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Jiang J, Chen S, Zhang S, Zeng Y, Liu J, Lei W, Liu X, Chen X, Xiao Q. A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia. BMC Med Imaging 2024; 24:202. [PMID: 39103756 DOI: 10.1186/s12880-024-01370-w] [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: 05/28/2024] [Accepted: 07/18/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes. METHODS A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set (n = 80) and a validation set (n = 35). RESULTS The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set. CONCLUSIONS The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making.
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Affiliation(s)
- Jia Jiang
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Siqin Chen
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Shaofeng Zhang
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Yaling Zeng
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Jiayi Liu
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Wei Lei
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Xiang Liu
- Departments of Hematology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China.
| | - Xin Chen
- Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510280, China.
| | - Qiang Xiao
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China.
- Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510280, China.
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Barbosa-Martins J, Mendonça J, Carvalho N, Carvalho C, Soutinho G, Sarmento H, Coutinho C, Cotter J. Development of a predictive score to discriminate community acquired pneumonia with underlying lung cancer: A retrospective case - control study. Respir Med 2024; 229:107675. [PMID: 38782137 DOI: 10.1016/j.rmed.2024.107675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND A pneumonic infiltrate might hide an occult lung cancer (LC). This awareness depends on each clinician personal experience, turning definitive LC diagnosis challenging and possibly delayed. In this study we aimed to develop a clinical score to better identify those cases. MATERIALS AND METHODS We conducted a retrospective case-control study, including previously undiagnosed LC patients admitted in our institution, with a presumptive suspicious of community acquired pneumonia (CAP). Cases were compared with random CAP inpatient controls, using a matched 2:1 ratio. Demographic, clinical, and laboratorial variables were assessed for a possible association with the presence of a CAP with underlying LC (CAP-uLC). RESULTS Among 535 hospitalized LC patients, 43 cases had a presentation compatible with CAP and were compared with 86 CAP controls. A scoring system was built using 6 independent variables, which positively correlated with CAP-uLC: smoking history (OR: 8.3 [1.9-36.2]; p = 0.005); absence of fever (6.5 [2.0-21.5]; p = 0.002); sputum with blood (5.9 [1.2-29.9]; p = 0.033); platelet count ≥ 232x103/μL (5.8 [1.6-20.6]; p = 0.006); putative alternative diagnosis than CAP (4.6 [1.5-14.7]; p = 0.009); and duration of symptoms ≥ 10 days (3.7 [1.1-13.0]; p = 0.037). Our score presented an AUC of 0.910 (95 % CI, 0.852-0.967; p < 0.001), a sensitivity of 88.1 % and specificity of 84.7 %, in predicting the risk of presenting a CAP-uLC, when set to a cutoff of 18. CONCLUSION We propose a novel risk score aimed to aid clinicians identifying patients with CAP-uLC in the acute setting, possibly prompting early LC diagnosis.
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Affiliation(s)
- João Barbosa-Martins
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Joana Mendonça
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Nuno Carvalho
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Carolina Carvalho
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Gustavo Soutinho
- EPIUnit, Institute of Public Health of the University of Porto (ISPUP), Porto, Portugal.
| | - Helena Sarmento
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Camila Coutinho
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Jorge Cotter
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
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Xie K, Cui C, Li X, Yuan Y, Wang Z, Zeng L. MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses. Acad Radiol 2024:S1076-6332(24)00207-1. [PMID: 38644089 DOI: 10.1016/j.acra.2024.03.042] [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: 02/29/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/23/2024]
Abstract
RATIONALE AND OBJECTIVES Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses. MATERIALS AND METHODS A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05). CONCLUSION The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.
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Affiliation(s)
- Kexin Xie
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Can Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Xiaoqing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Yongfeng Yuan
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Liang Zeng
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China.
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Zyla J, Marczyk M, Prazuch W, Sitkiewicz M, Durawa A, Jelitto M, Dziadziuszko K, Jelonek K, Kurczyk A, Szurowska E, Rzyman W, Widłak P, Polanska J. Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies. Biomolecules 2023; 14:44. [PMID: 38254644 PMCID: PMC10813699 DOI: 10.3390/biom14010044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/24/2024] Open
Abstract
Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.
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Affiliation(s)
- Joanna Zyla
- Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (J.Z.); (W.P.); (J.P.)
| | - Michal Marczyk
- Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (J.Z.); (W.P.); (J.P.)
- Yale Cancer Center, Yale School of Medicine, New Haven, CT 06510, USA
| | - Wojciech Prazuch
- Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (J.Z.); (W.P.); (J.P.)
| | - Magdalena Sitkiewicz
- Department of Thoracic Surgery, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.S.); (A.D.); (W.R.)
| | - Agata Durawa
- Department of Thoracic Surgery, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.S.); (A.D.); (W.R.)
| | - Malgorzata Jelitto
- 2nd Department of Radiology, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.J.); (K.D.); (E.S.); (P.W.)
| | - Katarzyna Dziadziuszko
- 2nd Department of Radiology, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.J.); (K.D.); (E.S.); (P.W.)
| | - Karol Jelonek
- Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-100 Gliwice, Poland;
| | - Agata Kurczyk
- Department of Biostatistics and Bioinformatics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-100 Gliwice, Poland;
| | - Edyta Szurowska
- 2nd Department of Radiology, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.J.); (K.D.); (E.S.); (P.W.)
| | - Witold Rzyman
- Department of Thoracic Surgery, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.S.); (A.D.); (W.R.)
| | - Piotr Widłak
- 2nd Department of Radiology, Medical University of Gdansk, 80-210 Gdansk, Poland; (M.J.); (K.D.); (E.S.); (P.W.)
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; (J.Z.); (W.P.); (J.P.)
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Ji H, Liu Q, Chen Y, Gu M, Chen Q, Guo S, Ning S, Zhang J, Li WH. Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study. Front Endocrinol (Lausanne) 2022; 13:997921. [PMID: 36726465 PMCID: PMC9884819 DOI: 10.3389/fendo.2022.997921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/17/2023] Open
Abstract
PURPOSE The purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters. METHODS A total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA). RESULTS The radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83-0.96) in the training cohort and 0.88 (95% CI, 0.79-0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90-0.98) and 0.91 (95% CI, 0.84-0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer-Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model. CONCLUSIONS The nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult.
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Affiliation(s)
- Huijun Ji
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qianqian Liu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yingxiu Chen
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Mengyao Gu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qi Chen
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shaolan Guo
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shangkun Ning
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Juntao Zhang
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wan-Hu Li
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
- *Correspondence: Wan-Hu Li,
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