1
|
Wang C, Shao J, He Y, Wu J, Liu X, Yang L, Wei Y, Zhou XS, Zhan Y, Shi F, Shen D, Li W. Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography. Nat Med 2024:10.1038/s41591-024-03211-3. [PMID: 39289570 DOI: 10.1038/s41591-024-03211-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 07/22/2024] [Indexed: 09/19/2024]
Abstract
The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.
Collapse
Affiliation(s)
- Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu, China.
| | - Jun Shao
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Yichu He
- Department of Research and Development, United Imaging Intelligence, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, United Imaging Intelligence, Shanghai, China
| | - Xingting Liu
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Liuqing Yang
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Ying Wei
- Department of Research and Development, United Imaging Intelligence, Shanghai, China
| | - Xiang Sean Zhou
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yiqiang Zhan
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Feng Shi
- Department of Research and Development, United Imaging Intelligence, Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu, China.
| |
Collapse
|
2
|
Chang AEB, Potter AL, Yang CFJ, Sequist LV. Early Detection and Interception of Lung Cancer. Hematol Oncol Clin North Am 2024; 38:755-770. [PMID: 38724286 DOI: 10.1016/j.hoc.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.
Collapse
Affiliation(s)
- Allison E B Chang
- Department of Medicine, Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, USA; Department of Hematology/Oncology, Dana Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Alexandra L Potter
- Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Chi-Fu Jeffrey Yang
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Lecia V Sequist
- Department of Medicine, Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| |
Collapse
|
3
|
Labaki WW, Agusti A, Bhatt SP, Bodduluri S, Criner GJ, Fabbri LM, Halpin DMG, Lynch DA, Mannino DM, Miravitlles M, Papi A, Sin DD, Washko GR, Kazerooni EA, Han MK. Leveraging Computed Tomography Imaging to Detect Chronic Obstructive Pulmonary Disease and Concomitant Chronic Diseases. Am J Respir Crit Care Med 2024; 210:281-287. [PMID: 38843079 PMCID: PMC11348973 DOI: 10.1164/rccm.202402-0407pp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/04/2024] [Indexed: 08/02/2024] Open
Affiliation(s)
| | - Alvar Agusti
- Cathedra Salut Respiratoria, University of Barcelona, Barcelona, Spain
- Pulmonary Service, Respiratory Institute, Clinic Barcelona, Barcelona, Spain
- Fundació Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Barcelona, Spain
| | - Surya P. Bhatt
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Sandeep Bodduluri
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Gerard J. Criner
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | | | - David M. G. Halpin
- Respiratory Medicine, University of Exeter Medical School, Exeter, United Kingdom
| | - David A. Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado
| | - David M. Mannino
- Department of Medicine, University of Kentucky, Lexington, Kentucky
| | - Marc Miravitlles
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Barcelona, Spain
- Neumología, Hospital Universitari Vall d’Hebron/Vall d’Hebron Institut de Recerca, Barcelona, Spain
| | - Alberto Papi
- Section of Respiratory Medicine, University of Ferrara, Ferrara, Italy
| | - Don D. Sin
- Centre for Heart Lung Innovation, St. Paul’s Hospital and University of British Columbia, Vancouver, British Columbia, Canada
- Division of Respiratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - George R. Washko
- Division of Pulmonary and Critical Care Medicine and
- Applied Chest Imaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ella A. Kazerooni
- Division of Pulmonary and Critical Care Medicine and
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - MeiLan K. Han
- Division of Pulmonary and Critical Care Medicine and
| |
Collapse
|
4
|
Chen D, Yang L, Zhang W, Shen J, Van Schil PEY, Divisi D, Seetharamu N, Gu J. Prevalence and management of pulmonary nodules: a systematic review and meta-analysis. J Thorac Dis 2024; 16:4619-4632. [PMID: 39144359 PMCID: PMC11320231 DOI: 10.21037/jtd-24-874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/16/2024]
Abstract
Background Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules. Methods We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence. Results We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies. Conclusions Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.
Collapse
Affiliation(s)
- Dan Chen
- International Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liyun Yang
- International Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wenhong Zhang
- International Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jieyun Shen
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China
| | - Paul E. Y. Van Schil
- Department of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium
| | - Duilio Divisi
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Thoracic Surgery Unit, “Giuseppe Mazzini” Hospital of Teramo, Teramo, Italy
| | - Nagarashee Seetharamu
- Division of Medical Oncology and Hematology, Northwell Health Cancer Institute, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, NY, USA
| | - Jie Gu
- International Medical Center, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
5
|
Yasaka K, Kanzawa J, Kanemaru N, Koshino S, Abe O. Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01186-8. [PMID: 38955964 DOI: 10.1007/s10278-024-01186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024]
Abstract
This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving and communication systems (PACS) and comparing it with that of radiologists. Patients whose radiological reports contained the term lung cancer (3111 for training, 124 for validation, and 288 for test) were included in this retrospective study. Based on clinical indication and diagnosis sections of the radiological report (used as input data), they were classified into four groups (used as reference data): group 0 (no lung cancer), group 1 (pretreatment lung cancer present), group 2 (after treatment for lung cancer), and group 3 (planning radiation therapy). Using the training and validation datasets, fine-tuning of the pretrained LLM was conducted ten times. Due to group imbalance, group 2 data were undersampled in the training. The performance of the best-performing model in the validation dataset was assessed in the independent test dataset. For testing purposes, two other radiologists (readers 1 and 2) were also involved in classifying radiological reports. The overall accuracy of the fine-tuned LLM, reader 1, and reader 2 was 0.983, 0.969, and 0.969, respectively. The sensitivity for differentiating group 0/1/2/3 by LLM, reader 1, and reader 2 was 1.000/0.948/0.991/1.000, 0.750/0.879/0.996/1.000, and 1.000/0.931/0.978/1.000, respectively. The time required for classification by LLM, reader 1, and reader 2 was 46s/2539s/1538s, respectively. Fine-tuned LLM effectively extracted patients on pretreatment for lung cancer from PACS with comparable performance to radiologists in a shorter time.
Collapse
Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Jun Kanzawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Noriko Kanemaru
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Saori Koshino
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| |
Collapse
|
6
|
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] [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.
Collapse
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
| |
Collapse
|
7
|
Chang PJ. Imaging Informatics: Maturing Beyond Adolescence to Enable the Return of the Doctor's Doctor. Radiology 2023; 309:e230936. [PMID: 37874240 DOI: 10.1148/radiol.230936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Affiliation(s)
- Paul J Chang
- From the Department of Radiology, University of Chicago Medicine, 5841 S Maryland Ave, MC2026, Chicago, IL 60637
| |
Collapse
|