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Yang Y, Zhang L, Wang H, Zhao J, Liu J, Chen Y, Lu J, Duan Y, Hu H, Peng H, Ye L. Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis. BMC Med Imaging 2024; 24:149. [PMID: 38886695 PMCID: PMC11184730 DOI: 10.1186/s12880-024-01313-5] [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: 01/31/2024] [Accepted: 05/27/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves. RESULTS In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model. CONCLUSION We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.
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Affiliation(s)
- Yantao Yang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Kunming, China
| | - Libin Zhang
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China
| | - Han Wang
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China
| | - Jie Zhao
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Kunming, China
| | - Jun Liu
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China
| | - Yun Chen
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China
| | - Jiagui Lu
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China
| | - Yaowu Duan
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Kunming, China
| | - Huilian Hu
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Kunming, China
| | - Hao Peng
- Department of Thoracic Surgery, The First People's Hospital Of Yunnan Province, Kunming City, Yunnan Province, China.
| | - Lianhua Ye
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Kunming, China.
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Li Z, Liu H, Wang M, Wang X, Pan D, Ma A, Chen Y. Nomogram for the preoperative prediction of Ki-67 expression and prognosis in stage IA lung adenocarcinoma based on clinical and multi-slice spiral computed tomography features. BMC Med Imaging 2024; 24:143. [PMID: 38867154 PMCID: PMC11167796 DOI: 10.1186/s12880-024-01305-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/21/2024] [Indexed: 06/14/2024] Open
Abstract
OBJECTIVE This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma. MATERIALS AND METHODS We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram's predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared. RESULTS In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS. CONCLUSION The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.
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Affiliation(s)
- Zhengteng Li
- Department of Radiology, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Hongmei Liu
- Thyroid and Breast Surgery, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Min Wang
- Department of Radiology, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Xiankai Wang
- Department of Radiology, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Dongmei Pan
- Department of Radiology, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Aidong Ma
- Department of Radiology, Jining No.1 People's Hospital, No. 6 Jiankang Road, Rencheng District, Jining, 272000, China
| | - Yang Chen
- Department of Radiology, Yantai Yeda Hospital, Yantai Economic and Technological Development Zone, No. 11 Taishan Road, Yantai, 264000, China.
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Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
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Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
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Zhai P, Cong H, Zhu E, Zhao G, Yu Y, Li J. MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7376-7390. [PMID: 36150004 DOI: 10.1109/tnnls.2022.3203412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient. This study presents a multiview contrastive network (MVCNet), which enhances the representations of 2-D views contrastively against other views of different spatial orientations. Specifically, MVCNet views each 3-D lesion from different orientations to collect multiple 2-D views; it learns to minimize a contrastive loss so that the 2-D views of the same 3-D lesion are aggregated, whereas those of different lesions are separated. To alleviate the issue of false negative examples, the uninformative negative samples are filtered out, which results in more discriminative features for downstream tasks. By linear evaluation, MVCNet achieves state-of-the-art accuracies on the lung image database consortium and image database resource initiative (LIDC-IDRI) (88.62%), lung nodule database (LNDb) (76.69%), and TianChi (84.33%) datasets for unsupervised representation learning. When fine-tuned on 10% of the labeled data, the accuracies are comparable to the supervised learning models (89.46% versus 85.03%, 73.85% versus 73.44%, 83.56% versus 83.34% on the three datasets, respectively), indicating the superiority of MVCNet in learning representations with limited annotations. Our findings suggest that contrasting multiple 2-D views is an effective approach to capturing the original 3-D information, which notably improves the utilization of the scarce and valuable annotated CT data.
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Xu H, Li C, Zhang L, Ding Z, Lu T, Hu H. Immunotherapy efficacy prediction through a feature re-calibrated 2.5D neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108135. [PMID: 38569256 DOI: 10.1016/j.cmpb.2024.108135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based biomarkers to predict the efficacy of lung cancer treatments offers a promising avenue for improving therapeutic outcomes. METHODS This study presents an automatic system for immunotherapy efficacy prediction on the subjects with lung cancer, facilitating significant clinical implications. Our model employs an advanced 2.5D neural network that incorporates 2D intra-slice feature extraction and 3D inter-slice feature aggregation. We further present a lesion-focused prior to guide the re-calibration for intra-slice features, and a attention-based re-calibration for the inter-slice features. Finally, we design an accumulated back-propagation strategy to optimize network parameters in a memory-efficient fashion. RESULTS We demonstrate that the proposed method achieves impressive performance on an in-house clinical dataset, surpassing existing state-of-the-art models. Furthermore, the proposed model exhibits increased efficiency in inference for each subject on average. To further validate the effectiveness of our model and its components, we conducted comprehensive and in-depth ablation experiments and discussions. CONCLUSION The proposed model showcases the potential to enhance physicians' diagnostic performance due to its impressive performance in predicting immunotherapy efficacy, thereby offering significant clinical application value. Moreover, we conduct adequate comparison experiments of the proposed methods and existing advanced models. These findings contribute to our understanding of the proposed model's effectiveness and serve as motivation for future work in immunotherapy efficacy prediction.
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Affiliation(s)
- Haipeng Xu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Chenxin Li
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, SAR, China.
| | - Longfeng Zhang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
| | - Zhiyuan Ding
- School of Informatics, Xiamen University, Fujian 350014, China.
| | - Tao Lu
- Department of Radiology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fujian 350014, China.
| | - Huihua Hu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
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Wu J, Li R, Zhang H, Zheng Q, Tao W, Yang M, Zhu Y, Ji G, Li W. Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study. Thorac Cancer 2024. [PMID: 38798230 DOI: 10.1111/1759-7714.15383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVES Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning. METHODS A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. RESULTS The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001). CONCLUSIONS The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.
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Affiliation(s)
- Jiaxuan Wu
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Chengdu, China
- Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China
| | - Ruicen Li
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huohuo Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Chengdu, China
- Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Zheng
- West China Clinical Medical College, Sichuan University, Chengdu, China
| | - Wenjuan Tao
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Yang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Zhu
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Guiyi Ji
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Chengdu, China
- Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, China
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Polanco D, González J, Gracia-Lavedan E, Pinilla L, Plana R, Molina M, Pardina M, Barbé F. Multidisciplinary virtual management of pulmonary nodules. Pulmonology 2024; 30:239-246. [PMID: 35115280 DOI: 10.1016/j.pulmoe.2021.12.003] [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: 08/25/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION AND OBJECTIVES Multidisciplinary nodule clinics provide high-quality care and favor adherence to guidelines. Virtual care has shown savings benefits along with patient satisfaction. Our aim is to describe the first year of operation of a multidisciplinary virtual lung nodule clinic, the population evaluated and issued decisions. Secondarily, among discharged patients, we aimed to analyze their follow-up prior to the existence of our consultation, evaluating its adherence to guidelines. MATERIALS AND METHODS Observational study including all patients evaluated at the Virtual Lung Nodule Clinic (VLNC) (March 2018- March 2019). Clinical and radiological data were recorded. Recommendations, based on 2017 Fleischner Society guidelines, were categorized into follow-up, discharge or referral to lung cancer consultation. Discharged patients were classified according to adherence to guidelines of their previous management, into adequate, prolonged and non-indicated follow-up. RESULTS A total of 365 patients (58.9% men; median age 64.0 years) were included. Sixty-four percent had smoking history and 23% had chronic obstructive pulmonary disease (COPD). Most nodules were solid (87.4%) and multiple (57.5%). The median diameter was 6.00 mm. 43.8% of patients were discharged following first VLNC evaluation. Among them, 27.5% had received appropriate follow-up, but 66.9% had received poor management. Patients with prolonged follow-up (33.1%) were older (67.0 vs 60.5 years) and had larger nodules (6.00 mm vs 5.00). Non-indicated follow-up patients (33.8%) were more non-smokers (77.8% vs 31.8%) and presented smaller nodules (4.00 vs 5.00 mm). CONCLUSIONS During its first year of operation, the VLNC has evaluated a population with a relevant risk profile for lung cancer development, management of which should be cautious and adhere to guidelines. After the first VLNC assessment, approximately one-half of this population was discharged. It was noticeable that previous follow-up of discharged patients was found poorly adherent to guidelines, with a marked tendency to overmanagement.
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Affiliation(s)
- D Polanco
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - J González
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - E Gracia-Lavedan
- Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain
| | - L Pinilla
- Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain
| | - R Plana
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain
| | - M Molina
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain
| | - M Pardina
- Department of Radiology, Arnau de Vilanova University Hospital, IRBLleida
| | - F Barbé
- Respiratory Department, University Hospital Arnau de Vilanova. Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Group of Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Spain.
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Deng Y, Xia L, Zhang J, Deng S, Wang M, Wei S, Li K, Lai H, Yang Y, Bai Y, Liu Y, Luo L, Yang Z, Chen Y, Kang R, Gan F, Pu Q, Mei J, Ma L, Lin F, Guo C, Liao H, Zhu Y, Liu Z, Liu C, Hu Y, Yuan Y, Zha Z, Yuan G, Zhang G, Chen L, Cheng Q, Shen S, Liu L. Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages. Cell Rep Med 2024; 5:101489. [PMID: 38554705 PMCID: PMC11031428 DOI: 10.1016/j.xcrm.2024.101489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/26/2024] [Accepted: 03/06/2024] [Indexed: 04/02/2024]
Abstract
Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.
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Affiliation(s)
- Yulan Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Liang Xia
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jian Zhang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Senyi Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Mengyao Wang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Shiyou Wei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Kaixiu Li
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Hongjin Lai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunhao Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yuquan Bai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yongcheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lanzhi Luo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhenyu Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yaohui Chen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Ran Kang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Fanyi Gan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Qiang Pu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jiandong Mei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lin Ma
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Feng Lin
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chenglin Guo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunke Zhu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chengwu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yang Hu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhengyu Zha
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gang Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gao Zhang
- Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
| | - Qing Cheng
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Shensi Shen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
| | - Lunxu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
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9
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Wang X, Cui Y, Wang Y, Liu S, Meng N, Wei W, Bai Y, Shen Y, Guo J, Guo Z, Wang M. Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI. J Magn Reson Imaging 2024. [PMID: 38602245 DOI: 10.1002/jmri.29388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities. PURPOSE To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules. STUDY TYPE Prospective. POPULATION 54 patients, including 21 men and 33 women. FIELD STRENGTH/SEQUENCE Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo. ASSESSMENT Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded. STATISTICAL TESTS The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant. RESULTS A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements. DATA CONCLUSION The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xinhui Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yingying Cui
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Ying Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Shuo Liu
- Department of Medical Imaging, Xinxiang Medical University and Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Wei Wei
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Yu Shen
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | | | - Zhiping Guo
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Health Management Center of Henan Province, Zhengzhou University People's Hospital and FuWai Central China Cardiovascular Hospital, Zhengzhou, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
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10
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Mai S, Liu H, Zeng H, Cheng Z, Huang J, Shi G, Li Y, Wu Z. Diagnostic challenge and survival analysis of pulmonary oligometastases and primary lung cancer in breast cancer patients. Thorac Cancer 2024; 15:1017-1028. [PMID: 38494913 PMCID: PMC11045338 DOI: 10.1111/1759-7714.15285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND The aim of this study was to compare breast cancer patients with pulmonary oligometastases (POM) and primary lung cancer (PLC) and to assess whether there were differences in clinical features, CT features, and survival outcomes between the two groups. METHODS From January 2010 to December 2021, the clinical records of 437 with malignant pulmonary nodules who had breast cancer patients were reviewed. POM was identified in 45 patients and PLC in 43 patients after the initial detection of pulmonary nodules. The clinicopathological characteristics, CT appearance of pulmonary nodules, and survival of the two groups were compared. RESULTS Stage II to IV breast tumors (p < 0.001), high pathological grade of breast cancer (p = 0.001), low proportion of luminal-type breast cancer (p = 0.003), and the higher serum CYFRA 21-1 level (p = 0.046) were the clinical characteristics of pulmonary nodules suggestive of POM rather than PLC. The CT features of lung nodules indicative of PLC rather than POM were the subsolid component (p < 0.001), lobulation (p = 0.010), air bronchogram (p < 0.001) and pleural indentation (p = 0.004). Ten-year survival rate for PLC was 93.2%, which was higher compared with 57.8% in those with POM (p = 0.001). CONCLUSIONS Elevated serum CYFRA 21-1 levels and late-stage breast cancer may be beneficial for the diagnosis of POM. CT imaging appearances of the subsolid component, lobulation, air bronchogram, and pleural indentation increase the likelihood of PLC. Breast cancer patients with PLC presented better survival with attentive monitoring than those with POM.
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Affiliation(s)
- Siyao Mai
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Haiqing Liu
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Hong Zeng
- Department of Pathology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Ziliang Cheng
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Jingwen Huang
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Guangzi Shi
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Yong Li
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Zhuo Wu
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhouChina
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11
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Chen M, Ding L, Deng S, Li J, Li X, Jian M, Xu Y, Chen Z, Yan C. Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features. Acad Radiol 2024:S1076-6332(24)00082-5. [PMID: 38508939 DOI: 10.1016/j.acra.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 03/22/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs). MATERIALS AND METHODS Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test. RESULTS Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05). CONCLUSION A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.
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Affiliation(s)
- Mingwang Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Li Ding
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Shuting Deng
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Jingxu Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
| | - Xiaomei Li
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Mingjue Jian
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Zhao Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Chenggong Yan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
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12
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Wang Z, Mortani Barbosa EJ. Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models. J Am Coll Radiol 2024:S1546-1440(24)00274-6. [PMID: 38461910 DOI: 10.1016/j.jacr.2024.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/19/2024] [Accepted: 02/02/2024] [Indexed: 03/12/2024]
Abstract
OBJECTIVE To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features. METHODS We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance. RESULTS On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840. CONCLUSION Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.
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Affiliation(s)
- Zhuoyang Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eduardo J Mortani Barbosa
- Director of CT Modality at the Thoracic Imaging Section, Division of Cardiothoracic Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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13
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Wang H, Chen A, Wang K, Yang H, Wen W, Ren Q, Chen L, Xu X, Zhu Q. CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology. Discov Oncol 2024; 15:29. [PMID: 38310621 PMCID: PMC10838864 DOI: 10.1007/s12672-024-00872-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
PURPOSE Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model. METHODS Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted. RESULTS The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS. CONCLUSION CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.
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Affiliation(s)
- Hongya Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Aiping Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Kun Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - He Yang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Wei Wen
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Qianrui Ren
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Liang Chen
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Xinfeng Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
| | - Quan Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
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14
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Liu M, Yang L, Sun X, Liang X, Li C, Feng Q, Li M, Zhang L. Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images. Radiol Cardiothorac Imaging 2024; 6:e220234. [PMID: 38206165 PMCID: PMC10912885 DOI: 10.1148/ryct.220234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/03/2023] [Accepted: 08/23/2023] [Indexed: 01/12/2024]
Abstract
Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Mengwen Liu
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Lin Yang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xujie Sun
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xin Liang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Cong Li
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Qianqian Feng
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
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15
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Salvatore MM, Liu Y, Peng B, Hsu HY, Saqi A, Tsai WY, Leu CS, Jambawalikar S. Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT. J Transl Med 2024; 22:67. [PMID: 38229113 DOI: 10.1186/s12967-023-04645-y] [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: 07/02/2023] [Accepted: 10/20/2023] [Indexed: 01/18/2024] Open
Abstract
PURPOSE Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma. METHODS This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented. RESULTS Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer. CONCLUSIONS Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.
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Affiliation(s)
- Mary M Salvatore
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA.
| | - Yucheng Liu
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Boyu Peng
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Hao Yun Hsu
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Anjali Saqi
- Department of Pathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wei-Yann Tsai
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Cheng-Shiun Leu
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
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Yamada D, Matsusako M, Yoneoka D, Oikado K, Ninomiya H, Nozaki T, Ishiyama M, Makidono A, Otsuji M, Itoh H, Ojiri H. Ex-vivo 1.5T MR Imaging versus CT in Estimating the Size of the Pathologically Invasive Component of Lung Adenocarcinoma Spectrum Lesions. Magn Reson Med Sci 2024; 23:92-101. [PMID: 36529498 PMCID: PMC10838715 DOI: 10.2463/mrms.mp.2022-0125] [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: 10/13/2022] [Accepted: 11/01/2022] [Indexed: 01/05/2024] Open
Abstract
PURPOSE The purpose of this study was to investigate whether ex-vivo MRI enables accurate estimation of the invasive component of lung adenocarcinoma. METHODS We retrospectively reviewed 32 patients with lung adenocarcinoma who underwent lung lobectomy. The specimens underwent MRI at 1.5T. The boundary between the lesion and the normal lung was evaluated on a 5-point scale in each three MRI sequences, and a one-way analysis of variance and post-hoc tests were performed. The invasive component size was measured histopathologically. The maximum diameter of each solid component measured on CT and MR T1-weighted (T1W) images and the maximum size obtained from histopathologic images were compared using the Wilcoxon signed-rank test. Inter-reader agreement was evaluated using intraclass correlation coefficients (ICC). RESULTS T1W images were determined to be optimal for the delineation of the lesions (P < 0.001). The histopathologic invasive area corresponded to the area where the T1W ex-vivo MR image showed a high signal intensity that was almost equal to the intravascular blood signal. The maximum diameter of the solid component on CT was overestimated compared with the maximum invasive size on histopathology (mean, 153%; P < 0.05), while that on MRI was evaluated mostly accurately without overestimation (mean, 108%; P = 0.48). The interobserver reliability of the measurements using CT and MRI was good (ICC = 0.71 on CT, 0.74 on MRI). CONCLUSION Ex-vivo MRI was more accurate than conventional CT in delineating the invasive component of lung adenocarcinoma.
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Affiliation(s)
- Daisuke Yamada
- Department of Radiology, St. Luke’s International University, Tokyo, Japan
| | - Masaki Matsusako
- Department of Radiology, St. Luke’s International University, Tokyo, Japan
| | - Daisuke Yoneoka
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Katsunori Oikado
- Diagnostic Imaging Center, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hironori Ninomiya
- Division of Pathology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke’s International University, Tokyo, Japan
| | - Mitsutomi Ishiyama
- Diagnostic Imaging Center, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Akari Makidono
- Department of Diagnostic Radiology, Tokyo Metropolitan Children’s Medical Center, Fuchu, Tokyo, Japan
| | - Mizuto Otsuji
- Department of Thoracic Surgery, Tokyo Metropolitan Bokutoh Hospital, Tokyo, Japan
| | - Harumi Itoh
- Department of Radiology, Faculty of Medical Sciences, University of Fukui, Yoshida-gun, Fukui, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine and University Hospital, Tokyo, Japan
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17
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Gamble JL, Ferguson D, Yuen J, Sheikh A. Limitations of GPT-3.5 and GPT-4 in Applying Fleischner Society Guidelines to Incidental Lung Nodules. Can Assoc Radiol J 2023:8465371231218250. [PMID: 38146205 DOI: 10.1177/08465371231218250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023] Open
Abstract
Purpose: To evaluate the accuracy of GPT-3.5, GPT-4, and a fine-tuned GPT-3.5 model in applying Fleischner Society recommendations to lung nodules. Methods: We generated 10 lung nodule descriptions for each of the 12 nodule categories from the Fleischner Society guidelines, incorporating them into a single fictitious report (n = 120). GPT-3.5 and GPT-4 were prompted to make follow-up recommendations based on the reports. We then incorporated the full guidelines into the prompts and re-submitted them. Finally, we re-submitted the prompts to a fine-tuned GPT-3.5 model. Results were analyzed using binary accuracy analysis in R. Results: GPT-3.5 accuracy in applying Fleischner Society guidelines was 0.058 (95% CI: 0.02, 0.12). GPT-4 accuracy was improved at 0.15 (95% CI: 0.09, 0.23; P = .02 for accuracy comparison). In recommending PET-CT and/or biopsy, both GPT-3.5 and GPT-4 had an F-score of 0.00. After explicitly including the Fleischner Society guidelines in the prompt, GPT-3.5 and GPT-4 significantly improved their accuracy to 0.42 (95% CI: 0.33, 0.51; P < .001) and to 0.66 (95% CI: 0.57, 0.74; P < .001), respectively. GPT-4 remained significantly better than GPT-3.5 (P < .001). The fine-tuned GPT-3.5 model accuracy was 0.46 (95% CI: 0.37, 0.55), not different from the GPT-3.5 model with guidelines included (P = .53). Conclusion: GPT-3.5 and GPT-4 performed poorly in applying widely known guidelines and never correctly recommended biopsy. Flawed knowledge and reasoning both contributed to their poor performance. While GPT-4 was more accurate than GPT-3.5, its inaccuracy rate was unacceptable for clinical practice. These results underscore the limitations of large language models for knowledge and reasoning-based tasks.
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Affiliation(s)
- Joel L Gamble
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Duncan Ferguson
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Joanna Yuen
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Adnan Sheikh
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
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Li S, Chen M, Wang Y, Li X, Gao G, Luo X, Tang L, Liu X, Wu N. An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans. Clin Lung Cancer 2023; 24:e301-e310. [PMID: 37596166 DOI: 10.1016/j.cllc.2023.08.001] [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: 06/26/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023]
Abstract
INTRODUCTION It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time. MATERIALS AND METHODS We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters. RESULTS The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals. CONCLUSION This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.
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Affiliation(s)
- Shaolei Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Mailin Chen
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yaqi Wang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiang Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | | | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | | | - Nan Wu
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
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Sun J, Zhang L, Hu B, Du Z, Cho WC, Witharana P, Sun H, Ma D, Ye M, Chen J, Wang X, Yang J, Zhu C, Shen J. Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma. Lung Cancer 2023; 186:107392. [PMID: 37816297 DOI: 10.1016/j.lungcan.2023.107392] [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: 05/18/2023] [Revised: 08/27/2023] [Accepted: 10/04/2023] [Indexed: 10/12/2023]
Abstract
BACKGROUND The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.
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Affiliation(s)
- Jiajing Sun
- Taizhou Hospital, Zhejiang University School of Medicine, Taizhou, China
| | - Li Zhang
- Dianei Technology, Shanghai, China
| | - Bingyu Hu
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University Guangzhou, China
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong, China
| | - Pasan Witharana
- Northern General Hospital, Herries Rd, Sheffield S5 7AU, UK; Imperial College London, London SW7 2BX, UK
| | - Hua Sun
- Taizhou Hospital, Zhejiang University School of Medicine, Taizhou, China
| | - Dehua Ma
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Minhua Ye
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | | | | | - Jiancheng Yang
- Dianei Technology, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China; EPFL, Lausanne, Switzerland
| | - Chengchu Zhu
- Taizhou Hospital, Zhejiang University School of Medicine, Taizhou, China; Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
| | - Jianfei Shen
- Taizhou Hospital, Zhejiang University School of Medicine, Taizhou, China; Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
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20
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Feng H, Huang G, Cao B, Zan Z, Wei Q. Maximum amplitude and mean platelet volume in the blood as biomarkers to detect lung adenocarcinoma cancer featured with ground-glass nodules. EUR J INFLAMM 2023. [DOI: 10.1177/1721727x231151530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Objectives The development and progression of malignancies are closely linked to hypercoagulability. As an early type of lung adenocarcinoma, ground glass nodules (GGNs) have been detected increasingly. Blood Maximum amplitude (MA) and mean platelet volume (MPV) are related to various conditions of hypercoagulability. Therefore, the role of MA and MPV in diagnosing lung adenocarcinoma cancer featured with GGNs was investigated in this case-control study. Methods The analyzed data of this study is derived from GGNs patients and healthy individuals in West China (Airport) Hospital Sichuan University. The differences between GGNs patients and healthy individuals were determined by one-way ANOVA, logistic regression or chi-squared test. The accuracy of diagnostic was performed by receiver operating characteristic curve (ROC). The relative mRNA expressions were studied by RT-qPCR. Results 470 patients diagnosed with GGNs which benign lesions (BN group) are inflammatory and malignant lesions (LC group) are adenocarcinoma in stage IA, and 235 healthy subjects (HC group) were enrolled in this study. Levels of MA and MPV were increased in LC group compared with BN and HC group ( p < 0.001, p < 0.001). When we combined MA and MPV, MA and MPV presented a sensitivity (SEN) of 0.809 and a specificity (SPE) of 0.774. And the area under the curve (AUC) increased to 0.871 (0.837–0.900) when confidence interval was 95%. Conclusion This study demonstrates that there have been systemic changes in coagulation disorders in the pathogenesis of GGNs. The diagnostic ability to different lung adenocarcinoma cancer featured with GGNs from benign or healthy controls can be improved by the combination of MA and MPV. Maximum amplitude and MPV may be used as biomarkers to detect lung adenocarcinoma cancer featured with GGNs.
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Affiliation(s)
- Hao Feng
- Department of Thoracic Surgery, The First People’s Hospital of Shuangliu District, Chengdu, China
| | - Gaigai Huang
- Department of Clinical Laboratory, The First People’s Hospital of Shuangliu District, Chengdu, China
| | - Boxiong Cao
- Department of Thoracic Surgery, The First People’s Hospital of Shuangliu District, Chengdu, China
| | - Ziliang Zan
- Department of Thoracic Surgery, The First People’s Hospital of Shuangliu District, Chengdu, China
| | - Qiang Wei
- Department of Thoracic Surgery, The First People’s Hospital of Shuangliu District, Chengdu, China
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Chang Y, Xing H, Shang Y, Liu Y, Yu L, Dai H. Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models. J Cancer Res Clin Oncol 2023; 149:15425-15438. [PMID: 37642725 DOI: 10.1007/s00432-023-05311-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE To construct and validate conventional and radiomics models based on dual-layer spectral CT radiomics for preoperative prediction of lung ground glass nodules (GGNs) invasiveness. MATERIALS AND METHODS A retrospective study was conducted on 176 GGNs patients who underwent chest non-contrast enhancement scan on dual-layer spectral detector CT at our hospital within 2 weeks before surgery. Patients were randomized into the training cohort and testing cohort. Clinical features, imaging features and spectral quantitative parameters were collected to establish a conventional model. Radiomics models were established by extracting 1781 radiomics features form regions of interest of each spectral image [120 kVp poly energetic images (PI), 60 keV images and electron density maps], respectively. After selecting the optimal radiomic features and integrating multiple machine learning models, the conventional model, PI model, 60 keV model, electron density (ED) model and combined model based on multimodal spectral images were finally established. The performance of these models was assessed through the evaluation of discrimination, calibration, and clinical application. RESULTS In the conventional model, age, vacuole sign, 60 keV and ED were independent risk factors of invasiveness. The combined model using logistic regression-least absolute shrinkage and selection operator classifiers was the optimal model with a higher area under the curve of the training (0.961, 95% confidence interval, CI: 0.932-0.991) and testing set (0.944, 0.890-0.999). CONCLUSION The combined models are helpful to predict the invasiveness of GGNs before surgery and guide the individualized treatment of patients.
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Affiliation(s)
- Yue Chang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Hanqi Xing
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Yi Shang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Yuanqing Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Lefan Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Hui Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China.
- Institute of Medical Imaging, Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China.
- Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, 215123, Jiangsu Province, People's Republic of China.
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22
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Wang TW, Chao HS, Chiu HY, Lin YH, Chen HC, Lu CF, Liao CY, Lee Y, Shiao TH, Chen YM, Huang JW, Wu YT. Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:5125. [PMID: 37958300 PMCID: PMC10647242 DOI: 10.3390/cancers15215125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.
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Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hung-Chun Chen
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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Cardillo G, Petersen RH, Ricciardi S, Patel A, Lodhia JV, Gooseman MR, Brunelli A, Dunning J, Fang W, Gossot D, Licht PB, Lim E, Roessner ED, Scarci M, Milojevic M. European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons. Eur J Cardiothorac Surg 2023; 64:ezad222. [PMID: 37243746 DOI: 10.1093/ejcts/ezad222] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/10/2023] [Accepted: 05/26/2023] [Indexed: 05/29/2023] Open
Affiliation(s)
- Giuseppe Cardillo
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Unicamillus-Saint Camillus University of Health Sciences, Rome, Italy
| | - René Horsleben Petersen
- Department of Cardiothoracic Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Sara Ricciardi
- Unit of Thoracic Surgery, Azienda Ospedaliera San Camillo Forlanini, Rome, Italy
- Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Akshay Patel
- Department of Thoracic Surgery, University Hospitals Birmingham, England, United Kingdom
- Institute of Immunology and Immunotherapy, University of Birmingham, United Kingdom
| | - Joshil V Lodhia
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Michael R Gooseman
- Department of Thoracic Surgery, Hull University Teaching Hospitals NHS Trust, and Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Alessandro Brunelli
- Department of Thoracic Surgery, St James University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Joel Dunning
- James Cook University Hospital Middlesbrough, United Kingdom
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Jiaotong University Medical School, Shangai, China
| | - Dominique Gossot
- Department of Thoracic Surgery, Curie-Montsouris Thoracic Institute, Paris, France
| | - Peter B Licht
- Department of Cardiothoracic Surgery, Odense University Hospital, Odense, Denmark
| | - Eric Lim
- Academic Division of Thoracic Surgery, The Royal Brompton Hospital and Imperial College London, United Kingdom
| | - Eric Dominic Roessner
- Department of Thoracic Surgery, Center for Thoracic Diseases, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Marco Scarci
- Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, Hammersmith Hospital, London, United Kingdom
| | - Milan Milojevic
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands
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24
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He Y, Xiong Z, Zhang J, Xie J, Zhu W, Zhao M, Li Z. Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol 2023; 149:9937-9946. [PMID: 37249644 DOI: 10.1007/s00432-023-04918-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/23/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period. METHODS Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth. RESULTS For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8 days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9 days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions. CONCLUSION Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.
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Affiliation(s)
- Yifan He
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang District, Dalian, 116011, Zhongshan, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang District, Dalian, 116011, Zhongshan, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang District, Dalian, 116011, Zhongshan, China
| | - Jiayue Xie
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang District, Dalian, 116011, Zhongshan, China
| | - Wen Zhu
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Min Zhao
- Pharmaceutical Diagnostics, GE Healthcare, Beijing, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang District, Dalian, 116011, Zhongshan, China.
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Yang Z, Cai Y, Chen Y, Ai Z, Chen F, Wang H, Han Q, Feng Q, Xiang Z. A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma. Acad Radiol 2023; 30:1946-1961. [PMID: 36567145 DOI: 10.1016/j.acra.2022.12.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/24/2022] [Accepted: 12/03/2022] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.
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Affiliation(s)
- Zhihe Yang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.); School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Yuqin Cai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Yirong Chen
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Fang Chen
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Hao Wang
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Qili Feng
- School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.).
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Fu C, Yang Z, Li P, Shan K, Wu M, Xu J, Ma C, Luo F, Zhou L, Sun J, Zhao F. Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study. Cancer Med 2023; 12:18460-18469. [PMID: 37723872 PMCID: PMC10557850 DOI: 10.1002/cam4.6402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.
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Affiliation(s)
- Chun‐Long Fu
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Ze‐Bin Yang
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Ping Li
- Department of Radiology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
- Department of RadiologyJiaxing Hospital of Traditional Chinese MedicineJiaxingChina
| | - Kang‐Fei Shan
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Mei‐Kang Wu
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Jie‐Ping Xu
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Chi‐Jun Ma
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
| | - Fang‐Hong Luo
- Department of Radiology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
| | - Long Zhou
- Department of Radiology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
| | - Ji‐Hong Sun
- Department of Radiology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang ProvinceNingboChina
- Cancer CenterZhejiang UniversityHangzhouChina
| | - Fen‐Hua Zhao
- Department of RadiologyAffiliated Dongyang Hospital of Wenzhou Medical UniversityDongyangChina
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Karaman A, Abbasguliyev H. Editorial for "Predictive Value of 18 F-FDG PET/MRI for Pleural Invasion in Solid and Subsolid Lung Adenocarcinomas Smaller Than 3 cm". J Magn Reson Imaging 2023; 58:187-188. [PMID: 36222571 DOI: 10.1002/jmri.28442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 06/11/2023] Open
Affiliation(s)
- Adem Karaman
- Department of Radiology, Medical Faculty, Ataturk University, Erzurum, Turkey
| | - Hasan Abbasguliyev
- Department of Radiology, Medical Faculty, Ataturk University, Erzurum, Turkey
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Qiu J, Li R, Wang Y, Ma X, Qu C, Liu B, Yue W, Tian H. A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter. Front Oncol 2023; 13:1196883. [PMID: 37361581 PMCID: PMC10285407 DOI: 10.3389/fonc.2023.1196883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Background With the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging. Methods A retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors. Results A total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful. Conclusion The nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery.
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Affiliation(s)
- Jianhao Qiu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yukai Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiuyuan Ma
- Department of Cardiology, Qianfoshan Hospital in the Shandong Province, Jinan, Shandong, China
| | - Chenghao Qu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Binyan Liu
- Department of Breast Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Weiming Yue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
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Ding X, Lin G, Wang P, Chen H, Li N, Yang Z, Qiu M. Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT. Front Oncol 2023; 13:1204435. [PMID: 37333820 PMCID: PMC10272389 DOI: 10.3389/fonc.2023.1204435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.
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Affiliation(s)
- Xiangxiang Ding
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
- Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Guihu Lin
- Department of Thoracic Surgery, Aerospace 731 Hospital, Beijing, China
| | - Peiyu Wang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China
- Thoracic Oncology Institute, Peking University People’s Hospital, Beijing, China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Nan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
- Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhi Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
- Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China
- Thoracic Oncology Institute, Peking University People’s Hospital, Beijing, China
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
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Lee K, Liu Z, Chandran U, Kalsekar I, Laxmanan B, Higashi MK, Jun T, Ma M, Li M, Mai Y, Gilman C, Wang T, Ai L, Aggarwal P, Pan Q, Oh W, Stolovitzky G, Schadt E, Wang X. Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing. JMIR AI 2023; 2:e44537. [PMID: 38875565 PMCID: PMC11041451 DOI: 10.2196/44537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 03/31/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.
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Affiliation(s)
| | | | - Urmila Chandran
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Iftekhar Kalsekar
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Balaji Laxmanan
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | | | - Tomi Jun
- Sema4, Stamford, CT, United States
| | - Meng Ma
- Sema4, Stamford, CT, United States
| | | | - Yun Mai
- Sema4, Stamford, CT, United States
| | | | | | - Lei Ai
- Sema4, Stamford, CT, United States
| | | | - Qi Pan
- Sema4, Stamford, CT, United States
| | - William Oh
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Li M, Zhu L, Lv Y, Shen L, Han Y, Ye B. Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study. Sci Rep 2023; 13:6999. [PMID: 37117233 PMCID: PMC10147622 DOI: 10.1038/s41598-023-33803-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002-1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304-1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value - 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481-0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748-413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004-1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and - 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.
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Affiliation(s)
- Min Li
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lei Zhu
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Yilv Lv
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Leilei Shen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yuchen Han
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
| | - Bo Ye
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
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Bulutay P, Atasoy Ç, Erus S, Tanju S, Dilege Ş, Fırat P. Scrape cytology and radiological solid size correlation can be used in the intraoperative management of subsolid lung nodules. Diagn Cytopathol 2023; 51:239-250. [PMID: 36519435 DOI: 10.1002/dc.25089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/07/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND The term radiologic subsolid lung nodule (SLN) represents a heterogeneous group of non-neoplastic and neoplastic lesions. Intraoperative evaluation (IO) is often required to differentiate and diagnose. The current study aims to investigate the feasibility and reliability of scrape cytology (SC) and radiologic solid size correlation for the IO diagnosis of SLNs. METHODS Sixty-eight patients with SLN signs were eligible to take part in the study due to intraoperatively prepared SC slides. We managed to complete the blind radiologic solid size measurement and cytologic evaluation retrospectively. Cases were grouped into three categories based on their cytological features: Group-0 (Benign), Group-1 (mild atypical features), and Group-2 (severe atypical features/unequivocally carcinoma). IO diagnoses were given by combining the radiologic solid size and cytological findings. RESULTS Cytological features of Group-1 were observed in 100%, 93%, 32.5%, and 17% of the AIS, MIA, IA, and benign lesions, respectively. Cytological features of Group-2 were observed in 67.5%, and 7% of the IA and MIA, respectively. By combining cytology with radiologic solid size, 100%, 85%, 71%, and 83% of the AIS, IA, MIA, and benign lesions respectively were diagnosed correctly. Fifteen (15%) percent of the IA cases were underdiagnosed as MIA since their radiological solid sizes were less than 0.5 cm with cytological features of Group-1. Conversely, 29% of the MIA cases were overdiagnosed as IA since their radiological solid sizes were greater than 0.5 cm. CONCLUSION SLNs should be handled with caution in terms of IO management. SC and radiologic solid size correlation both provide a practical and tissue-protecting approach for the IO evaluation of SLNs, ensuring a high consistency between IO and definitive diagnosis.
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Affiliation(s)
- Pınar Bulutay
- Department of Pathology, Koç University Hospital, Istanbul, Turkey
| | - Çetin Atasoy
- Department of Radiology, Koç University Hospital, Istanbul, Turkey
| | - Suat Erus
- Department of Thoracic Surgery, Koç University Hospital, Istanbul, Turkey
| | - Serhan Tanju
- Department of Thoracic Surgery, Koç University Hospital, Istanbul, Turkey
| | - Şükrü Dilege
- Department of Thoracic Surgery, Koç University Hospital, Istanbul, Turkey
| | - Pınar Fırat
- Department of Pathology, Koç University Hospital, Istanbul, Turkey
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Zhang J, Tang K, Liu L, Guo C, Zhao K, Li S. Management of pulmonary nodules in women with pregnant intention: A review with perspective. Ann Thorac Med 2023; 18:61-69. [PMID: 37323371 PMCID: PMC10263075 DOI: 10.4103/atm.atm_270_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/04/2022] [Accepted: 09/13/2022] [Indexed: 06/17/2023] Open
Abstract
The process for the management of pulmonary nodules in women with pregnant intention remains a challenge. There was a certain proportion of targeted female patients with high-risk lung cancer, and anxiety for suspicious lung cancer in early stage also exists. A comprehensive review of hereditary of lung cancer, effects of sexual hormone on lung cancer, natural history of pulmonary nodules, and computed tomography imaging with radiation exposure based on PubMed search was completed. The heredity of lung cancer and effects of sexual hormone on lung cancer are not the decisive factors, and the natural history of pulmonary nodules and the radiation exposure of imaging should be the main concerns. The management of incidental pulmonary nodules in young women with pregnant intention is an intricate and indecisive problem we have to encounter. The balance between the natural history of pulmonary nodules and the radiation exposure of imaging should be weighed.
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Affiliation(s)
- Jiaqi Zhang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kun Tang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
- Institute of Respiratory Disease of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lei Liu
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Guo
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ke Zhao
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shanqing Li
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li Q, Zhu L, von Stackelberg O, Triphan SMF, Biederer J, Weinheimer O, Eichinger M, Vogelmeier CF, Jörres RA, Kauczor HU, Heußel CP, Jobst BJ, Wielpütz MO. MRI Compared with Low-Dose CT for Incidental Lung Nodule Detection in COPD: A Multicenter Trial. Radiol Cardiothorac Imaging 2023; 5:e220176. [PMID: 37124637 PMCID: PMC10141334 DOI: 10.1148/ryct.220176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 05/02/2023]
Abstract
Purpose To investigate morphofunctional chest MRI for the detection and management of incidental pulmonary nodules in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods In this prospective study, 567 participants (mean age, 66 years ± 9 [SD]; 340 men) underwent same-day contrast-enhanced MRI and nonenhanced low-dose CT (LDCT) in a nationwide multicenter trial (clinicaltrials.gov: NCT01245933). Nodule dimensions, morphologic features, and Lung Imaging Reporting and Data System (Lung-RADS) category were assessed at MRI by two blinded radiologists, and consensual LDCT results served as the reference standard. Comparisons were performed using the Student t test, and agreements were assessed using the Cohen weighted κ. Results A total of 525 nodules larger than 3 mm in diameter were detected at LDCT in 178 participants, with a mean diameter of 7.2 mm ± 6.1 (range, 3.1-63.1 mm). Nodules were not detected in the remaining 389 participants. Sensitivity and positive predictive values with MRI for readers 1 and 2, respectively, were 63.0% and 84.8% and 60.2% and 83.9% for solid nodules (n = 495), 17.6% and 75.0% and 17.6% and 60.0% for part-solid nodules (n = 17), and 7.7% and 100% and 7.7% and 50.0% for ground-glass nodules (n = 13). For nodules 6 mm or greater in diameter, sensitivity and positive predictive values were 73.3% and 92.2% for reader 1 and 71.4% and 93.2% for reader 2, respectively. Readers underestimated the long-axis diameter at MRI by 0.5 mm ± 1.7 (reader 1) and 0.5 mm ± 1.5 (reader 2) compared with LDCT (P < .001). For Lung-RADS categorization per nodule using MRI, there was substantial to perfect interreader agreement (κ = 0.75-1.00) and intermethod agreement compared with LDCT (κ = 0.70-1.00 and 0.69-1.00). Conclusion In a multicenter setting, morphofunctional MRI showed moderate sensitivity for detection of incidental pulmonary nodules in participants with COPD but high agreement with LDCT for Lung-RADS classification of nodules.Clinical trial registration no. NCT01245933 and NCT02629432Keywords: MRI, CT, Thorax, Lung, Chronic Obstructive Pulmonary Disease, Screening© RSNA, 2023 Supplemental material is available for this article.
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Goceri E. Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev 2023; 56:1-45. [PMID: 37362888 PMCID: PMC10027281 DOI: 10.1007/s10462-023-10453-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.
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Affiliation(s)
- Evgin Goceri
- Department of Biomedical Engineering, Engineering Faculty, Akdeniz University, Antalya, Turkey
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He W, Guo G, Du X, Guo S, Zhuang X. CT imaging indications correlate with the degree of lung adenocarcinoma infiltration. Front Oncol 2023; 13:1108758. [PMID: 36969028 PMCID: PMC10036829 DOI: 10.3389/fonc.2023.1108758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
BackgroundGround glass nodules (GGN) of the lung may be a precursor of lung cancer and have received increasing attention in recent years with the popularity of low-dose high-resolution computed tomography (CT). Many studies have discussed imaging features that suggest the benignity or malignancy of GGN, but the extent of its postoperative pathological infiltration is poorly understood. In this study, we identified CT imaging features that indicate the extent of GGN pathological infiltration.MethodsA retrospective analysis of 189 patients with pulmonary GGN from January 2020 to December 2021 at Shanxi Cancer Hospital was performed. Patients were classified according to their pathological type into non-invasive adenocarcinoma [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) in a total of 34 cases], micro-invasive adenocarcinoma (MIA) in 80 cases, and invasive adenocarcinoma (IAC) in a total of 75 cases. The general demographic data, nodule size, nodule area, solid component, CT indications and pathological findings of the three groups of patients were analyzed to predict the correlation between GGN and the degree of lung adenocarcinoma infiltration.ResultsNo statistically significant differences were found among the three groups in general information, vascular signs, and vacuolar signs (P > 0.05). Statistically significant differences among the three groups were found in nodule size, nodule area, lobar signs, pleural traction, burr signs, bronchial signs, and solid components (P < 0.05). Logistic regression equation tests based on the statistically significant indicators showed that nodal area, lobar sign, pleural pull, burr sign, bronchial sign, and solid component were independent predictors of lung adenocarcinoma infiltration. The subject operating characteristic (ROC) curve analysis showed that nodal area is valuable in predicting GGN infiltration.ConclusionCT-based imaging indications are useful predictors of infiltrative adenocarcinoma manifested as pulmonary ground glass nodules.
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Affiliation(s)
- Wenchen He
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Gang Guo
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoxiang Du
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Shiping Guo
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
- *Correspondence: Shiping Guo, ; Xiaofei Zhuang,
| | - Xiaofei Zhuang
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Cardiothoracic Surgery, Lvliang People's Hospital, Lvliang, Shanxi, China
- *Correspondence: Shiping Guo, ; Xiaofei Zhuang,
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Preoperative Localization Using Methylene Blue, Coils, and Per-operative Ultrasound for Small Lung Lesions During Thoracoscopic Surgery. Indian J Surg 2023. [DOI: 10.1007/s12262-023-03715-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
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Beddok A, Chabi-Charvillat ML, Kennel T, de Wolf J, Pricopi C, Crequit P, Girard N, Otz J, Vallée A, Longchampt E, Sage E, Glorion M. Prospective Radiologic-Pathologic Correlation of Macroscopic Volume and Microscopic Extension of Nonsolid Lung Nodules on Thin-section CT Images for Sublobar Resection and Stereotactic Radiotherapy Planning. Clin Lung Cancer 2023; 24:98-106. [PMID: 36509664 DOI: 10.1016/j.cllc.2022.11.001] [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: 07/14/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION The objective of this study was to determine whether computed tomography (CT) could be a useful tool for nonsolid lung nodule (NSN) treatment planning, surgery or stereotactic body radiation therapy (SBRT), by assessing the macroscopic and microscopic extension of these nodules. METHODS The study prospectively included 23 patients undergoing anatomic resection at the Foch Hospital in 2020/2021 for NSN with a ground-glass component of more than 50%. Firstly, for each patient, both the macroscopic dimensions of the NSN were assessed on CT and during pathologic analysis. Secondly, the microscopic extension was assessed during pathologic examination. Wilcoxon sign rank tests were used to compare these dimensions. Spearman correlation test and Bland-Altman analysis were used to evaluate the agreement between radiological and pathologic measurements. RESULTS On CT, the median largest diameter and volume of NSN were 21 mm and 3780 cc, while on pathologic analysis, they were 15 mm and 1800 cc, respectively. Therefore, the largest diameter and volume of the NSN were significantly higher on CT than on pathological analysis. For microscopic extension, the median largest diameter and volume of NSN were 17 mm and 2040 cc, respectively. No significant difference was observed between the macroscopic size and the microscopic extension assessed during pathologic analysis. Moreover, correlation analysis and Bland-Altman plots showed that radiological and pathologic measurements could provide equivalent precision. CONCLUSION Our study showed that CT did not underestimate the macroscopic size and microscopic extension of NSN and confirmed that CT can be used for NSN treatment planning.
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Affiliation(s)
- Arnaud Beddok
- Radiation Oncology Department, Proton Therapy Centre, Centre Universitaire, Institut Curie, PSL Research University, Orsay, France; Laboratory of Translational Imaging in Oncology (LITO), Institut Curie, PSL Research University, University Paris Saclay, Inserm, Orsay, France.
| | | | - Titouan Kennel
- Department of Epidemiology-Data-Biostatistics, Delegation of Clinical Research and Innovation (DRCI), Foch hospital, Suresnes, France
| | - Julien de Wolf
- Department of Thoracic Surgery, Hôpital Foch, Suresnes, France
| | - Ciprian Pricopi
- Department of Thoracic Oncology, Hôpital Foch, Suresnes, France
| | - Perrine Crequit
- Department of Epidemiology-Data-Biostatistics, Delegation of Clinical Research and Innovation (DRCI), Foch hospital, Suresnes, France
| | | | - Joelle Otz
- Radiation Oncology Department, Institut Curie, Saint-Cloud, France
| | - Alexandre Vallée
- Department of Epidemiology-Data-Biostatistics, Delegation of Clinical Research and Innovation (DRCI), Foch hospital, Suresnes, France
| | | | - Edouard Sage
- Department of Thoracic Surgery, Hôpital Foch, Suresnes, France
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Chen Z, Long Y, Zhang Y, Zhang B, He Q, Zhang X. Detection efficacy of analog [ 18F]FDG PET/CT, digital [ 18F]FDG, and [ 13N]NH 3 PET/CT: a prospective, comparative study of patients with lung adenocarcinoma featuring ground glass nodules. Eur Radiol 2023; 33:2118-2127. [PMID: 36322193 DOI: 10.1007/s00330-022-09186-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES This prospective study compared the detection efficacy of analog 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) (aF PET/CT), digital [18F]FDG PET/CT (dF PET/CT), and digital 13N-ammonia (13N-NH3) PET/CT (dN PET/CT) for patients with lung adenocarcinoma featuring ground glass nodules (GGNs). METHODS Eighty-seven patients with lung adenocarcinoma featuring GGNs who underwent dF and dN PET/CT were enrolled. Based on the GGN component, diameter, and solid-part size, 87 corresponding patients examined using aF PET/CT were included, with age, sex, and lesion characteristics closely matched. Images were visually evaluated, and the tumor to background ratio (TBR) was used for semi-quantitative analysis. RESULTS Ultimately, 40 and 47 patients with pure GGNs (pGGNs) and mixed GGNs (mGGNs), respectively, were included. dF PET/CT revealed more positive lesions and higher tracer uptake in GGNs than did aF PET/CT (53/87 vs. 26/87, p < 0.05; TBR: 3.08 ± 4.85 vs. 1.42 ± 0.93, p < 0.05), especially in mGGNs (44/47 vs. 26/47, p < 0.05; TBR: 4.48 ± 6.17 vs. 1.78 ± 1.16, p < 0.05). However, dN PET/CT detected more positive lesions than did dF PET/CT (71/87 vs. 53/87, p < 0.05), especially in pGGNs (24/40 vs. 9/40, p < 0.05). CONCLUSIONS dF PET/CT provides superior detection efficacy over aF PET/CT for patients with lung adenocarcinoma featuring GGNs, particularly mGGNs. dN PET/CT revealed superior detection efficacy over dF PET/CT, particularly in pGGNs. aF, dF, and dN PET/CT are valuable non-invasive examinations for lung cancer featuring GGNs, with dN PET/CT offering the best detection performance. KEY POINTS • Digital PET/CT provides superior detection efficacy over analog PET/CT in patients with lung adenocarcinoma featuring GGNs. • dN PET/CT can offer more help in the early detection of malignant GGN.
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Affiliation(s)
- Zhifeng Chen
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Yali Long
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Yuying Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Bing Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Qiao He
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China
| | - Xiangsong Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Er Road, Guangzhou, 510080, Guangdong Province, People's Republic of China.
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Ma H, Li S, Zhu Y, Zhang W, Luo Y, Liu B, Gou W, Xie C, Li Q. A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator? Ann Surg Oncol 2023; 30:3769-3778. [PMID: 36820932 DOI: 10.1245/s10434-023-13248-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/29/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.
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Affiliation(s)
- Huiyun Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China
| | - Shuangjiang Li
- Department of Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, China
| | - Ying Zhu
- Department of Radiology, Sun Yat-sen University First Affiliated Hospital, Guangzhou, 510080, Guangdong, China
| | - Wenbiao Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China
| | - Yingwei Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China
| | - Baocong Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China
| | - Wenjing Gou
- Department of Radiology, Sichuan Provincial People's Hospital, Chengdu, 610072, Sichuan, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China.
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, China.
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Park J, Doo KW, Sung YE, Jung JI, Chang S. Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules. Can Assoc Radiol J 2023; 74:137-146. [PMID: 35840350 DOI: 10.1177/08465371221110913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Purpose: To comprehensively evaluate qualitative and quantitative features for predicting invasiveness of pure ground-glass nodules (pGGNs) using multiplanar computed tomography. Methods: Ninety-three resected pGGNs (16 atypical adenomatous hyperplasia [AAH], 18 adenocarcinoma in situ [AIS], 31 minimally invasive adenocarcinoma [MIA], and 28 invasive adenocarcinoma [IA]) were retrospectively included. Two radiologists analyzed qualitative and quantitative features on three standard planes. Univariable and multivariable logistic regression analyses were performed to identify features to distinguish the pre-invasive (AAH/AIS) from the invasive (MIA/IA) group. Results: Tumor size showed high area under the curve (AUC) for predicting invasiveness (.860, .863, .874, and .893, for axial long diameter [AXLD], multiplanar long diameter, mean diameter, and volume, respectively). The AUC for AXLD (cutoff, 11 mm) was comparable to that of the volume (P = .202). The invasive group had a significantly higher number of qualitative features than the pre-invasive group, regardless of tumor size. Six out of 59 invasive nodules (10.2%) were smaller than 11 mm, and all had at least one qualitative feature. pGGNs smaller than 11 mm without any qualitative features (n = 16) were all pre-invasive. In multivariable analysis, AXLD, vessel change, and the presence or number of qualitative features were independent predictors for invasiveness. The model with AXLD and the number of qualitative features achieved the highest AUC (.902, 95% confidence interval .833-.971). Conclusion: In adenocarcinomas manifesting as pGGNs on computed tomography, AXLD and the number of qualitative features are independent risk factors for invasiveness; small pGGNs (<11 mm) without qualitative features have low probability of invasiveness.
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Affiliation(s)
- Jeaneun Park
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung Won Doo
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeoun Eun Sung
- Department of Hospital Pathology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
| | - Suyon Chang
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, 37128The Catholic University of Korea, Seoul, Republic of Korea
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Ai M. Safety and effectiveness of simultaneous localization of multiple lung nodules using coils and risk factors for pneumothorax: a retrospective study. Acta Radiol 2023; 64:581-587. [PMID: 35521822 DOI: 10.1177/02841851221093764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Localization of lung nodule before video-assisted thoracoscopic surgery (VATS) can help the surgeon to quickly and accurately find the lesion during surgery. PURPOSE To evaluate the safety and effectiveness of using coils to simultaneously locate multiple lung nodules under computed tomography guidance and to clarify the risk factors for pneumothorax. MATERIAL AND METHODS From January 2020 to December 2020, 61 patients underwent simultaneous localization of multiple lung nodules (Group A) and 120 patients underwent localization of a single lung nodule (Group B). The demographics, information related to localization procedure, and incidence of pulmonary hemorrhage and pneumothorax were compared between the patients in Groups A and B. Group A was further divided into a pneumothorax group and non-pneumothorax group. Univariate and multivariate regression analyses were used to determine the risk factors for pneumothorax in patients who underwent simultaneous localization of multiple lung nodules using coils. RESULTS The success rates in Groups A and B were 96.9% and 96.7%, respectively (P = 1.000). The number of pleural punctures (P<0.001), the positioning operation time (P<0.001), the rates of pneumothorax (P<0.001), and hemorrhage (P = 0.034) were higher in Group A than in Group B. The pneumothorax and bleeding in Group A did not require special treatment. Transfissural puncture (odds ratio [OR]=16.798; P = 0.033) and the numbers of pleural punctures (OR=2.437; P = 0.013) were independent risk factors for pneumothorax caused by simultaneous localization of multiple lung nodules, and hemorrhage was a protective factor against pneumothorax (OR=0.069; P = 0.002). CONCLUSION Simultaneous localization of multiple lung nodules using coils under computed tomography guidance is safe and effective. Transfissural puncture and higher numbers of pleural punctures will increase the risk of pneumothorax, whereas hemorrhage will reduce the risk of pneumothorax.
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Affiliation(s)
- Min Ai
- Department of Interventional Therapy, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, PR China
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Sugawara H, Yatabe Y, Watanabe H, Akai H, Abe O, Watanabe SI, Kusumoto M. Radiological precursor lesions of lung squamous cell carcinoma: Early progression patterns and divergent volume doubling time between hilar and peripheral zones. Lung Cancer 2023; 176:31-37. [PMID: 36584605 DOI: 10.1016/j.lungcan.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/28/2022] [Accepted: 12/17/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES This study investigated the early progression patterns of lung squamous cell carcinoma (SqCC) on computed tomography (CT) images. MATERIALS AND METHODS In total, 65 patients with SqCC who underwent surgical resection and two CT scans separated by an interval of at least 6 months were enrolled. We categorized the findings of the initial and at-diagnosis CT images into five patterns as previously reported. The volume doubling time (VDT) was calculated for measurable lesions. RESULTS A single nodule pattern on CT images at-diagnosis was most common in 56 (86.2 %) patients, in line with practical clinical findings. However, the patterns were diverse in the initial images, with 28 (43.1 %) patients displaying atypical findings, including multiple nodules (3.1 %), endobronchial lesions (20.0 %), subsolid nodules (10.8 %), and cyst wall thickening (9.2 %). All endobronchial lesions were located in the central/middle zone of the lung field, whereas lesions presented as multiple nodules, subsolid nodules, and cyst wall thickening were predominantly observed in the peripheral zone. The differences in the developed zones were reflected in the median VDT, and the tumors with an initial endobronchial pattern had a significantly shorter VDT than those with a subsolid nodule pattern (median: 140 days vs 276 days, p < 0.001). CONCLUSIONS Lung SqCC initiated with various CT image patterns, although most tumors ultimately developed a single nodule pattern by diagnosis. The initial CT image patterns differed between the hilar and peripheral zones, suggesting a difference in the progression scheme, which was also supported by differences in VDT.
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Affiliation(s)
- Haruto Sugawara
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan; Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan.
| | - Hirokazu Watanabe
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Masahiko Kusumoto
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
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Deng J, Zeng Z, Liao Y, Zhong H, Zhang H. Cyanoacrylate glue foreign body after CT-guided localization of a pulmonary nodule during video-assisted thoracoscopic surgery: a case report. BMC Pulm Med 2023; 23:24. [PMID: 36653826 PMCID: PMC9847023 DOI: 10.1186/s12890-023-02321-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND A tracheal foreign body is a common airway aspiration that creates an emergency, which often causes unobserved respiratory problems and requires management. Iatrogenic tracheal foreign bodies are rarely observed, which results in tracheal obstruction. If the foreign body were removed from the tracheobronchial system, it would save lives. A similar case of a tracheal foreign body was focused on, which was caused by medical glue used during preoperative computed tomography localization of pulmonary nodules. CASE PRESENTATION The foreign body was deposited in the right upper bronchi, accidentally discovered after anesthesia when a double-lumen tube was located by fiber bronchoscopy. Following a video-assisted thoracoscopic surgery, the foreign body was removed using a respiratory endoscopy without subsequent adverse consequences for the patient. CONCLUSIONS There is a risk of complications from iatrogenic airway foreign bodies for preoperative localization of pulmonary nodules by injecting cyanoacrylate glue.
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Affiliation(s)
- Jingdan Deng
- grid.459766.fDepartment of Anesthesiology, Meizhou People’s Hospital, Meizhou City, 514031 Guangdong Province China
| | - Zhiwen Zeng
- grid.459766.fDepartment of Anesthesiology, Meizhou People’s Hospital, Meizhou City, 514031 Guangdong Province China
| | - Yilin Liao
- grid.459766.fDepartment of Anesthesiology, Meizhou People’s Hospital, Meizhou City, 514031 Guangdong Province China
| | - Haihui Zhong
- grid.459766.fDepartment of Thoracic Surgery, Meizhou People’s Hospital, Meizhou City, 514031 Guangdong Province China
| | - Huanrong Zhang
- grid.459766.fDepartment of Thoracic Surgery, Meizhou People’s Hospital, Meizhou City, 514031 Guangdong Province China
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He Y, Xiong Z, Tian D, Zhang J, Chen J, Li Z. Natural progression of persistent pure ground-glass nodules 10 mm or smaller: long-term observation and risk factor assessment. Jpn J Radiol 2023; 41:605-616. [PMID: 36607551 DOI: 10.1007/s11604-022-01382-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/26/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Semi-automatic segmentation was used to investigate the natural progression of pure ground-glass nodules (pGGNs) of 5-10 mm in long-term follow-up and to analyze independent risk factors for subsequent growth. MATERIALS AND METHODS A total of 154 pGGNs of 5-10 mm from 132 patients with 698 follow-up CT scans were retrospectively identified. Subsequently, enrolled pGGNs were semiautomatically segmented on initial and follow-up CT to obtain diameter, density and volume, thus calculating mass, volume doubling time (VDT), and mass doubling time (MDT). Kaplan‒Meier analysis and multivariate Cox proportional risk regression were performed to explore independent predictors of pGGN growth. We analyzed growth differences among different pathological results of pGGNs confirmed by surgery. The prognosis was analyzed using the total diameter or solid size of the nodules on the last preoperative CT. RESULTS Among the 85 (55.2%) pGGNs with growth, 5.9%, 51.8%, and 80.0% showed growth within 1, 3, and 5 years, respectively. The median VDT and MDT were 1206.4 (range 349.8-5134.4) days and 1161.3 (range 339.4-6630.4) days, respectively. The multivariate Cox risk regression analysis showed that mean CT attenuation (m-CTA) [hazard ratio (HR) = 2.098, p = 0.010] and roundness index (HR = 1.892, p = 0.021) were independent risk factors for pGGN growth. In total, 67.6% of surgically resected and growing pGGNs were invasive non-mucinous adenocarcinoma (IA), including 2 cases of endpoint events, showing a PSN with solid components of 5.6 mm and a solid nodule with a diameter of 19.9 mm. CONCLUSIONS pGGNs of 5-10 mm showed an indolent clinical course. Follow-up CT imaging of pGGNs in the latter half of the first two years should be a rational management strategy. Small pGGNs with a larger overall m-CTA and roundness index on baseline CT are more likely to grow.
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Affiliation(s)
- Yifan He
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan, Xigang District, Dalian, 116011, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan, Xigang District, Dalian, 116011, China
| | - Di Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan, Xigang District, Dalian, 116011, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan, Xigang District, Dalian, 116011, China
| | - Jianzhou Chen
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan, Xigang District, Dalian, 116011, China.
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Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H, Kang R, Zhang L, Liu S, Sun X, Zhang S. Special issue "The advance of solid tumor research in China": Participants with a family history of cancer have a higher participation rate in low-dose computed tomography for lung cancer screening. Int J Cancer 2023; 152:7-14. [PMID: 35362560 PMCID: PMC9790604 DOI: 10.1002/ijc.34010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 12/30/2022]
Abstract
We aimed to determine participation in low-dose computed tomography (LDCT) of individuals with a family history of common cancers in a population-based screening program to provide timely evidence in high-risk populations in China. The analysis was conducted using data from the Cancer Screening Program in Urban China (CanSPUC), which recruited 282 377 participants aged 40 to 74 years from eight cities in the Henan province. Using the CanSPUC risk score system, 55 428 participants were evaluated to have high risk for lung cancer and were recommended for LDCT. We calculated the overall and group-specific participation rates using family history of common cancers and compared differences in participation rates between different groups. Odds ratios (ORs) and 95% confidence intervals were derived by multivariable logistic regression. Of the 55 428 participants, 22 260 underwent LDCT (participation rate, 40.16%). Family history of lung, esophageal, stomach, liver and colorectal cancer was associated with increased participation in LDCT screening. The odds of participants with a family history of one, two, three and four or more cancer cases undergoing LDCT screening were 1.9, 2.7, 2.8 and 3.5 times, respectively, than those without a family history of cancer. Compared to those without a history of cancer, participation in LDCT gradually increased as the number of cancer cases in the family increased (P < .001). Our findings suggest that there is room for improvement in lung cancer screening given the relatively low participation rate. Lung cancer screening in populations with a family history of cancer may improve efficiency and cost-effectiveness; however, this requires further verification.
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Affiliation(s)
- Lan‐Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Qing‐Cheng Meng
- Department of RadiologyThe Affiliated Cancer Hospital of Zhengzhou University &Henan Cancer HospitalZhengzhouChina
| | - Li‐Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Hui‐Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Rui‐Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Lu‐Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Shu‐Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Xi‐Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
| | - Shao‐Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer PreventionThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouChina
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Gao J, Qi Q, Li H, Wang Z, Sun Z, Cheng S, Yu J, Zeng Y, Hong N, Wang D, Wang H, Yang F, Li X, Li Y. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules. Front Oncol 2023; 13:1096453. [PMID: 36910632 PMCID: PMC9996279 DOI: 10.3389/fonc.2023.1096453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Background Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness. Methods We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves. Results In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058-3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004-1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259-27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057-9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399-0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750-7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886). Conclusion We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.
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Affiliation(s)
- Jian Gao
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Qingyi Qi
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Hao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zhenfan Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Zewen Sun
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Sida Cheng
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Jie Yu
- Department of Thoracic Surgery, Qingdao Women and Children's Hospital, Qingdao, China
| | - Yaqi Zeng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Huiyang Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Feng Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Xiao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Yun Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.,Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
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Koike H, Ashizawa K, Tsutsui S, Fukuda M, Okano S, Matsumoto K, Nagayasu T, Honda S, Uetani M. Surgically resected lung adenocarcinoma: do heterogeneous GGNs and part-solid nodules on thin-section CT show different prognosis? Jpn J Radiol 2023; 41:164-171. [PMID: 36219310 PMCID: PMC9889431 DOI: 10.1007/s11604-022-01345-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/27/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE This study aimed to evaluate the clinical courses of patients with surgically resected stage IA pulmonary adenocarcinoma (Ad) who exhibited heterogeneous ground-glass nodules (GGNs) or part-solid nodules on thin-section computed tomography (TSCT) and to clarify the prognostic differences between them. MATERIALS AND METHODS The cases of 242 patients with proven pulmonary Ad with heterogeneous GGN or part-solid nodule who underwent surgical resection were retrospectively reviewed. After surgery, they were examined pathologically. Disease-free survival (DFS) and overall survival (OS) were also investigated. RESULTS There were no cases of recurrent pulmonary Ad or death from the primary disease in the heterogeneous GGN group. In the part-solid nodule group, recurrent pulmonary Ad and death from the primary disease were observed in 12 and 6 of 181 patients, respectively. Heterogeneous GGNs were associated with significantly longer DFS than part-solid nodules (p = 0.042). While, there was no significant difference in OS between the two groups (p = 0.134). Pathological diagnoses were available for all 242 patients. 181 part-solid nodules were classified into 116 invasive Ads, 54 minimally invasive Ads (MIAs), and 11 Ad in situ (AIS) lesions, and 61 heterogeneous GGNs were classified into 18 invasive Ads, 25 MIAs, and 18 AIS lesions. CONCLUSION Heterogeneous GGNs were significantly associated with longer DFS than part-solid nodules. Pathologically, there were significant differences between the heterogeneous GGNs and part-solid nodules.
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Affiliation(s)
- Hirofumi Koike
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Kazuto Ashizawa
- Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Shin Tsutsui
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Minoru Fukuda
- Clinical Oncology Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Shinji Okano
- Depatment of Pathology, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Keitaro Matsumoto
- Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Takeshi Nagayasu
- Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
| | - Sumihisa Honda
- Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nursing, Nagasaki, 852-8501 Japan
| | - Masataka Uetani
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501 Japan
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Li X, Zhang G, Gao S, Xue Q, He J. Knowledge mapping visualization of the pulmonary ground-glass opacity published in the web of science. Front Oncol 2022; 12:1075350. [PMID: 36620580 PMCID: PMC9815441 DOI: 10.3389/fonc.2022.1075350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives With low-dose computed tomography(CT) lung cancer screening, many studies with an increasing number of patients with ground-glass opacity (GGO) are published. Hence, the present study aimed to analyze the published studies on GGO using bibliometric analysis. The findings could provide a basis for future research in GGO and for understanding past advances and trends in the field. Methods Published studies on GGO were obtained from the Web of Science Core Collection. A bibliometric analysis was conducted using the R package and VOSviewer for countries, institutions, journals, authors, keywords, and articles relevant to GGO. In addition, a bibliometric map was created to visualize the relationship. Results The number of publications on GGO has been increasing since 2011. China is ranked as the most prolific country; however, Japan has the highest number of citations for its published articles. Seoul National University and Professor Jin Mo Goo from Korea had the highest publications. Most top 10 journals specialized in the field of lung diseases. Radiology is a comprehensive journal with the greatest number of citations and highest H-index than other journals. Using bibliometric analysis, research topics on "prognosis and diagnosis," "artificial intelligence," "treatment," "preoperative positioning and minimally invasive surgery," and "pathology of GGO" were identified. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO. In addition, most top 10 literatures in this field were guidelines for lung cancer and pulmonary nodules. Conclusions The publication volume of GGO has increased rapidly. The top three countries with the highest number of published articles were China, Japan, and the United States. Japan had the most significant number of citations for published articles. Most key journals specialized in the field of lung diseases. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO.
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Affiliation(s)
| | | | | | - Qi Xue
- *Correspondence: Qi Xue, ; Jie He,
| | - Jie He
- *Correspondence: Qi Xue, ; Jie He,
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Dong H, Zhang J, Min W, Shen Q. Osimertinib showed efficacy on contralateral multiple ground-glass nodules after segmentectomy for lung adenocarcinoma harboring primary EGFR-T790M mutation: a case report and review of the literature. J Cardiothorac Surg 2022; 17:324. [PMID: 36536456 PMCID: PMC9761993 DOI: 10.1186/s13019-022-02071-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Multiple ground-glass nodules (mGGNs) in the lung has been defined as synchronous multiple primary lung cancer (SMPLC), it is has been very difficult challenging to differentiate SMPLC from intrapulmonary metastases, and its treatment remains controversial. CASE PRESENTATION We report a case simultaneously involving mGGNs and lung adenocarcinoma harboring primary EGFR-T790M mutation, in which the patient underwent the radical resection of lesions in the left upper lung, and continued the osimertinib treatment for the residual mGGNs in all lobes of the right lung. These mGGNs displayed different responses to osimertinib. CONCLUSIONS We reported a successful strategy on the postoperative treatment for mGGNs. For those that cannot be completely resected, the chemotherapy, radiotherapy, stereotactic body radiation therapy, immunotherapy and targeted therapy have been performed instead. The EGFR-TKI therapy strategy showed significant advantages, but how to achieve even better therapeutic effect needs more researches.
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Affiliation(s)
- Haijun Dong
- grid.413679.e0000 0004 0517 0981Department of Thoracic Surgery, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China ,grid.413679.e0000 0004 0517 0981Department of Pneumology, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China
| | - Jianbin Zhang
- grid.413679.e0000 0004 0517 0981Department of Thoracic Surgery, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China ,grid.413679.e0000 0004 0517 0981Department of Pneumology, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China
| | - Weiwei Min
- grid.413679.e0000 0004 0517 0981Department of Thoracic Surgery, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China ,grid.413679.e0000 0004 0517 0981Department of Pneumology, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China
| | - Qibin Shen
- grid.413679.e0000 0004 0517 0981Department of Thoracic Surgery, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China ,grid.413679.e0000 0004 0517 0981Department of Pneumology, Huzhou Central Hospital, Affiliated Central Hospital of HuZhou University, 1558 Third Ring North Road, Huzhou, 313000 Zhejiang China
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