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Baeza S, Gil D, Sanchez C, Torres G, Carmezim J, Tebé C, Guasch I, Nogueira I, García-Reina S, Martínez-Barenys C, Mate JL, Andreo F, Rosell A. Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model. Arch Bronconeumol 2024:S0300-2896(24)00192-3. [PMID: 38876917 DOI: 10.1016/j.arbres.2024.05.027] [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: 03/16/2024] [Revised: 05/16/2024] [Accepted: 05/21/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN). METHODOLOGY Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem. RESULTS The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80. CONCLUSIONS Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.
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Affiliation(s)
- Sonia Baeza
- Respiratory Medicine Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - Debora Gil
- Computer Vision Center and Computer Science Department, UAB, Barcelona, Spain
| | - Carles Sanchez
- Computer Vision Center and Computer Science Department, UAB, Barcelona, Spain
| | - Guillermo Torres
- Computer Vision Center and Computer Science Department, UAB, Barcelona, Spain
| | - João Carmezim
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Biostatistics Support and Research Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Cristian Tebé
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Biostatistics Support and Research Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Ignasi Guasch
- Radiodiagnostic Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Isabel Nogueira
- Radiodiagnostic Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Samuel García-Reina
- Thoracic Surgery Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; Departament de Cirugia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carlos Martínez-Barenys
- Thoracic Surgery Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; Departament de Cirugia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jose Luis Mate
- Pathology Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Felipe Andreo
- Respiratory Medicine Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Antoni Rosell
- Respiratory Medicine Department, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain; Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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Rocco G, Pennazza G, Tan KS, Vanstraelen S, Santonico M, Corba RJ, Park BJ, Sihag S, Bott MJ, Crucitti P, Isbell JM, Ginsberg MS, Weiss H, Incalzi RA, Finamore P, Longo F, Zompanti A, Grasso S, Solomon SB, Vincent A, McKnight A, Cirelli M, Voli C, Kelly S, Merone M, Molena D, Gray K, Huang J, Rusch VW, Bains MS, Downey RJ, Adusumilli PS, Jones DR. A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology. J Thorac Oncol 2024:S1556-0864(24)00211-9. [PMID: 38762120 DOI: 10.1016/j.jtho.2024.05.006] [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: 03/07/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
INTRODUCTION Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.
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Affiliation(s)
- Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Giorgio Pennazza
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marco Santonico
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Robert J Corba
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Bernard J Park
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Smita Sihag
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew J Bott
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pierfilippo Crucitti
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - James M Isbell
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michelle S Ginsberg
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hallie Weiss
- Department of Anesthesiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Raffaele Antonelli Incalzi
- Department of Geriatrics, Research Unit of Internal Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Panaiotis Finamore
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Filippo Longo
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Alessandro Zompanti
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Simone Grasso
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Stephen B Solomon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alain Vincent
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexa McKnight
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael Cirelli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Carmela Voli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Susan Kelly
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mario Merone
- Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Daniela Molena
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gray
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Huang
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Valerie W Rusch
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manjit S Bains
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Robert J Downey
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
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Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark 2024:CBM230360. [PMID: 38427470 DOI: 10.3233/cbm-230360] [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] [Indexed: 03/03/2024]
Abstract
Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.
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Bhalla S, Yi S, Gerber DE. Emerging Strategies in Lung Cancer Screening: Blood and Beyond. Clin Chem 2024; 70:60-67. [PMID: 38175587 PMCID: PMC11161198 DOI: 10.1093/clinchem/hvad137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/02/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection. CONTENT This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies. SUMMARY Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.
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Affiliation(s)
- Sheena Bhalla
- Department of Internal Medicine (Division of Hematology-Oncology), UT Southwestern Medical Center, Dallas, TX, United States
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, United States
| | - Sofia Yi
- School of Medicine, UT Southwestern Medical Center, Dallas, TX, United States
| | - David E Gerber
- Department of Internal Medicine (Division of Hematology-Oncology), UT Southwestern Medical Center, Dallas, TX, United States
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, United States
- Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX, United States
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van den Broek D, Groen HJM. Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities. Tumour Biol 2024; 46:S65-S80. [PMID: 37393461 DOI: 10.3233/tub-230004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2023] Open
Abstract
Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.
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Affiliation(s)
- Daniel van den Broek
- Department of laboratory Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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任 益, 马 琼, 李 芳, 曾 潇, 谭 施, 付 西, 郑 川, 由 凤, 李 雪. [Analysis of Salivary Microbiota Characteristics in Patients With Pulmonary Nodules: A Prospective Nonrandomized Concurrent Controlled Trial]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:1208-1218. [PMID: 38162086 PMCID: PMC10752765 DOI: 10.12182/20231160103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 01/03/2024]
Abstract
Objective To uncover and identify the differences in salivary microbiota profiles and their potential roles between patients with pulmonary nodules (PN) and healthy controls, and to propose new candidate biomarkers for the early warning of PN. Methods 16S rRNA amplicon sequencing was performed with the saliva samples of 173 PN patients, or the PN group, and 40 health controls, or the HC group, to compare the characteristics, including diversity, community composition, differential species, and functional changes of salivary microbiota in the two groups. Random forest algorithm was used to identify salivary microbial markers of PN and their predictive value for PN was assessed by area under the curve (AUC). Finally, the biological functions and potential mechanisms of differentially-expressed genes in saliva samples were preliminarily investigated on the basis of predictive functional profiling of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). Results The α diversity and β diversity of salivary microbiota in the PN group were higher than those in the HC group (P<0.05). Furthermore, there were significant differences in the community composition and the abundance of oral microorganisms between the PN and the HC groups (P<0.05). Random forest algorithm was applied to identify differential microbial species. Porphyromonas, Haemophilus, and Fusobacterium constituted the optimal marker sets (AUC=0.79, 95% confidence interval: 0.71-0.86), which can be used to effectively identify patients with PN. Bioinformatics analysis of the differentially-expressed genes revealed that patients with PN showed significant enrichment in protein/molecular functions involved in immune deficiency and redox homeostasis. Conclusion Changes in salivary microbiota are closely associated with PN and may induce the development of PN or malignant transformation of PN, which indicates the potential of salivary microbiota to be used as a new non-invasive humoral marker for the early diagnosis of PN.
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Affiliation(s)
- 益锋 任
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
- 成都中医药大学肿瘤研究所 (成都 610075)Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 琼 马
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 芳 李
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 潇 曾
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 施言 谭
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 西 付
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 川 郑
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 凤鸣 由
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
- 成都中医药大学肿瘤研究所 (成都 610075)Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - 雪珂 李
- 成都中医药大学附属医院 代谢性疾病中医药调控四川省重点实验室 (成都 610075)Sichuan Provincial Key Laboratory of TCM Regulation of Metabolic Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
- 成都中医药大学肿瘤研究所 (成都 610075)Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
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Godfrey CM, Shipe ME, Welty VF, Maiga AW, Aldrich MC, Montgomery C, Crockett J, Vaszar LT, Regis S, Isbell JM, Rickman OB, Pinkerman R, Lambright ES, Nesbitt JC, Maldonado F, Blume JD, Deppen SA, Grogan EL. The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation. Chest 2023; 164:1305-1314. [PMID: 37421973 PMCID: PMC10635839 DOI: 10.1016/j.chest.2023.06.009] [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: 02/09/2023] [Revised: 05/03/2023] [Accepted: 06/01/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND METHODS Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots. RESULTS Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23. INTERPRETATION The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.
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Affiliation(s)
- Caroline M Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Maren E Shipe
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Valerie F Welty
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Amelia W Maiga
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Melinda C Aldrich
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Jerod Crockett
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | | | - Shawn Regis
- Department of Radiation Oncology, Lahey Hospital and Medical Center, Burlington, MA
| | - James M Isbell
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Otis B Rickman
- Division of Pulmonary Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Rhonda Pinkerman
- Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Eric S Lambright
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Jonathan C Nesbitt
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN
| | - Fabien Maldonado
- Division of Pulmonary Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jeffrey D Blume
- School of Data Science, University of Virginia, Charlottesville, VA
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Division of Thoracic Surgery, Veterans Hospital, Tennessee Valley Healthcare System, Nashville, TN.
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8
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Paez R, Rowe DJ, Deppen SA, Grogan EL, Kaizer A, Bornhop DJ, Kussrow AK, Barón AE, Maldonado F, Kammer MN. Assessing the clinical utility of biomarkers using the intervention probability curve (IPC). Cancer Biomark 2023:CBM230054. [PMID: 38073376 PMCID: PMC11055936 DOI: 10.3233/cbm-230054] [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] [Indexed: 01/01/2024]
Abstract
BACKGROUND Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease. OBJECTIVE To assess the potential impact of a new biomarker for lung cancer using the IPC. METHODS The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker. RESULTS The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds. CONCLUSIONS The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.
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Affiliation(s)
- Rafael Paez
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Dianna J. Rowe
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Stephen A. Deppen
- Tennessee Valley Healthcare System, Nashville, Tennessee, United States of America
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Eric L. Grogan
- Tennessee Valley Healthcare System, Nashville, Tennessee, United States of America
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Alexander Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Darryl J. Bornhop
- Department of Chemistry, and The Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN
| | - Amanda K. Kussrow
- Department of Chemistry, and The Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN
| | - Anna E. Barón
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Fabien Maldonado
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Michael N. Kammer
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Multidisciplinary Approach to Stratification of Lung Cancer with Biomarkers, MASLAB, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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9
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He J, Wang B, Tao J, Liu Q, Peng M, Xiong S, Li J, Cheng B, Li C, Jiang S, Qiu X, Yang Y, Ye Z, Zeng F, Zhang J, Liu D, Li W, Chen Z, Zeng Q, Fan JB, Liang W. Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study. Lancet Digit Health 2023; 5:e647-e656. [PMID: 37567793 DOI: 10.1016/s2589-7500(23)00125-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 05/30/2023] [Accepted: 06/16/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND There is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. We propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules. METHODS We conducted a prospective specimen collection and retrospective masked evaluation study. We recruited participants with a solitary pulmonary nodule sized 5-30 mm from 24 hospitals across 20 cities in China. Participants who were aged 18 years or older and had been referred with 5-30 mm non-calcified and solitary pulmonary nodules, including solid nodules, part solid nodules, and pure ground-glass nodules, were included. We developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). We then integrated the CIBM model with our previously established circulating tumour DNA methylation model (PulmoSeek) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The clinical utility of the models was evaluated using decision curve analysis. A low cutoff (0·65) for high sensitivity and a high cutoff (0·89) for high specificity were applied simultaneously to stratify pulmonary nodules into low-risk, medium-risk, and high-risk groups. The primary outcome was the diagnostic performance of the CIBM, PulmoSeek, and PulmoSeek Plus models. Participants in this study were drawn from two prospective clinical studies that were registered (NCT03181490 and NCT03651986), the first of which was completed, and the second of which is ongoing because 25% of participants have not yet finished the required 3-year follow-up. FINDINGS We recruited a total of 1380 participants. 1097 participants were enrolled from July 7, 2017, to Feb 12, 2019; 839 participants were used for the CIBM model training set, and the rest (n=258) for the first CIBM validation set and the PulmoSeek Plus training set. 283 participants were enrolled from Oct 26, 2018, to March 20, 2020, as an independent validation set for the PulmoSeek Plus model and the second validation set for the CIBM model. The CIBM model validation cohorts had area under the curves (AUCs) of 0·85 (95% CI 0·80-0·89) and 0·85 (0·81-0·89). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models with an increase of 0·05 in AUC (PulmoSeek Plus vs CIBM, 95% CI 0·022-0·087, p=0·001; and PulmoSeek Plus vs PulmoSeek, 0·018-0·083, p=0·002). The overall sensitivity of the PulmoSeek Plus model was 0·98 (0·97-0·99) at a fixed specificity of 0·50 for ruling out lung cancer. A high sensitivity of 0·98 (0·96-0·99) was maintained in early-stage lung cancer (stages 0 and I) and 0·99 (0·96-1·00) in 5-10 mm nodules. The decision curve showed that if an invasive intervention, such as surgical resection or biopsy, was deemed necessary at more than the risk threshold score of 0·54, the PulmoSeek Plus model would provide a standardised net benefit of 82·38% (76·06-86·79%), equivalent to correctly identifying approximately 83 of 100 people with lung cancer. Using the PulmoSeek Plus model to classify pulmonary nodules with two cutoffs (0·65 and 0·89) would have reduced 89% (105/118) of unnecessary surgeries and 73% (308/423) of delayed treatments. INTERPRETATION The PulmoSeek Plus Model combining clinical, imaging, and cell-free DNA methylation biomarkers aids the early diagnosis of pulmonary nodules, with potential application in clinical decision making for the management of pulmonary nodules. FUNDING The China National Science Foundation, the Key Project of Guangzhou Scientific Research Project, the High-Level University Construction Project of Guangzhou Medical University, the National Key Research & Development Programme, the Guangdong High Level Hospital Construction "Reaching Peak" Plan, the Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, The Leading Projects of Guangzhou Municipal Health Sciences Foundation, the Key Research and Development Plan of Shaanxi Province of China, the Scheme of Guangzhou Economic and Technological Development District for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Team in Innovation, the Guangzhou Development Zone International Science and Technology Cooperation Project, and the Science and Technology Planning Project of Guangzhou.
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Affiliation(s)
- Jianxing He
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | - Bo Wang
- AnchorDx Medical, Guangzhou, China
| | | | - Qin Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | | | - Shan Xiong
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | - Jianfu Li
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | - Bo Cheng
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | - Caichen Li
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | - Shunjun Jiang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China
| | | | | | | | - Fanrui Zeng
- Department of Radiation Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jian Zhang
- Xijing Hospital of Air Force Military Medical University, Xi'an, China
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiwei Chen
- AnchorDx Medical, Guangzhou, China; AnchorDx, Fremont, CA, USA
| | - Qingsi Zeng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jian-Bing Fan
- AnchorDx Medical, Guangzhou, China; Department of Pathology, School of Basic Medical Science, Southern Medical University, Guangzhou, China.
| | - Wenhua Liang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China.
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10
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Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest 2023; 164:1028-1041. [PMID: 37244587 PMCID: PMC10645597 DOI: 10.1016/j.chest.2023.05.025] [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: 02/21/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.
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Affiliation(s)
- Rafael Paez
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Michael N Kammer
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Nicole T Tanner
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC
| | - Samira Shojaee
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Brent E Heideman
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Tobias Peikert
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Meridith L Balbach
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Wade T Iams
- Department of Medicine, Division of Hematology-Oncology, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN
| | - Boting Ning
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA
| | - Christopher Mallow
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami, Miami, FL
| | - Lonny Yarmus
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD
| | - Kwun M Fong
- University of Queensland Thoracic Research Centre, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Stephen Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Tennessee Valley Healthcare System, Nashville, TN
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Tennessee Valley Healthcare System, Nashville, TN
| | - Fabien Maldonado
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN.
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11
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Basran PS, Shcherban N, Forman M, Chang J, Nelissen S, Recchia BK, Porter IR. Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning. Vet Radiol Ultrasound 2023; 64:890-903. [PMID: 37394240 DOI: 10.1111/vru.13250] [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: 01/06/2023] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 07/04/2023] Open
Abstract
This retrospective analytical observational cohort study aimed to model and predict the classification of feline intestinal diseases from segmentations of a transverse section from small intestine ultrasound (US) image, complete blood count (CBC), and serum biochemical profile data using a variety of machine-learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy-confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology ("healthy"), and other conditions (warrant a biopsy for further diagnosis). CBC, blood serum chemistry, small intestinal ultrasound, and small intestinal biopsy were obtained within a 2-week interval. CBC and serum biomarkers and radiomic features were combined for modeling. Four classification schemes were investigated: (1) normal versus abnormal; (2) warranting or not warranting a biopsy; (3) lymphoma, IBD, healthy, or other conditions; and (4) lymphoma, IBD, or other conditions. Two feature selection methods were used to identify the top 3, 5, 10, and 20 features, and six machine learning models were trained. The average (95% CI) performance of models for all combinations of features, numbers of features, and types of classifiers was 0.886 (0.871-0.912) for Model 1 (normal vs. abnormal), 0.751 (0.735-0.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450-0.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426-0.589), for Model 4 (lymphoma, IBD, or other). Our findings suggest model accuracies above 0.85 can be achieved in Model 1 and 2, and that including CBC and biochemistry data with US radiomics data did not significantly improve accuracy in our models.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
- Department of Biological Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Natalya Shcherban
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Marnin Forman
- Cornell University Veterinary Specialists, Stamford, Connecticut, USA
| | - Jasmine Chang
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Sophie Nelissen
- Department of Biomedical Sciences, Section of Anatomic Pathology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | | | - Ian R Porter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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12
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Marmor HN, Kammer MN, Deppen SA, Shipe M, Welty VF, Patel K, Godfrey C, Billatos E, Herman JG, Wilson DO, Kussrow AK, Bornhop DJ, Maldonado F, Chen H, Grogan EL. Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers. J Thorac Cardiovasc Surg 2023; 166:669-678.e4. [PMID: 36792410 PMCID: PMC10287834 DOI: 10.1016/j.jtcvs.2022.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer. METHODS A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach. RESULTS A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16). CONCLUSIONS The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.
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Affiliation(s)
- Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Michael N Kammer
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tenn.
| | - Maren Shipe
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Valerie F Welty
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tenn
| | - Khushbu Patel
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Caroline Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Boston Medical Center, Boston, Mass
| | - James G Herman
- Division of Hematology/Oncology, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - David O Wilson
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | | | | | - Fabien Maldonado
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tenn
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tenn
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13
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Craig DJ, Crawford EL, Chen H, Grogan EL, Deppen SA, Morrison T, Antic SL, Massion PP, Willey JC. TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk. BMC Cancer 2023; 23:783. [PMID: 37612638 PMCID: PMC10464352 DOI: 10.1186/s12885-023-11266-7] [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: 05/19/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort. METHODS Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. RESULTS TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03). CONCLUSIONS These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.
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Affiliation(s)
- Daniel J Craig
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Erin L Crawford
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Heidi Chen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Eric L Grogan
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
- Tennessee Valley VA Healthcare System, 1310 24Th Avenue South, Nashville, TN, 37212, USA
| | - Steven A Deppen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Thomas Morrison
- Accugenomics Inc, 1410 Commonwealth Dr #105, Wilmington, NC, 28403, USA
| | - Sanja L Antic
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Pierre P Massion
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - James C Willey
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA.
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14
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Pritchett MA, Sigal B, Bowling MR, Kurman JS, Pitcher T, Springmeyer SC. Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study. PLoS One 2023; 18:e0287409. [PMID: 37432960 DOI: 10.1371/journal.pone.0287409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/05/2023] [Indexed: 07/13/2023] Open
Abstract
A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8-30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. A total of 280 IC tested, and 278 control patients met eligibility and analysis criteria and 197 were in each group after PSM (IC and control groups). Patients in the IC group were 74% less likely to undergo an invasive procedure as compared to the control group (absolute difference 14%, p <0.001) indicating that for every 7 patients tested, one unnecessary invasive procedure was avoided. Invasive procedure reduction corresponded to a reduction in risk classification, with 71 patients (36%) in the IC group classified as low risk (pCA < 5%). The proportion of IC group patients with malignant PNs sent to surveillance were not statistically different than the control group, 7.5% vs 3.5% for the IC vs. control groups, respectively (absolute difference 3.91%, p 0.075). The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules. Trial registration: Clinical trial registration: ClinicalTrials.gov NCT03766958.
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Affiliation(s)
- Michael A Pritchett
- Department of Pulmonary Medicine, FirstHealth of the Carolinas & Pinehurst Medical Clinic, Pinehurst, North Carolina, United States of America
| | - Barry Sigal
- Southeastern Research Center, Winston-Salem, North Carolina, United States of America
| | - Mark R Bowling
- Division of Pulmonary, Critical Care, and Sleep Medicine, Brody School of Medicine, Eastern Carolina University, Greenville, North Carolina, United States of America
| | - Jonathan S Kurman
- Division of Critical Care Medicine, Interventional Pulmonology, Pulmonary Disease, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Trevor Pitcher
- Medical Affairs, Biodesix, Inc., Boulder, Colorado, United States of America
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15
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Li TZ, Hin Lee H, Xu K, Gao R, Dawant BM, Maldonado F, Sandler KL, Landman BA. Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation. J Med Imaging (Bellingham) 2023; 10:044002. [PMID: 37469854 PMCID: PMC10353481 DOI: 10.1117/1.jmi.10.4.044002] [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: 12/16/2022] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/21/2023] Open
Abstract
Purpose Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT). Approach In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort (n = 1189 ) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence. Results Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (p < 0.01 ). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCO m 2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (χ 2 = 7.48 , p = 0.02 ). Conclusions We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.
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Affiliation(s)
- Thomas Z. Li
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, School of Medicine, Nashville, Tennessee, United States
| | - Ho Hin Lee
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Kaiwen Xu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Riqiang Gao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Benoit M. Dawant
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Fabien Maldonado
- Vanderbilt University Medical Center, Department of Medicine, Nashville, Tennessee, United States
| | - Kim L. Sandler
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
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16
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Lastwika KJ, Wu W, Zhang Y, Ma N, Zečević M, Pipavath SNJ, Randolph TW, Houghton AM, Nair VS, Lampe PD, Kinahan PE. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment. Cancers (Basel) 2023; 15:3418. [PMID: 37444527 PMCID: PMC10341085 DOI: 10.3390/cancers15133418] [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: 05/09/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
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Affiliation(s)
- Kristin J. Lastwika
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Wei Wu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Yuzheng Zhang
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - Ningxin Ma
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
| | - Mladen Zečević
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Sudhakar N. J. Pipavath
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Timothy W. Randolph
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - A. McGarry Houghton
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Viswam S. Nair
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Paul D. Lampe
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
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17
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Tahvilian S, Kuban JD, Yankelevitz DF, Leventon D, Henschke CI, Zhu J, Baden L, Yip R, Hirsch FR, Reed R, Brown A, Muldoon A, Trejo M, Katchman BA, Donovan MJ, Pagano PC. The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules. BMC Pulm Med 2023; 23:193. [PMID: 37277788 PMCID: PMC10240808 DOI: 10.1186/s12890-023-02433-4] [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: 02/07/2023] [Accepted: 04/13/2023] [Indexed: 06/07/2023] Open
Abstract
PURPOSE Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. METHODS LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. RESULTS Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). CONCLUSION Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.
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Affiliation(s)
- Shahram Tahvilian
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Joshua D Kuban
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David F Yankelevitz
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel Leventon
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Claudia I Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey Zhu
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lara Baden
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fred R Hirsch
- Icahn School of Medicine, Center for Thoracic Oncology, Tisch Cancer Institute at Mount Sinai, New York, NY, USA
| | - Rebecca Reed
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Ashley Brown
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Allison Muldoon
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Michael Trejo
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Benjamin A Katchman
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Michael J Donovan
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
- Department of Pathology, Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul C Pagano
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
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18
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Shi Y, Guo D, Chun Y, Liu J, Liu L, Tu L, Xu J. A lung cancer risk warning model based on tongue images. Front Physiol 2023; 14:1154294. [PMID: 37324390 PMCID: PMC10267397 DOI: 10.3389/fphys.2023.1154294] [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: 01/30/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Abstract
Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.
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Affiliation(s)
- Yulin Shi
- Experimental Education Center of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dandan Guo
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yi Chun
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiayi Liu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lingshuang Liu
- Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Liping Tu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiatuo Xu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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19
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Luo H, Zu R, Li L, Deng Y, He S, Yin X, Zhang K, He Q, Yin Y, Yin G, Yao D, Wang D. Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules. iScience 2023; 26:106693. [PMID: 37197326 PMCID: PMC10183669 DOI: 10.1016/j.isci.2023.106693] [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: 11/21/2022] [Revised: 02/23/2023] [Accepted: 04/13/2023] [Indexed: 05/19/2023] Open
Abstract
It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.
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Affiliation(s)
- Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Corresponding author
| | - Ruiling Zu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lintao Li
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yao Deng
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Shuya He
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xing Yin
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Kaijiong Zhang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Qiao He
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Yin
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dongsheng Wang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Corresponding author
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20
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Vachani A, Lam S, Massion PP, Brown JK, Beggs M, Fish AL, Carbonell L, Wang SX, Mazzone PJ. Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors. Chest 2023; 163:966-976. [PMID: 36368616 PMCID: PMC10258433 DOI: 10.1016/j.chest.2022.10.038] [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: 10/20/2022] [Accepted: 10/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis. RESEARCH QUESTION What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model? STUDY DESIGN AND METHODS Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model. RESULTS In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold. INTERPRETATION The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.
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Affiliation(s)
- Anil Vachani
- Pulmonary, Allergy, and Critical Care Division, Department of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael J. Crescenz VA Medical Center, Department of Medicine, Philadelphia, PA.
| | - Stephen Lam
- Department of Integrative Oncology, British Columbia Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Pierre P Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University, Nashville, TN
| | - James K Brown
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California, San Francisco, CA; VA Medical Center San Francisco, Department of Medicine, San Francisco, CA
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21
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Marmor HN, Deppen SA, Welty V, Kammer MN, Godfrey CM, Patel K, Maldonado F, Chen H, Starnes SL, Wilson DO, Billatos E, Grogan EL. Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing. Cancer Epidemiol Biomarkers Prev 2023; 32:329-336. [PMID: 36535650 PMCID: PMC10128087 DOI: 10.1158/1055-9965.epi-22-0532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/24/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.
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Affiliation(s)
- Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.,Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tennessee
| | - Valerie Welty
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael N Kammer
- Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Caroline M Godfrey
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Khushbu Patel
- Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Fabien Maldonado
- Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sandra L Starnes
- Division of Thoracic Surgery, University of Cincinnati, Cincinnati, Ohio
| | - David O Wilson
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Boston Medical Center, Boston, Massachusetts
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.,Section of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, Tennessee
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22
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Kammer MN, Paez R. It Doesn't Smell Like Cancer to Me: The Promise of Exhaled Breath Biomarkers for Lung Cancer Diagnosis. Chest 2023; 163:479-480. [PMID: 36894259 DOI: 10.1016/j.chest.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 03/09/2023] Open
Affiliation(s)
- Michael N Kammer
- Vanderbilt University Medical Center, Vanderbilt-Ingram Cancer Center, Nashville, TN.
| | - Rafael Paez
- Vanderbilt University Medical Center, Vanderbilt-Ingram Cancer Center, Nashville, TN
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23
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Marmor HN, Zorn JT, Deppen SA, Massion PP, Grogan EL. Biomarkers in Lung Cancer Screening: a Narrative Review. CURRENT CHALLENGES IN THORACIC SURGERY 2023; 5:5. [PMID: 37016707 PMCID: PMC10069480 DOI: 10.21037/ccts-20-171] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.
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Affiliation(s)
- Hannah N. Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - J. Tyler Zorn
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Stephen A. Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Pierre P. Massion
- Vanderbilt Ingram Cancer Center, Nashville, TN; Department of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Eric L. Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Thoracic Surgery, Tennessee Valley VA Healthcare System, Nashville, TN
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24
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Predicting Tumor Mutation Burden and EGFR Mutation Using Clinical and Radiomic Features in Patients with Malignant Pulmonary Nodules. J Pers Med 2022; 13:jpm13010016. [PMID: 36675677 PMCID: PMC9865229 DOI: 10.3390/jpm13010016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Pulmonary nodules (PNs) shown as persistent or growing ground-glass opacities (GGOs) are usually lung adenocarcinomas or their preinvasive lesions. Tumor mutation burden (TMB) and somatic mutations are important determinants for the choice of strategy in patients with lung cancer during therapy. A total of 93 post-operative patients with 108 malignant PNs were enrolled for analysis (75 cases in the training cohort and 33 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast computed tomography (CT) images of the entire tumor. Using commercial next generation sequencing, we detected TMB status and somatic mutations of all FFPE samples. Here, 870 quantitative radiomics features were extracted from the segmentations of PNs, and pathological and clinical characteristics were collected from medical records. The LASSO (least absolute shrinkage and selection operator) regression and stepwise logistic regressions were performed to establish the predictive model. For the epidermal growth factor receptor (EGFR) mutation, the AUCs of the clinical model and the integrative model validated by the validation set were 0.6726 (0.4755-0.8697) and 0.7421 (0.5698-0.9144). For the TMB status, the ROCs showed that AUCs of the clinical model and the integrative model validated by the validation set were 0.7808 (0.6231-0.9384) and 0.8462 (0.7132-0.9791). The quantitative radiomics signatures showed potential value in predicting the EGFR mutant and TMB status in GGOs. Moreover, the integrative model provided sufficient information for the selection of therapy and deserves further analysis.
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25
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Abstract
Lung cancer screening with low-dose computed tomography (LDCT) reduces lung cancer deaths by early detection. The United States Preventive Services Task Force recommends lung cancer screening with LDCT in adults of age 50 years to 80 years who have at least a 20 pack-year smoking history and are currently smoking or have quit within the past 15 years. The implementation of a lung-cancer-screening program is complex. High-quality screening requires the involvement of a multidisciplinary team. The aim of a screening program is to find balance between mortality reduction and avoiding potential harms related to false-positive findings, overdiagnosis, invasive procedures, and radiation exposure. Components and processes of a high-quality lung-cancer-screening program include the identification of eligible individuals, shared decision-making, performing and reporting LDCT results, management of screen-detected lung nodules and non-nodule findings, smoking cessation, ensuring adherence, data collection, and quality improvement.
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Affiliation(s)
- Humberto K Choi
- Respiratory Institute, Cleveland Clinic, 9500 Euclid Avenue Mail Code A90, Cleveland, OH 44195, USA.
| | - Peter J Mazzone
- Respiratory Institute, Cleveland Clinic, 9500 Euclid Avenue Mail Code A90, Cleveland, OH 44195, USA
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26
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Kussrow A, Kammer MN, Massion PP, Webster R, Bornhop DJ. Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination. ACS OMEGA 2022; 7:31916-31923. [PMID: 36120008 PMCID: PMC9476196 DOI: 10.1021/acsomega.2c02763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.
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Affiliation(s)
- Amanda
K. Kussrow
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Michael N. Kammer
- Division
of Allergy, Pulmonary and Critical Care Medicine and Vanderbilt-Ingram
Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Pierre P. Massion
- Division
of Allergy, Pulmonary and Critical Care Medicine and Vanderbilt-Ingram
Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rebekah Webster
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Darryl J. Bornhop
- Department
of Chemistry and The Vanderbilt Institute for Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
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27
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Kammer MN, Rowe DJ, Deppen SA, Grogan EL, Kaizer AM, Barón AE, Maldonado F. The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers. Cancer Epidemiol Biomarkers Prev 2022; 31:1752-1759. [PMID: 35732292 PMCID: PMC9491691 DOI: 10.1158/1055-9965.epi-22-0190] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 05/11/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect. METHODS Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets. RESULTS Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 > 0.97 for each) with fitted parameters closely aligned with professional society guidelines. CONCLUSIONS The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice. IMPACT We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.
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Affiliation(s)
| | - Dianna J Rowe
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephen A Deppen
- Vanderbilt University Medical Center, Nashville, Tennessee.,Tennessee Valley Healthcare Administration Nashville Campus, Nashville, Tennessee
| | - Eric L Grogan
- Vanderbilt University Medical Center, Nashville, Tennessee.,Tennessee Valley Healthcare Administration Nashville Campus, Nashville, Tennessee
| | - Alexander M Kaizer
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Anna E Barón
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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28
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Marmor HN, Jackson L, Gawel S, Kammer M, Massion PP, Grogan EL, Davis GJ, Deppen SA. Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers. Clin Chim Acta 2022; 534:106-114. [PMID: 35870539 PMCID: PMC10057862 DOI: 10.1016/j.cca.2022.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/05/2022] [Accepted: 07/12/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.
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Affiliation(s)
- Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA.
| | - Laurel Jackson
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Susan Gawel
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Michael Kammer
- Department of Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA.
| | - Pierre P Massion
- Department of Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA; Tennessee Valley Healthcare System, Veterans Affairs, 1310 24th Avenue South, Nashville, TN 37212, USA
| | - Gerard J Davis
- Abbott Diagnostics Division, 100 Abbott Park Road, Abbott Park, IL 60064, USA.
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA; Tennessee Valley Healthcare System, Veterans Affairs, 1310 24th Avenue South, Nashville, TN 37212, USA.
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29
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Rampariag R, Chernyavskiy I, Al-Ajam M, Tsay JCJ. Controversies and challenges in lung cancer screening. Semin Oncol 2022; 49:S0093-7754(22)00056-2. [PMID: 35907666 DOI: 10.1053/j.seminoncol.2022.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/01/2022] [Accepted: 07/01/2022] [Indexed: 11/11/2022]
Abstract
Two large randomized controlled trials have shown mortality benefit from lung cancer screening (LCS) in high-risk groups. Updated guidelines by the United State Preventative Service Task Force in 2020 will allow for inclusion of more patients who are at high risk of developing lung cancer and benefit from screening. As medical clinics and lung cancer screening programs around the country continue to work on perfecting the LCS workflow, it is important to understand some controversial issues surrounding LCS that should be addressed. In this article, we identify some of these issues, including false positive rates of low-dose CT, over-diagnosis, cost expenditure, LCS disparities in minorities, and utility of biomarkers. We hope to provide clarity, potential solutions, and future directions on how to address these controversies.
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Affiliation(s)
- Ravindra Rampariag
- Section of Pulmonary, Critical Care and Sleep Medicine, Medical Service, Veterans Administration (VA) New York Harbor Healthcare System, NY, USA
| | - Igor Chernyavskiy
- Section of Pulmonary, Critical Care and Sleep Medicine, Medical Service, Veterans Administration (VA) New York Harbor Healthcare System, NY, USA; Section of Pulmonary, Critical Care and Sleep Medicine, Medical Service, Veterans Administration (VA) Northport Healthcare System, NY, USA
| | - Mohammad Al-Ajam
- Section of Pulmonary, Critical Care and Sleep Medicine, Medical Service, Veterans Administration (VA) New York Harbor Healthcare System, NY, USA; Division of Pulmonary, Critical Care, and Sleep, Department of Medicine, SUNY Downstate Medical Center, NY, USA
| | - Jun-Chieh J Tsay
- Section of Pulmonary, Critical Care and Sleep Medicine, Medical Service, Veterans Administration (VA) New York Harbor Healthcare System, NY, USA; Division of Pulmonary, Critical Care, and Sleep, Department of Medicine, New York University Grossman School of Medicine, NY, USA.
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30
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Zhao M, She Y, Chen C. Integrated Biomarkers for Indeterminate Pulmonary Nodules: Is 2-Year Imaging Follow-Up Enough for Suspected Benign Lesions? Am J Respir Crit Care Med 2022; 205:1485-1486. [PMID: 35373720 PMCID: PMC9875898 DOI: 10.1164/rccm.202111-2648le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
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31
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Kammer MN, Maldonado F, Deppen SA, Baron A, Grogan EL. Reply to Zhao et al.: Integrated Biomarkers for Indeterminate Pulmonary Nodules: Is 2-Year Imaging Follow-Up Enough for Suspected Benign Lesions? Am J Respir Crit Care Med 2022; 205:1486. [PMID: 35373716 PMCID: PMC9875903 DOI: 10.1164/rccm.202201-0082le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Michael N. Kammer
- Vanderbilt UniversityNashville, Tennessee,Corresponding author (e-mail: )
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32
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Fong KM, Marshall HM, Lam DCL. Lung cancer screening-Marching along … …. Respirology 2022; 27:578-580. [PMID: 35581523 PMCID: PMC9545866 DOI: 10.1111/resp.14295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/09/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Kwun M Fong
- Thoracic Medicine, The Prince Charles Hospital, Brisbane, Queensland.,UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland
| | - Henry M Marshall
- Thoracic Medicine, The Prince Charles Hospital, Brisbane, Queensland.,UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland
| | - David C L Lam
- Department of Medicine, University of Hong Kong, Hong Kong
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33
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Papalampidou A, Papoutsi E, Katsaounou P. Pulmonary nodule malignancy probability: a diagnostic accuracy meta-analysis of the Mayo model. Clin Radiol 2022; 77:443-450. [DOI: 10.1016/j.crad.2022.01.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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34
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Arenberg D. Integrated Biomarkers for Pulmonary Nodules: Proving What Is Possible. Am J Respir Crit Care Med 2021; 204:1247-1248. [PMID: 34582716 PMCID: PMC8786076 DOI: 10.1164/rccm.202108-2002ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Douglas Arenberg
- Department of Internal Medicine University of Michigan Medical School Ann Arbor, Michigan
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