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He XQ, Huang XT, Luo TY, Liu X, Li Q. The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes. Quant Imaging Med Surg 2024; 14:1348-1358. [PMID: 38415140 PMCID: PMC10895103 DOI: 10.21037/qims-23-995] [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: 07/11/2023] [Accepted: 11/20/2023] [Indexed: 02/29/2024]
Abstract
Background Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes. Methods From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs. Results In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001). Conclusions CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.
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Affiliation(s)
- Xiao-Qun He
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xing-Tao Huang
- Department of Radiology, the Fifth People’s Hospital of Chongqing, Chongqing, China
| | - Tian-You Luo
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao Liu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Hunter B, Argyros C, Inglese M, Linton-Reid K, Pulzato I, Nicholson AG, Kemp SV, L Shah P, Molyneaux PL, McNamara C, Burn T, Guilhem E, Mestas Nuñez M, Hine J, Choraria A, Ratnakumar P, Bloch S, Jordan S, Padley S, Ridge CA, Robinson G, Robbie H, Barnett J, Silva M, Desai S, Lee RW, Aboagye EO, Devaraj A. Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. Br J Cancer 2023; 129:1949-1955. [PMID: 37932513 PMCID: PMC10703918 DOI: 10.1038/s41416-023-02480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/21/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis. METHODS Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate "Safety-Net" and "Early Diagnosis" decision-support tools. RESULTS In total, 810 patients with 990 nodules were included. The AUC for malignancy prediction was 0.85 (95% CI: 0.82-0.87), 0.78 (95% CI: 0.70-0.85) and 0.78 (95% CI: 0.59-0.92) for the training, test and external test datasets, respectively. The test set accuracy was 73% (95% CI: 65-81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers. CONCLUSIONS SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
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Affiliation(s)
- Benjamin Hunter
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Christos Argyros
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Marianna Inglese
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
- Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Italy
| | - Kristofer Linton-Reid
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Ilaria Pulzato
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Andrew G Nicholson
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Histopathology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Samuel V Kemp
- Nottingham University Hospitals NHS Trust, Department of Respiratory Medicine, Nottingham, UK
| | - Pallav L Shah
- Imperial College London, National Heart and Lung Institute, London, UK
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Philip L Molyneaux
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Cillian McNamara
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Toby Burn
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Emily Guilhem
- King's College Hospital, Department of Radiology, London, UK
| | | | - Julia Hine
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Anika Choraria
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Prashanthi Ratnakumar
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Susannah Bloch
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Simon Jordan
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Thoracic Surgery, London, UK
| | - Simon Padley
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Carole A Ridge
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Graham Robinson
- The Royal United Hospital, Bath, Department of Radiology, Bath, UK
| | - Hasti Robbie
- King's College Hospital, Department of Radiology, London, UK
| | - Joseph Barnett
- Department of Radiology, Royal Free Hospital, London, UK
| | - Mario Silva
- Section of "Scienze Radiologiche", Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Sujal Desai
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
- Imperial College London, Margaret Turner-Warwick Centre for Fibrosing Lung Disease, London, UK
| | - Richard W Lee
- Imperial College London, National Heart and Lung Institute, London, UK
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
- Early Diagnosis and Detection, Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Eric O Aboagye
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Anand Devaraj
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK.
- Imperial College London, National Heart and Lung Institute, London, UK.
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Xie RL, Wang Y, Zhao YN, Zhang J, Chen GB, Fei J, Fu Z. Lung nodule pre-diagnosis and insertion path planning for chest CT images. BMC Med Imaging 2023; 23:22. [PMID: 36737717 PMCID: PMC9896815 DOI: 10.1186/s12880-023-00973-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.
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Affiliation(s)
- Rong-Li Xie
- grid.16821.3c0000 0004 0368 8293Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yao Wang
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Yan-Na Zhao
- grid.24516.340000000123704535Department of Ultrasound, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065 China
| | - Jun Zhang
- grid.16821.3c0000 0004 0368 8293Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Guang-Biao Chen
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jian Fei
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zhuang Fu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Weir-McCall JR, Debruyn E, Harris S, Qureshi NR, Rintoul RC, Gleeson FV, Gilbert FJ. Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging. Chest 2023; 163:444-454. [PMID: 36087795 PMCID: PMC9899635 DOI: 10.1016/j.chest.2022.08.2227] [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: 01/08/2022] [Revised: 08/19/2022] [Accepted: 08/24/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. RESEARCH QUESTION What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? STUDY DESIGN AND METHODS This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. RESULTS Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only). INTERPRETATION An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. TRIAL REGISTRATION ClinicalTrials.gov Identifier; No.: NCT02013063.
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Affiliation(s)
- Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge; Department of Radiology, Royal Papworth Hospital, Cambridge
| | - Elise Debruyn
- College of Medicine, University of Illinois at Chicago, Chicago, IL
| | - Scott Harris
- Faculty of Public Health Sciences and Medical Statistics, University of Southampton, Southampton
| | | | - Robert C Rintoul
- Department of Oncology, University of Cambridge; Department of Thoracic Oncology, Royal Papworth Hospital
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital and University of Oxford, Oxford, England
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge.
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Lee YS, Kim JD, Park HO, Lee CE, Jang IS, Choi JY. Video-Assisted Thoracic Surgery Core Needle Biopsy for Pulmonary Nodules in Patients with Impaired Lung Function: Is It Feasible and Safe? J Chest Surg 2023; 56:1-5. [PMID: 36598118 PMCID: PMC9845864 DOI: 10.5090/jcs.22.063] [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: 07/14/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 01/05/2023] Open
Abstract
Background The number of patients with incidentally identified pulmonary nodules is increasing. This study attempted to confirm the usefulness and safety of video-assisted thoracic surgery (VATS) core needle biopsy of pulmonary nodules. Methods Data from 18 patients diagnosed with pulmonary nodules who underwent VATS core need biopsy were retrospectively reviewed. Results Of the 18 patients, 15 had malignancies (primary lung cancer, n=14; metastatic lung cancer, n=1), and 3 had benign nodules. Mortality and pleural metastasis did not occur during the follow-up period. Conclusion In patients with solitary pulmonary nodules that require tissue confirmation, computed tomography-guided percutaneous cutting needle biopsy or diagnostic pulmonary resection sometimes may not be feasible choices due to the location of the solitary pulmonary nodule or the patient's impaired pulmonary function, VATS core needle biopsy may be performed in these patients as an alternative method.
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Affiliation(s)
- Yong-Seong Lee
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
| | - Jong Duk Kim
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea,Corresponding author Jong Duk Kim Tel 82-55-750-8000 Fax 82-55-753-8138 E-mailORCIDhttps://orcid.org/0000-0003-0268-1674
| | - Hyun-Oh Park
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
| | - Chung-Eun Lee
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
| | - In-Seok Jang
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
| | - Jun-Young Choi
- Department of Cardiothoracic Surgery, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
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Hunter B, Chen M, Ratnakumar P, Alemu E, Logan A, Linton-Reid K, Tong D, Senthivel N, Bhamani A, Bloch S, Kemp SV, Boddy L, Jain S, Gareeboo S, Rawal B, Doran S, Navani N, Nair A, Bunce C, Kaye S, Blackledge M, Aboagye EO, Devaraj A, Lee RW. A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules. EBioMedicine 2022; 86:104344. [PMID: 36370635 PMCID: PMC9664396 DOI: 10.1016/j.ebiom.2022.104344] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).
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Affiliation(s)
- Benjamin Hunter
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK; Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Prashanthi Ratnakumar
- Department of Respiratory Medicine, Charing Cross Hospital, Imperial College Healthcare Trust, Fulham Palace Road, London, W6 8RF, UK
| | - Esubalew Alemu
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Andrew Logan
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Kristofer Linton-Reid
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Daniel Tong
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Nishanthi Senthivel
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Amyn Bhamani
- Department of Respiratory Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Susannah Bloch
- Department of Respiratory Medicine, Charing Cross Hospital, Imperial College Healthcare Trust, Fulham Palace Road, London, W6 8RF, UK
| | - Samuel V Kemp
- Department of Respiratory Medicine, Nottingham University Hospitals NHS Foundation Trust, Hucknall Road, Nottingham, NG5 1PB, UK
| | - Laura Boddy
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Sejal Jain
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Shafick Gareeboo
- Department of Respiratory Medicine, Queen Elizabeth Hospital, Stadium Road, Woolwich, London, SE18 4QH, UK
| | - Bhavin Rawal
- Department of Radiology, The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK
| | - Simon Doran
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, UK
| | - Neal Navani
- Department of Respiratory Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Arjun Nair
- Department of Radiology, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK
| | - Catey Bunce
- Clinical Trials Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, UK
| | - Stan Kaye
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, UK
| | - Matthew Blackledge
- Computational Imaging Group, The Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Anand Devaraj
- Department of Radiology, The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Richard W Lee
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK.
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7
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Zhu Y, Yang L, Li Q, Chen B, Hao Q, Sun X, Tan J, Li W. Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations. J Evid Based Med 2022; 15:106-122. [PMID: 35794787 DOI: 10.1111/jebm.12481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting. METHODS The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs. RESULTS A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs. CONCLUSION Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.
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Affiliation(s)
- Yuqi Zhu
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qianrui Li
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qiukui Hao
- The Center of Gerontology and Geriatrics, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Tan
- Chinese Evidence-Based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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Zhang R, Sun H, Chen B, Xu R, Li W. Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. J Thorac Dis 2021; 13:4156-4168. [PMID: 34422345 PMCID: PMC8339772 DOI: 10.21037/jtd-21-80] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
Background Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features. Methods This study enrolled 5–20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model. Results Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model. Conclusions Predictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Renjie Xu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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Patel N, Xu W, Deng Y, Jin J, Zhang H. Cross-Scale Integration of Nano-Sized Extracellular Vesicle-Based Biomarker and Radiomics Features for Predicting Suspected Sub-Solid Pulmonary Nodules. J Biomed Nanotechnol 2021; 17:1109-1122. [PMID: 34167625 DOI: 10.1166/jbn.2021.3097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Sub-solid nodules (SSN) are common radiographic findings. Due to possibility of malignancy, further evaluation is urgentlyneeded for prevention and management of lung cancer (LC). This current study enrolled patients with SSN, including LC, benign nodules (BN), and healthy individuals as a control, to discover small extracellular vesicles (sEVs) differentially expressed miRNAs (DEMs) as biomarker by next-generation sequencing (NGS) and validation by RT-qPCR. Through cross-scale integration of validated small-molecule and macro-imaging, the prediction model was developed by logistic algorithms and further interpreted into an easy-to-use Nomogram by Cox-proportional hazards modeling. Present study has discovered various sEVs DEMs and sEVs-miR-424-5p that were selected and validated as novel potential biomarkers for cancerous nodule, namely LC. Furthermore, the 10 radiomics signs and 4 clinical features of SSN were merged with sEVs-miR-424-5p and proceeded in multivariate logistic regression analysis to develop the cross-scale integrated modeling, which yielded a significantly higher area under the curve (AUC). Finally, visualization of an easy-to-use nomogram was invented to potentially predict suspected SSN. sEVs-miR-424-5p could be a novel biomarker for distinguishing SSN from LC and BN populations. Its association with cross-scale fusion of radiomics-clinical features will provide great potential to be an errorless prediction of malignant SSN.
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Affiliation(s)
- Nishant Patel
- Department of Oncology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
| | - Wenwen Xu
- Department of Oncology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
| | - Yuxia Deng
- Department of Oncology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
| | - Jiyang Jin
- Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
| | - Haijun Zhang
- Department of Oncology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
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