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Pan X, Fu L, Lv J, Feng L, Li K, Chen S, Deng X, Long L. Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less. Front Oncol 2025; 14:1426284. [PMID: 39845317 PMCID: PMC11752897 DOI: 10.3389/fonc.2024.1426284] [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: 05/01/2024] [Accepted: 12/11/2024] [Indexed: 01/24/2025] Open
Abstract
Purpose To develop and validate a radiomics nomogram model for predicting the micropapillary pattern (MPP) in lung adenocarcinoma (LUAD) tumors of ≤2 cm in size. Methods In this study, 300 LUAD patients from our institution were randomly divided into the training cohort (n = 210) and an internal validation cohort (n = 90) at a ratio of 7:3, besides, we selected 65 patients from another hospital as the external validation cohort. The region of interest of the tumor was delineated on the computed tomography (CT) images, and radiomics features were extracted. A nomogram model was established using radiomics features, clinical features and conventional radiographic features. The nomogram model was compared with the radiomics model and the clinical model alone to test its diagnostic validity. Receiver operating characteristic (ROC) curves, areas under the ROC curves and decision curve analysis (DCA) results were plotted to evaluate the model performance and clinical application. Results The nomogram model exhibited superior performance, with an AUC of 0.905 (95% confidence interval [CI]: 0.857-0.951) in the training cohort, which decreased to 0.817 (95% CI: 0.698-0.936) in the external validation cohort. The clinical model had AUCs of 0.820 (95% CI: 0.753-0.886) and 0.730 (95% CI: 0.572-0.888) in the training and external validation cohorts, respectively. The radiomics model had AUCs of 0.895 (95% CI: 0.840-0.949) and 0.800 (95% CI: 0.675-0.924) for training and external validation, respectively. DCA confirmed that the nomogram model had the better clinical benefit. Conclusions The nomogram model achieved promising prediction efficiency for identifying the presence of the MPP in LUAD tumors ≤2 cm, allowing clinicians to develop more rational and efficacious personalized treatment strategies.
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Affiliation(s)
- Xiaoyu Pan
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liang Fu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jiecai Lv
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lijuan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Kai Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Siqi Chen
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xi Deng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Lu G, Su Z, Yu X, He Y, Sha T, Yan K, Guo H, Tao Y, Liao L, Zhang Y, Lu G, Gong W. Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study. Cancer Med 2025; 14:e70545. [PMID: 39777868 PMCID: PMC11706237 DOI: 10.1002/cam4.70545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 12/08/2024] [Accepted: 12/15/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored. METHODS Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. RESULTS A total of 267 participants were enrolled, including 210 with low-risk and 57 with moderate-risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate-risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611-0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974-1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data. CONCLUSIONS The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability. TRIAL REGISTRATION Number ChiCTR2400081283.
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Affiliation(s)
- Guangyu Lu
- Department of Health Management CenterAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
- School of Public HealthMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Zhixia Su
- School of Public HealthMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Xiaoping Yu
- Department of Health Management CenterAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Yuhang He
- School of NursingMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Taining Sha
- School of Public HealthMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Kai Yan
- School of Public HealthMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Hong Guo
- Department of Thoracic SurgeryAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Yujian Tao
- Department of Respiratory and Critical Care MedicineAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Liting Liao
- Department of Basic MedicineMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Yanyan Zhang
- Testing Center of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Guotao Lu
- Yangzhou Key Laboratory of Pancreatic DiseaseInstitute of Digestive Diseases, Affiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
- Pancreatic Center, Department of GastroenterologyAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
| | - Weijuan Gong
- Department of Health Management CenterAffiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
- Department of Basic MedicineMedical College of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
- Yangzhou Key Laboratory of Pancreatic DiseaseInstitute of Digestive Diseases, Affiliated Hospital of Yangzhou University, Yangzhou UniversityYangzhouJiangsuChina
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Sun Y, Liang F, Yang J, Liu Y, Shen Z, Zhou C, Xia Y. Pilot study: radiomic analysis for predicting treatment response to whole-brain radiotherapy combined temozolomide in lung cancer brain metastases. Front Oncol 2024; 14:1395313. [PMID: 39193384 PMCID: PMC11347322 DOI: 10.3389/fonc.2024.1395313] [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: 03/03/2024] [Accepted: 07/23/2024] [Indexed: 08/29/2024] Open
Abstract
Objective The objective of this study is to assess the viability of utilizing radiomics for predicting the treatment response of lung cancer brain metastases (LCBM) to whole-brain radiotherapy (WBRT) combined with temozolomide (TMZ). Methods Fifty-three patients diagnosed with LCBM and undergoing WBRT combined with TMZ were enrolled. Patients were divided into responsive and non-responsive groups based on the RANO-BM criteria. Radiomic features were extracted from contrast-enhanced the whole brain tissue CT images. Feature selection was performed using t-tests, Pearson correlation coefficients, and Least Absolute Shrinkage And Selection (LASSO) regression. Logistic regression was employed to construct the radiomics model, which was then integrated with clinical data to develop the nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, and clinical utility was assessed using decision curve analysis (DCA). Results A total of 1834 radiomic features were extracted from each patient's images, and 3 features with predictive value were selected. Both the radiomics and nomogram models exhibited satisfactory predictive performance and clinical utility, with the nomogram model demonstrating superior predictive value. The ROC analysis revealed that the AUC of the radiomics model in the training and testing sets were 0.776 and 0.767, respectively, while the AUC of the nomogram model were 0.799 and 0.833, respectively. DCA curves demonstrated that both models provided benefits to patients across various thresholds. Conclusion Radiomic-defined image biomarkers can effectively predict the treatment response of WBRT combined with TMZ in patients with LCBM, offering potential to optimize treatment decisions for this condition.
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Affiliation(s)
- Yichu Sun
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Fei Liang
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Jing Yang
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Yong Liu
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Ziqiang Shen
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Chong Zhou
- Department of Radiation Oncology, Xuzhou Central Hospital, Xuzhou, Jiangsu, China
| | - Youyou Xia
- Department of Radiation Oncology, The First People's Hospital of Lianyungang/Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China
- Department of Radiation Oncology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
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Long T, Zhu X, Tang D, Li H, Zhang P. Application of a nomogram from coagulation-related biomarkers and C1q and total bile acids in distinguishing advanced and early-stage lung cancer. Int J Biol Markers 2024; 39:130-140. [PMID: 38303516 DOI: 10.1177/03936155241229454] [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: 02/03/2024]
Abstract
BACKGROUND This study aimed to establish a nomogram to distinguish advanced- and early-stage lung cancer based on coagulation-related biomarkers and liver-related biomarkers. METHODS A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling. RESULTS The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P-Hosmer-Lemeshow = 0.697) between the predicted probability and the actual probability. CONCLUSIONS We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.
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Affiliation(s)
- Tingting Long
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Xinyu Zhu
- Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, PR China
| | - Dongling Tang
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Huan Li
- Department of Clinical Laboratory, Jiangxi Provincial People's Hospital, The First Hospital Affiliated to Nanchang Medical College, Nanchang, PR China
| | - Pingan Zhang
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, PR China
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Qu X, Zhang L, Ji W, Lin J, Wang G. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics. Front Oncol 2023; 13:1267838. [PMID: 37941552 PMCID: PMC10628597 DOI: 10.3389/fonc.2023.1267838] [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: 08/01/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Objective This study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer. Methods A retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA). Results A total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model. Conclusion The radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.
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Affiliation(s)
- Xueting Qu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China
| | - Liang Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Weina Ji
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jizheng Lin
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China
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Baidya Kayal E, Ganguly S, Sasi A, Sharma S, DS D, Saini M, Rangarajan K, Kandasamy D, Bakhshi S, Mehndiratta A. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models. Front Oncol 2023; 13:1212526. [PMID: 37671060 PMCID: PMC10476362 DOI: 10.3389/fonc.2023.1212526] [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: 04/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
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Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Dheeksha DS
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Manish Saini
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Krithika Rangarajan
- Radiodiagnosis, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | | | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India
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