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Chu Y, Zhang S, Wan W, Yang J, Zhang Y, Nie C, Xing W, Tong S, Liu J, Tian G, Wang B, Ji L. Pathological image profiling identifies onco-microbial, tumor immune microenvironment, and prognostic subtypes of colorectal cancer. APMIS 2024; 132:416-429. [PMID: 38403979 DOI: 10.1111/apm.13387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/02/2024] [Indexed: 02/27/2024]
Abstract
Histology slide, tissue microbes, and the host gene expression can be independent prognostic factors of colorectal cancer (CRC), but the underlying associations and biological significance of these multimodal omics remain unknown. Here, we comprehensively profiled the matched pathological images, intratumoral microbes, and host gene expression characteristics in 527 patients with CRC. By clustering these patients based on histology slide features, we classified the patients into two histology slide subtypes (HSS). Onco-microbial community and tumor immune microenvironment (TIME) were also significantly different between the two subtypes (HSS1 and HSS2) of patients. Furthermore, variation in intratumoral microbes-host interaction was associated with the prognostic heterogeneity between HSS1 and HSS2. This study proposes a new CRC classification based on pathological image features and elucidates the process by which tumor microbes-host interactions are reflected in pathological images through the TIME.
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Affiliation(s)
- Yuwen Chu
- School of Electrical & Information Engineering, Anhui University of Technology, Anhui, China
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Shuo Zhang
- School of management, Harbin Institute of Technology, Harbin, China
| | - Wei Wan
- Department of Colorectal and Anal Surgery, Yidu Central Hospital of Weifang, Shandong, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yumeng Zhang
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Chuanqi Nie
- School of Electrical & Information Engineering, Anhui University of Technology, Anhui, China
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Weipeng Xing
- School of Electrical & Information Engineering, Anhui University of Technology, Anhui, China
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Shanhe Tong
- School of Electrical & Information Engineering, Anhui University of Technology, Anhui, China
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jinyang Liu
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Bing Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Anhui, China
| | - Lei Ji
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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Jackson A, Hua CH, Olch A, Yorke ED, Rancati T, Milano MT, Constine LS, Marks LB, Bentzen SM. Reporting Standards for Complication Studies of Radiation Therapy for Pediatric Cancer: Lessons From PENTEC. Int J Radiat Oncol Biol Phys 2024; 119:697-707. [PMID: 38760117 DOI: 10.1016/j.ijrobp.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 01/14/2024] [Accepted: 02/08/2024] [Indexed: 05/19/2024]
Abstract
The major aim of Pediatric Normal Tissue Effects in the Clinic (PENTEC) was to synthesize quantitative published dose/-volume/toxicity data in pediatric radiation therapy. Such systematic reviews are often challenging because of the lack of standardization and difficulty of reporting outcomes, clinical factors, and treatment details in journal articles. This has clinical consequences: optimization of treatment plans must balance between the risks of toxicity and local failure; counseling patients and their parents requires knowledge of the excess risks encountered after a specific treatment. Studies addressing outcomes after pediatric radiation therapy are particularly challenging because: (a) survivors may live for decades after treatment, and the latency time to toxicity can be very long; (b) children's maturation can be affected by radiation, depending on the developmental status of the organs involved at time of treatment; and (c) treatment regimens frequently involve chemotherapies, possibly modifying and adding to the toxicity of radiation. Here we discuss: basic reporting strategies to account for the actuarial nature of the complications; the reporting of modeling of abnormal development; and the need for standardized, comprehensively reported data sets and multivariate models (ie, accounting for the simultaneous effects of radiation dose, age, developmental status at time of treatment, and chemotherapy dose). We encourage the use of tools that facilitate comprehensive reporting, for example, electronic supplements for journal articles. Finally, we stress the need for clinicians to be able to trust artificial intelligence models of outcome of radiation therapy, which requires transparency, rigor, reproducibility, and comprehensive reporting. Adopting the reporting methods discussed here and in the individual PENTEC articles will increase the clinical and scientific usefulness of individual reports and associated pooled analyses.
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Affiliation(s)
- Andrew Jackson
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York.
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Arthur Olch
- Radiation Oncology Department, University of Southern California and Children's Hospital, Los Angeles, California
| | - Ellen D Yorke
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Michael T Milano
- Department of Radiation Oncology, University of Rochester Medical Center, Wilmot Cancer Institute, Rochester, New York
| | - Louis S Constine
- Department of Radiation Oncology, University of Rochester Medical Center, Wilmot Cancer Institute, Rochester, New York; Pediatrics, University of Rochester Medical Center, Wilmot Cancer Institute, Rochester, New York
| | - Lawrence B Marks
- Department of Radiation Oncology and Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Soren M Bentzen
- Department of Epidemiology and Public Health, University of Maryland, Baltimore, Maryland
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Ahmad S, Raza K. An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives. J Drug Target 2024:1-12. [PMID: 38662768 DOI: 10.1080/1061186x.2024.2347358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024]
Abstract
There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.
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Affiliation(s)
- Shaban Ahmad
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
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Zhu E, Zhang L, Liu Y, Ji T, Dai J, Tang R, Wang J, Hu C, Chen K, Yu Q, Lu Q, Ai Z. Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning. Clin Transl Oncol 2024:10.1007/s12094-024-03459-8. [PMID: 38678522 DOI: 10.1007/s12094-024-03459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients. OBJECTIVE To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL). METHODS Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses. RESULTS Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST. CONCLUSIONS Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.
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Affiliation(s)
- Enzhao Zhu
- School of Medicine, Tongji University, Shanghai, China
| | - Linmei Zhang
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Prosthodontics, Stomatological Hospital and Dental School, Tongji University, Shanghai, 200072, China
| | - Yixian Liu
- Department of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Tianyu Ji
- School of Medicine, Tongji University, Shanghai, China
| | - Jianmeng Dai
- School of Medicine, Tongji University, Shanghai, China
| | - Ruichen Tang
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Jiayi Wang
- School of Medicine, Tongji University, Shanghai, China
| | - Chunyu Hu
- Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China
| | - Kai Chen
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Qianyi Yu
- School of Medicine, Tongji University, Shanghai, China
| | - Qiuyi Lu
- School of Medicine, Tongji University, Shanghai, China
| | - Zisheng Ai
- Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China.
- Clinical Research Center for Mental Disorders, School of Medicine, Chinese-German Institute of Mental Health, Shanghai Pudong New Area Mental Health Center, Tongji University, Shanghai, China.
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胡 伦, 夏 威, 李 琼, 高 欣. [Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2024; 41:205-212. [PMID: 38686399 PMCID: PMC11058493 DOI: 10.7507/1001-5515.202309060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/08/2024] [Indexed: 05/02/2024]
Abstract
Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as "image transformation to image restoration" to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network's feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.
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Affiliation(s)
- 伦瑜 胡
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - 威 夏
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - 琼 李
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
| | - 欣 高
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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Zeng J, Song D, Li K, Cao F, Zheng Y. Deep learning model for predicting postoperative survival of patients with gastric cancer. Front Oncol 2024; 14:1329983. [PMID: 38628668 PMCID: PMC11018873 DOI: 10.3389/fonc.2024.1329983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Background Prognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients. Methods Eleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI). Results It was discovered that the DeepSurv model predicted postoperative gastric cancer patients' overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer. Conclusion In this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.
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Affiliation(s)
| | | | | | | | - Yongbin Zheng
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Saeki Y, Maki N, Nemoto T, Inada K, Minami K, Tamura R, Imamura G, Cho-Isoda Y, Kitazawa S, Kojima H, Yoshikawa G, Sato Y. Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array. Lung Cancer 2024; 190:107514. [PMID: 38447302 DOI: 10.1016/j.lungcan.2024.107514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/12/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.
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Affiliation(s)
- Yusuke Saeki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Naoki Maki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Takahiro Nemoto
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Katsushige Inada
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Kosuke Minami
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Ryo Tamura
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan; Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Ibaraki, Japan; Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Gaku Imamura
- Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Yukiko Cho-Isoda
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Shinsuke Kitazawa
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Hiroshi Kojima
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan; Ibaraki Clinical Education and Training Center, University of Tsukuba Hospital, Ibaraki, Japan
| | - Genki Yoshikawa
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Materials Science and Engineering, Graduate School of Pure and Applied Science, University of Tsukuba, Ibaraki, Japan
| | - Yukio Sato
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan.
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Coroller T, Sahiner B, Amatya A, Gossmann A, Karagiannis K, Moloney C, Samala RK, Santana-Quintero L, Solovieff N, Wang C, Amiri-Kordestani L, Cao Q, Cha KH, Charlab R, Cross FH, Hu T, Huang R, Kraft J, Krusche P, Li Y, Li Z, Mazo I, Paul R, Schnakenberg S, Serra P, Smith S, Song C, Su F, Tiwari M, Vechery C, Xiong X, Zarate JP, Zhu H, Chakravartty A, Liu Q, Ohlssen D, Petrick N, Schneider JA, Walderhaug M, Zuber E. Methodology for Good Machine Learning with Multi-Omics Data. Clin Pharmacol Ther 2024; 115:745-757. [PMID: 37965805 DOI: 10.1002/cpt.3105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
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Affiliation(s)
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Anup Amatya
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Alexej Gossmann
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Konstantinos Karagiannis
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Ravi K Samala
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Luis Santana-Quintero
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Solovieff
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Craig Wang
- Novartis Pharma AG, Rotkreuz, Switzerland
| | - Laleh Amiri-Kordestani
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qian Cao
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Kenny H Cha
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rosane Charlab
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Frank H Cross
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tingting Hu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ruihao Huang
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffrey Kraft
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Yutong Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Zheng Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Ilya Mazo
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul Paul
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Paolo Serra
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Sean Smith
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chi Song
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Fei Su
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Mohit Tiwari
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Colin Vechery
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Xin Xiong
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Hao Zhu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Qi Liu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - David Ohlssen
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Julie A Schneider
- Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Walderhaug
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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10
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Zeng J, Li K, Cao F, Zheng Y. The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study. Sci Rep 2024; 14:6609. [PMID: 38504089 PMCID: PMC10951333 DOI: 10.1038/s41598-024-56701-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 03/09/2024] [Indexed: 03/21/2024] Open
Abstract
Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.
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Affiliation(s)
- Junjie Zeng
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China
| | - Kai Li
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China
| | - Fengyu Cao
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China
| | - Yongbin Zheng
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
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11
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Pai S, Bontempi D, Hadzic I, Prudente V, Sokač M, Chaunzwa TL, Bernatz S, Hosny A, Mak RH, Birkbak NJ, Aerts HJWL. Foundation model for cancer imaging biomarkers. NAT MACH INTELL 2024; 6:354-367. [PMID: 38523679 PMCID: PMC10957482 DOI: 10.1038/s42256-024-00807-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/08/2024] [Indexed: 03/26/2024]
Abstract
Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.
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Affiliation(s)
- Suraj Pai
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Ibrahim Hadzic
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Vasco Prudente
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Mateo Sokač
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Tafadzwa L. Chaunzwa
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Simon Bernatz
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
| | - Raymond H. Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
| | - Nicolai J. Birkbak
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Hugo J. W. L. Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA USA
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
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12
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Ruan D, Fang J, Teng X. Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer. Q J Nucl Med Mol Imaging 2024; 68:70-83. [PMID: 35420272 DOI: 10.23736/s1824-4785.22.03441-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy. METHODS We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and support vector machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). RESULTS The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively. CONCLUSIONS Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.
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Affiliation(s)
- Dan Ruan
- Department of Nuclear Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Fujian, China -
| | - Janyao Fang
- Department of Nuclear Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Fujian, China
| | - Xinyu Teng
- Department of Nuclear Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Fujian, China
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13
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Göndöcs D, Dörfler V. AI in medical diagnosis: AI prediction & human judgment. Artif Intell Med 2024; 149:102769. [PMID: 38462271 DOI: 10.1016/j.artmed.2024.102769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/02/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
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Affiliation(s)
| | - Viktor Dörfler
- University of Strathclyde Business School, United Kingdom.
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14
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Xu N, Wang J, Dai G, Lu T, Li S, Deng K, Song J. EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC. J Imaging Inform Med 2024:10.1007/s10278-024-01022-z. [PMID: 38361006 DOI: 10.1007/s10278-024-01022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840-0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.
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Affiliation(s)
- Nan Xu
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiajun Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Gang Dai
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230036, China
| | - Tao Lu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230036, China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China.
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15
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Boubnovski Martell M, Linton-Reid K, Hindocha S, Chen M, Moreno P, Álvarez-Benito M, Salvatierra Á, Lee R, Posma JM, Calzado MA, Aboagye EO. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. NPJ Precis Oncol 2024; 8:28. [PMID: 38310164 PMCID: PMC10838282 DOI: 10.1038/s41698-024-00502-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/04/2024] [Indexed: 02/05/2024] Open
Abstract
The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
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Affiliation(s)
| | | | - Sumeet Hindocha
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
| | - Mitchell Chen
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Paula Moreno
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Departamento de Cirugía Toráxica y Trasplante de Pulmón, Hospital Universitario Reina Sofía, Córdoba, 14014, Spain
| | - Marina Álvarez-Benito
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Ángel Salvatierra
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Richard Lee
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Joram M Posma
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Marco A Calzado
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain.
- Departamento de Biología Celular, Fisiología e Inmunología, Universidad de Córdoba, Córdoba, 14014, Spain.
| | - Eric O Aboagye
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK.
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16
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Demircioğlu A. The effect of data resampling methods in radiomics. Sci Rep 2024; 14:2858. [PMID: 38310165 PMCID: PMC10838284 DOI: 10.1038/s41598-024-53491-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/05/2024] Open
Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
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17
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Zhong R, Gao T, Li J, Li Z, Tian X, Zhang C, Lin X, Wang Y, Gao L, Hu K. The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis. Front Oncol 2024; 14:1346010. [PMID: 38371616 PMCID: PMC10869611 DOI: 10.3389/fonc.2024.1346010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/18/2024] [Indexed: 02/20/2024] Open
Abstract
Background Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. Objective This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. Results A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations). Conclusions Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.
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Affiliation(s)
- Ruikang Zhong
- Beijing University of Chinese Medicine, Beijing, China
| | - Tangke Gao
- Beijing University of Chinese Medicine, Beijing, China
| | - Jinghua Li
- Beijing University of Chinese Medicine, Beijing, China
| | - Zexing Li
- Beijing University of Chinese Medicine, Beijing, China
| | - Xue Tian
- Guang'an Men Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chi Zhang
- Beijing University of Chinese Medicine, Beijing, China
| | - Ximing Lin
- Beijing University of Chinese Medicine, Beijing, China
| | - Yuehui Wang
- Beijing University of Chinese Medicine, Beijing, China
| | - Lei Gao
- Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Kaiwen Hu
- Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
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18
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Brandenberger D, White LM. Radiomics in Musculoskeletal Tumors. Semin Musculoskelet Radiol 2024; 28:49-61. [PMID: 38330970 DOI: 10.1055/s-0043-1776428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.
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Affiliation(s)
- Daniel Brandenberger
- Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
- Institut für Radiologie und Nuklearmedizin, Kantonsspital Baselland, Liestal, Switzerland
- Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Lawrence M White
- Department of Medical Imaging, Musculoskeletal Imaging, University of Toronto, Toronto, Ontario, Canada
- Toronto Joint Department of Medical Imaging, University Health Network, Sinai Health System, and Women's College Hospital, Mount Sinai Hospital, Toronto, Ontario, Canada
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20
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Chen YF, Chawla S, Mousa-Doust D, Nichol A, Ng R, Isaac KV. Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction. Plast Reconstr Surg Glob Open 2024; 12:e5599. [PMID: 38322813 PMCID: PMC10846766 DOI: 10.1097/gox.0000000000005599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/15/2023] [Indexed: 02/08/2024]
Abstract
Background Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65-0.80]; RF 0.77 AUC (95% CI, 0.74-0.81); EN 0.77 AUC (95% CI, 0.73-0.81); logistic lasso 0.76 AUC (95% CI, 0.72-0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730-0.858)]. Conclusion A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.
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Affiliation(s)
- Yi-Fu Chen
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sahil Chawla
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Dorsa Mousa-Doust
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alan Nichol
- Department of Radiation Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Raymond Ng
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kathryn V Isaac
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
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21
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Schön F, Kieslich A, Nebelung H, Riediger C, Hoffmann RT, Zwanenburg A, Löck S, Kühn JP. Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma. Sci Rep 2024; 14:590. [PMID: 38182664 PMCID: PMC10770355 DOI: 10.1038/s41598-023-50451-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024] Open
Abstract
To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.
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Affiliation(s)
- Felix Schön
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
| | - Aaron Kieslich
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
| | - Heiner Nebelung
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Carina Riediger
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Ralf-Thorsten Hoffmann
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Alex Zwanenburg
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Steffen Löck
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Jens-Peter Kühn
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
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22
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Nie T, Chen Z, Cai J, Ai S, Xue X, Yuan M, Li C, Shi L, Liu Y, Verma V, Bi J, Han G, Yuan Z. Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy. Radiother Oncol 2024; 190:110047. [PMID: 38070685 DOI: 10.1016/j.radonc.2023.110047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023]
Abstract
PURPOSE This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy. MATERIALS AND METHODS This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting. RESULTS HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts. CONCLUSIONS In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.
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Affiliation(s)
- Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zien Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China; School of Biomedical Engineering, South-Central Minzu University, Wuhan, PR China
| | - Jun Cai
- Department of Oncology, First Affiliated Hospital of Yangtze University, Nanhuan Road, Jingzhou, Hubei, PR China
| | - Shuangquan Ai
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China; School of Biomedical Engineering, South-Central Minzu University, Wuhan, PR China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Mengting Yuan
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Chao Li
- Department of Oncology, First Affiliated Hospital of Yangtze University, Nanhuan Road, Jingzhou, Hubei, PR China
| | - Liting Shi
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Vivek Verma
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Jianping Bi
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Guang Han
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.
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Ohki T, Kunii N, Chao ZC. Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0. Rev Neurosci 2023; 34:839-868. [PMID: 36960579 DOI: 10.1515/revneuro-2022-0137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/26/2023] [Indexed: 03/25/2023]
Abstract
There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.
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Affiliation(s)
- Takefumi Ohki
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Naoto Kunii
- Department of Neurosurgery, The University of Tokyo, Tokyo 113-0033, Japan
| | - Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Dudas D, Saghand PG, Dilling TJ, Perez BA, Rosenberg SA, El Naqa I. Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2023:S0360-3016(23)08185-3. [PMID: 38056778 DOI: 10.1016/j.ijrobp.2023.11.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term local control of some lesions. In this study, we analyzed such patients using explainable deep learning techniques and consequently proposed appropriate treatment planning criteria. These novel criteria could help planners achieve optimized treatment plans for maximal local control. METHODS AND MATERIALS A total of 535 patients treated with 50 Gy/5 fractions were used to develop a novel deep learning local response model. A multimodality approach, incorporating computed tomography images, 3-dimensional dose distribution, and patient demographics, combined with a discrete-time survival model, was applied to predict time to failure and the probability of local control. Subsequently, an integrated gradient-weighted class activation mapping method was used to identify the most significant dose-volume metrics predictive of local failure and their optimal cut-points. RESULTS The model was cross-validated, showing an acceptable performance (c-index: 0.72, 95% CI, 0.68-0.75); the testing c-index was 0.69. The model's spatial attention was concentrated mostly in the tumors' periphery (planning target volume [PTV] - internal gross target volume [IGTV]) region. Statistically significant dose-volume metrics in improved local control were BED Dnear-min ≥ 103.8 Gy in IGTV (hazard ratio [HR], 0.31; 95% CI, 015-0.63), V104 ≥ 98% in IGTV (HR, 0.30; 95% CI, 0.15-0.60), gEUD ≥ 103.8 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.50), and Dmean ≥ 104.5 Gy in PTV-IGTV (HR, 0.25; 95% CI, 0.12-0.51). CONCLUSIONS Deep learning-identified dose-volume metrics have shown significant prognostic power (log-rank, P = .003) and could be used as additional actionable criteria for treatment planning in NSCLC stereotactic body radiation therapy patients receiving 50 Gy in 5 fractions. Although our data do not confirm or refute that a significantly higher BED for the prescription dose is necessary for tumor control in NSCLC, it might be clinically effective to escalate the nominal prescribed dose from BED 100 to 105 Gy.
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Affiliation(s)
| | | | - Thomas J Dilling
- Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Bradford A Perez
- Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Stephen A Rosenberg
- Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Issam El Naqa
- Departments of Machine Learning; Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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Felefly T, Roukoz C, Fares G, Achkar S, Yazbeck S, Meyer P, Kordahi M, Azoury F, Nasr DN, Nasr E, Noël G, Francis Z. An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection. J Digit Imaging 2023; 36:2335-2346. [PMID: 37507581 PMCID: PMC10584786 DOI: 10.1007/s10278-023-00886-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and surgical procedures. We explore herein the performance and interpretability of an MRI-radiomics variational quantum neural network (QNN) using a quantum-annealing mutual-information (MI) feature selection approach. We retrospectively included 423 patients with HGG and LBM (> 2 cm) who had a contrast-enhanced T1-weighted (CE-T1) MRI between 2012 and 2019. After exclusion, 72 HGG and 129 LBM were kept. Tumors were manually segmented, and a 5-mm peri-tumoral ring was created. MRI images were pre-processed, and 1813 radiomic features were extracted. A set of best features based on MI was selected. MI and conditional-MI were embedded into a quadratic unconstrained binary optimization (QUBO) formulation that was mapped to an Ising-model and submitted to D'Wave's quantum annealer to solve for the best combination of 10 features. The 10 selected features were embedded into a 2-qubits QNN using PennyLane library. The model was evaluated for balanced-accuracy (bACC) and area under the receiver operating characteristic curve (ROC-AUC) on the test set. The model performance was benchmarked against two classical models: dense neural networks (DNN) and extreme gradient boosting (XGB). Shapley values were calculated to interpret sample-wise predictions on the test set. The best 10-feature combination included 6 tumor and 4 ring features. For QNN, DNN, and XGB, respectively, training ROC-AUC was 0.86, 0.95, and 0.94; test ROC-AUC was 0.76, 0.75, and 0.79; and test bACC was 0.74, 0.73, and 0.72. The two most influential features were tumor Laplacian-of-Gaussian-GLRLM-Entropy and sphericity. We developed an accurate interpretable QNN model with quantum-informed feature selection to differentiate between LBM and HGG on CE-T1 brain MRI. The model performance is comparable to state-of-the-art classical models.
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Affiliation(s)
- Tony Felefly
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon.
- ICube Laboratory, University of Strasbourg, Strasbourg, France.
- Radiation Oncology Department, Hôtel-Dieu de Lévis, Lévis, QC, Canada.
| | - Camille Roukoz
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Georges Fares
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
- Physics Department, Saint Joseph University, Beirut, Lebanon
| | - Samir Achkar
- Radiation Oncology Department, Gustave Roussy Cancer Campus, 94805, Villejuif, France
| | - Sandrine Yazbeck
- Department of Radiology, University of Maryland School of Medicine, 655 W Baltimore St S, Baltimore, MD, 21201, USA
| | - Philippe Meyer
- Medical Physics Department, Institut de Cancérologie de Strasbourg (ICANS), 67200, Strasbourg, France
- IMAGeS Unit, IRIS Platform, ICube, University of Strasbourg, 67085, Strasbourg Cedex, France
| | | | - Fares Azoury
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Dolly Nehme Nasr
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Elie Nasr
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Georges Noël
- Radiotherapy Department, Institut de Cancérologie de Strasbourg (ICANS), 67200, Strasbourg, France
- Radiobiology Department, IMIS Unit, IRIS Platform, ICube, University of Strasbourg, 67085, Strasbourg Cedex, France
- Faculty of Medicine, University of Strasbourg, 67000, Strasbourg, France
| | - Ziad Francis
- Physics Department, Saint Joseph University, Beirut, Lebanon
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Ma Y, Li Q. An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning. Cancer Radiother 2023; 27:705-711. [PMID: 37932182 DOI: 10.1016/j.canrad.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 11/08/2023]
Abstract
PURPOSE The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-treated CT images. MATERIALS AND METHODS This study enrolled 462 patients with NSCLC who received chemoradiation. On the basis of pretreated CT images, we developed three models to compare the prediction of chemoradiation: intratumoral, peritumoral and combined regions. To further illustrate each model, we established different feature integration methods: a) radiomics model with 1500 features; b) deep learning model with a multiple instance learning algorithm; c) integrated model by integrating radiomic and deep learning features. For radiomics and integrated models, support vector machine and the least absolute shrinkage and selection operator were used to extract and select features. Transfer learning and max pooling algorithms were used to identify high informative features in deep learning models. We applied ten-fold cross validation in model training and testing. RESULTS The best area under the curve (AUC) of intratumoral, peritumoral and combined models were 0.89 (95% CI, 0.74-0.93), 0.86 (95% CI, 0.75-0.92) and 0.92 (95% CI, 0.81-0.95), respectively. It indicated the importance of the peritumoral region for treatment response prediction and should be used in combination with the intratumoral region. Integrated models gave better results than models with radiomics and deep learning features alone in all regions of interest and radiomics models outperformed deep learning models in any comparative models. CONCLUSIONS The model that integrate radiomic and deep learning features and combined intra- and peritumoral regions provide valuable information in predicting treatment response of chemoradiation. It can help oncologists customize personalized clinical treatment plans for NSCLC patients.
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Affiliation(s)
- Y Ma
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
| | - Q Li
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
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Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking. AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients. METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis. RESULTS 3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%. CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
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Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Hagar S Mahmoud
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Vincenzo K Wong
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Carlos Ignacio Gonzalez Baerga
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Miti Parikh
- Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
| | - Sherif B Elsherif
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Roisman LC, Kian W, Anoze A, Fuchs V, Spector M, Steiner R, Kassel L, Rechnitzer G, Fried I, Peled N, Bogot NR. Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer. NPJ Precis Oncol 2023; 7:125. [PMID: 37990050 PMCID: PMC10663598 DOI: 10.1038/s41698-023-00473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
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Affiliation(s)
- Laila C Roisman
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
- Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Waleed Kian
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
- The Institute of Oncology, Assuta Ashdod, Ashdod, Israel
| | - Alaa Anoze
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Vered Fuchs
- Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maria Spector
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Roee Steiner
- The Institute for Nuclear Medicine, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Levi Kassel
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Gilad Rechnitzer
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Iris Fried
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Nir Peled
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
| | - Naama R Bogot
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
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Wang Y, Zhu J, Lu X, Cheng W, Xu L, Wang X, Wang J, Yang J, Niu F, Chen W, Sun X, Li W, Wen Z, Guan H, Yan F. Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells. Medicine (Baltimore) 2023; 102:e35830. [PMID: 37932991 PMCID: PMC10627624 DOI: 10.1097/md.0000000000035830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/06/2023] [Indexed: 11/08/2023] Open
Abstract
To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.
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Affiliation(s)
- Yang Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, P.R. China
| | - Junkai Zhu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Xiaofan Lu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Wenxuan Cheng
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Li Xu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Xin Wang
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Jian Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, P.R. China
| | - Jun Yang
- Department of Pathology, Nanjing Drum Tower Hospital, Nanjing, P.R. China
| | - Fengnan Niu
- Department of Pathology, Nanjing Drum Tower Hospital, Nanjing, P.R. China
| | - Wenping Chen
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Xu Sun
- Université Paris Cité, Paris, France
| | - Wenyi Li
- Suzhou Science & Technology Town Hospital, Suzhou, P.R. China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, Guangzhou, Guangdong, P.R. China
| | - Haitao Guan
- Department of Endocrinology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Fangrong Yan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
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Saha PK, Nadeem SA, Comellas AP. A Survey on Artificial Intelligence in Pulmonary Imaging. Wiley Interdiscip Rev Data Min Knowl Discov 2023; 13:e1510. [PMID: 38249785 PMCID: PMC10796150 DOI: 10.1002/widm.1510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 06/21/2023] [Indexed: 01/23/2024]
Abstract
Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
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Affiliation(s)
- Punam K Saha
- Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242
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Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
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Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
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Shen XM, Mao L, Yang ZY, Chai ZK, Sun TG, Xu Y, Sun ZJ. Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging. Oral Dis 2023; 29:3325-3336. [PMID: 36520552 DOI: 10.1111/odi.14474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/23/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Imaging interpretation of the benignancy or malignancy of parotid gland tumors (PGTs) is a critical consideration prior to surgery in view of therapeutic and prognostic values of such discrimination. This study investigates the application of a deep learning-based method for preoperative stratification of PGTs. MATERIALS AND METHODS Using the 3D DenseNet-121 architecture and a dataset consisting of 117 volumetric arterial-phase contrast-enhanced CT scans, we developed a binary classifier for PGT distinction and tested it. We compared the discriminative performance of the model on the test set to that of 12 junior and 12 senior head and neck clinicians. Besides, potential clinical utility of the model was evaluated by measuring changes in unassisted and model-assisted performance of junior clinicians. RESULTS The model finally reached the sensitivity, specificity, PPV, NPV, F1-score of 0.955 (95% CI 0.751-0.998), 0.667 (95% CI 0.241-0.940), 0.913 (95% CI 0.705-0.985), 0.800 (95% CI 0.299-0.989) and 0.933, respectively, comparable to that of practicing clinicians. Furthermore, there were statistically significant increases in junior clinicians' specificity, PPV, NPV and F1-score in differentiating benign from malignant PGTs when unassisted and model-assisted performance of junior clinicians were compared. CONCLUSION Our results provide evidence that deep learning-based method may offer assistance for PGT's binary distinction.
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Affiliation(s)
- Xue-Meng Shen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Liang Mao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhi-Yi Yang
- School of Computer Science, Wuhan University, Wuhan, China
| | - Zi-Kang Chai
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Ting-Guan Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Yongchao Xu
- School of Computer Science, Wuhan University, Wuhan, China
| | - Zhi-Jun Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
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Li X, Yu X, Tian D, Liu Y, Li D. Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning. Med Phys 2023; 50:7049-7059. [PMID: 37722701 DOI: 10.1002/mp.16748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 08/17/2023] [Accepted: 09/08/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics. PURPOSE The purpose of this study was to explore the potential application value of pathomics signatures. METHODS Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links. RESULTS The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.
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Affiliation(s)
- Xiaoyuan Li
- Department of Traditional Chinese Medicine, The affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaoqian Yu
- Department of Dermatology, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong, China
| | - Duanliang Tian
- Department of Tuina, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, Shandong, China
| | - Yiran Liu
- Department of Traditional Chinese Medicine, Weifang Medical College, Weifang, Shandong, China
| | - Ding Li
- Department of Traditional Chinese Medicine, The affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Lee T, Lee KH, Lee JH, Park S, Kim YT, Goo JM, Kim H. Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study. Eur Radiol 2023:10.1007/s00330-023-10306-x. [PMID: 37861801 DOI: 10.1007/s00330-023-10306-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVES To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).
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Affiliation(s)
- Taehee Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Jong Hyuk Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Samina Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Seoul National University Cancer Research Institute, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Seoul National University Cancer Research Institute, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
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Starke S, Zwanenburg A, Leger K, Lohaus F, Linge A, Kalinauskaite G, Tinhofer I, Guberina N, Guberina M, Balermpas P, von der Grün J, Ganswindt U, Belka C, Peeken JC, Combs SE, Boeke S, Zips D, Richter C, Troost EGC, Krause M, Baumann M, Löck S. Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients. Cancers (Basel) 2023; 15:4897. [PMID: 37835591 PMCID: PMC10571894 DOI: 10.3390/cancers15194897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.
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Affiliation(s)
- Sebastian Starke
- Helmholtz-Zentrum Dresden–Rossendorf, Department of Information Services and Computing, 01328 Dresden, Germany
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
| | - Alex Zwanenburg
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
| | - Karoline Leger
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Fabian Lohaus
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Annett Linge
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
| | - Goda Kalinauskaite
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Berlin, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Inge Tinhofer
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Berlin, 10117 Berlin, Germany; (G.K.); (I.T.)
- Department of Radiooncology and Radiotherapy, Charité University Hospital, 10117 Berlin, Germany
| | - Nika Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Essen, 45147 Essen, Germany (M.G.)
- Department of Radiotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Maja Guberina
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Essen, 45147 Essen, Germany (M.G.)
- Department of Radiotherapy, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
| | - Panagiotis Balermpas
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Frankfurt, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Jens von der Grün
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Frankfurt, 60596 Frankfurt, Germany; (P.B.); (J.v.d.G.)
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, 60596 Frankfurt, Germany
| | - Ute Ganswindt
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
- Department of Radiation Oncology, Medical University of Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria
| | - Claus Belka
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Clinical Cooperation Group, Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
| | - Jan C. Peeken
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Stephanie E. Combs
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Munich, 80336 Munich, Germany; (U.G.); (C.B.); (J.C.P.); (S.E.C.)
- Department of Radiation Oncology, Technische Universität München, 81675 Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Simon Boeke
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Tübingen, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Daniel Zips
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Tübingen, 72076 Tübingen, Germany; (S.B.); (D.Z.)
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, 72076 Tübingen, Germany
| | - Christian Richter
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Esther G. C. Troost
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Mechthild Krause
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), 01307 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, 01328 Dresden, Germany
| | - Michael Baumann
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
- German Cancer Research Center (DKFZ), Division Radiooncology/Radiobiology, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center DKFZ, 69120 Heidelberg, Germany
| | - Steffen Löck
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, 01309 Dresden, Germany; (A.Z.); (K.L.); (F.L.); (A.L.); (C.R.); (E.G.C.T.); (M.K.); (M.B.); (S.L.)
- German Cancer Research Center (DKFZ), Heidelberg and German Cancer Consortium (DKTK) Partner Site Dresden, 01309 Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01309 Dresden, Germany
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Ramkumar M, Shanmugaraja P, Anusuya V, Dhiyanesh B. Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment. Comput Methods Biomech Biomed Engin 2023:1-13. [PMID: 37791591 DOI: 10.1080/10255842.2023.2262662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/17/2023] [Indexed: 10/05/2023]
Abstract
Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis.
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Affiliation(s)
- M Ramkumar
- Department of CSBS, Knowledge Institute of Technology, Salem, Tamil Nadu, India
| | - P Shanmugaraja
- Department of IT, Sona College of Technology, Salem, Tamil Nadu, India
| | - V Anusuya
- Department of IT, Ramco Institute of Technology, Virudhunagar, Tamil Nadu, India
| | - B Dhiyanesh
- Department of CSE, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India
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Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, El Naqa I, Wei L. Interpretable artificial intelligence in radiology and radiation oncology. Br J Radiol 2023; 96:20230142. [PMID: 37493248 PMCID: PMC10546466 DOI: 10.1259/bjr.20230142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, Netherlands
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Jiaren Zou
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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Yanagawa M, Ito R, Nozaki T, Fujioka T, Yamada A, Fujita S, Kamagata K, Fushimi Y, Tsuboyama T, Matsui Y, Tatsugami F, Kawamura M, Ueda D, Fujima N, Nakaura T, Hirata K, Naganawa S. New trend in artificial intelligence-based assistive technology for thoracic imaging. Radiol Med 2023; 128:1236-1249. [PMID: 37639191 PMCID: PMC10547663 DOI: 10.1007/s11547-023-01691-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023]
Abstract
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.
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Affiliation(s)
- Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan.
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-2621, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nish I 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Kinoshita F, Takenaka T, Yamashita T, Matsumoto K, Oku Y, Ono Y, Wakasu S, Haratake N, Tagawa T, Nakashima N, Mori M. Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer. Sci Rep 2023; 13:15683. [PMID: 37735585 PMCID: PMC10514331 DOI: 10.1038/s41598-023-42964-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 09/17/2023] [Indexed: 09/23/2023] Open
Abstract
There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.
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Affiliation(s)
- Fumihiko Kinoshita
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
- Department of Thoracic Oncology, National Hospital Organization Kyushu Cancer Center, Fukuoka, Japan
| | - Tomoyoshi Takenaka
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | | | | | - Yuka Oku
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
- Department of Thoracic Oncology, National Hospital Organization Kyushu Cancer Center, Fukuoka, Japan
| | - Yuki Ono
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Sho Wakasu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Naoki Haratake
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Tetsuzo Tagawa
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Masaki Mori
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Lu N, Guan X, Zhu J, Li Y, Zhang J. A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation. Cancers (Basel) 2023; 15:4497. [PMID: 37760468 PMCID: PMC10526233 DOI: 10.3390/cancers15184497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients' preoperative staging and RAS gene mutation status. METHODS The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset. RESULTS In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images. CONCLUSION In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients' chances of survival and quality of life.
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Affiliation(s)
- Na Lu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Xiao Guan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Jianguo Zhu
- Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China;
| | - Yuan Li
- Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
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Pai S, Bontempi D, Prudente V, Hadzic I, Sokač M, Chaunzwa TL, Bernatz S, Hosny A, Mak RH, Birkbak NJ, Aerts HJWL. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging. medRxiv 2023:2023.09.04.23294952. [PMID: 37732237 PMCID: PMC10508804 DOI: 10.1101/2023.09.04.23294952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.
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Affiliation(s)
- Suraj Pai
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Vasco Prudente
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Ibrahim Hadzic
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Mateo Sokač
- Department of Molecular Medicine, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus, Denmark
| | - Tafadzwa L. Chaunzwa
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Simon Bernatz
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Nicolai J Birkbak
- Department of Molecular Medicine, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus, Denmark
| | - Hugo JWL Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, MA 02115, United States of America
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
- Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, MA 02115, USA
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Chiappetta M, Sassorossi C, Cusumano G. Surgery for Non-Small Cell Lung Cancer in the Personalized Therapy Era. Curr Oncol 2023; 30:7773-7776. [PMID: 37623044 PMCID: PMC10453037 DOI: 10.3390/curroncol30080563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023] Open
Abstract
Lung cancer remains one of the tumours with the highest incidence and the poorestprognosis, with an estimated incidence of more than 220,000 cases with 135,000 cancerrelateddeaths annually in the United States [1,2].[...].
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Affiliation(s)
- Marco Chiappetta
- Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Carolina Sassorossi
- Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Giacomo Cusumano
- Thoracic Surgery Unit, Policlinico—San Marco Hospital, University of Catania, 95124 Catania, Italy;
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Thanoon MA, Zulkifley MA, Mohd Zainuri MAA, Abdani SR. A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images. Diagnostics (Basel) 2023; 13:2617. [PMID: 37627876 PMCID: PMC10453592 DOI: 10.3390/diagnostics13162617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023] Open
Abstract
One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient's probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.
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Affiliation(s)
- Mohammad A. Thanoon
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
- System and Control Engineering Department, College of Electronics Engineering, Ninevah University, Mosul 41002, Iraq
| | - Mohd Asyraf Zulkifley
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
| | - Muhammad Ammirrul Atiqi Mohd Zainuri
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, University Kebangsaan Malaysia, Bangi 43600, Malaysia;
| | - Siti Raihanah Abdani
- School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Shah Alam 40450, Malaysia;
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Moon JW, Yang E, Kim JH, Kwon OJ, Park M, Yi CA. Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI. Diagnostics (Basel) 2023; 13:2555. [PMID: 37568918 PMCID: PMC10417371 DOI: 10.3390/diagnostics13152555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). METHODS DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. RESULTS 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. CONCLUSIONS Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.
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Affiliation(s)
- Jung Won Moon
- Department of Radiology, Kangnam Sacred Heart Hospital, Hallym University School of Medicine, Seoul 07441, Republic of Korea;
| | - Ehwa Yang
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - O Jung Kwon
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Minsu Park
- Department of Information and Statistics, Chungnam National University, Daejeon 34134, Republic of Korea;
| | - Chin A Yi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
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Xing W, Gao W, Lv X, Zhao Z, Xu X, Wu Z, Mao G, Chen J. Artificial intelligence predicts lung cancer radiotherapy response: A meta-analysis. Artif Intell Med 2023; 142:102585. [PMID: 37316099 DOI: 10.1016/j.artmed.2023.102585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/06/2023] [Accepted: 05/16/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) technology has clustered patients based on clinical features into sub-clusters to stratify high-risk and low-risk groups to predict outcomes in lung cancer after radiotherapy and has gained much more attention in recent years. Given that the conclusions vary considerably, this meta-analysis was conducted to investigate the combined predictive effect of AI models on lung cancer. METHODS This study was performed according to PRISMA guidelines. PubMed, ISI Web of Science, and Embase databases were searched for relevant literature. Outcomes, including overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and local control (LC), were predicted using AI models in patients with lung cancer after radiotherapy, and were used to calculate the pooled effect. Quality, heterogeneity, and publication bias of the included studies were also evaluated. RESULTS Eighteen articles with 4719 patients were eligible for this meta-analysis. The combined hazard ratios (HRs) of the included studies for OS, LC, PFS, and DFS of lung cancer patients were 2.55 (95 % confidence interval (CI) = 1.73-3.76), 2.45 (95 % CI = 0.78-7.64), 3.84 (95 % CI = 2.20-6.68), and 2.66 (95 % CI = 0.96-7.34), respectively. The combined area under the receiver operating characteristics curve (AUC) of the included articles on OS and LC in patients with lung cancer was 0.75 (95 % CI = 0.67-0.84), and 0.80 (95%CI = 0.0.68-0.95), respectively. CONCLUSION The clinical feasibility of predicting outcomes using AI models after radiotherapy in patients with lung cancer was demonstrated. Large-scale, prospective, multicenter studies should be conducted to more accurately predict the outcomes in patients with lung cancer.
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Affiliation(s)
- Wenmin Xing
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China
| | - Wenyan Gao
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences&Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Xiaoling Lv
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China
| | - Zhenlei Zhao
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China
| | - Xiaogang Xu
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China
| | - Zhibing Wu
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China
| | - Genxiang Mao
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China.
| | - Jun Chen
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, China.
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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Libling WA, Korn R, Weiss GJ. Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer. Transl Lung Cancer Res 2023; 12:1575-1589. [PMID: 37577298 PMCID: PMC10413018 DOI: 10.21037/tlcr-23-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 08/15/2023]
Abstract
Background and Objective Radiomics is an emerging field of advanced image analysis that has shown promise as a non-invasive, companion diagnostic in predicting clinical outcomes and response assessments in solid tumors. Radiomics aims to extract high-content information from medical images not visible to the naked eye, especially in early-stage non-small cell lung cancer (NSCLC) patients. Although these patients are being identified by early detection programs, it remains unclear which patients would benefit from adjuvant treatment versus active surveillance. Having a radiomic signature(s) that could predict early recurrence would be beneficial. In this review, an overview of the basic radiomic approaches used to evaluate solid tumors on radiologic scans, including NSCLC is provided followed by a review of relevant literature that supports the use of radiomics to help predict tumor recurrence in early-stage NSCLC patients. Methods A review of the radiomic literature from 1985 to present focusing on the prediction of disease recurrence in early-stage NSCLC was conducted. PubMed database was searched using key terms for radiomics and NSCLC. A total of 41 articles were identified and 13 studies were considered suitable for inclusion based upon study population, patient number (n>50), use of well described radiomic methodologies, suitable model building features, and well-defined testing/training and validation where feasible. Key Content and Findings Examples of using radiomics in early-stage NSCLC patients will be presented, where disease free survival is a primary consideration. A summary of the findings demonstrates the importance of both the intratumor and peritumoral radiomic signals as a marker of outcomes. Conclusions The value of radiomic information for predicting disease recurrence in early-stage NSCLC patients is accumulating. However, overcoming several challenges along with the lack of prospective trials, has inhibited it use as a clinical decision-making support tool in early-stage NSCLC.
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Affiliation(s)
- William Adam Libling
- Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, USA
| | - Ronald Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA
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Sun H, Plawinski J, Subramaniam S, Jamaludin A, Kadir T, Readie A, Ligozio G, Ohlssen D, Baillie M, Coroller T. A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs). PLoS One 2023; 18:e0280316. [PMID: 37410795 PMCID: PMC10325103 DOI: 10.1371/journal.pone.0280316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 12/27/2022] [Indexed: 07/08/2023] Open
Abstract
Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.
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Affiliation(s)
- Hanxi Sun
- Department of Statistics, Purdue University, West Lafayette, IN, United States of America
| | - Jason Plawinski
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Sajanth Subramaniam
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | | | | | - Aimee Readie
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Gregory Ligozio
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - David Ohlssen
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Mark Baillie
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Thibaud Coroller
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
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