1
|
Yan Y, Liu Z, Pang W, Huang S, Deng M, Yao J, Huang Q, Jin M, Shui L. Integrated biosensor array for multiplex biomarkers cancer diagnosis via in-situ self-assembly carbon nanotubes with an ordered inverse-opal structure. Biosens Bioelectron 2024; 262:116528. [PMID: 38943855 DOI: 10.1016/j.bios.2024.116528] [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: 03/25/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
To enhance the precision and reliability of early disease detection, especially in malignancies, an exhaustive investigation of multi-target biomarkers is essential. In this study, an advanced integrated electrochemical biosensor array that demonstrates exceptional performance was constructed. This biosensor was developed through a controllable porous-size mechanism and in-situ modification of carbon nanotubes (CNTs) to quantify multiplex biomarkers-specifically, C-reaction protein (CRP), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA)-in human serum plasma. The fabrication process involved creating a highly ordered three-dimensional inverse-opal structure with the CNTs (pCNTs) modifier through microdroplet-based microfluidics, confined spatial self-assembly of nanoparticles, and chemical wet-etching. This innovative approach allowed for direct in-situ modification of nanomaterial onto the surface of electrode array, eliminating secondary transfer and providing exceptional control over structure and stability. The outstanding electrochemical performance was achieved through the synergistic effect of the pCNTs nanomaterial, aptamer, and horseradish peroxidase-labeled (HRP-) antibody. Additionally, the integrated biosensor array platform comprised multiple individually addressable electrode units (n = 11), enabling simultaneous multi-parallel/target testing, thereby ensuring accuracy and high throughput. Crucially, this integrated biosensor array accurately quantified multiplex biomarkers in human serum, yielding results comparable to commercial methods. This integrated technology holds promise for point-of-care testing (POCT) in early disease diagnosis and biological analysis.
Collapse
Affiliation(s)
- Yu Yan
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China
| | - Zhenping Liu
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China.
| | - Wenbin Pang
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China
| | - Shijian Huang
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510006, PR China
| | - Mengxin Deng
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China
| | - Jiyuan Yao
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China
| | - Qiuju Huang
- Guangxi Key Laboratory of Bioactive Molecules Research and Evaluation, College of Pharmacy, Guangxi Medical University, Nanning, 530021, PR China.
| | - Mingliang Jin
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China; International Academy of Optoelectronics at Zhaoqing, South China Normal University, Zhaoqing, 526238, PR China
| | - Lingling Shui
- Joint Laboratory of Optofluidic Technology and System, National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, PR China; Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510006, PR China.
| |
Collapse
|
2
|
Brims F. Lung cancer and lymphangioleiomyomatosis: A common complication in an uncommon condition. Respirology 2024; 29:751-752. [PMID: 38857971 DOI: 10.1111/resp.14768] [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: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024]
Abstract
See related article
Collapse
Affiliation(s)
- Fraser Brims
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, Perth, Western Australia, Australia
| |
Collapse
|
3
|
Liu S, Wang S, Guo J, Wang C, Zhang H, Lin D, Wang Y, Hu X. Crosstalk among disulfidptosis-related lncRNAs in lung adenocarcinoma reveals a correlation with immune profile and clinical prognosis. Noncoding RNA Res 2024; 9:772-781. [PMID: 38590434 PMCID: PMC10999374 DOI: 10.1016/j.ncrna.2024.03.006] [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: 12/17/2023] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.
Collapse
Affiliation(s)
- Shifeng Liu
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Song Wang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jian Guo
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Congxiao Wang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hao Zhang
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Dongliang Lin
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuanyong Wang
- Department of Thoracic Surgery, Tangdu Hospital of Air Force Military Medical University, Xi'an, China
| | - Xiaokun Hu
- Department of Interventional Medical Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
4
|
Zhu X, Ye R, Jiang X, Zhang J. Smoking as a mediator in the association between major depressive disorder and schizophrenia on lung cancer risk: a bidirectional/multivariable and mediation Mendelian randomization study. Front Psychiatry 2024; 15:1367858. [PMID: 39176232 PMCID: PMC11338888 DOI: 10.3389/fpsyt.2024.1367858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 07/17/2024] [Indexed: 08/24/2024] Open
Abstract
Background & Aims Major depressive disorder and schizophrenia have been hypothesized to be closely associated with cancer. However, the associations between these psychiatric conditions and the development of lung cancer remain uncertain. This study aimed to explore the causal relationship among major depressive disorder, schizophrenia, and the risk of lung cancer. Methods Two-sample bidirectional/multivariable and mediation Mendelian randomization (MR) analyses were conducted. Genome-wide summary data on major depressive disorder (N=500,199) and schizophrenia (N=127,906) were utilized. Data on the risk of lung cancer (overall, adenocarcinoma, and squamous cell) were collected from a cohort of individuals of European ancestry (N=27,209). Three smoking-related behaviors (smoking initiation, pack years of smoking, and cigarettes smoked per day) were included in the multivariable and mediation MR analyses. Results Patients with schizophrenia had a significantly greater risk of developing lung cancer (odds ratio (OR) = 1.144, 95% confidence interval (95% CI): 1.048-1.248, P = 0.003). The number of cigarettes smoked per day partially mediated the relationship between schizophrenia and the overall risk of lung cancer (OR = 1.185, 95% CI: 1.112-1.264, P = 0.021, proportion of mediation effect: 61.033%). However, there is no reliable evidence indicating an association between major depressive disorder and the risk of lung cancer (overall, adenocarcinoma, and squamous cell cancer). Conclusions The findings indicated an association between schizophrenia and an increased risk of lung cancer, with smoking served as a partial mediator. When smoking was included in the regression analysis, the explanatory power of schizophrenia diagnosis was reduced, suggesting that smoking may be an important causal contributor to lung cancer in this population. Given the high prevalence of smoking among individuals with schizophrenia, these results underscore the need for further research to explore the underlying mechanisms of smoking's impact. Consequently, greater emphasis should be placed on monitoring the respiratory health of individuals with schizophrenia and implementing early interventions to address smoking-related behaviors.
Collapse
Affiliation(s)
- Xirong Zhu
- Taizhou Cancer Hospital, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou, China
| | - Ruizhi Ye
- Taizhou Cancer Hospital, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou, China
| | - Xianming Jiang
- Taizhou Cancer Hospital, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou, China
| | - Jing Zhang
- Wenling Hospital of Traditional Chinese Medicine, Wenling, China
- Department of General Practice, Wenling, China
| |
Collapse
|
5
|
Wang J, Zhou J, Wang M. Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework. Med Phys 2024; 51:5427-5440. [PMID: 38436455 DOI: 10.1002/mp.17014] [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: 07/25/2023] [Revised: 01/24/2024] [Accepted: 02/20/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning-based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi-scale collaboration when segmenting objects. PURPOSE This paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi-scale cooperation in target segmentation. OBJECTIVE Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non-cancer, inflammation, death, and connective tissue. METHODS First, the method uses whole-slide images in the pan-cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R-CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non-synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module. RESULTS Extensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R-CNN framework. CONCLUSIONS It is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.
Collapse
Affiliation(s)
- Juan Wang
- School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, China
| | - Jian Zhou
- National Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology Chinese Academy of Science, Shanghai, China
| | - Man Wang
- School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, China
| |
Collapse
|
6
|
Sheu CC, Wang CC, Hsu JS, Chung WS, Hsu HY, Shi HY. Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model. World J Oncol 2024; 15:550-561. [PMID: 38993243 PMCID: PMC11236381 DOI: 10.14740/wjon1882] [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: 04/09/2024] [Accepted: 06/10/2024] [Indexed: 07/13/2024] Open
Abstract
Background Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Methods Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. Results The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. Conclusion LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.
Collapse
Affiliation(s)
- Chau-Chyun Sheu
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan, Republic of China
- Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Chun-Chun Wang
- Medical Intensive Care Unit, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Jui-Sheng Hsu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan, Republic of China
- Department of Radiology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Wei-Shiuan Chung
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan, Republic of China
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Hong-Yi Hsu
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, Republic of China
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan, Republic of China
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan, Republic of China
| |
Collapse
|
7
|
Lv L, Zhou F, Quan Y, Fan Y, Bao Y, Dou Y, Qu H, Dai X, Zhao H, Zheng S, Zhao C, Yang L. Demethylzeylasteral exerts potent efficacy against non-small-cell lung cancer via the P53 signaling pathway. Transl Oncol 2024; 46:101989. [PMID: 38781861 PMCID: PMC11141460 DOI: 10.1016/j.tranon.2024.101989] [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: 01/02/2024] [Revised: 04/18/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Lung cancer has one of the highest mortality rates worldwide, with non-small-cell lung cancer (NSCLC) constituting approximately 85% of all cases. Demethylzeylasteral (DEM), extracted from Tripterygium wilfordii Hook F, exhibits notable anti-tumor properties. In this study, we revealed that DEM could effectively induce NSCLC cell apoptosis. Specifically, DEM can dose-dependently suppress the viability and migration of human NSCLC cells. RNA-seq analysis revealed that DEM regulates the P53-signaling pathway, which was further validated by assessing crucial proteins involved in this pathway. Biacore analysis indicated that DEM has high affinity with the P53 protein. The CDX model demonstrated DEM's anti-tumor actions. This work provided evidence that DEM-P53 interaction stabilizes P53 protein and triggers downstream anti-tumor activities. These findings indicate that DEM treatment holds promise as a potential therapeutic approach for NSCLC, which warrants further clinical assessment in patients with NSCLC.
Collapse
Affiliation(s)
- Linxi Lv
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; The Institute of Life Sciences, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Feng Zhou
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China; The Institute of Life Sciences, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Yizhou Quan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Yiwei Fan
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Yunjia Bao
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Yaning Dou
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Hongyan Qu
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Xuanxuan Dai
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
| | - Haiyang Zhao
- The Institute of Life Sciences, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Suqing Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
| | - Chengguang Zhao
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
| | - Lehe Yang
- The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; The Institute of Life Sciences, Wenzhou University, Wenzhou, Zhejiang 325035, China.
| |
Collapse
|
8
|
Chang AEB, Potter AL, Yang CFJ, Sequist LV. Early Detection and Interception of Lung Cancer. Hematol Oncol Clin North Am 2024; 38:755-770. [PMID: 38724286 DOI: 10.1016/j.hoc.2024.03.004] [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] [Indexed: 07/05/2024]
Abstract
Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.
Collapse
Affiliation(s)
- Allison E B Chang
- Department of Medicine, Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, USA; Department of Hematology/Oncology, Dana Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Alexandra L Potter
- Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Chi-Fu Jeffrey Yang
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Lecia V Sequist
- Department of Medicine, Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| |
Collapse
|
9
|
Bastone SA, Patirelis A, Luppichini M, Ambrogi V. Ultrasound for Intra-Operative Detection of Peri-Centimetric Pulmonary Nodules in Uniportal Video-Assisted Thoracic Surgery (VATS): A Comparison with Conventional Techniques in Multiportal VATS. J Clin Med 2024; 13:4448. [PMID: 39124715 PMCID: PMC11313293 DOI: 10.3390/jcm13154448] [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: 05/13/2024] [Revised: 07/02/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Background: Video-assisted thoracic surgery (VATS) has become the gold-standard approach for lung resections. Given the impossibility of digital palpation, we witnessed the progressive development of peri-centimetric and deeply located pulmonary nodule alternative detection techniques. Intra-operative lung ultrasound is an increasingly effective diagnostic method, although only a few small studies have evaluated its accuracy. This study analyzed the effectiveness and sensitivity of uniportal VATS with intra-operative lung ultrasound (ILU), in comparison to multiportal VATS, for visualizing solitary and deep-sited pulmonary nodules. Methods: Patient data from October 2021 to October 2023, from a single center, were retrospectively gathered and analyzed. In total, 31 patients who received ILU-aided uniportal VATS (Group A) were matched for localization time, operative time, sensitivity, and post-operative complications, with 33 undergoing nodule detection with conventional techniques, such as manual or instrumental palpation, in multiportal VATS (Group B). Surgeries were carried out by the same team and ILU was performed by a certified operator. Results: Group A presented a significantly shorter time for nodule detection [median (IQR): 9 (8-10) vs. 14 (12.5-15) min; p < 0.001] and operative time [median (IQR): 33 (29-38) vs. 43 (39-47) min; p < 0.001]. All nodules were correctly localized and resected in Group A (sensitivity 100%), while three were missed in Group B (sensitivity 90.9%). Two patients in Group B presented with a prolonged air leak that was conservatively managed, compared to none in Group A, resulting in a post-operative morbidity rate of 6.1% vs. 0% (p = 0.16). Conclusions: ILU-aided uniportal VATS was faster and more effective than conventional techniques in multiportal VATS for nodule detection.
Collapse
Affiliation(s)
- Sebastiano Angelo Bastone
- Department of Thoracic Surgery, Tor Vergata University Polyclinic, 00133 Rome, Italy; (S.A.B.); (A.P.); (M.L.)
- Ph.D. Program in Applied Medical-Surgical Sciences, Department of Surgical Sciences, Tor Vergata University, 00133 Rome, Italy
| | - Alexandro Patirelis
- Department of Thoracic Surgery, Tor Vergata University Polyclinic, 00133 Rome, Italy; (S.A.B.); (A.P.); (M.L.)
| | - Matilde Luppichini
- Department of Thoracic Surgery, Tor Vergata University Polyclinic, 00133 Rome, Italy; (S.A.B.); (A.P.); (M.L.)
| | - Vincenzo Ambrogi
- Department of Thoracic Surgery, Tor Vergata University Polyclinic, 00133 Rome, Italy; (S.A.B.); (A.P.); (M.L.)
| |
Collapse
|
10
|
Wu Z, Li J, Zhong M, Xu Z, Yang M, Xu C. HTR3A Promotes Non-small Cell Lung Cancer Through the FOXH1/Wnt3A Signaling Pathway. Biochem Genet 2024:10.1007/s10528-024-10872-9. [PMID: 39046651 DOI: 10.1007/s10528-024-10872-9] [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: 05/05/2024] [Accepted: 06/15/2024] [Indexed: 07/25/2024]
Abstract
5-Hydroxytryptamine receptors (5-HTRs) are strongly correlated with tumor progression in various types of cancer. Despite this, the underlying mechanisms responsible for the role of 5-HTRs in non-small cell lung cancer (NSCLC) remains unclear. This study aimed to investigate the relationship between 5-hydroxytryptamine receptor 3A (HTR3A) and NSCLC development. Our findings indicated a higher distribution of HTR3A expression in NSCLC tissues when compared with normal tissues, where patients with high HTR3A levels demonstrated shorter overall survival times. In vitro analyses revealed that overexpression of HTR3A facilitated the proliferation and migration of NSCLC cell lines (A549 and NCI-H3255). Similarly, a notable acceleration of tumor growth and enhanced pulmonary tumorigenic potential were observed in HTR3A-overexpressing tumor-bearing mice. Mechanistically, upregulation of Forkhead Box H1 (FOXH1) by HTR3A led to the activation of Wnt3A/β-catenin signaling pathways, thereby promoting the development of NSCLC. Our report thus highlights the significance of the HTR3A/FOXH1 axis during tumor progression in NSCLC, proposing HTR3A as a possible diagnostic indicator and candidate target for clinical therapy.
Collapse
Affiliation(s)
- Zeqin Wu
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China
| | - Jiufei Li
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China
| | - Minglian Zhong
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China
| | - Zhiyuan Xu
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Mulan Yang
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China.
| | - Chenyang Xu
- Thoracic Surgery Department, Ganzhou People's Hospital, 18 MeiGuan Ave, Zhanggong District, Ganzhou, 341000, China.
| |
Collapse
|
11
|
Díaz MA, Fusco M, Benítez CA, Gayet F, García L, Victoria L, Jaramillo S, Bayo J, Zubieta MR, Rizzo MM, Piccioni F, Malvicini M. Targeting hyaluronan metabolism-related molecules associated with resistant tumor-initiating cells potentiates chemotherapy efficacy in lung cancer. Sci Rep 2024; 14:16803. [PMID: 39039104 PMCID: PMC11263553 DOI: 10.1038/s41598-024-66914-0] [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: 05/10/2024] [Accepted: 07/05/2024] [Indexed: 07/24/2024] Open
Abstract
The success of chemotherapy regimens in patients with non-small cell lung cancer (NSCLC) could be restricted at least in part by cancer stem cells (CSC) niches within the tumor microenvironment (TME). CSC express CD133, CD44, CD47, and SOX2, among other markers and factors. Analysis of public data revealed that high expression of hyaluronan (HA), the main glycosaminoglycan of TME, correlated positively with CSC phenotype and decreased disease-free interval in NSCLC patients. We aimed to cross-validate these findings on human and murine lung cancer cells and observed that CD133 + CSC differentially expressed higher levels of HA, HAS3, ABCC5, SOX2, and CD47 (p < 0.01). We modulated HA expression with 4-methylumbelliferone (4Mu) and detected an increase in sensitivity to paclitaxel (Pa). We evaluated the effect of 4Mu + chemotherapy on survival, HA metabolism, and CSC profile. The combination of 4Mu with Pa reduced the clonogenic and tumor-forming ability of CSC. Pa-induced HAS3, ABCC5, SOX2, and CD47 expression was mitigated by 4Mu. Pa + 4Mu combination significantly reduced in vivo tumor growth, enhancing animal survival and restoring the CSC profile in the TME to basal levels. Our results suggest that HA is involved in lung CSC phenotype and chemosensitivity, and its modulation by 4Mu improves treatment efficacy to inhibit tumor progression.
Collapse
Affiliation(s)
- Marco Aurelio Díaz
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
| | - Mariel Fusco
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
| | - Constanza Arriola Benítez
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
| | - Fernando Gayet
- Servicio de Oncología, Hospital Universitario Austral, Buenos Aires, Argentina
| | - Ludmila García
- Laboratorio Central, Hospital Universitario Austral, Buenos Aires, Argentina
| | - Lucia Victoria
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
| | - Sebastián Jaramillo
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
| | - Juan Bayo
- Programa de Hepatología Experimental y Terapia Génica, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Tecnicas, Pilar, Argentina
| | | | - Manglio M Rizzo
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina
- Servicio de Oncología, Hospital Universitario Austral, Buenos Aires, Argentina
| | - Flavia Piccioni
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina.
| | - Mariana Malvicini
- Cancer Immunobiology Laboratory, Instituto de Investigaciones en Medicina Traslacional, Universidad Austral-Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar, Argentina.
| |
Collapse
|
12
|
Ledda RE, Funk GC, Sverzellati N. The pros and cons of lung cancer screening. Eur Radiol 2024:10.1007/s00330-024-10939-6. [PMID: 39014085 DOI: 10.1007/s00330-024-10939-6] [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: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/14/2024] [Indexed: 07/18/2024]
Abstract
Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.
Collapse
Affiliation(s)
| | - Georg-Christian Funk
- Department of Medicine II with Pneumology, Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Ottakring, Vienna, Austria
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| |
Collapse
|
13
|
Wei W, Wang Y, Ouyang R, Wang T, Chen R, Yuan X, Wang F, Wu S, Hou H. Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data. Ann Surg Oncol 2024:10.1245/s10434-024-15762-3. [PMID: 39014163 DOI: 10.1245/s10434-024-15762-3] [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: 02/19/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. METHODS Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. RESULTS For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. CONCLUSION This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.
Collapse
Affiliation(s)
- Wei Wei
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yun Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Renren Ouyang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rujia Chen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Hongyan Hou
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| |
Collapse
|
14
|
Jiang Y, Zhang G, Zhu J, Wang X, Tao Z, Yu P. Development and validation of a TAAbs and TAAs based non-invasive model for diagnosing lung cancer. Heliyon 2024; 10:e33888. [PMID: 39027487 PMCID: PMC11255565 DOI: 10.1016/j.heliyon.2024.e33888] [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: 11/16/2023] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/20/2024] Open
Abstract
Background Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection. Methods 1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis. Results The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis. Conclusions This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.
Collapse
Affiliation(s)
- Yan Jiang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Gong Zhang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Jiayi Zhu
- Department of Pathology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Xuchu Wang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Zhihua Tao
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| | - Pan Yu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
| |
Collapse
|
15
|
Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [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: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
Collapse
Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| |
Collapse
|
16
|
Wong LY, Choudhary S, Kapula N, Lin M, Elliott IA, Guenthart BA, Liou DZ, Backhus LM, Berry MF, Shrager JB, Lui NS. Barriers to Completing Low Dose Computed Tomography Scan for Lung Cancer Screening. Clin Lung Cancer 2024; 25:424-430. [PMID: 38749902 DOI: 10.1016/j.cllc.2024.04.014] [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: 03/04/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 06/30/2024]
Abstract
INTRODUCTION Annual low-dose computed tomography (LDCT) screening has been shown to reduce lung cancer mortality in high-risk individuals by detecting the disease at an earlier stage. This study aims to assess the barriers to completing LDCT in a cohort of patients who were determined eligible for lung cancer screening (LCS). METHODS We performed a single institution, mixed methods, cross-sectional study of patients who had a LDCT ordered from July to December 2022. We then completed phone surveys with patients who did not complete LDCT to assess knowledge, attitude, and perceptions toward LCS. RESULTS We identified 380 patients who met inclusion criteria, including 331 (87%) who completed LDCT and 49 (13%) who did not. Patients who completed a LDCT and those who did not were similar regarding age, sex, race, primary language, household income, body mass index, median pack years, and quit time. Positive predictors of LDCT completion were: meeting USPSTF guidelines (97.9% vs 81.6%), being married (58.3% vs 44.9%), former versus current smokers (55% vs 41.7%), personal history of emphysema (60.4% vs 42.9%), and family history of lung cancer (13.9% vs 4.1%) (all P < .05). Of the patients who participated in the phone survey, only 7% of respondents thought they were high risk for developing lung cancer despite attending a shared decision-making visit and only 10% wanted to re-schedule their LDCT. CONCLUSION There exist barriers to completing LDCT even after patients are identified as eligible and complete a shared decision-making visit secondary to knowledge barriers, misperceptions, and patient disinterest.
Collapse
Affiliation(s)
- Lye-Yeng Wong
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Sania Choudhary
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Ntemena Kapula
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Margaret Lin
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305
| | - Irmina A Elliott
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Brandon A Guenthart
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Douglas Z Liou
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Leah M Backhus
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305; VA Palo Alto Health Care System, Palo Alto CA
| | - Mark F Berry
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305
| | - Joseph B Shrager
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305; VA Palo Alto Health Care System, Palo Alto CA
| | - Natalie S Lui
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk Building, Stanford, CA 94305.
| |
Collapse
|
17
|
Jiang J, Mo W, Lian X, Cao D, Cheng H, Wang H. Detection of PD‑L1 expression and epithelial‑mesenchymal transition of circulating tumor cells in non‑small cell lung cancer. Exp Ther Med 2024; 28:294. [PMID: 38827467 PMCID: PMC11140314 DOI: 10.3892/etm.2024.12583] [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: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 06/04/2024] Open
Abstract
The present study aimed to assess the roles of peripheral circulating tumor cell (CTC) count, CTC subtypes and programmed death ligand 1 (PD-L1) expression in the clinical staging and prognosis of patients with non-small cell lung cancer (NSCLC). A total of 100 patients with NSCLC with available tumor tissues were enrolled in the present study, and 7.5 ml peripheral blood was collected. Patients were divided into PD-L1-positive and PD-L1-negative groups according to PD-L1 immunohistochemical staining. Peripheral blood samples from both groups were analyzed to determine the CTC count, epithelial-type CTCs (E-CTCs), mesenchymal-type CTCs (M-CTCs) and PD-L1 expression. Clinical data were collected, and patients were followed up for a maximum of 36 months, with patient death as the endpoint event. Patients with PD-L1-positive tumors had a worse prognosis compared with those with PD-L1-negative tumors (P=0.045). The PD-L1-positive group exhibited significantly higher numbers of CTCs and M-CTCs compared with the PD-L1-negative group (P≤0.05). However, the number of E-CTCs did not differ significantly between the two groups (P>0.05). PD-L1-positive patients with higher CTC and M-CTC counts had relatively poorer prognoses (P≤0.05), while the number of E-CTCs had no significant effect on prognosis (P>0.05). Compared with the early-stage NSCLC group, the late-stage NSCLC group exhibited a significant increase in the CTC count (P≤0.05), while E-CTC and M-CTC counts did not significantly differ between the two groups (P>0.05). The PD-L1-positive group exhibited a significant increase in the number of PD-L1+ CTCs and PD-L1+ M-CTCs compared with the PD-L1-negative group (P≤0.05), while PD-L1+ E-CTC counts did not differ significantly between the two groups (P>0.05). The PD-L1-positive patients with a higher number of PD-L1+ CTCs and PD-L1+ M-CTCs had relatively poorer prognoses (P≤0.05), while the PD-L1+ E-CTC count had no significant effect on prognosis (P>0.05). Compared with the early-stage NSCLC group, the late-stage NSCLC group exhibited a significant increase in the number of PD-L1+ CTCs and PD-L1+ M-CTCs (P≤0.05), while PD-L1+ E-CTC counts did not significantly differ between the two groups (P>0.05). Based on univariate and multivariate analyses, the number of PD-L1+ M-CTCs was identified as an independent prognostic factor for NSCLC. In conclusion, the presence of CTCs in peripheral blood, particularly PD-L1+ M-CTC subtype, indicated poorer clinical staging and prognosis in patients with NSCLC. These findings suggested that CTCs, specifically the PD-L1+ M-CTC subtype, could serve as a monitoring indicator for the clinical staging and prognosis of patients with NSCLC.
Collapse
Affiliation(s)
- Jianping Jiang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Weiqiang Mo
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Xue Lian
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Dakui Cao
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Haiying Cheng
- Department of Nursing Administration, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Haiqin Wang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| |
Collapse
|
18
|
Li B, Huang Z, Wang Y, Guo C, Liang N, Yang H, Li S. Causal relationships between immune cell phenotypes and lung adenocarcinoma: A bidirectional two-sample Mendelian randomization study. Thorac Cancer 2024; 15:1673-1680. [PMID: 39034427 PMCID: PMC11260555 DOI: 10.1111/1759-7714.15394] [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: 03/27/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common type of lung cancer and closely associated with the immune system. Emerging evidence suggests that blood immune cell phenotypes in patients with LUAD may undergo alterations. Nevertheless, the limited amount of relevant research makes it difficult to understand the causal links between LUAD and changes in the immune cells. This study aimed to reveal the potential causal relationships between 731 immune cell phenotypes and LUAD. METHODS A bidirectional two-sample Mendelian randomization (MR) analysis was used to clarify causal relationships. Four types of immune phenotypes, absolute cell counts, relative cell counts, median fluorescence intensities (MFIs) of surface antigens, and morphological parameters, were investigated in this study. Heterogeneity tests, horizontal pleiotropy tests, and leave-one-out analyses were performed to validate the reliability of our study. RESULTS A total of 26 immune cell characteristics were identified as contributing to the occurrence of LUAD. Memory B cells, IgD-CD38br cells, CD4+ regulatory T cells (Tregs), and plasmacytoid dendritic cells (DCs) may play a role in the development of LUAD. Through reverse MR, our study discovered that the presence of LUAD also induced changes in the expression levels of 16 immune cell traits involving specific surface markers and various types of immune cells, some of which pertain to antigen presentation and immune activation processes. CONCLUSION Our study demonstrated causal links between several immune cell phenotypes and LUAD, thereby providing indications of the potentially oncogenic physiological state and early screening biomarkers for future research.
Collapse
Affiliation(s)
- Bowen Li
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhicheng Huang
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yadong Wang
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Chao Guo
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Naixin Liang
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huaxia Yang
- Department of Rheumatology and Clinical ImmunologyPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Shanqing Li
- Department of Thoracic SurgeryPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| |
Collapse
|
19
|
Tang Y, Zhou L, Wang F, Huang Y, Wang J, Zhao S, Qi L, Liu L, Liang M, Hou D, Xu Z, Zhang K, Tang W, Wu N. Assessing the efficiency of eligibility criteria for low-dose computed tomography lung screening in China according to current guidelines. BMC Med 2024; 22:267. [PMID: 38926820 PMCID: PMC11210050 DOI: 10.1186/s12916-024-03445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Evidence from observational studies indicates that lung cancer screening (LCS) guidelines with high rates of lung cancer (LC) underdiagnosis, and although current screening guidelines have been updated and eligibility criteria for screening have been expanded, there are no studies comparing the efficiency of LCS guidelines in Chinese population. METHODS Between 2005 and 2022, 31,394 asymptomatic individuals were screened using low-dose computed tomography (LDCT) at our institution. Demographic data and relevant LC risk factors were collected. The efficiency of the LCS for each guideline criteria was expressed as the efficiency ratio (ER). The inclusion rates, eligibility rates, LC detection rates, and ER based on the different eligibility criteria of the four guidelines were comparatively analyzed. The four guidelines were as follows: China guideline for the screening and early detection of lung cancer (CGSL), the National Comprehensive Cancer Network (NCCN), the United States Preventive Services Task Force (USPSTF), and International Early Lung Cancer Action Program (I-ELCAP). RESULTS Of 31,394 participants, 298 (155 women, 143 men) were diagnosed with LC. For CGSL, NCCN, USPSTF, and I-ELCAP guidelines, the eligibility rates for guidelines were 13.92%, 6.97%, 6.81%, and 53.46%; ERe for eligibility criteria were 1.46%, 1.64%, 1.51%, and 1.13%, respectively; and for the inclusion rates, they were 19.0%, 9.5%, 9.3%, and 73.0%, respectively. LCs which met the screening criteria of CGSL, NCCN, USPSTF, and I-ELCAP guidelines were 29.2%, 16.4%, 14.8%, and 86.6%, respectively. The age and smoking criteria for CGSL were stricter, hence resulting in lower rates of LC meeting the screening criteria. The CGSL, NCCN, and USPSTF guidelines showed the highest underdiagnosis in the 45-49 age group (17.4%), while the I-ELCAP guideline displayed the highest missed diagnosis rate (3.0%) in the 35-39 age group. Males and females significantly differed in eligibility based on the criteria of the four guidelines (P < 0.001). CONCLUSIONS The I-ELCAP guideline has the highest eligibility rate for both males and females. But its actual efficiency ratio for those deemed eligible by the guideline was the lowest. Whereas the NCCN guideline has the highest ERe value for those deemed eligible by the guideline.
Collapse
Affiliation(s)
- Yanyan Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lina Zhou
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Fei Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yao Huang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shijun Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Li Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Min Liang
- Department of Diagnostic Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China
| | - Donghui Hou
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhijian Xu
- Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100020, China
| | - Kai Zhang
- Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100020, China
| | - Wei Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Ning Wu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| |
Collapse
|
20
|
Wang X, Bai L, Kong L, Guo Z. Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer. Front Oncol 2024; 14:1411731. [PMID: 38974237 PMCID: PMC11224453 DOI: 10.3389/fonc.2024.1411731] [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: 04/03/2024] [Accepted: 06/07/2024] [Indexed: 07/09/2024] Open
Abstract
Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.
Collapse
Affiliation(s)
- Xiaochen Wang
- Department of Pathology and Pathophysiology, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China
- Department of Pathology, Cancer Hospital Affiliated to Inner Mongolia Medical University / Peking University Cancer Hospital Inner Mongolia Hospital, Hohhot, Inner Mongolia, China
| | - Lu Bai
- Department of Pathology and Pathophysiology, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China
- Department of Pathology, Cancer Hospital Affiliated to Inner Mongolia Medical University / Peking University Cancer Hospital Inner Mongolia Hospital, Hohhot, Inner Mongolia, China
| | - Linghui Kong
- Department of Pathology, Cancer Hospital Affiliated to Inner Mongolia Medical University / Peking University Cancer Hospital Inner Mongolia Hospital, Hohhot, Inner Mongolia, China
| | - Zhijuan Guo
- Department of Pathology, Cancer Hospital Affiliated to Inner Mongolia Medical University / Peking University Cancer Hospital Inner Mongolia Hospital, Hohhot, Inner Mongolia, China
| |
Collapse
|
21
|
Panagiotou E, Vathiotis IA, Makrythanasis P, Hirsch F, Sen T, Syrigos K. Biological and therapeutic implications of the cancer-related germline mutation landscape in lung cancer. THE LANCET. RESPIRATORY MEDICINE 2024:S2213-2600(24)00124-3. [PMID: 38885686 DOI: 10.1016/s2213-2600(24)00124-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 06/20/2024]
Abstract
Although smoking is the primary cause of lung cancer, only about 15% of lifelong smokers develop the disease. Moreover, a substantial proportion of lung cancer cases occur in never-smokers, highlighting the potential role of inherited genetic factors in the cause of lung cancer. Lung cancer is significantly more common among those with a positive family history, especially for early-onset disease. Therefore, the presence of pathogenic germline variants might act synergistically with environmental factors. The incorporation of next-generation sequencing in routine clinical practice has led to the identification of cancer-predisposing mutations in an increasing proportion of patients with lung cancer. This Review summarises the landscape of germline susceptibility in lung cancer and highlights the importance of germline testing in patients diagnosed with the disease, which has the potential to identify individuals at risk, with implications for tailored therapeutic approaches and successful prevention through genetic counselling and screening.
Collapse
Affiliation(s)
- Emmanouil Panagiotou
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis A Vathiotis
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece.
| | - Periklis Makrythanasis
- Laboratory of Medical Genetics, Medical School, National and Kapodistrian University of Athens, Athens, Greece; Department of Genetic Medicine and Development, Medical School, University of Geneva, Geneva, Switzerland; Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Fred Hirsch
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Triparna Sen
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Konstantinos Syrigos
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece
| |
Collapse
|
22
|
Wang Y, Duan Y, Guo D, Lv H, Li Q, Liu X, Qiao N, Meng H, Zhang X, Lan L, Liu X, Liu X. Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis. Clin Transl Oncol 2024:10.1007/s12094-024-03556-8. [PMID: 38869739 DOI: 10.1007/s12094-024-03556-8] [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: 04/11/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice. METHODS A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software. RESULTS The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93). CONCLUSIONS Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.
Collapse
Affiliation(s)
- Yixian Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Yuqing Duan
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Dingjie Guo
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Hongbo Lv
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Qiong Li
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Xuan Liu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Na Qiao
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Hengyu Meng
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Xin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Linwei Lan
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China
| | - Xiumin Liu
- Department of Clinical Laboratory, The Second Hospital of Jilin University, Changchun, Jilin, 130041, China.
| | - Xin Liu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, 130021, China.
| |
Collapse
|
23
|
Fang K, Zheng X, Lin X, Dai Z. A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques. Front Endocrinol (Lausanne) 2024; 15:1296047. [PMID: 38894742 PMCID: PMC11183288 DOI: 10.3389/fendo.2024.1296047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Purpose The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to identify and screen for osteoporosis in patients. Method A total of 488 patients who had undergone chest CT and bone turnover marker testing, and had known bone mineral density, were included in this study. ITK-SNAP software was used to delineate regions of interest, while radiomics features were extracted using Python. Multiple 2D and 3D deep learning models were trained to identify these regions of interest. The effectiveness of these techniques in screening for osteoporosis in patients was compared. Result Clinical models based on gender, age, and β-cross achieved an accuracy of 0.698 and an AUC of 0.665. Radiomics models, which utilized 14 selected radiomics features, achieved a maximum accuracy of 0.750 and an AUC of 0.739. The test group yielded promising results: the 2D Deep Learning model achieved an accuracy of 0.812 and an AUC of 0.855, while the 3D Deep Learning model performed even better with an accuracy of 0.854 and an AUC of 0.906. Similarly, the 2D Transfer Learning model achieved an accuracy of 0.854 and an AUC of 0.880, whereas the 3D Transfer Learning model exhibited an accuracy of 0.740 and an AUC of 0.737. Overall, the application of 3D deep learning and 2D transfer learning techniques on chest CT scans showed excellent screening performance in the context of osteoporosis. Conclusion Bone turnover markers may not be necessary for osteoporosis screening, as 3D deep learning and 2D transfer learning techniques utilizing chest CT scans proved to be equally effective alternatives.
Collapse
Affiliation(s)
- Kaibin Fang
- Department of Orthopaedic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Xiaoling Zheng
- Aviation College, Liming Vocational University, Quanzhou, China
| | - Xiaocong Lin
- Department of Orthopaedic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Zhangsheng Dai
- Department of Orthopaedic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| |
Collapse
|
24
|
Teng X, Han K, Jin W, Ma L, Wei L, Min D, Chen L, Du Y. Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism. EClinicalMedicine 2024; 72:102617. [PMID: 38707910 PMCID: PMC11066529 DOI: 10.1016/j.eclinm.2024.102617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Background Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. Methods The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Findings Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. Interpretation This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. Funding This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).
Collapse
Affiliation(s)
- Xiaoyan Teng
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Kun Han
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Wei Jin
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Liru Ma
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Lirong Wei
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Daliu Min
- Department of Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Libo Chen
- Department of Nuclear Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Yuzhen Du
- Department of Laboratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| |
Collapse
|
25
|
Gao GB, Chen L, Pan JF, Lei T, Cai X, Hao Z, Wang Q, Shan G, Li J. LncRNA RGMB-AS1 inhibits HMOX1 ubiquitination and NAA10 activation to induce ferroptosis in non-small cell lung cancer. Cancer Lett 2024; 590:216826. [PMID: 38574881 DOI: 10.1016/j.canlet.2024.216826] [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: 12/21/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Ferroptosis, an iron-dependent regulated cell death caused by excessive lipid peroxide accumulation, has emerged as a promising therapeutic target in various cancers, including non-small cell lung cancer (NSCLC). In this study, we identified the long non-coding RNA RGMB-AS1 as a key regulator of ferroptosis in NSCLC. Mechanistically, RGMB-AS1 interacted with heme oxygenase 1 (HMOX1) and prevented its ubiquitination by the E3 ligase TRC8, leading to increased HMOX1 stability and enhanced ferroptosis. Additionally, RGMB-AS1 bound to the 82-87 amino acid region of N-alpha-acetyltransferase 10 (NAA10), stimulating its acetyltransferase activity and promoting the conversion of acetyl-CoA to HMG-CoA, further contributing to ferroptosis. The RGMB-AS1-HMOX1 and RGMB-AS1-NAA10 axes synergistically inhibited NSCLC growth both in vitro and in vivo. Clinically, low RGMB-AS1 expression was associated with advanced tumor stage and poor overall survival in NSCLC patients. Furthermore, adeno-associated virus-mediated RGMB-AS1 overexpression significantly suppressed tumor growth in mouse xenograft models. Our findings uncover a novel lncRNA-mediated regulatory mechanism of ferroptosis and highlight the potential of RGMB-AS1 as a prognostic biomarker and therapeutic target in NSCLC.
Collapse
Affiliation(s)
- Gui-Bin Gao
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Liang Chen
- Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Jia-Feng Pan
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Tao Lei
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Xin Cai
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Zhexue Hao
- Department of Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Qi Wang
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Ge Shan
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, the CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Jin Li
- The Key Laboratory of Advanced Interdisciplinary Studies, The State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China; Department of Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510530, China; The People's Hospital of Anshun City, Anshun, Guizhou, 561000, China.
| |
Collapse
|
26
|
Xue C, Lu X, Sun G, Wang N, He G, Xu W, Xi Z, Xie L. Efficacy of Opportunistic Screening with Chest CT in Identifying Osteoporosis and Osteopenia in Patients with T2DM. Diabetes Metab Syndr Obes 2024; 17:2155-2163. [PMID: 38827165 PMCID: PMC11143444 DOI: 10.2147/dmso.s462065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/08/2024] [Indexed: 06/04/2024] Open
Abstract
Purpose To explore the validity of the thoracic spine Hounsfield Unit (HU) measured by chest computed tomography (CT) for opportunistic screening of diabetic osteoporosis. The current study attempted to establish a diagnostic threshold for thoracic spine HU in a type 2 diabetes mellitus (T2DM) population with osteoporosis. Patients and Methods The current study retrospectively included 334 patients with T2DM. They underwent chest CT and Dual-energy X-ray (DXA) between August 2021 and January 2022 in our hospital. HU values were measured on the resulting chest CT images at thoracic spine 11 and 12 to construct regions of interest. All patients were grouped according to the lowest T-value of DXA examination: osteoporosis, osteopenia and normal bone density. HU values were compared with T-values in each group of patients, and receiver operating characteristics curves were plotted to calculate diagnostic thresholds as well as sensitivity and specificity. Results There was a strong correlation between the HU values of chest CT and the T-values of DXA (p < 0.01). The sensitivity for osteoporosis was 88.7% for T11 attenuation≤ 98 HU and the specificity for osteoporosis was 87.5% for T12 attenuation ≤ 117HU; the specificity for normal BMD was 85.4% for T11 attenuation ≥ 147 HU and 82% for T12 attenuation ≥ 146 HU. Conclusion Chest CT can be used to screen patients with T2DM for opportunistic osteoporosis and help determine if they need DXA screening. The current study suggests that when the HU threshold of T11 ≤ 98/T12 ≤ 117, patients may need further osteoporosis screening.
Collapse
Affiliation(s)
- Congyang Xue
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Xiaopei Lu
- Department of Traditional Chinese Medicine Surgery, Nanjing Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Guangda Sun
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Nan Wang
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Gansheng He
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Wenqiang Xu
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Zhipeng Xi
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Lin Xie
- Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| |
Collapse
|
27
|
Notsuda H, Oshio H, Onodera K, Hirama T, Watanabe Y, Watanabe T, Suzuki T, Oishi H, Niikawa H, Saito-Koyama R, Noda M, Tominaga J, Okada Y. Morphological Predictors of Primary Lung Cancer among Part-Solid Ground-Grass Nodules on High-Resolution CT. TOHOKU J EXP MED 2024; 263:35-42. [PMID: 38355111 DOI: 10.1620/tjem.2024.j016] [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] [Indexed: 02/16/2024]
Abstract
Recent advancements in computed tomography (CT) scanning have improved the detection rates of peripheral pulmonary nodules, including those with ground-glass opacities (GGOs). This study focuses on part-solid pure ground-glass nodules (GGNs) and aims to identify imaging predictors that can reliably differentiate primary lung cancer from nodules with other diagnoses among part-solid GGNs on high-resolution CT (HRCT). A retrospective study was conducted on 609 patients who underwent surgical treatment or observation for lung nodules. Radiological findings from pre-operative HRCT scans were reviewed and several CT imaging features of part-solid GGNs were examined for their positive predictive value to identify primary lung cancer. The proportions of the nodules with a final diagnosis of primary lung cancer were significantly higher in part-solid GGNs (91.9%) compared with solid nodules (70.3%) or pure GGNs (66.7%). Among CT imaging features of part-solid GGNs that were evaluated, consolidation-to-tumor ratio (CTR) < 0.5 (98.1%), pleural indentation (96.4%), and clear tumor border (96.7%) had high positive predictive value to identify primary lung cancer. When two imaging features were combined, the combination of CTR < 0.5 and a clear tumor border was identified to have 100% positive predictive values with a sensitivity of 40.8%. Thus we conclude that part-solid GGNs with a CTR < 0.5 accompanied by a clear tumor border evaluated by HRCT are very likely to be primary lung cancers with an acceptable sensitivity. Preoperative diagnostic procedures to obtain a pathological diagnosis may potentially be omitted in patients harboring such part-solid GGNs.
Collapse
Affiliation(s)
- Hirotsugu Notsuda
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | | | - Ken Onodera
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Takashi Hirama
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Yui Watanabe
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Tatsuaki Watanabe
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Takaya Suzuki
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Hisashi Oishi
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Hiromichi Niikawa
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Ryoko Saito-Koyama
- Department of Pathology, National Hospital Organization, Sendai Medical Center
| | - Masafumi Noda
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| | - Junya Tominaga
- Department of Diagnostic Radiology, Tohoku University Hospital
| | - Yoshinori Okada
- Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University
| |
Collapse
|
28
|
Ren S, Yu C, Huang Q. Diagnostic value of combined detection of plasma cfDNA concentration and integrity in NSCLC. Lung Cancer Manag 2024; 13:LMT64. [PMID: 38812772 PMCID: PMC11131340 DOI: 10.2217/lmt-2023-0009] [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: 08/28/2023] [Accepted: 12/12/2023] [Indexed: 05/31/2024] Open
Abstract
Aim: To evaluate the value of combined detection of plasma cfDNA concentration and integrity in the early diagnosis of NSCLC. Methods: Real-time fluorescence quantitative PCR was used to determine the concentration and integrity of plasma cfDNA in 71 NSCLC patients and 53 healthy people. Results: Combined detection of plasma cfDNA concentration and integrity had higher diagnostic power in differentiating NSCLC patients with stage I/II from healthy people than detection of plasma cfDNA concentration alone or integrity alone. The AUC, sensitivity and specificity of the combined detection of plasma cfDNA concentration and integrity were 0.781, 0.62 and 0.85. Conclusion: Combined detection of plasma cfDNA concentration and integrity could improve the diagnostic value in NSCLC detection.
Collapse
Affiliation(s)
- Sai Ren
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, PR China
- Department of Laboratory Medicine, People's Hospital of Chongqing Liang Jiang New Area, Chongqing, 401121, PR China
| | - Chunli Yu
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, PR China
- Department of Laboratory Medicine, Chengdu Xinhua Hospital, Chengdu, 610055, PR China
| | - Qing Huang
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, PR China
| |
Collapse
|
29
|
Shen H, Li C. Global research trends in immunotherapy for non-small cell lung cancer patients with KRAS mutations: a bibliometric analysis. Front Oncol 2024; 14:1385761. [PMID: 38817907 PMCID: PMC11137258 DOI: 10.3389/fonc.2024.1385761] [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: 02/13/2024] [Accepted: 04/22/2024] [Indexed: 06/01/2024] Open
Abstract
Background Immunotherapy, frequently combined with conventional chemotherapy, is crucial for treating NSCLC. Kirsten rat sarcoma virus (KRAS) is a poor prognostic factor in patients with NSCLC, particularly lung adenocarcinoma, where binding of conventional inhibitors to mutated KRAS proteins is challenging. Field profiles, research hotspots, and prospects for immunotherapy for patients with NSCLC-carrying KRAS mutations were uncovered in this study. Methods Microsoft Excel 2019, Bibliometrix, VOSviewer software, and Citespace were utilized to conduct a comprehensive scientometric analysis and understand a specific research field's knowledge base and frontiers aided by bibliometrics. Results Between 2014 and 2023, 398 eligible documents in the English language were acquired using the WoSCC database, of which 113 and 285 were reviews and articles, respectively. The growth rate per year was 34.25 %. The most cited articles were from the United States, and China published the highest number of articles. Cancers was the journal, with increased publications in recent years. The keywords with the strongest citation bursts were analyzed using Citespace. "Immune checkpoint inhibitors," "co-occurring genomic alterations," and "KRAS" are among the research hotspots in this field. Conclusion Using bibliometric and visual analyses, we examined immunotherapy for patients with KRAS-mutant NSCLC over the previous decade. The whole analysis showed a steady, quick increase in yearly publications in this area. Our findings will provide a roadmap for future research on the mechanisms of immunotherapy and immune checkpoint inhibitor action in treating KRAS-mutant NSCLC.
Collapse
Affiliation(s)
- Hanyu Shen
- Department of Clinical Laboratory, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People’s Hospital, Wuxi, Jiangsu, China
| | - Chunxiao Li
- Department of Surgery, Wuxi Huishan No.2 People’s Hospital, Wuxi, Jiangsu, China
| |
Collapse
|
30
|
Wang A, Hao Y, Huo Y, Xu X, Zhang Y. An analysis of the influencing factors of false negative autoantibodies in patients with non-small cell lung cancer. Front Oncol 2024; 14:1358387. [PMID: 38800369 PMCID: PMC11116597 DOI: 10.3389/fonc.2024.1358387] [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: 12/19/2023] [Accepted: 04/09/2024] [Indexed: 05/29/2024] Open
Abstract
Objectives To analyze the clinical significance of seven autoantibodies (P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE, and CAGE) in patients with non-small cell lung cancer (NSCLC) and the factors that influence false-negative results. Methods Seven autoantibodies were measured in the serum of 502 patients with non-small cell lung cancer (NSCLC) using ELISA, and their correlations with age, sex, smoking history, pathological type, clinical stage, and PD-L1 gene expression were analyzed. The clinicopathological data of the false-negative and positive groups for the seven autoantibodies were compared to determine the influencing factors. Results P53 antibody expression level was correlated with lobulation sign, PGP9.5 antibody expression level with sex and vascular convergence; SOX2 antibody expression level with pathological type, clinical stage, and enlarged lymph nodes; and MAGE antibody expression level with the pathological type (P<0.05). False-negative autoantibodies are prone to occur in lung cancer patients with ground-glass nodules, no enlarged lymph nodes, no vascular convergence, and PD-L1 gene expression <1% (P <0.05). Conclusion Detection of seven autoantibodies was clinically significant in patients with NSCLC. However, poor sensitivity should be considered in clinical diagnoses to prevent missed diagnoses.
Collapse
Affiliation(s)
- Ailin Wang
- Department of Gerontology and Geriatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Ying Hao
- Department of Gerontology and Geriatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Yunlong Huo
- Department of Pathology, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Xiaoman Xu
- Department of Pulmonary and Critical Care Medicine, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Yi Zhang
- Department of Gerontology and Geriatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
31
|
Kasuga I, Yokoe Y, Gamo S, Sugiyama T, Tokura M, Noguchi M, Okayama M, Nagakura R, Ohmori N, Tsuchiya T, Sofuni A, Itoi T, Ohtsubo O. Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries. Medicine (Baltimore) 2024; 103:e38161. [PMID: 38728453 PMCID: PMC11081589 DOI: 10.1097/md.0000000000038161] [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: 09/29/2023] [Accepted: 04/17/2024] [Indexed: 05/12/2024] Open
Abstract
Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.
Collapse
Affiliation(s)
- Ikuma Kasuga
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
- Department of Internal Medicine, Faculty of Medicine, Tokyo Medical University, Tokyo, Japan
- Department of Nursing, Faculty of Human Care, Tohto University, Saitama, Japan
| | - Yoshimi Yokoe
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Sanae Gamo
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Tomoko Sugiyama
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Michiyo Tokura
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Maiko Noguchi
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Mayumi Okayama
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Rei Nagakura
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Nariko Ohmori
- Department of Medicine, Healthcare Center, Shinjuku Oiwake Clinic and Ladies Branch, Seikokai, Tokyo, Japan
| | - Takayoshi Tsuchiya
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Atsushi Sofuni
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
- Department of Clinical Oncology, Tokyo Medical University, Tokyo Japan
| | - Takao Itoi
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Osamu Ohtsubo
- Department of Nursing, Faculty of Human Care, Tohto University, Saitama, Japan
- Department of Medicine, Kenkoigaku Association, Tokyo Japan
| |
Collapse
|
32
|
Lorenzon M, Zabotti A, Cereser L. Cancer screening before treatments in rheumatoid arthritis: lights and shadows. Rheumatology (Oxford) 2024; 63:1475-1476. [PMID: 37707539 DOI: 10.1093/rheumatology/kead478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- Michele Lorenzon
- Department of Medicine, Institute of Radiology, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
| | - Alen Zabotti
- Department of Medicine, Rheumatology Institute, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, Institute of Radiology, University of Udine-University Hospital 'S. Maria della Misericordia', Udine, Italy
| |
Collapse
|
33
|
Ezegbogu M, Wilkinson E, Reid G, Rodger EJ, Brockway B, Russell-Camp T, Kumar R, Chatterjee A. Cell-free DNA methylation in the clinical management of lung cancer. Trends Mol Med 2024; 30:499-515. [PMID: 38582623 DOI: 10.1016/j.molmed.2024.03.007] [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: 12/07/2023] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 04/08/2024]
Abstract
The clinical use of cell-free DNA (cfDNA) methylation in managing lung cancer depends on its ability to differentiate between malignant and healthy cells, assign methylation changes to specific tissue sources, and elucidate opportunities for targeted therapy. From a technical standpoint, cfDNA methylation analysis is primed as a potential clinical tool for lung cancer screening, early diagnosis, prognostication, and treatment, pending the outcome of elaborate validation studies. Here, we discuss the current state of the art in cfDNA methylation analysis, examine the unique features and limitations of these new methods in a clinical context, propose two models for applying cfDNA methylation data for lung cancer screening, and discuss future research directions.
Collapse
Affiliation(s)
- Mark Ezegbogu
- Department of Pathology, Dunedin School of Medicine, University of Otago, New Zealand
| | - Emma Wilkinson
- Department of Pathology, Dunedin School of Medicine, University of Otago, New Zealand
| | - Glen Reid
- Department of Pathology, Dunedin School of Medicine, University of Otago, New Zealand
| | - Euan J Rodger
- Department of Pathology, Dunedin School of Medicine, University of Otago, New Zealand
| | - Ben Brockway
- Department of Medicine, Dunedin School of Medicine, University of Otago, New Zealand
| | - Takiwai Russell-Camp
- Department of Medicine, Dunedin School of Medicine, University of Otago, New Zealand
| | - Rajiv Kumar
- St George's Cancer Care Centre, 131 Leinster Road, Christchurch, 8014, New Zealand
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, New Zealand; SoHST Faculty, UPES University, Dehradun 248007, India.
| |
Collapse
|
34
|
Wang H, Yang M, Xiong W, Wang Q, Zheng B, Bai Y, Zou K, Li J, Ren J, Chen W, Zhai J, Li J. Noteworthy impacts of COVID-19 pandemic on cancer screening: A systematic review. FUNDAMENTAL RESEARCH 2024; 4:484-494. [PMID: 38933198 PMCID: PMC11197616 DOI: 10.1016/j.fmre.2023.12.016] [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: 05/04/2023] [Revised: 12/18/2023] [Accepted: 12/26/2023] [Indexed: 06/28/2024] Open
Abstract
The sudden onset of the coronavirus disease 2019 (COVID-19) in January 2020 has affected essential global health services. Cancer-screening services that can reduce cancer mortality are strongly affected. However, the specific role of COVID-19 in cancer screening is not fully understood. This study aimed to assess the efficiency of global cancer screening programs before and during the COVID-19 pandemic and to promote potential cancer-screening strategies for the next pandemic. Electronic searches in PubMed, Embase, and Web of Science, and manual searches were performed between January 1, 2020 and March 1, 2023. Cohort studies that reported the number of participants who underwent cancer screening before and during the COVID-19 pandemic were included. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Differences in cancer-screening rates were estimated using the incidence rate ratio (IRR). Fifty-five cohort studies were included in this meta-analysis. The screening rates of colorectal cancer using invasive screening methods (Pooled IRR = 0.52, 95% CI: 0.42 to 0.65, p < 0.01), cervical cancer (Pooled IRR = 0.56, 95% CI: 0.47 to 0.67, p < 0.01), breast cancer (Pooled IRR = 0.57, 95% CI: 0.49 to 0.66, p < 0.01) and prostate cancer (Pooled IRR = 0.71, 95% CI: 0.56 to 0.90, p < 0.01) during the COVID-19 pandemic were significantly lower than those before the COVID-19 pandemic. The screening rates of lung cancer (Pooled IRR = 0.77, 95% CI: 0.58 to 1.03, p = 0.08) and colorectal cancer using noninvasive screening methods (Pooled IRR = 0.74, 95% CI: 0.50 to 1.09, p = 0.13) were reduced with no statistical differences. The subgroup analyses revealed that the reduction in cancer-screening rates varied across economies. Our results suggest that the COVID-19 pandemic has had a noteworthy impact on colorectal, cervical, breast, and prostate cancer screening. Developing innovative cancer-screening technologies is important to promote the efficiency of cancer-screening services in the post-COVID-19 era and prepare for the next pandemic.
Collapse
Affiliation(s)
- Huilin Wang
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Min Yang
- Department of Comprehensive Intervention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wei Xiong
- Department of Gynecology Endocrine & Reproductive Center, National Clinical Research Center for Obstetric & Gynecologic Diseases Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College/Chinese Academy of Medical Sciences, Beijing 100005, China
| | - Quan Wang
- Ambulatory Surgery Center of Xijing Hospital, Fourth Military Medical University, Xi'an 710068, China
| | - Bobo Zheng
- Department of General Surgery, Shaanxi Provincial People’ s Hospital, Xi'an 710068, China
| | - Yang Bai
- College of Clinical Medicine, Capital Medical University, Beijing 100069, China
| | - Kaiyong Zou
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jibin Li
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jiansong Ren
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wanqing Chen
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jingbo Zhai
- School of Public Health, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jiang Li
- Office for Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| |
Collapse
|
35
|
Zha C, Li L, Zhu F, Zhao Y. The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2818. [PMID: 38732924 PMCID: PMC11086312 DOI: 10.3390/s24092818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.
Collapse
Affiliation(s)
| | - Lei Li
- Department of Electronics and Electrical Engineering, Changchun University of Technology, Changchun 130012, China; (C.Z.); (F.Z.); (Y.Z.)
| | | | | |
Collapse
|
36
|
Oskarsdottir GN, Lampa E, Berglund A, Rosengren L, Ulvestad M, Boros M, Daumont MJ, Rault C, Emanuel G, Leal C, Schoemaker MJ, Wagenius G. Real-World Treatment Patterns and Survival Outcomes for Patients with Non-Metastatic Non-Small-Cell Lung Cancer in Sweden: A Nationwide Registry Analysis from the I-O Optimise Initiative. Cancers (Basel) 2024; 16:1655. [PMID: 38730607 PMCID: PMC11083854 DOI: 10.3390/cancers16091655] [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: 03/14/2024] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, with ~40-50% of patients diagnosed with non-metastatic disease (stages IA-IIIC). The treatment landscape is evolving rapidly as immunotherapies and targeted therapy are introduced in the non-metastatic setting, creating a need to assess patient outcomes prior to their introduction. This real-world study using Swedish National Lung Cancer Registry data examined outcomes (overall survival (OS) and time to next treatment or death (TTNTD)) and treatment patterns for adults diagnosed with non-metastatic NSCLC. Baseline characteristics and OS from diagnosis were described for all patients; OS, treatment patterns, and TTNTD from treatment start were described for the treatment subgroup (patients diagnosed from 2014 onwards), stratified by disease stage and initial treatment. OS and TTNTD were described using the Kaplan-Meier estimator. The overall population (2008-2019) included 17,433 patients; the treatment subgroup included 5147 patients. Median OS (interquartile range) overall ranged from 83.3 (31.6-165.3) months (stage I patients) to 10.4 (4.3-24.2) months (stage IIIB patients). Among the treatment subgroup, median OS and TTNTD were longest among patients receiving surgery versus other anticancer treatments. These findings provide a baseline upon which to evaluate the epidemiology of non-metastatic NSCLC as newer treatments are introduced.
Collapse
Affiliation(s)
- Gudrun N. Oskarsdottir
- Department of Respiratory Medicine and Allergology, Skåne University Hospital, V/O Hjärt- och Lungmedicin, 222 42 Lund, Sweden
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, 22 381 Lund, Sweden
| | - Erik Lampa
- Epistat AB, 753 30 Uppsala, Sweden; (E.L.); (A.B.)
| | | | - Linda Rosengren
- Medical Department Sweden, Bristol Myers Squibb, Hemvärnsgatan 9, 171 23 Solna, Sweden
| | - Maria Ulvestad
- Medical Department Nordics, Bristol Myers Squibb, Lysaker Torg 35, 1366 Lysaker, Norway;
| | - Miklos Boros
- Department of Cardiothoracic Surgery, Linköping University Hospital, 581 85 Linköping, Sweden;
| | - Melinda J. Daumont
- Worldwide Health Economics & Outcomes Research, Bristol Myers Squibb, 1420 Braine-L’Alleud, Belgium;
| | | | - Gabrielle Emanuel
- Real-World Data Analytics Markets, Bristol Myers Squibb, Uxbridge Business Park, Sanderson Road, Uxbridge UB8 1DH, UK;
| | | | | | - Gunnar Wagenius
- Department of Oncology-Pathology, Karolinska Institute, 141 86 Stockholm, Sweden;
- Thoracic Oncology Centre, The Cancer Theme, Karolinska University Hospital, 171 76 Stockholm, Sweden
| |
Collapse
|
37
|
Yang D, Zheng X. Enhanced recovery after surgery program focusing on chest tube management improves surgical recovery after video-assisted thoracoscopic surgery. J Cardiothorac Surg 2024; 19:253. [PMID: 38643197 PMCID: PMC11031910 DOI: 10.1186/s13019-024-02762-3] [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: 11/13/2023] [Accepted: 04/01/2024] [Indexed: 04/22/2024] Open
Abstract
OBJECTIVE Chest drainage is a standard procedure in thoracoscopic surgery for lung cancer. However, chest tube placement may deteriorate the ventilation capacity and increase difficulty of postoperative management of patients. The study investigated on the effects of enhanced recovery after surgery (ERAS) program focusing on chest tube management on surgical recovery of lung cancer patients. METHODS The study population consisted of 60 patients undergoing video-assisted thoracoscopic surgery (VATS) after implementation of ERAS program and another group of 60 patients undergoing VATS before implementation of ERAS program. RESULTS The mean time of first food intake was 12.9 h required for the ERAS group, which was significantly shorter than 18.4 h required for the control group (p < 0.0001). The mean time of out-of-bed activity was 14.2 h taken for the ERAS group, which was notably shorter than 22.8 h taken for the control group (p < 0.0001). The duration of chest tube placement was 68.6 h in the ERAS group, which was remarkably shorter than 92.8 h in the control group (p < 0.0001). The rate overall postoperative complications were notably lower in the ERAS group than in the control group (p = 0.018). The visual analogue score (VAS) scores on the second postoperative day exhibited significant differences between the ERAS group and the control group (p = 0.017). The patients in the ERAS group had a shorter hospitalization stay than those in the control group (p < 0.0001). CONCLUSION The study suggests the ERAS program focusing on chest tube management could improve surgical recovery, remove patient chest tube earlier, and relieve patient pain after VATS.
Collapse
Affiliation(s)
- Dan Yang
- Lung Cancer Center, West China Hospital, Sichuan University/West China School of Nursing, Chengdu, 610000, Sichuan, China
| | - Xi Zheng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, 610000, Sichuan, China.
| |
Collapse
|
38
|
Mulshine JL, Avila RS, Rizzo AA, Estepar RSJ, McGlothlin A, Pyenson B, Hoyos J, Aldigé CR, Yankelevitz DF. Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary. Int J Cancer 2024; 154:1365-1370. [PMID: 38156720 DOI: 10.1002/ijc.34825] [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: 09/20/2023] [Revised: 10/27/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024]
Abstract
Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Jody Hoyos
- Prevent Cancer Foundation, Alexandria, Virginia, USA
| | | | - David F Yankelevitz
- Icahn School of Medicine, The Mount Sinai Health System, New York, New York, USA
| |
Collapse
|
39
|
Shi Y, Yao M, Shen S, Wang L, Yao D. Abnormal expression of Krüppel-like transcription factors and their potential values in lung cancer. Heliyon 2024; 10:e28292. [PMID: 38560274 PMCID: PMC10979174 DOI: 10.1016/j.heliyon.2024.e28292] [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: 07/27/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
Lung cancer still is one of the most common malignancy tumors in the world. However, the mechanisms of its occurrence and development have not been fully elucidated. Zinc finger protein family (ZNFs) is the largest transcription factor family in human genome. Recently, the more and more basic and clinical evidences have confirmed that ZNFs/Krüppel-like factors (KLFs) refer to a group of conserved zinc finger-containing transcription factors that are involved in lung cancer progression, with the functions of promotion, inhibition, dual roles and unknown classifications. Based on the recent literature, some of the oncogenic KLFs are promising molecular biomarkers for diagnosis, prognosis or therapeutic targets of lung cancer. Interestingly, a novel computational approach has been proposed by using machine learning on features calculated from primary sequences, the XGBoost-based model with accuracy of 96.4 % is efficient in identifying KLF proteins. This paper reviews the recent some progresses of the oncogenic KLFs with their potential values for diagnosis, prognosis and molecular target in lung cancer.
Collapse
Affiliation(s)
- Yang Shi
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University & Department of Medical Immunology, Medical School of Nantong University, Nantong 226001, China
- Department of Thoracic Surgery, First People's Hospital of Yancheng, Yancheng 224001, China
| | - Min Yao
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University & Department of Medical Immunology, Medical School of Nantong University, Nantong 226001, China
| | - Shuijie Shen
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University & Department of Medical Immunology, Medical School of Nantong University, Nantong 226001, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, Jiangsu, China
| | - Dengfu Yao
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University & Department of Medical Immunology, Medical School of Nantong University, Nantong 226001, China
| |
Collapse
|
40
|
Lei J, Huang K, Wu S, Xu J, Xu Y, Zhao J, Zhang X, Bai C, Song Y, Kang J, Ran P, Zhou Y, Shen H, Wen F, Huang K, Chen Y, Yao W, Sun T, Lin Y, Zhu J, Shan G, Yang T, Wang C. Heterogeneities and impact profiles of early chronic obstructive pulmonary disease status: findings from the China Pulmonary Health Study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 45:101021. [PMID: 38352242 PMCID: PMC10862401 DOI: 10.1016/j.lanwpc.2024.101021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/09/2024] [Accepted: 01/18/2024] [Indexed: 02/16/2024]
Abstract
Background The prevalence, epidemiological and clinical heterogeneities, and impact profiles of individuals with preserved ratio impaired spirometry (PRISm), pre-COPD, young COPD, and mild COPD in general Chinese population were not known yet. Methods Data were obtained from the China Pulmonary Health study (2012-2015), a nationally representative cross-sectional survey that recruited 50,991 adults aged 20 years or older. Definitions of the four early disease status were consistent with the latest publications and the Global Initiative for Chronic Obstructive Lung Disease criteria. Findings The age-standardised prevalences of PRISm, pre-COPD, young COPD, and mild COPD were 5.5% (95% confidence interval, 4.3-6.9), 7.2% (5.9-8.8), 1.1% (0.7-1.8), and 3.1% (2.5-3.8), respectively. In summary, mild COPD was under more direct or established impact factor exposures, such as older age, male gender, lower education level, lower family income, biomass use, air pollution, and more accumulative cigarette exposures; young COPD and pre-COPD experienced more personal and parents' events in earlier lives, such as history of bronchitis or pneumonia in childhood, frequent chronic cough in childhood, parental history of respiratory diseases, passive smoke exposure in childhood, and mother exposed to passive smoke while pregnant; pre-COPD coexisted with heavier symptoms and comorbidities burdens; young COPD exhibited worse airway obstruction; and most of the four early disease status harbored small airway dysfunction. Overall, older age, male gender, lower education level, living in the urban area, occupational exposure, frequent chronic cough in childhood, more accumulated cigarette exposure, comorbid with cardiovascular disease and gastroesophageal reflux disease were all associated with increased presence of the four early COPD status; different impact profiles were additionally observed with distinct entities. Over the four categories, less than 10% had ever taken pulmonary function test; less than 1% reported a previously diagnosed COPD; and no more than 13% had received pharmaceutical treatment. Interpretation Significant heterogeneities in prevalence, epidemiological and clinical features, and impact profiles were noted under varied defining criteria of early COPD; a unified and validated definition for an early disease stage is warranted. Closer attention, better management, and further research need to be administrated to these population. Funding Chinese Academy of Medical Sciences Institute of Respiratory Medicine Grant for Young Scholars (No. 2023-ZF-9); China International Medical Foundation (No. Z-2017-24-2301); Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (No. 2021-I2M-1-049); National High Level Hospital Clinical Research Funding (No. 2022-NHLHCRF-LX-01); Major Program of National Natural Science Foundation of China (No. 82090011).
Collapse
Affiliation(s)
- Jieping Lei
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Clinical Research and Data Management, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
| | - Ke Huang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
| | - Sinan Wu
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Clinical Research and Data Management, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
| | - Jianying Xu
- Department of Pulmonary and Critical Care Medicine, Shanxi Bethune Hospital Shanxi Academy of Medical Sciences, Taiyuan, Shanxi Province, PR China
| | - Yongjian Xu
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Jianping Zhao
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Xiangyan Zhang
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, Guizhou Province, PR China
| | - Chunxue Bai
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, PR China
| | - Yuanlin Song
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, PR China
| | - Jian Kang
- Department of Pulmonary and Critical Care Medicine, The First Hospital of China Medical University, Shenyang, Liaoning Province, PR China
| | - Pixin Ran
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Diseases, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, PR China
| | - Yumin Zhou
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Diseases, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, PR China
| | - Huahao Shen
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang Province, PR China
| | - Fuqiandg Wen
- State Key Laboratory of Biotherapy of China and Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan Province, PR China
| | - Kewu Huang
- Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders, Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing, PR China
- Department of Respiratory Medicine, Capital Medical University, Beijing, PR China
- Beijing Institute of Respiratory Medicine, Beijing, PR China
| | - Yahong Chen
- Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, PR China
| | - Wanzhen Yao
- Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, PR China
| | - Tieying Sun
- Department of Respiratory and Critical Care Medicine, Beijing Hospital, Beijing, PR China
- National Center of Gerontology, Beijing, PR China
| | - Yingxiang Lin
- Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders, Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing, PR China
- Beijing Institute of Respiratory Medicine, Beijing, PR China
| | - Jianguo Zhu
- National Center of Gerontology, Beijing, PR China
| | - Guangliang Shan
- Department of Epidemiology and Biostatistics, School of Basic Medicine of Peking Union Medical College, Institute of Basic Medical Sciences of Chinese Academy of Medical Sciences, Beijing, PR China
| | - Ting Yang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
| | - Chen Wang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
- Department of Respiratory Medicine, Capital Medical University, Beijing, PR China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - China Pulmonary Health (CPH) Study Investigators
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Clinical Research and Data Management, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, PR China
- Department of Pulmonary and Critical Care Medicine, Shanxi Bethune Hospital Shanxi Academy of Medical Sciences, Taiyuan, Shanxi Province, PR China
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, Guizhou Province, PR China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, PR China
- Department of Pulmonary and Critical Care Medicine, The First Hospital of China Medical University, Shenyang, Liaoning Province, PR China
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Diseases, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, PR China
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang Province, PR China
- State Key Laboratory of Biotherapy of China and Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan Province, PR China
- Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders, Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing, PR China
- Department of Respiratory Medicine, Capital Medical University, Beijing, PR China
- Beijing Institute of Respiratory Medicine, Beijing, PR China
- Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, PR China
- Department of Respiratory and Critical Care Medicine, Beijing Hospital, Beijing, PR China
- National Center of Gerontology, Beijing, PR China
- Department of Epidemiology and Biostatistics, School of Basic Medicine of Peking Union Medical College, Institute of Basic Medical Sciences of Chinese Academy of Medical Sciences, Beijing, PR China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| |
Collapse
|
41
|
Sohn JH, Fields BKK. Radiomics and Deep Learning to Predict Pulmonary Nodule Metastasis at CT. Radiology 2024; 311:e233356. [PMID: 38591975 DOI: 10.1148/radiol.233356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Affiliation(s)
- Jae Ho Sohn
- From the Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging and Division of Cardiothoracic Imaging, University of California San Francisco (UCSF), 185 Berry St, Ste 350, San Francisco, CA 94107
| | - Brandon K K Fields
- From the Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging and Division of Cardiothoracic Imaging, University of California San Francisco (UCSF), 185 Berry St, Ste 350, San Francisco, CA 94107
| |
Collapse
|
42
|
Pan Z, Hu G, Zhu Z, Tan W, Han W, Zhou Z, Song W, Yu Y, Song L, Jin Z. Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models. Radiology 2024; 311:e232057. [PMID: 38591974 DOI: 10.1148/radiol.232057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.
Collapse
Affiliation(s)
- Zhengsong Pan
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Ge Hu
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Zhenchen Zhu
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Weixiong Tan
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Wei Han
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Zhen Zhou
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Wei Song
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Yizhou Yu
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Lan Song
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| | - Zhengyu Jin
- From the Department of Radiology (Z.P., Z. Zhu, W.S., L.S., Z.J.), Medical Research Center (G.H.), State Key Laboratory of Complex Severe and Rare Disease (G.H.), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China; 4 + 4 Medical Doctor Program (Z.P., Z. Zhu), Department of Epidemiology and Health Statistics (W.H.), Institute of Basic Medicine Sciences (W.H.), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Deepwise AI Laboratory, Beijing Deepwise & League of PhD Technology, Beijing, China (W.T., Z. Zhou, Y.Y.); and Department of Computer Science, The University of Hong Kong, Hong Kong, China (Y.Y.)
| |
Collapse
|
43
|
Henschke C, Huber R, Jiang L, Yang D, Cavic M, Schmidt H, Kazerooni E, Zulueta JJ, Sales Dos Santos R, Ventura L. Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee. J Thorac Oncol 2024; 19:565-580. [PMID: 37979778 DOI: 10.1016/j.jtho.2023.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/24/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.
Collapse
Affiliation(s)
- Claudia Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Rudolf Huber
- Division of Respiratory Medicine and Thoracic Oncology, Department of Medicine, University of Munich - Campus Innenstadt, Ziemssenstrabe, Munich, Germany
| | - Long Jiang
- Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary Medicine and Critical Care, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Milena Cavic
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia
| | - Heidi Schmidt
- Department of Medical Imaging, Toronto General Hospital, Toronto, Canada
| | - Ella Kazerooni
- Division of Cardiothoracic Radiology and Internal Medicine, University of Michigan Medical School, Frankel Cardiovascular Center, Ann Arbor, Michigan
| | - Javier J Zulueta
- Department of Medicine, Mount Sinai Morningside, New York, New York
| | - Ricardo Sales Dos Santos
- Department of Minimally Invasive Thoracic and Robotic Surgery, Albert Einstein Israeli Hospital, Sao Paulo, Brazil
| | - Luigi Ventura
- Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
| |
Collapse
|
44
|
Yuan F, Hu Y, Xu F, Feng X. A review of obstructive sleep apnea and lung cancer: epidemiology, pathogenesis, and therapeutic options. Front Immunol 2024; 15:1374236. [PMID: 38605948 PMCID: PMC11007033 DOI: 10.3389/fimmu.2024.1374236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 03/12/2024] [Indexed: 04/13/2024] Open
Abstract
Despite undeniable advances in modern medicine, lung cancer still has high morbidity and mortality rates. Lung cancer is preventable and treatable, and it is important to identify new risk factors for lung cancer, especially those that can be treated or reversed. Obstructive sleep apnea (OSA) is a very common sleep-breathing disorder that is grossly underestimated in clinical practice. It can cause, exacerbate, and worsen adverse outcomes, including death and various diseases, but its relationship with lung cancer is unclear. A possible causal relationship between OSA and the onset and progression of lung cancer has been established biologically. The pathophysiological processes associated with OSA, such as sleep fragmentation, intermittent hypoxia, and increased sympathetic nervous excitation, may affect normal neuroendocrine regulation, impair immune function (especially innate and cellular immunity), and ultimately contribute to the occurrence of lung cancer, accelerate progression, and induce treatment resistance. OSA may be a contributor to but a preventable cause of the progression of lung cancer. However, whether this effect exists independently of other risk factors is unclear. Therefore, by reviewing the literature on the epidemiology, pathogenesis, and treatment of lung cancer and OSA, we hope to understand the relationships between the two and promote the interdisciplinary exchange of ideas between basic medicine, clinical medicine, respiratory medicine, sleep medicine, and oncology.
Collapse
Affiliation(s)
- Fang Yuan
- Department of Respiratory, The First Hospital of Jiujiang City, Jiujiang, China
| | - Yanxia Hu
- Department of Respiratory, The First Hospital of Jiujiang City, Jiujiang, China
| | - Fei Xu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xujun Feng
- Department of Respiratory, The First Hospital of Jiujiang City, Jiujiang, China
- Department of Respiratory and Critical Care Medicine, Sleep Medicine Center, Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
45
|
Pereira LFF, dos Santos RS, Bonomi DO, Franceschini J, Santoro IL, Miotto A, de Sousa TLF, Chate RC, Hochhegger B, Gomes A, Schneider A, de Araújo CA, Escuissato DL, Prado GF, Costa-Silva L, Zamboni MM, Ghefter MC, Corrêa PCRP, Torres PPTES, Mussi RK, Muglia VF, de Godoy I, Bernardo WM. Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging. J Bras Pneumol 2024; 50:e20230233. [PMID: 38536982 PMCID: PMC11095927 DOI: 10.36416/1806-3756/e20230233] [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: 07/13/2023] [Accepted: 12/13/2023] [Indexed: 05/18/2024] Open
Abstract
Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.
Collapse
Affiliation(s)
- Luiz Fernando Ferreira Pereira
- . Serviço de Pneumologia, Hospital das Clínicas, Faculdade de Medicina, Universidade Federal de Minas Gerais - UFMG - Belo Horizonte (MG) Brasil
| | - Ricardo Sales dos Santos
- . Serviço de Cirurgia Torácica, Hospital Israelita Albert Einstein, São Paulo (SP) Brasil
- . Programa ProPulmão, SENAI CIMATEC e SDS Healthline, Salvador (BA) Brasil
| | - Daniel Oliveira Bonomi
- . Departamento de Cirurgia Torácica, Faculdade de Medicina, Universidade Federal de Minas Gerais - UFMG - Belo Horizonte (MG) Brasil
| | - Juliana Franceschini
- . Programa ProPulmão, SENAI CIMATEC e SDS Healthline, Salvador (BA) Brasil
- . Fundação ProAR, Salvador (BA) Brasil
| | - Ilka Lopes Santoro
- . Disciplina de Pneumologia, Departamento de Medicina, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil
| | - André Miotto
- . Disciplina de Cirurgia Torácica, Departamento de Cirurgia, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil
| | - Thiago Lins Fagundes de Sousa
- . Serviço de Pneumologia, Hospital Universitário Alcides Carneiro, Universidade Federal de Campina Grande - UFCG - Campina Grande (PB) Brasil
| | - Rodrigo Caruso Chate
- . Serviço de Radiologia, Hospital Israelita Albert Einstein, São Paulo (SP) Brasil
| | - Bruno Hochhegger
- . Department of Radiology, University of Florida, Gainesville (FL) USA
| | - Artur Gomes
- . Serviço de Cirurgia Torácica, Santa Casa de Misericórdia de Maceió, Maceió (AL) Brasil
| | - Airton Schneider
- . Serviço de Cirurgia Torácica, Hospital São Lucas, Escola de Medicina, Pontifícia Universidade Católica do Rio Grande do Sul - PUCRS - Porto Alegre (RS) Brasil
| | - César Augusto de Araújo
- . Programa ProPulmão, SENAI CIMATEC e SDS Healthline, Salvador (BA) Brasil
- . Departamento de Radiologia, Faculdade de Medicina da Bahia - UFBA - Salvador (BA) Brasil
| | - Dante Luiz Escuissato
- . Departamento de Clínica Médica, Universidade Federal Do Paraná - UFPR - Curitiba (PR) Brasil
| | | | - Luciana Costa-Silva
- . Serviço de Diagnóstico por Imagem, Instituto Hermes Pardini, Belo Horizonte (MG) Brasil
| | - Mauro Musa Zamboni
- . Instituto Nacional de Câncer José Alencar Gomes da Silva, Rio de Janeiro (RJ) Brasil
- . Centro Universitário Arthur Sá Earp Neto/Faculdade de Medicina de Petrópolis -UNIFASE - Petrópolis (RJ) Brasil
| | - Mario Claudio Ghefter
- . Serviço de Cirurgia Torácica, Hospital Israelita Albert Einstein, São Paulo (SP) Brasil
- . Serviço de Cirurgia Torácica, Hospital do Servidor Público Estadual, São Paulo (SP) Brasil
| | | | | | - Ricardo Kalaf Mussi
- . Serviço de Cirurgia Torácica, Hospital das Clínicas, Universidade Estadual de Campinas - UNICAMP - Campinas (SP) Brasil
| | - Valdair Francisco Muglia
- . Departamento de Imagens Médicas, Oncologia e Hematologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo - USP - Ribeirão Preto (SP) Brasil
| | - Irma de Godoy
- . Disciplina de Pneumologia, Departamento de Clínica Médica, Faculdade de Medicina de Botucatu, Universidade Estadual Paulista, Botucatu (SP) Brasil
| | | |
Collapse
|
46
|
Jia K, Wang Y, Jiang L, Lai M, Liu W, Wang L, Liu H, Cao X, Li Y, Nie Z. Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO 3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16:12302-12309. [PMID: 38414269 DOI: 10.1021/acsami.3c19007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.
Collapse
Affiliation(s)
- Ke Jia
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Lixia Jiang
- Gannan Medical University, Ganzhou 341000, China
| | - Mi Lai
- Gannan Medical University, Ganzhou 341000, China
| | - Wenlan Liu
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Liping Wang
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohua Cao
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Yuze Li
- State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, College of Chemistry and Chemical Engineering, Ningxia University, Yinchuan 750021, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
47
|
Marino P, Mininni M, Deiana G, Marino G, Divella R, Bochicchio I, Giuliano A, Lapadula S, Lettini AR, Sanseverino F. Healthy Lifestyle and Cancer Risk: Modifiable Risk Factors to Prevent Cancer. Nutrients 2024; 16:800. [PMID: 38542712 PMCID: PMC10974142 DOI: 10.3390/nu16060800] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 08/07/2024] Open
Abstract
Cancer has become a serious problem worldwide, as it represents the main cause of death, and its incidence has increased over the years. A potential strategy to counter the growing spread of various forms of cancer is the adoption of prevention strategies, in particular, the use of healthy lifestyles, such as maintaining a healthy weight, following a healthy diet; being physically active; avoiding smoking, alcohol consumption, and sun exposure; and vitamin D supplementation. These modifiable risk factors are associated with this disease, contributing to its development, progression, and severity. This review evaluates the relationship between potentially modifiable risk factors and overall cancer development, specifically breast, colorectal, and prostate cancer, and highlights updated recommendations on cancer prevention. The results of numerous clinical and epidemiological studies clearly show the influence of lifestyles on the development and prevention of cancer. An incorrect diet, composed mainly of saturated fats and processed products, resulting in increased body weight, combined with physical inactivity, alcohol consumption, and smoking, has induced an increase in the incidence of all three types of cancer under study. Given the importance of adopting correct and healthy lifestyles to prevent cancer, global institutions should develop strategies and environments that encourage individuals to adopt healthy and regular behaviors.
Collapse
Affiliation(s)
- Pasquale Marino
- Unit of Oncological Gynecology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (G.D.); (F.S.)
| | - Mariangela Mininni
- Department Direzione Generale per la Salute e le Politiche della Persona, Regione Basilicata, Via Vincenzo Verrastro, 4, 85100 Potenza, Italy;
| | - Giovanni Deiana
- Unit of Oncological Gynecology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (G.D.); (F.S.)
| | - Graziella Marino
- Unit of Breast Surgery, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Potenza, Italy;
| | - Rosa Divella
- Nutritionist’s Studio at the Gravina in Puglia, C.so Giuseppe Di Vittorio, 14, 70024 Bari, Italy;
| | - Ilaria Bochicchio
- Unit of Clinical Psychology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (I.B.); (A.G.); (S.L.); (A.R.L.)
| | - Alda Giuliano
- Unit of Clinical Psychology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (I.B.); (A.G.); (S.L.); (A.R.L.)
| | - Stefania Lapadula
- Unit of Clinical Psychology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (I.B.); (A.G.); (S.L.); (A.R.L.)
| | - Alessandro Rocco Lettini
- Unit of Clinical Psychology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (I.B.); (A.G.); (S.L.); (A.R.L.)
| | - Francesca Sanseverino
- Unit of Oncological Gynecology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Via Padre Pio, 1, 85028 Potenza, Italy; (G.D.); (F.S.)
| |
Collapse
|
48
|
Zhou J, Yu B, Guo P, Wang S. The insufficiency of CT examination in early detection of central lung squamous cell carcinoma and squamous epithelial precancerous lesions. BMC Cancer 2024; 24:299. [PMID: 38443800 PMCID: PMC10916110 DOI: 10.1186/s12885-024-12052-9] [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: 12/05/2023] [Accepted: 02/25/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND CT examination for lung cancer has been carried out for more than 20 years and great achievements have been made in the early detection of lung cancer. However, in the clinical work, a large number of advanced central lung squamous cell carcinoma are still detected through bronchoscopy. Meanwhile, a part of CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions are also accidentally detected through bronchoscopy. METHODS This study retrospectively collects the medical records of patients in the bronchoscopy room of the Endoscopy Department of Zhejiang Cancer Hospital from January 2014 to December 2018. The inclusion criteria for patients includes: 1.Patient medical records completed, 2.Without history of lung cancer before the diagnosis and first pathological diagnosis of primary lung cancer, 3.Have the lung CT data of the same period, 4.Have the bronchoscopy records and related pathological diagnosis, 5.The patients undergoing radical surgical treatment must have a complete postoperative pathological diagnosis. Finally, a total of 10,851 patients with primary lung cancer are included in the study, including 7175 males and 3676 females, aged 22-98 years. Firstly, 130 patients with CT-occult lesions are extracted and their clinical features are analyzed. Then, 604 cases of single central squamous cell carcinoma and 3569 cases of peripheral adenocarcinoma are extracted and compares in postoperative tumor diameter and lymph node metastasis. RESULTS 115 cases of CT-occult central lung squamous cell carcinoma and 15 cases of squamous epithelial precancerous lesions are found. In the total lung cancer, the proportion of CT-occult lesions is 130/10,851 (1.20%). Meanwhile, all these patients are middle-aged and elderly men with a history of heavy smoking. There are statistically significant differences in postoperative median tumor diameter (3.65 cm vs.1.70 cm, P < 0.0001) and lymph node metastasis rate (50.99% vs.13.06%, P < 0.0001) between 604 patients with operable single central lung squamous cell carcinoma and 3569 patients with operable peripheral lung adenocarcinoma. Of the 604 patients with squamous cell carcinoma, 96.52% (583/604) are male with a history of heavy smoking and aged 40-82 years with a median age of 64 years. CONCLUSIONS This study indicates that the current lung CT examination of lung cancer is indeed insufficiency for the early diagnosis of central squamous cell carcinoma and squamous epithelial precancerous lesions. Further bronchoscopy in middle-aged and elderly men with a history of heavy smoking can make up for the lack of routine lung CT examination.
Collapse
Affiliation(s)
- Jiaming Zhou
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bijun Yu
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Peng Guo
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shi Wang
- Department of Endoscopy, Zhejiang Cancer Hospital, Hangzhou, China.
| |
Collapse
|
49
|
Pan H, Zou N, Tian Y, Shen Y, Chen H, Zhu H, Zhang J, Jin W, Gu Z, Ning J, Jiang L, Huang J, Luo Q. Robotic Versus Thoracoscopic Sub-lobar Resection for Octogenarians with Clinical Stage IA Non-small Cell Lung Cancer: A Propensity Score-Matched Real-World Study. Ann Surg Oncol 2024; 31:1568-1580. [PMID: 38071721 PMCID: PMC10838251 DOI: 10.1245/s10434-023-14689-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/16/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Minimally invasive sub-lobectomy is sufficient in treating small early-stage non-small cell lung cancer (NSCLC). However, comparison of the feasibility and oncologic efficacy between robot-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in performing sub-lobectomy for early-stage NSCLC patients age 80 years or older is scarce. METHODS Octogenarians with clinical stage IA NSCLC (tumor size, ≤ 2 cm) undergoing minimally invasive wedge resection or segmentectomy at Shanghai Chest Hospital from 2011 to 2020 were retrospectively reviewed from a prospectively maintained database. Propensity score-matching (PSM) with a RATS versus VATS ratio of 1:4 was performed. Perioperative and long-term outcomes were analyzed. RESULTS The study identified 594 patients (48 RATS and 546 VATS patients), and PSM resulted in 45 cases in the RATS group and 180 cases in the VATS group. The RATS patients experienced less intraoperative bleeding (60 mL [interquartile range (IQR), 50-100 mL] vs. 80 mL [IQR, 50-100 mL]; P = 0.027) and a shorter postoperative hospital stay (4 days [IQR, 3-5 days] vs. 5 days [IQR, 4-6 days]; P = 0.041) than the VATS patients. The two surgical approaches were comparable concerning other perioperative outcomes and postoperative complications (20.00% vs. 26.11%; P = 0.396). Additionally, during a median follow-up period of 66 months, RATS and VATS achieved comparable 5-year overall survival (90.48% vs. 87.93%; P = 0.891), recurrence-free survival (83.37% vs. 83.18%; P = 0.782), and cumulative incidence of death. Further subgroup comparison also demonstrated comparable long-term outcomes between the two approaches. Finally, multivariate Cox analysis indicated that the surgical approach was not independently correlated with long-term outcomes. CONCLUSIONS The RATS approach shortened the postoperative hospital stay, reduced intraoperative bleeding by a statistically notable but clinically insignificant amount, and achieved long-term outcomes comparable with VATS in performing sub-lobectomy for octogenarians with early-stage small NSCLC.
Collapse
Affiliation(s)
- Hanbo Pan
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningyuan Zou
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Tian
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaofeng Shen
- Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hang Chen
- Department of Cardiothoracic Surgery, The Affiliated Lihuili Hospital of Ningbo University, Zhejiang, China
| | - Hongda Zhu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiaqi Zhang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenan Gu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junwei Ning
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Thoracic Surgery, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Long Jiang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Huang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| |
Collapse
|
50
|
Adams SJ, Flores EJ, Little BP, Sharma A, Lennes IT, Shepard JAO, Fintelmann FJ. RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program. Radiographics 2024; 44:e230057. [PMID: 38329900 PMCID: PMC10878164 DOI: 10.1148/rg.230057] [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: 04/11/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 02/10/2024]
Abstract
Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.
Collapse
Affiliation(s)
- Scott J. Adams
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Efren J. Flores
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Brent P. Little
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Amita Sharma
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Inga T. Lennes
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Jo-Anne O. Shepard
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| | - Florian J. Fintelmann
- From the Departments of Radiology (S.J.A., E.J.F., A.S., J.O.S.,
F.J.F.) and Medicine (I.T.L.), Massachusetts General Hospital, Harvard Medical
School, 55 Fruit St, Boston, MA 02111; and Department of Radiology, Mayo Clinic
Florida, Jacksonville, Fla (B.P.L.)
| |
Collapse
|