longfei Wang
- Department of Orthopedics, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Sen Zhong
- Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tianyang Sheng
- Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guoxin Fan
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
- Correspondence: Guoxin Fan Xiang Liao Yunfei Xu
| | - Xiang Liao
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
- Correspondence: Guoxin Fan Xiang Liao Yunfei Xu
| | - Yunfei Xu
- Department of urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Correspondence: Guoxin Fan Xiang Liao Yunfei Xu
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13
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Shi M, Zhai GQ. Models for Predicting Early Death in Patients With Stage IV Esophageal Cancer: A Surveillance, Epidemiology, and End Results-Based Cohort Study. Cancer Control 2022; 29:10732748211072976. [PMID: 35037487 PMCID: PMC8777366 DOI: 10.1177/10732748211072976] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background Despite enormous progress in the stage IV esophageal cancer (EC) treatment,
some patients experience early death after diagnosis. This study aimed to
identify the early death risk factors and construct models for predicting
early death in stage IV EC patients. Methods Stage IV EC patients diagnosed between 2010 and 2015 in the Surveillance,
Epidemiology, and End Results (SEER) database were selected. Early death was
defined as death within 3 months of diagnosis, with or without therapy.
Early death risk factors were identified using logistic regression analyses
and further used to construct predictive models. The concordance index
(C-index), calibration curves, and decision curve analyses (DCA) were used
to assess model performance. Results Out of 4411 patients enrolled, 1779 died within 3 months. Histologic grade,
therapy, the status of the bone, liver, brain and lung metastasis, marriage,
and insurance were independent factors for early death in stage IV EC
patients. Histologic grade and the status of the bone and liver metastases
were independent factors for early death in both chemoradiotherapy and
untreated groups. Based on these variables, predictive models were
constructed. The C-index was .613 (95% confidence interval (CI),
[.573–.653]) and .635 (95% CI, [.596–.674]) in the chemoradiotherapy and
untreated groups, respectively, while calibration curves and DCA showed
moderate performance. Conclusions More than 40% of stage IV EC patients suffered from an early death. The
models could help clinicians discriminate between low and high risks of
early death and strategize individually-tailed therapeutic interventions in
stage IV EC patients.
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Affiliation(s)
- Min Shi
- Department of Gastroenterology, Changzhou Maternal and Child Health Care Hospital, Changzhou, China
| | - Guo-Qing Zhai
- Department of Gastroenterology, Liyang People's Hospital, Liyang Branch of Jiangsu Province Hospital, Liyang, China
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ECM Remodeling in Squamous Cell Carcinoma of the Aerodigestive Tract: Pathways for Cancer Dissemination and Emerging Biomarkers. Cancers (Basel) 2021. [DOI: 10.3390/cancers13112759
expr 955442319 + 839973387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
Squamous cell carcinomas (SCC) include a number of different types of tumors developing in the skin, in hollow organs, as well as the upper aerodigestive tract (UADT) including the head and neck region and the esophagus which will be dealt with in this review. These tumors are often refractory to current therapeutic approaches with poor patient outcome. The most important prognostic determinant of SCC tumors is the presence of distant metastasis, significantly correlating with low patient survival rates. Rapidly emerging evidence indicate that the extracellular matrix (ECM) composition and remodeling profoundly affect SSC metastatic dissemination. In this review, we will summarize the current knowledge on the role of ECM and its remodeling enzymes in affecting the growth and dissemination of UADT SCC. Taken together, these published evidence suggest that a thorough analysis of the ECM composition in the UADT SCC microenvironment may help disclosing the mechanism of resistance to the treatments and help defining possible targets for clinical intervention.
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ECM Remodeling in Squamous Cell Carcinoma of the Aerodigestive Tract: Pathways for Cancer Dissemination and Emerging Biomarkers. Cancers (Basel) 2021; 13:cancers13112759. [PMID: 34199373 PMCID: PMC8199582 DOI: 10.3390/cancers13112759] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Local and distant metastasis of patients affected by squamous cell carcinoma of the upper aerodigestive tract predicts poor prognosis. In the latest years, the introduction of new therapeutic approaches, including targeted and immune therapies, has improved the overall survival. However, a large number of these patients do not benefit from these treatments. Thus, the identification of suitable prognostic and predictive biomarkers, as well as the discovery of new therapeutic targets have emerged as a crucial clinical need. In this context, the extracellular matrix represents a suitable target for the development of such therapeutic tools. In fact, the extracellular matrix is composed by complex molecules able to interact with a plethora of receptors and growth factors, thus modulating the dynamic crosstalk between cancer cells and the tumor microenvironment. In this review, we summarize the current knowledge of the role of the extracellular matrix in affecting squamous cell carcinoma growth and dissemination. Despite extracellular matrix is known to affect the development of many cancer types, only a restricted number of these molecules have been recognized to impact on squamous cell carcinoma progression. Thus, we consider that a thorough analysis of these molecules may be key to develop new potential therapeutic targets/biomarkers. Abstract Squamous cell carcinomas (SCC) include a number of different types of tumors developing in the skin, in hollow organs, as well as the upper aerodigestive tract (UADT) including the head and neck region and the esophagus which will be dealt with in this review. These tumors are often refractory to current therapeutic approaches with poor patient outcome. The most important prognostic determinant of SCC tumors is the presence of distant metastasis, significantly correlating with low patient survival rates. Rapidly emerging evidence indicate that the extracellular matrix (ECM) composition and remodeling profoundly affect SSC metastatic dissemination. In this review, we will summarize the current knowledge on the role of ECM and its remodeling enzymes in affecting the growth and dissemination of UADT SCC. Taken together, these published evidence suggest that a thorough analysis of the ECM composition in the UADT SCC microenvironment may help disclosing the mechanism of resistance to the treatments and help defining possible targets for clinical intervention.
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