1
|
Cellina M, De Padova G, Caldarelli N, Libri D, Cè M, Martinenghi C, Alì M, Papa S, Carrafiello G. Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy. Crit Rev Oncog 2024; 29:1-13. [PMID: 38505877 DOI: 10.1615/critrevoncog.2023050439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
Collapse
Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, Ospedale San Raffaele, Via Olgettina, 60 - 20132 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| |
Collapse
|
2
|
Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
Collapse
Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
| |
Collapse
|
3
|
Chen X, Feng B, Xu K, Chen Y, Duan X, Jin Z, Li K, Li R, Long W, Liu X. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Eur Radiol 2023; 33:6804-6816. [PMID: 37148352 DOI: 10.1007/s00330-023-09690-1] [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: 08/03/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.
Collapse
Affiliation(s)
- Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Kuncai Xu
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Yehang Chen
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Xiaobei Duan
- Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Zhifa Jin
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, 519000, People's Republic of China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China.
| | - Xueguo Liu
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong Province, 518107, People's Republic of China.
| |
Collapse
|
4
|
Sun J, Li F, Yang J, Lin C, Zhou X, Liu N, Zhang B, Song G, Wang W, Huang C, Song Z, Shi L. Pretherapy investigations using highly robust visualized biomarkers from CT imaging by multiple machine-learning techniques toward its prognosis prediction for ALK-inhibitor therapy in NSCLC: a feasibility study. J Cancer Res Clin Oncol 2023; 149:7341-7353. [PMID: 36928998 DOI: 10.1007/s00432-023-04615-3] [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: 10/29/2022] [Accepted: 01/27/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE Molecularly targeted therapy has revolutionized the therapeutic landscape and is emerging as the first-line treatment option for ALK-rearranged non-small-cell lung cancer (NSCLC). In this study, the highly informative and robust biomarkers based on pre-treatment CT images and clinicopathologic features will be developed and validated to predict the prognosis for ALK-inhibitor therapy in NSCLC patients. METHODS A total of 161 ALK-positive NSCLC patients treated with ALK inhibitors were retrospectively collected as training, validation and test sets from multi-center institutions. Cox proportional hazard regression (CPH) penalized by LASSO and random survival forest (RSF) coupled with recursive feature elimination (RFE) were used for radiomics and clinical features identification and model construction. An overlapping post-processing method was extra added to training process to investigate the stronger biomarker on the whole set. RESULTS 123 of the collected cases progressed after a median follow-up of 15.5 months (IQR, 8.3-25.3). The T and M staging, pericardial effusion, age and ALK inhibitor-alectinib were determined as significant predictors in the survival analysis. Furthermore, we visualized the finally retained 4 radiomics feature. The RSF models built from overlapping-processed clinical and radiomics features respectively reached the maximum C-index of 0.68 and 0.75,but the combination of them,radioclinical signature, improved the score to 0.78. The model on the validation and external test datasets yielded the C-index of 0.73 and 0.79, with the iAUC of 0.76 and 0.83, the IBS of 0.119 and 0.112. CONCLUSION With respect to a simple selection strategy of overlapping optimal radiomics and clinical features from different survival models may promote better progression-free survival(PFS) prediction than conventional survival analysis, which provides a potential method for guiding personalized pre-treatment options of NSCLC.
Collapse
Affiliation(s)
- Jingjing Sun
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Feng Li
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Jiantao Yang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Xianglan Zhou
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Na Liu
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Bingqian Zhang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Ge Song
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wenxian Wang
- Department of Medical Oncology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Zhengbo Song
- Department of Clinical Trial, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
| |
Collapse
|
5
|
Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif Intell Med 2023; 141:102572. [PMID: 37295902 DOI: 10.1016/j.artmed.2023.102572] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/16/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
Collapse
Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Danial Javaheri
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
| |
Collapse
|
6
|
Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Semin Roentgenol 2023; 58:184-195. [PMID: 37087139 DOI: 10.1053/j.ro.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
Collapse
|
7
|
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
Collapse
|
8
|
Lei R, Yu Y, Li Q, Yao Q, Wang J, Gao M, Wu Z, Ren W, Tan Y, Zhang B, Chen L, Lin Z, Yao H. Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer. Front Oncol 2022; 12:895177. [PMID: 36505880 PMCID: PMC9727155 DOI: 10.3389/fonc.2022.895177] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 10/18/2022] [Indexed: 11/24/2022] Open
Abstract
Objective The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI). Methods In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared. Results A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients. Conclusion The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.
Collapse
Affiliation(s)
- Ruilin Lei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yunfang Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Faculty of Medicine, Macau University of Science and Technology, Macao, Macao SAR, China
| | - Qingjian Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qinyue Yao
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Jin Wang
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Ming Gao
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Ren
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yujie Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bingzhong Zhang
- Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liliang Chen
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Zhongqiu Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,*Correspondence: Zhongqiu Lin, ; Herui Yao,
| | - Herui Yao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,*Correspondence: Zhongqiu Lin, ; Herui Yao,
| |
Collapse
|
9
|
Fan L, Yang W, Tu W, Zhou X, Zou Q, Zhang H, Feng Y, Liu S. Thoracic Imaging in China: Yesterday, Today, and Tomorrow. J Thorac Imaging 2022; 37:366-373. [PMID: 35980382 PMCID: PMC9592175 DOI: 10.1097/rti.0000000000000670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Thoracic imaging has been revolutionized through advances in technology and research around the world, and so has China. Thoracic imaging in China has progressed from anatomic observation to quantitative and functional evaluation, from using traditional approaches to using artificial intelligence. This article will review the past, present, and future of thoracic imaging in China, in an attempt to establish new accepted strategies moving forward.
Collapse
Affiliation(s)
- Li Fan
- Second Affiliated Hospital, Naval Medical University
| | - Wenjie Yang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenting Tu
- Second Affiliated Hospital, Naval Medical University
| | - Xiuxiu Zhou
- Second Affiliated Hospital, Naval Medical University
| | - Qin Zou
- Second Affiliated Hospital, Naval Medical University
| | - Hanxiao Zhang
- Second Affiliated Hospital, Naval Medical University
| | - Yan Feng
- Second Affiliated Hospital, Naval Medical University
| | - Shiyuan Liu
- Second Affiliated Hospital, Naval Medical University
| |
Collapse
|
10
|
Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics (Basel) 2022; 12:diagnostics12112644. [PMID: 36359485 PMCID: PMC9689810 DOI: 10.3390/diagnostics12112644] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/26/2022] [Accepted: 10/29/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.
Collapse
|
11
|
Hao P, Deng BY, Huang CT, Xu J, Zhou F, Liu ZX, Zhou W, Xu YK. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images. Front Oncol 2022; 12:994285. [PMID: 36338735 PMCID: PMC9630325 DOI: 10.3389/fonc.2022.994285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/26/2022] [Indexed: 12/01/2023] Open
Abstract
PURPOSE To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features. METHOD AND MATERIALS This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves. RESULTS The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort. CONCLUSION Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.
Collapse
Affiliation(s)
- Peng Hao
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bo-Yu Deng
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chan-Tao Huang
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jun Xu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Fang Zhou
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhe-Xing Liu
- School of Biomedical Engineering, Southern Medical Uinversity, Guangzhou, China
| | - Wu Zhou
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi-Kai Xu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| |
Collapse
|
12
|
Terada Y, Takahashi T, Hayakawa T, Ono A, Kawata T, Isaka M, Muramatsu K, Tone K, Kodama H, Imai T, Notsu A, Mori K, Ohde Y, Nakajima T, Sugino T, Takahashi T. Artificial Intelligence-Powered Prediction of ALK Gene Rearrangement in Patients With Non-Small-Cell Lung Cancer. JCO Clin Cancer Inform 2022; 6:e2200070. [PMID: 36162012 DOI: 10.1200/cci.22.00070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Several studies reported the possibility of predicting genetic abnormalities in non-small-cell lung cancer by deep learning (DL). However, there are no data of predicting ALK gene rearrangement (ALKr) using DL. We evaluated the ALKr predictability using the DL platform. MATERIALS AND METHODS We selected 66 ALKr-positive cases and 142 ALKr-negative cases, which were diagnosed by ALKr immunohistochemical staining in our institution from January 2009 to March 2019. We generated virtual slide of 300 slides (150 ALKr-positive slides and 150 ALKr-negative slides) using NanoZoomer. HALO-AI was used to analyze the whole-slide imaging data, and the DenseNet network was used to build the learning model. Of the 300 slides, we randomly assigned 172 slides to the training cohort and 128 slides to the test cohort to ensure no duplication of cases. In four resolutions (16.0/4.0/1.0/0.25 μm/pix), ALKr prediction models were built in the training cohort and ALKr prediction performance was evaluated in the test cohort. We evaluated the diagnostic probability of ALKr by receiver operating characteristic analysis in each ALKr probability threshold (50%, 60%, 70%, 80%, 90%, and 95%). We expected the area under the curve to be 0.64-0.85 in the model of a previous study. Furthermore, in the test cohort data, an expert pathologist also evaluated the presence of ALKr by hematoxylin and eosin staining on whole-slide imaging. RESULTS The maximum area under the curve was 0.73 (50% threshold: 95% CI, 0.65 to 0.82) in the resolution of 1.0 μm/pix. In this resolution, with an ALKr probability of 50% threshold, the sensitivity and specificity were 73% and 73%, respectively. The expert pathologist's sensitivity and specificity in the same test cohort were 13% and 94%. CONCLUSION The ALKr prediction by DL was feasible. Further study should be addressed to improve accuracy of ALKr prediction.
Collapse
Affiliation(s)
- Yukihiro Terada
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Akira Ono
- Division of Thoracic Oncology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Takuya Kawata
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Mitsuhiro Isaka
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Koji Muramatsu
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kiyoshi Tone
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Hiroaki Kodama
- Division of Thoracic Oncology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Toru Imai
- Department of Biostatistics, Clinical Research Center, Shizuoka Cancer Center, Shizuoka, Japan
| | - Akifumi Notsu
- Department of Biostatistics, Clinical Research Center, Shizuoka Cancer Center, Shizuoka, Japan
| | - Keita Mori
- Department of Biostatistics, Clinical Research Center, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yasuhisa Ohde
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | | |
Collapse
|
13
|
Dogan I, Gurbuz M, Paksoy N, Ferhatoglu F, Vatansever S, Saip P, Demirkazik A, Aydiner A. Evaluation of clinicopathological features determining treatment response in patients with ALK mutant NSCLC. Medicine (Baltimore) 2022; 101:e30188. [PMID: 36042659 PMCID: PMC9410652 DOI: 10.1097/md.0000000000030188] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
ALK (anaplastic lymphoma kinase) inhibitors may be used to treat patients with ALK mutant metastatic nonsmall cell cancer (NSCLC). This study aimed to investigate the factors affecting the patients response to treatment with ALK-positive metastatic NSCLC. Data of the patients were investigated retrospectively. Binary regression analysis was performed to evaluate response predictors of treatment. Furthermore, we determined the cut-off value of the ALK-positivity for objective response to the therapy using ROC analysis. A total of 68 patients were included in the research. The median overall survival was observed 39.2 months. The overall response rate was 66.2%. The ratio of ALK positivity (P = .02), gender (P = .04), and the total number of metastatic sites (P = .02) all were detected as predictors of the response to ALK inhibitor in binary regression analysis. ALK inhibitor type (P = .56), primary tumor location (P = .35), pathological subtype (P = .68), de-novo metastatic disease (P = .28), and age (P = .94) were not predictive indicators for response. The cut-off level of ALK positivity was found to be 33% in patients with an objective response. The real-life effectiveness of ALK inhibitors in NSCLC patients with ALK mutations was shown in this research. We determined that having less than 3 metastatic sites, having a high ALK positivity ratio, and being female were all good predictors of ALK inhibitor response.
Collapse
Affiliation(s)
- Izzet Dogan
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
- *Correspondence: Izzet Dogan, MD, Istanbul University Institute of Oncology, Department of Medical Oncology, Çapa/Fatih- Istanbul 34093 (e-mail: )
| | - Mustafa Gurbuz
- Ankara University Faculty of Medicine, Medical Oncology, Ankara, Turkey
| | - Nail Paksoy
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
| | - Ferhat Ferhatoglu
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
| | - Sezai Vatansever
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
| | - Pinar Saip
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
| | - Ahmet Demirkazik
- Ankara University Faculty of Medicine, Medical Oncology, Ankara, Turkey
| | - Adnan Aydiner
- Istanbul University Institute of Oncology, Medical Oncology, Istanbul, Turkey
| |
Collapse
|
14
|
Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
Collapse
Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
| |
Collapse
|
15
|
Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
Collapse
Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| |
Collapse
|
16
|
Jiang Y, Wang Y, Fu S, Chen T, Zhou Y, Zhang X, Chen C, He LN, Du W, Li H, Lin Z, Zhao Y, Yang Y, Zhao H, Fang W, Huang Y, Hong S, Zhang L. A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment. Thorac Cancer 2022; 13:1558-1569. [PMID: 35437945 PMCID: PMC9161316 DOI: 10.1111/1759-7714.14386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022] Open
Abstract
Background Brain metastasis (BM) comprises the most common reason for crizotinib failure in patients with anaplastic lymphoma kinase (ALK)‐rearranged non–small cell lung cancer (NSCLC). We hypothesize that its occurrence could be predicted by a computed tomography (CT)‐based radiomics model, therefore, allowing for selection of enriched patient populations for prevention therapies. Methods A total of 75 eligible patients were enrolled from Sun Yat‐sen University Cancer Center between June 2014 and September 2019. The primary endpoint was brain metastasis‐free survival (BMFS), estimated from the initiation of crizotinib to the date of the occurrence of BM. Patients were randomly divided into two cohorts for model training (n = 51) and validation (n = 24), respectively. A radiomics signature was constructed based on features extracted from chest CT before crizotinib treatment. Clinical model was developed using the Cox proportional hazards model. Log‐rank test was performed to describe the difference of BMFS risk. Results Patients with low radiomics score had significantly longer BMFS than those with higher, both in the training cohort (p = 0.019) and validation cohort (p = 0.048). The nomogram combining smoking history and the radiomics signature showed good performance for the estimation of BMFS, both in the training (concordance index [C‐index], 0.762; 95% confidence interval [CI], 0.663–0.861) and validation cohort (C‐index, 0.724; 95% CI, 0.601–0.847). Conclusion We have developed a CT‐based radiomics model to predict subsequent BM in patients with non‐brain metastatic NSCLC undergoing crizotinib treatment. Selection of an enriched patient population at high BM risk will facilitate the design of clinical trials or strategies to prevent BM.
Collapse
Affiliation(s)
- Yongluo Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixing Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha Fu
- Cellular & Molecular Diagnostics Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixin Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of VIP region, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xuanye Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chen Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-Na He
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Du
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haifeng Li
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zuan Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuanyuan Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yunpeng Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongyun Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shaodong Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| |
Collapse
|
17
|
Li J, Wu J, Zhao Z, Zhang Q, Shao J, Wang C, Qiu Z, Li W. Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review. J Thorac Dis 2022; 13:7021-7033. [PMID: 35070384 PMCID: PMC8743400 DOI: 10.21037/jtd-21-864] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 08/30/2021] [Indexed: 02/05/2023]
Abstract
Objective In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field. Background Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients. Methods We searched literature from 2000 through July 31th, 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as “artificial intelligence”, “AI”, “deep learning”, “lung cancer”, “NSCLC”, “SCLC” were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions. Conclusions AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients’ information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.
Collapse
Affiliation(s)
- Jingwei Li
- Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China.,West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Jiayang Wu
- West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhehao Zhao
- West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Qiran Zhang
- West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Chengdi Wang
- Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Zhixin Qiu
- Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
18
|
The application of radiomics in predicting gene mutations in cancer. Eur Radiol 2022; 32:4014-4024. [DOI: 10.1007/s00330-021-08520-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/11/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022]
|
19
|
Wang C, Xu X, Shao J, Zhou K, Zhao K, He Y, Li J, Guo J, Yi Z, Li W. Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images. JOURNAL OF ONCOLOGY 2021; 2021:5499385. [PMID: 35003258 PMCID: PMC8741343 DOI: 10.1155/2021/5499385] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 10/25/2021] [Accepted: 11/14/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. METHODS Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. RESULTS The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94-0.98), 0.80 (95% CI: 0.72-0.88), and 0.73 (95% CI: 0.63-0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). CONCLUSION In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.
Collapse
Affiliation(s)
- Chengdi Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Xiuyuan Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Kai Zhou
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Kefu Zhao
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Yanqi He
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Jixiang Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| |
Collapse
|
20
|
Zhan X, Long H, Gou F, Duan X, Kong G, Wu J. A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer. SENSORS 2021; 21:s21237996. [PMID: 34884000 PMCID: PMC8659811 DOI: 10.3390/s21237996] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 12/15/2022]
Abstract
In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.
Collapse
Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
- Correspondence: (H.L.); (J.W.)
| | - Fangfang Gou
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
| | - Xun Duan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Guangqian Kong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Jia Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
- Research Center for Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia
- Correspondence: (H.L.); (J.W.)
| |
Collapse
|
21
|
Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, Su Q, Dai D, Xu W. Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma. Front Oncol 2021; 11:709137. [PMID: 34367993 PMCID: PMC8340023 DOI: 10.3389/fonc.2021.709137] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/01/2021] [Indexed: 12/14/2022] Open
Abstract
Objective The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). Methods Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET. Results The AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SECT (p<0.05). Conclusion The stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.
Collapse
Affiliation(s)
- Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Yingchao Song
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Yiwen Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Lei Zhu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Qian Su
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Dong Dai
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin, China
| |
Collapse
|
22
|
Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors. Sci Rep 2021; 11:9984. [PMID: 33976268 PMCID: PMC8113313 DOI: 10.1038/s41598-021-88239-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan–Meier (K–M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K–M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.
Collapse
|