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Dernbach G, Kazdal D, Ruff L, Alber M, Romanovsky E, Schallenberg S, Christopoulos P, Weis CA, Muley T, Schneider MA, Schirmacher P, Thomas M, Müller KR, Budczies J, Stenzinger A, Klauschen F. Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study. Eur J Cancer 2024; 211:114292. [PMID: 39276594 DOI: 10.1016/j.ejca.2024.114292] [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: 03/12/2024] [Revised: 07/05/2024] [Accepted: 08/11/2024] [Indexed: 09/17/2024]
Abstract
INTRODUCTION Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. METHODS This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. RESULTS Models trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DISCUSSION Model explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. CONCLUSION Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.
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Affiliation(s)
- Gabriel Dernbach
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany; BIFOLD, Berlin, Germany; Aignostics GmbH, Berlin, Germany
| | - Daniel Kazdal
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | | | - Maximilian Alber
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany; Aignostics GmbH, Berlin, Germany
| | - Eva Romanovsky
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Petros Christopoulos
- Department of Thoracic Oncology, Thoraxklinik and National Centre for Tumour Diseases (NCT) at Heidelberg University Hospital, 69126 Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Thomas Muley
- Translational Research Unit, Thoraxklinik at Heidelberg University Hospital, 69126 Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | - Marc A Schneider
- Translational Research Unit, Thoraxklinik at Heidelberg University Hospital, 69126 Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Michael Thomas
- Department of Thoracic Oncology, Thoraxklinik and National Centre for Tumour Diseases (NCT) at Heidelberg University Hospital, 69126 Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany
| | - Klaus-Robert Müller
- BIFOLD, Berlin, Germany; Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany; Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea; Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 4, 66123 Saarbrücken, Germany
| | - Jan Budczies
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany.
| | - Frederick Klauschen
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany; BIFOLD, Berlin, Germany; German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Germany; Institute of Pathology, LMU München, München, Germany.
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Park JH, Lim JH, Kim S, Kim CH, Choi JS, Lim JH, Kim L, Chang JW, Park D, Lee MW, Kim S, Park IS, Han SH, Shin E, Roh J, Heo J. Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images. J Pathol Clin Res 2024; 10:e70004. [PMID: 39358807 PMCID: PMC11446692 DOI: 10.1002/2056-4538.70004] [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: 07/19/2024] [Revised: 08/27/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024]
Abstract
EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.
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Affiliation(s)
- Jun Hyeong Park
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, Republic of Korea
| | - June Hyuck Lim
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seonhwa Kim
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Chul-Ho Kim
- Department of Otolaryngology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jeong-Seok Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Inha University College of Medicine, Incheon, Republic of Korea
| | - Jun Hyeok Lim
- Division of Pulmonology, Department of Internal Medicine, Inha University College of Medicine, Incheon, Republic of Korea
| | - Lucia Kim
- Department of Pathology, Inha University College of Medicine, Incheon, Republic of Korea
| | - Jae Won Chang
- Department of Otolaryngology-Head and Neck Surgery, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Dongil Park
- Division of Pulmonary, Allergy and Critical Care Medicine, Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Myung-Won Lee
- Division of Hematology and Oncology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Sup Kim
- Department of Radiation Oncology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Il-Seok Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Seung Hoon Han
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Eun Shin
- Department of Pathology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Jin Roh
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jaesung Heo
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
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Prassas I, Clarke B, Youssef T, Phlamon J, Dimitrakopoulos L, Rofaeil A, Yousef GM. Computational pathology: an evolving concept. Clin Chem Lab Med 2024; 62:2148-2155. [PMID: 38646706 DOI: 10.1515/cclm-2023-1124] [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: 10/24/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024]
Abstract
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.
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Affiliation(s)
- Ioannis Prassas
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Timothy Youssef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - Juliana Phlamon
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | | | - Andrew Rofaeil
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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Zhang W, Wang W, Xu Y, Wu K, Shi J, Li M, Feng Z, Liu Y, Zheng Y, Wu H. Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning. J Transl Med 2024; 104:102094. [PMID: 38871058 DOI: 10.1016/j.labinv.2024.102094] [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: 11/16/2023] [Revised: 04/28/2024] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-small cell lung cancer from hematoxylin and eosin-stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with The Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.
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Affiliation(s)
- Wanqiu Zhang
- Department of Pathology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Wei Wang
- Department of Pathology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yao Xu
- Department of Pathology, Wannan Medical College First Affiliated Hospital, Yijishan Hospital, Wuhu, China
| | - Kun Wu
- The Image Processing Center, School of Astronautics, Beihang University, Beijing, China
| | - Jun Shi
- School of Software, Hefei University of Technology, Hefei, China
| | - Ming Li
- Department of Pathology, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhengzhong Feng
- Department of Pathology, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
| | - Yinhua Liu
- Department of Pathology, Wannan Medical College First Affiliated Hospital, Yijishan Hospital, Wuhu, China.
| | - Yushan Zheng
- School of Engineering Medicine, Beijing Advanced Innovation Center on Biomedical Engineering, Beihang University, Beijing, China.
| | - Haibo Wu
- Department of Pathology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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Chen J, Chen A, Yang S, Liu J, Xie C, Jiang H. Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis. Radiother Oncol 2024; 196:110325. [PMID: 38734145 DOI: 10.1016/j.radonc.2024.110325] [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: 11/17/2023] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND PURPOSE We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. MATERIALS AND METHODS We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR. RESULTS We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7. CONCLUSION The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
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Affiliation(s)
- Jinzhan Chen
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Ayun Chen
- Department of Endocrinology, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian 361000, People's Republic of China
| | - Shuwen Yang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Jiaxin Liu
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Congyi Xie
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
| | - Hongni Jiang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
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Overkamp F. [A look into the neighboring discipline: eHealth in oncology]. CHIRURGIE (HEIDELBERG, GERMANY) 2024; 95:451-458. [PMID: 38727743 DOI: 10.1007/s00104-024-02089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/16/2024]
Abstract
Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.
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Affiliation(s)
- Friedrich Overkamp
- OncoConsult Overkamp GmbH, Europaplatz 2, 10557, Berlin, Deutschland.
- onkowissen.de GmbH, Würzburg, Deutschland.
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Weng L, Xu Y, Chen Y, Chen C, Qian Q, Pan J, Su H. Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma. Clin Transl Oncol 2024; 26:1438-1445. [PMID: 38194018 DOI: 10.1007/s12094-023-03366-4] [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: 10/17/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, we proposed a deep learning model using non-invasive CT images to predict EGFR mutation status with robustness and generalizability. METHODS A total of 525 patients were enrolled at the local hospital to serve as the internal data set for model training and validation. In addition, a cohort of 30 patients from the publicly available Cancer Imaging Archive Data Set was selected for external testing. All patients underwent plain chest CT, and their EGFR mutation status labels were categorized as either mutant or wild type. The CT images were analyzed using a self-attention-based ViT-B/16 model to predict the EGFR mutation status, and the model's performance was evaluated. To produce an attention map indicating the suspicious locations of EGFR mutations, Grad-CAM was utilized. RESULTS The ViT deep learning model achieved impressive results, with an accuracy of 0.848, an AUC of 0.868, a sensitivity of 0.924, and a specificity of 0.718 on the validation cohort. Furthermore, in the external test cohort, the model achieved comparable performances, with an accuracy of 0.833, an AUC of 0.885, a sensitivity of 0.900, and a specificity of 0.800. CONCLUSIONS The ViT model demonstrates a high level of accuracy in predicting the EGFR mutation status of lung adenocarcinoma patients. Moreover, with the aid of attention maps, the model can assist clinicians in making informed clinical decisions.
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Affiliation(s)
- Luoqi Weng
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yilun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yuhan Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Chengshui Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Qinqing Qian
- Department of Respiratory Medicine, Shaoxing People's Hospital, Shaoxing, 312000, Zhejiang, China
| | - Jie Pan
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
- Department of Gastroenterology, The Second Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China
| | - Huang Su
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China.
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
- Department of Gastroenterology, The Second Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
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Abbaker N, Minervini F, Guttadauro A, Solli P, Cioffi U, Scarci M. The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review. Front Oncol 2024; 14:1347464. [PMID: 38414748 PMCID: PMC10897973 DOI: 10.3389/fonc.2024.1347464] [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: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
Objectives To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.
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Affiliation(s)
- Namariq Abbaker
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
| | - Fabrizio Minervini
- Division of Thoracic Surgery, Luzerner Kantonsspital, Lucern, Switzerland
| | - Angelo Guttadauro
- Division of Surgery, Università Milano-Bicocca and Istituti Clinici Zucchi, Monza, Italy
| | - Piergiorgio Solli
- Division of Thoracic Surgery, Policlinico S. Orsola-Malpighi, Bologna, Italy
| | - Ugo Cioffi
- Department of Surgery, University of Milan, Milan, Italy
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom
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Aldea M, Ghigna MR, Lacroix-Triki M, Andre F. Unlocking the potential of AI-assisted pathology for molecular alteration screening. Eur J Cancer 2024; 197:113467. [PMID: 38103329 DOI: 10.1016/j.ejca.2023.113467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/19/2023]
Affiliation(s)
- Mihaela Aldea
- Department of Medical Oncology, Gustave Roussy, Villejuif, France; Paris Saclay University, Kremlin-Bicetre, France.
| | | | | | - Fabrice Andre
- Department of Medical Oncology, Gustave Roussy, Villejuif, France; Paris Saclay University, Kremlin-Bicetre, France.
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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: 6] [Impact Index Per Article: 6.0] [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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