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Rowe DJ, Khalil TA, Kammer MN, Godfrey CM, Zou Y, Vnencak-Jones CL, Xiao D, Deppen S, Grogan EL. A deeper evaluation of cytokeratin fragment 21-1 as a lung cancer tumor marker and comparison of different assays. BIOSENSORS & BIOELECTRONICS: X 2025; 23:100593. [PMID: 40329987 PMCID: PMC12055278 DOI: 10.1016/j.biosx.2025.100593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/08/2025]
Abstract
Studies show CYFRA 21-1 fragments of cytokeratin 19 (CK19) to be promising biomarkers for non-small cell lung cancer (NSCLC). Although previous literature identifies specific CYFRA 21-1 antibody binding epitopes, the exact molecular weight of the CK19 fragment being detected by current assays is not well-documented. Serum samples from 58 patients (lung cancer (N = 36), control (N = 22)) were used to measure CYFRA 21-1 across four different quantification assays: enzyme-linked immunosorbent assay (ELISA), chemiluminescent assay (ChLIA), electrochemiluminescence immunoassay (ECLIA), and compensated interferometric reader (CIR). In the cancer group, correlation between ECLIA and ELISA was high (R(Pearson) = 0.948, r(Spearman) = 0.868) while correlation between ECLIA vs ChLIA and ECLIA vs CIR was low (R= 0.005, r = -0.0593), (R = 0.0275, r = 0.167), respectively. In the control group, correlation between ECLIA and ELISA was high (R = 0.861, r = 0.927) while correlation between ECLIA vs ChLIA and ECLIA vs CIR was low (R = 0.0079, r = -0.0593), (R = 0.0244, r = -0.102), respectively. Compared to ECLIA, concordance coefficients (p c ) were poor (p c < 0.90) across all assays except for cancers group in ELISA (p c = 0.913). ECLIA was the only assay to report control ranges above 1 ng/mL CYFRA 21-1 (ECLIA, 1.14-21.59 ng/mL; ELISA, 0.79-24.26 ng/mL; ChLIA, 0.062-0.691 ng/mL; 0.08-7.68 ng/mL). Differing sizes of the protein being measured by each assay may have a role in the discrepancies observed. Given the different CYFRA 21-1 concentration estimates among assays, further characterization of the fragment and its release during epithelial malignancies, such as NSCLC, is imperative to developing effective biomarker assays.
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Affiliation(s)
- Dianna J. Rowe
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy A. Khalil
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael N. Kammer
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Caroline M. Godfrey
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yong Zou
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cindy L. Vnencak-Jones
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David Xiao
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric L. Grogan
- Tennessee Valley Healthcare Systems, Nashville Campus, Nashville, TN, USA
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
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Li H, Salehjahromi M, Godoy MCB, Qin K, Plummer CM, Zhang Z, Hong L, Heeke S, Le X, Vokes N, Zhang B, Araujo HA, Altan M, Wu CC, Antonoff MB, Ostrin EJ, Gibbons DL, Heymach JV, Lee JJ, Gerber DE, Wu J, Zhang J. Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model. Cancers (Basel) 2025; 17:1499. [PMID: 40361426 PMCID: PMC12070823 DOI: 10.3390/cancers17091499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2025] [Revised: 04/26/2025] [Accepted: 04/28/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND/OBJECTIVES Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction. METHODS Patients with persistent pulmonary nodules-defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage-were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort. RESULTS In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil's AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729. CONCLUSIONS For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception.
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Affiliation(s)
- Hui Li
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Morteza Salehjahromi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Kang Qin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Courtney M. Plummer
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Zheng Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Lingzhi Hong
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Simon Heeke
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Natalie Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Bingnan Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Haniel A. Araujo
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Mehmet Altan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Carol C. Wu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Mara B. Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Edwin J. Ostrin
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - David E. Gerber
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Jia Wu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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Kotoulas SC, Spyratos D, Porpodis K, Domvri K, Boutou A, Kaimakamis E, Mouratidou C, Alevroudis I, Dourliou V, Tsakiri K, Sakkou A, Marneri A, Angeloudi E, Papagiouvanni I, Michailidou A, Malandris K, Mourelatos C, Tsantos A, Pataka A. A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2025; 17:882. [PMID: 40075729 PMCID: PMC11898928 DOI: 10.3390/cancers17050882] [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: 09/15/2024] [Revised: 02/06/2025] [Accepted: 02/25/2025] [Indexed: 03/14/2025] Open
Abstract
According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5-10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities-such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans-but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis.
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Affiliation(s)
- Serafeim-Chrysovalantis Kotoulas
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Dionysios Spyratos
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Konstantinos Porpodis
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Kalliopi Domvri
- Pulmonary Department, Unit of thoracic Malignancies Research, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece; (D.S.); (K.P.); (K.D.)
| | - Afroditi Boutou
- Pulmonary Department General, Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (A.B.); (A.T.)
| | - Evangelos Kaimakamis
- 1st ICU, Medical Informatics Laboratory, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece;
| | - Christina Mouratidou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Ioannis Alevroudis
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Vasiliki Dourliou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Kalliopi Tsakiri
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Agni Sakkou
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Alexandra Marneri
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Elena Angeloudi
- Adult ICU, General Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (C.M.); (I.A.); (V.D.); (K.T.); (A.S.); (A.M.); (E.A.)
| | - Ioanna Papagiouvanni
- 4th Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Anastasia Michailidou
- 2nd Propaedeutic Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Konstantinos Malandris
- 2nd Internal Medicine Department, General Hospital of Thessaloniki “Ippokrateio”, Aristotle’s University of Thessaloniki, Konstantinoupoleos 49, 54642 Thessaloniki, Greece;
| | - Constantinos Mourelatos
- Biology and Genetics Laboratory, Aristotle’s University of Thessaloniki, 54624 Thessaloniki, Greece;
| | - Alexandros Tsantos
- Pulmonary Department General, Hospital of Thessaloniki “Ippokrateio”, Konstantinoupoleos 49, 54642 Thessaloniki, Greece; (A.B.); (A.T.)
| | - Athanasia Pataka
- Respiratory Failure Clinic and Sleep Laboratory, General Hospital of Thessaloniki “G. Papanikolaou”, Aristotle’s University of Thessaloniki, Leoforos Papanikolaou Municipality of Chortiatis, 57010 Thessaloniki, Greece;
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Chen S, Lin WL, Liu WT, Zou LY, Chen Y, Lu F. Pulmonary nodule malignancy probability: a meta-analysis of the Brock model. Clin Radiol 2025; 82:106788. [PMID: 39842180 DOI: 10.1016/j.crad.2024.106788] [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: 09/03/2024] [Revised: 11/13/2024] [Accepted: 12/17/2024] [Indexed: 01/24/2025]
Abstract
AIM This study aims to quantify the performance of the Brock model through a systematic review and meta-analysis and to clarify its overall accuracy in predicting malignant pulmonary nodules. MATERIALS AND METHODS A systematic search was conducted in databases including the Cochrane Library, Excerpta Medica database (EMBASE), MEDLINE, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), VIP, and Wanfang from their inception until May 1, 2024, to collect observational cohort studies involving the Brock model. The primary outcome was the pooled area under the receiver operating characteristic curve (ROC) the area under curve (AUC) for the Brock model. Secondary outcomes included sensitivity and specificity. The metaprotocol was registered in the International Prospective Register of Systematic Reviews (CRD42024538163). RESULTS A total of 52 studies involving 85,558 patients were included. The pooled AUC was 0.796 (95% confidence interval [CI]: 0.771-0.820), with a pooled sensitivity of 0.82 (95% CI: 0.76-0.87) and specificity of 0.80 (95% CI: 0.72-0.86). Subgroup analysis showed that the performance of the full model was significantly better than that of the simplified model (0.822, 95% CI: 0.794-0.849 versus 0.687, 95% CI: 0.611-0.763). The model performed excellently for pulmonary nodules with diameters of 1- to 8 mm (AUC: 0.927, 95% CI: 0.900-0.954). However, its performance was lower in Asian populations (AUC = 0.741, 95% CI: 0.703-0.780), solitary pulmonary nodules (AUC = 0.767, 95% CI: 0.693-0.842), and subsolid pulmonary nodules (AUC = 0.747, 95% CI: 0.661-0.832). CONCLUSION This meta-analysis confirms the Brock model's overall strong performance. However, the results indicate certain application limitations of the Brock model, with reduced accuracy for larger nodules (>15 mm), solitary pulmonary nodules, subsolid nodules, and in Asian populations.
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Affiliation(s)
- S Chen
- The Second People's Hospital Affiliated to Fujian University of Chinese Medicine, Fuzhou, China; Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - W L Lin
- The Second People's Hospital Affiliated to Fujian University of Chinese Medicine, Fuzhou, China; Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - W T Liu
- School of Nursing and Midwifery, Edith Cowan University, Perth, Australia
| | - L Y Zou
- Zhangzhou Hospital, Zhangzhou, China
| | - Y Chen
- School of Business, Nanjing University, Nanjing, China
| | - F Lu
- The Second People's Hospital Affiliated to Fujian University of Chinese Medicine, Fuzhou, China; Fujian Clinical Medical Research Center for Integrated Chinese and Western Medicine Diagnosis and Treatment of Early Stage Lung Cancer, Fuzhou, China.
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Piskorski L, Debic M, von Stackelberg O, Schlamp K, Welzel L, Weinheimer O, Peters AA, Wielpütz MO, Frauenfelder T, Kauczor HU, Heußel CP, Kroschke J. Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups. Eur Radiol 2025:10.1007/s00330-024-11256-8. [PMID: 39747589 DOI: 10.1007/s00330-024-11256-8] [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: 05/29/2024] [Revised: 10/14/2024] [Accepted: 10/30/2024] [Indexed: 01/04/2025]
Abstract
OBJECTIVES Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-network (LCP-CNN), a deep learning-based approach, in comparison to multiparametric statistical methods (Brock model and Lung-RADS®) for risk classification of nodules in cohorts with different risk profiles and underlying pulmonary diseases. MATERIALS AND METHODS Retrospective analysis was conducted on non-contrast and contrast-enhanced CT scans containing pulmonary nodules measuring 5-30 mm. Ground truth was defined by histology or follow-up stability. The final analysis was performed on 297 patients with 422 eligible nodules, of which 105 nodules were malignant. Classification performance of the LCP-CNN, Brock model, and Lung-RADS® was evaluated in terms of diagnostic accuracy measurements including ROC-analysis for different subcohorts (total, screening, emphysema, and interstitial lung disease). RESULTS LCP-CNN demonstrated superior performance compared to the Brock model in total and screening cohorts (AUC 0.92 (95% CI: 0.89-0.94) and 0.93 (95% CI: 0.89-0.96)). Superior sensitivity of LCP-CNN was demonstrated compared to the Brock model and Lung-RADS® in total, screening, and emphysema cohorts for a risk threshold of 5%. Superior sensitivity of LCP-CNN was also shown across all disease groups compared to the Brock model at a threshold of 65%, compared to Lung-RADS® sensitivity was better or equal. No significant differences in the performance of LCP-CNN were found between subcohorts. CONCLUSION This study offers further evidence of the potential to integrate deep learning-based decision support systems into pulmonary nodule classification workflows, irrespective of the individual patient risk profile and underlying pulmonary disease. KEY POINTS Question Is a deep-learning approach (LCP-CNN) superior to multiparametric models (Brock model, Lung-RADS®) in classifying pulmonary nodule risk across varied patient profiles? Findings LCP-CNN shows superior performance in risk classification of pulmonary nodules compared to multiparametric models with no significant impact on risk profiles and structural pulmonary diseases. Clinical relevance LCP-CNN offers efficiency and accuracy, addressing limitations of traditional models, such as variations in manual measurements or lack of patient data, while producing robust results. Such approaches may therefore impact clinical work by complementing or even replacing current approaches.
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Affiliation(s)
- Lars Piskorski
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Manuel Debic
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Kai Schlamp
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany
| | - Linn Welzel
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Oliver Weinheimer
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Alan Arthur Peters
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Department for Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mark Oliver Wielpütz
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Hans-Ulrich Kauczor
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Claus Peter Heußel
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany
| | - Jonas Kroschke
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
- Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
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Yin K, Chen W, Qin G, Liang J, Bao X, Yu H, Li H, Xu J, Chen X, Wang Y, Savage RH, Schoepf UJ, Mu D, Zhang B. Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness. Quant Imaging Med Surg 2024; 14:5708-5720. [PMID: 39144022 PMCID: PMC11320525 DOI: 10.21037/qims-24-247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 07/05/2024] [Indexed: 08/16/2024]
Abstract
Background The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.
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Affiliation(s)
- Kejie Yin
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenping Chen
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guochu Qin
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jing Liang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xue Bao
- Department of Cardiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hongming Yu
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hui Li
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jianhua Xu
- Department of Radiology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Yujie Wang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Rock H. Savage
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - U. Joseph Schoepf
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Dan Mu
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Zhuang W, Xu H, Wu J, Li Z, Tang Y, Wu H, Chen Y, Qiao G. Patient-reported respiratory symptoms and relevant factors in patients with pulmonary nodules. J Thorac Dis 2024; 16:4097-4105. [PMID: 39144361 PMCID: PMC11320227 DOI: 10.21037/jtd-23-1939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/10/2024] [Indexed: 08/16/2024]
Abstract
Background Pulmonary nodules (PNs) are commonly considered too small to cause respiratory symptoms. However, many PN patients present with respiratory symptoms of unknown origin. This study aims to explore these symptoms and identify the associated factors. Methods Demographic and clinical information were retrospectively collected from 1,633 patients with incidental PNs who visited the thoracic outpatient clinic of Guangdong Provincial People's Hospital. Hospital Anxiety and Depression Scale was used to assess their anxiety and depression level. Logistic regression analyzes were employed to assess the independent risk factors for respiratory symptoms and the psychological impact on patients. Results Among the 1,633 patients, 37.2% reported at least one respiratory symptom. The most common symptoms in patients with PNs were cough (23.6%), followed by chest pain (14.0%), expectoration (13.8%) and hemoptysis (1.3%). Patients with large PNs (>20 mm) showed significantly higher odds of having cough [odds ratio (OR) =2.5; P=0.011] and expectoration (OR =3.6; P=0.001). Patients with multiple PNs were more susceptible to chest pain compared to those with solitary PNs (OR =1.5; P=0.007). Environmental factors such as passive smoking, kitchen fume pollution, environmental dust were the consistent risk contributors to the presence of these respiratory symptoms. Comparable findings were observed among the subgroup of individuals who undergo chest computed tomography scans as a part of their routine health check-up. Presence of respiratory symptoms, especially chest pain, was associated with increased the odds of anxiety (OR =2.2; P<0.001) and depression (OR =2.5; P<0.001) in patients. Conclusions Respiratory symptoms are common in PN patients, exhibiting a higher prevalence in patients with larger and multiple PNs and there is a strong association with exposure to environmental risk factors. These symptoms might exacerbate the anxiety and depression level in patients.
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Affiliation(s)
- Weitao Zhuang
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haijie Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Shantou University Medical College, Shantou, China
| | - Junhan Wu
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Zijie Li
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Yong Tang
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hansheng Wu
- Department of Thoracic Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yali Chen
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Guibin Qiao
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
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Al-Ghoula F, Patel K, Falde S, Rajagopalan S, Bartholmai B, Maldonado F, Peikert T. Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA. Sci Rep 2023; 13:21725. [PMID: 38066214 PMCID: PMC10709549 DOI: 10.1038/s41598-023-48567-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
While CT lung cancer screening reduces lung cancer-specific mortality, there are remaining challenges. Radiomic tools promiss to address these challenges, however, they are subject to interobserver variability if semi-automated segmentation techniques are used. Herein we report interobserver variability for two validated radiomic tools, BRODERS (Benign versus aggRessive nODule Evaluation using Radiomic Stratification) and CANARY (Computer-Aided Nodule Assessment and Risk Yield). We retrospectively analyzed the CT images of 95 malignant lung nodules of the adenocarcinoma spectrum using BRODERS and CANARY. Cases were identified at Mayo Clinic (n = 45) and Vanderbilt University Medical Center and Nashville/Veteran Administration Tennessee Valley Health Care System (n = 50). Three observers with different training levels (medical student, internal medicine resident and thoracic radiology fellow) each performed lung nodule segmentation. All methods were carried out in accordance with relevant guidelines and regulations. Interclass correlation coefficients (ICC) of 0.77, 0.98 and 0.97 for the average nodule volume, BRODERS cancer probability and Score Indicative of Lesion Aggression (SILA) which summarizes the distribution of the CANARY exemplars indicated good to excellent reliability, respectively. The dice similarity coefficient was 0.79 and 0.81 for the data sets from the two institutions. BRODERS and CANARY are robust radiomics tools with excellent interobserver variability. These tools are simple and reliable regardless the observer/operator's level of training.
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Affiliation(s)
| | - Khushbu Patel
- Vanderbilt University Medical Center, Nashville, USA
- Nashville/Veteran Administration Tennessee Valley Health System, Nashville, USA
| | | | | | | | - Fabien Maldonado
- Vanderbilt University Medical Center, Nashville, USA
- Nashville/Veteran Administration Tennessee Valley Health System, Nashville, USA
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