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Sun X, Li J, Gao X, Huang Y, Pang Z, Lv L, Li H, Liu H, Zhu L. Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma. Oncol Lett 2024; 28:342. [PMID: 38855504 PMCID: PMC11157670 DOI: 10.3892/ol.2024.14476] [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: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 06/11/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and disulfidptosis is a newly discovered mechanism of programmed cell death. However, the effects of disulfidptosis-related lncRNAs (DR-lncRNAs) in LUAD have yet to be fully elucidated. The aim of the present study was to identify and validate a novel lncRNA-based prognostic marker that was associated with disulfidptosis. RNA-sequencing and associated clinical data were obtained from The Cancer Genome Atlas database. Univariate Cox regression and lasso algorithm analyses were used to identify DR-lncRNAs and to establish a prognostic model. Kaplan-Meier curves, receiver operating characteristic curves, principal component analysis, Cox regression, nomograms and calibration curves were used to assess the reliability of the prognostic model. Functional enrichment analysis, immune infiltration analysis, somatic mutation analysis, tumor microenvironment and drug predictions were applied to the risk model. Reverse transcription-quantitative PCR was subsequently performed to validate the mRNA expression levels of the lncRNAs in normal cells and tumor cells. These analyses enabled a DR-lncRNA prognosis signature to be constructed, consisting of nine lncRNAs; U91328.1, LINC00426, MIR1915HG, TMPO-AS1, TDRKH-AS1, AL157895.1, AL512363.1, AC010615.2 and GCC2-AS1. This risk model could serve as an independent prognostic tool for patients with LUAD. Numerous immune evaluation algorithms indicated that the low-risk group may exhibit a more robust and active immune response against the tumor. Moreover, the tumor immune dysfunction exclusion algorithm suggested that immunotherapy would be more effective in patients in the low-risk group. The drug-sensitivity results showed that patients in the high-risk group were more sensitive to treatment with crizotinib, erlotinib or savolitinib. Finally, the expression levels of AL157895.1 were found to be lower in A549. In summary, a novel DR-lncRNA signature was constructed, which provided a new index to predict the efficacy of therapeutic interventions and the prognosis of patients with LUAD.
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Affiliation(s)
- Xiaoming Sun
- Department of Thoracic Surgery, Jinan Central Hospital, Jinan, Shandong 250013, P.R. China
| | - Jia Li
- Department of Thoracic Surgery, Jinan Central Hospital, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Xuedi Gao
- Department of Ophthamology, Jinan Mingshui Eye Hospital, Jinan, Shandong 250200, P.R. China
| | - Yubin Huang
- Department of Thoracic Surgery, Jinan Central Hospital, Shandong First Medical University, Jinan, Shandong 250013, P.R. China
| | - Zhanyue Pang
- Department of Thoracic Surgery, Jinan Central Hospital, Jinan, Shandong 250013, P.R. China
| | - Lin Lv
- Department of Thoracic Surgery, Jinan Central Hospital, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Hao Li
- Department of Thoracic Surgery, Jinan Central Hospital, Shandong First Medical University, Jinan, Shandong 250013, P.R. China
| | - Haibo Liu
- Department of Thoracic Surgery, Jinan Central Hospital, Jinan, Shandong 250013, P.R. China
| | - Liangming Zhu
- Department of Thoracic Surgery, Jinan Central Hospital, Shandong University, Jinan, Shandong 250013, P.R. China
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Pan X, Feng S, Wang Y, Chen J, Lin H, Wang Z, Hou F, Lu C, Chen X, Liu Z, Li Z, Cui Y, Liu Z. Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma. Heliyon 2024; 10:e30779. [PMID: 38779006 PMCID: PMC11109847 DOI: 10.1016/j.heliyon.2024.e30779] [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: 11/26/2023] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Background and objective Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence suggests that the spatial distance between tumor cells and lymphocytes also influences the immune responses, but the distance analysis based on Hematoxylin and Eosin (H&E) -stained whole-slide images (WSIs) remains insufficient. To address this issue, we aim to explore the relationship between distance and prognosis prediction of patients with LUAD in this study. Methods We recruited patients with resectable LUAD from three independent cohorts in this multi-center study. We proposed a simple but effective deep learning-driven workflow to automatically segment different cell types in the tumor region using the HoVer-Net model, and quantified the spatial distance (DIST) between tumor cells and lymphocytes based on H&E-stained WSIs. The association of DIST with disease-free survival (DFS) was explored in the discovery set (D1, n = 276) and the two validation sets (V1, n = 139; V2, n = 115). Results In multivariable analysis, the low DIST group was associated with significantly better DFS in the discovery set (D1, HR, 0.61; 95 % CI, 0.40-0.94; p = 0.027) and the two validation sets (V1, HR, 0.54; 95 % CI, 0.32-0.91; p = 0.022; V2, HR, 0.44; 95 % CI, 0.24-0.81; p = 0.009). By integrating the DIST with clinicopathological factors, the integrated model (full model) had better discrimination for DFS in the discovery set (C-index, D1, 0.745 vs. 0.723) and the two validation sets (V1, 0.621 vs. 0.596; V2, 0.671 vs. 0.650). Furthermore, the computerized DIST was associated with immune phenotypes such as immune-desert and inflamed phenotypes. Conclusions The integration of DIST with clinicopathological factors could improve the stratification performance of patients with resectable LUAD, was beneficial for the prognosis prediction of LUAD patients, and was also expected to assist physicians in individualized treatment.
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Affiliation(s)
- Xipeng Pan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Siyang Feng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Yumeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Jiale Chen
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Zimin Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Feihu Hou
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Cheng Lu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Xin Chen
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Zhenbing Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Zhenhui Li
- Department of Radiology, The Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China
| | - Zaiyi Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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Caranfil E, Lami K, Uegami W, Fukuoka J. Artificial Intelligence and Lung Pathology. Adv Anat Pathol 2024:00125480-990000000-00110. [PMID: 38780094 DOI: 10.1097/pap.0000000000000448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.
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Affiliation(s)
- Emanuel Caranfil
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Wataru Uegami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
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Bai H, Wang R, Dai Y, Xue Y. Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model. Technol Health Care 2024; 32:201-214. [PMID: 37302049 DOI: 10.3233/thc-220812] [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: 06/12/2023]
Abstract
BACKGROUND Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. OBJECTIVE In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. METHODS A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. RESULTS Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. CONCLUSION This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.
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Affiliation(s)
- He Bai
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Rui Wang
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Dai
- Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Yuan Xue
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
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5
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Dong H, He Z, Wang H, Ding M, Huang Y, Li H, Shi H, Mao L, Hu C, Wang J. Identification of potential biomarkers for progression and prognosis of renal clear cell carcinoma by comprehensive bioinformatics analysis. Technol Health Care 2024; 32:897-914. [PMID: 37483037 DOI: 10.3233/thc-230282] [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: 07/25/2023]
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cell carcinoma (RCC), and effective biomarkers will improve diagnosis and treatment. OBJECTIVE This study investigated NPEPL1 expression in ccRCC through public databases and clinical samples and assessed its correlation with clinicopathological features and patient prognosis. METHOD Data from The Cancer Genome Atlas and clinical specimens were gathered, NPEPL1 expression levels were analyzed; a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of NPEPL1; and clinicopathological data was used to study the correlations between expression and clinical parameters. NPEPL1's prognostic value was appraised using a Kaplan-Meier (K-M) survival curve, Cox regression analysis, and a nomogram model; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differently expressed genes between tissues with high and low NPEPL1 expression were used to estimate the underlying mechanisms involved. RESULTS NPEPL1 was significantly higher-expressed in ccRCC tissue. ROC analysis showed that NPEPL1 had noteworthy diagnostic efficacy. NPEPL1 expression was closely related to clinicopathological parameters, such as T and M stage. K-M analysis showed that overall survival was significantly shortened with high NPEPL1 expression. Cox regression analysis showed that NPEPL1 expression was an independent risk factor predicting overall survival. The nomogram showed a significantly high clinical value in predicting the 1-, 3-, and 5-year survival probabilities in ccRCC. GO and KEGG enrichment analysis suggested that NPEPL1 may promote the occurrence and development of ccRCC via the Ras signaling and other pathways. CONCLUSION NPEPL1 expression in ccRCC was higher than that in normal kidney tissues and was significantly associated with advanced clinical stage and poor prognosis. Therefore, NPEPL1 is a promising prognostic biomarker.
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Affiliation(s)
- Haonan Dong
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Zexi He
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
- Department of Urology, The Second People's Hospital of Foshan, Foshan, Guangdong, China
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Haifeng Wang
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Mingxia Ding
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Yinglong Huang
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Haihao Li
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Hongjin Shi
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Lan Mao
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Chongzhi Hu
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
| | - Jiansong Wang
- Department of Urology, The Second Affiliate Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, Yunnan, China
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Collins GS, Whittle R, Bullock GS, Logullo P, Dhiman P, de Beyer JA, Riley RD, Schlussel MM. Open science practices need substantial improvement in prognostic model studies in oncology using machine learning. J Clin Epidemiol 2024; 165:111199. [PMID: 37898461 DOI: 10.1016/j.jclinepi.2023.10.015] [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: 08/02/2023] [Revised: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 10/30/2023]
Abstract
OBJECTIVE To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. RESULTS We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. CONCLUSION The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.
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Affiliation(s)
- Gary S Collins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
| | - Rebecca Whittle
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Garrett S Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA; Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
| | - Patricia Logullo
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Paula Dhiman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Jennifer A de Beyer
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Michael M Schlussel
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
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Wang Y, Lin H, Yao N, Chen X, Qiu B, Cui Y, Liu Y, Li B, Han C, Li Z, Zhao W, Wang Z, Pan X, Lu C, Liu J, Liu Z, Liu Z. Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma. iScience 2023; 26:107635. [PMID: 37664636 PMCID: PMC10474456 DOI: 10.1016/j.isci.2023.107635] [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: 05/20/2023] [Revised: 07/17/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
The increased amount of tertiary lymphoid structures (TLSs) is associated with a favorable prognosis in patients with lung adenocarcinoma (LUAD). However, evaluating TLSs manually is an experience-dependent and time-consuming process, which limits its clinical application. In this multi-center study, we developed an automated computational workflow for quantifying the TLS density in the tumor region of routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The association between the computerized TLS density and disease-free survival (DFS) was further explored in 802 patients with resectable LUAD of three cohorts. Additionally, a Cox proportional hazard regression model, incorporating clinicopathological variables and the TLS density, was established to assess its prognostic ability. The computerized TLS density was an independent prognostic biomarker in patients with resectable LUAD. The integration of the TLS density with clinicopathological variables could support individualized clinical decision-making by improving prognostic stratification.
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Affiliation(s)
- Yumeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Ningning Yao
- Department of Radiobiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan 030013, China
| | - Xiaobo Chen
- First Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Bingjiang Qiu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangzhou 510080, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
- Department of Radiobiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University; Taiyuan 030013, China
- Guangdong Cardiovascular Institute, Guangzhou 510080, China
| | - Yu Liu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Bingbing Li
- Department of Pathology, Ganzhou Hospital of Guangdong Provincial People’s Hospital, Ganzhou Municipal Hospital, 49 Dagong Road, Ganzhou 341000, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zimin Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Xipeng Pan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Cheng Lu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhenbing Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Zaiyi Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [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: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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Feng Z, Lin H, Liu Z, Yan L, Wang Y, Li B, Liu E, Han C, Shi Z, Lu C, Liu Z, Pang C, Li Z, Cui Y, Pan X, Chen X. Artificial intelligence-quantified tumour-lymphocyte spatial interaction predicts disease-free survival in resected lung adenocarcinoma: A graph-based, multicentre study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107617. [PMID: 37235970 DOI: 10.1016/j.cmpb.2023.107617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 05/01/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVE A high degree of lymphocyte infiltration is related to superior outcomes amongst patients with lung adenocarcinoma. Recent evidence indicates that the spatial interactions between tumours and lymphocytes also influence the anti-tumour immune responses, but the spatial analysis at the cellular level remains insufficient. METHODS We proposed an artificial intelligence-quantified Tumour-Lymphocyte Spatial Interaction score (TLSI-score) by calculating the ratio between the number of spatial adjacent tumour-lymphocyte and the number of tumour cells based on topology cell graph constructed using H&E-stained whole-slide images. The association of TLSI-score with disease-free survival (DFS) was explored in 529 patients with lung adenocarcinoma across three independent cohorts (D1, 275; V1, 139; V2, 115). RESULTS After adjusting for pTNM stage and other clinicopathologic risk factors, a higher TLSI-score was independently associated with longer DFS than a low TLSI-score in the three cohorts [D1, adjusted hazard ratio (HR), 0.674; 95% confidence interval (CI) 0.463-0.983; p = 0.040; V1, adjusted HR, 0.408; 95% CI 0.223-0.746; p = 0.004; V2, adjusted HR, 0.294; 95% CI 0.130-0.666; p = 0.003]. By integrating the TLSI-score with clinicopathologic risk factors, the integrated model (full model) improves the prediction of DFS in three independent cohorts (C-index, D1, 0.716 vs. 0.701; V1, 0.666 vs. 0.645; V2, 0.708 vs. 0.662) CONCLUSIONS: TLSI-score shows the second highest relative contribution to the prognostic prediction model, next to the pTNM stage. TLSI-score can assist in the characterising of tumour microenvironment and is expected to promote individualized treatment and follow-up decision-making in clinical practice.
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Affiliation(s)
- Zhengyun Feng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; School of Medicine, South China University of Technology, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Lixu Yan
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yumeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Bingbing Li
- Department of Pathology, Guangdong Provincial People's Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), Ganzhou, 341000, China
| | - Entao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Cheng Lu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Zhenbing Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Cheng Pang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Zhenhui Li
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, China.
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangzhou, 510080, China; Department of Radiology, Shanxi Province Cancer Hospital; Shanxi Hospital Affiliated to Cancer Hospital; Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China.
| | - Xipeng Pan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
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Rangamuwa K, Aloe C, Christie M, Asselin-Labat ML, Batey D, Irving L, John T, Bozinovski S, Leong TL, Steinfort D. Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review. Front Oncol 2023; 13:1129195. [PMID: 37143952 PMCID: PMC10151669 DOI: 10.3389/fonc.2023.1129195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/28/2023] [Indexed: 05/06/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.
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Affiliation(s)
- Kanishka Rangamuwa
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine Royal Melbourne Hospital (RMH), University of Melbourne, Parkville, VIC, Australia
- *Correspondence: Kanishka Rangamuwa,
| | - Christian Aloe
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Michael Christie
- Department of Pathology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | | | - Daniel Batey
- Personalised Oncology Division, Walter Eliza Hall Institute, Melbourne, VIC, Australia
| | - Lou Irving
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Thomas John
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Steven Bozinovski
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Tracy L. Leong
- Personalised Oncology Division, Walter Eliza Hall Institute, Melbourne, VIC, Australia
- Department of Respiratory Medicine, Austin Hospital, Heidelberg, VIC, Australia
| | - Daniel Steinfort
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine Royal Melbourne Hospital (RMH), University of Melbourne, Parkville, VIC, Australia
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