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Wang Y, Li C, Wang Z, Wu R, Li H, Meng Y, Liu H, Song Y. Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features. Asia Pac J Clin Oncol 2024. [PMID: 38952146 DOI: 10.1111/ajco.14099] [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: 08/09/2023] [Revised: 05/17/2024] [Accepted: 06/11/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features. METHODS We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3). RESULTS The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85). CONCLUSIONS The prediction model based on radiomics and genomics features has a good prediction performance for STAS.
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Affiliation(s)
- Yimin Wang
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Chuling Li
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Zhaofeng Wang
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Ranpu Wu
- Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing, China
| | - Huijuan Li
- Department of Respiratory and Critical Care Medicine, The First School of Clinical Medicine, Jinling Hospital, Southern Medical University (Guangzhou), Nanjing, China
| | - Yunchang Meng
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Hongbing Liu
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Yong Song
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
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Ou DX, Lu CW, Chen LW, Lee WY, Hu HW, Chuang JH, Lin MW, Chen KY, Chiu LY, Chen JS, Chen CM, Hsieh MS. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers (Basel) 2024; 16:2132. [PMID: 38893251 PMCID: PMC11172106 DOI: 10.3390/cancers16112132] [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: 04/19/2024] [Revised: 05/25/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
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Affiliation(s)
- De-Xiang Ou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Chao-Wen Lu
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
| | - Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Wen-Yao Lee
- Division of Thoracic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, No. 69, Guizi Road, Taishan District, New Taipei City 24352, Taiwan;
| | - Hsiang-Wei Hu
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
| | - Jen-Hao Chuang
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Mong-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Kuan-Yu Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Ling-Ying Chiu
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Jin-Shing Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Min-Shu Hsieh
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
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Xu Y, Liang J, Zhuo Y, Liu L, Xiao Y, Zhou L. TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108103. [PMID: 38484410 DOI: 10.1016/j.cmpb.2024.108103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/06/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVES Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals. METHODS First, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics. RESULTS Our TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data. CONCLUSIONS The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.
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Affiliation(s)
- Yidan Xu
- Institutes of Biomedical Sciences, Fudan University, 138 Yi xue yuan Road, Shanghai, 200032, China.
| | - Jiaqing Liang
- School of Data Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.
| | - Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China; Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, 200032, China.
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, 138 Yi xue yuan Road, Shanghai, 200032, China; Intelligent Medicine Institute, Fudan University, 131 Dongan Road, Shanghai, 200032, China; Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, 200120, China.
| | - Yanghua Xiao
- School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, 200438, China; Shanghai Key Laboratory of Data Science, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
| | - Lingxiao Zhou
- Institute of Microscale Optoelectronics, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, 518000, China.
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Liu C, Wang YF, Wang P, Guo F, Zhao HY, Wang Q, Shi ZW, Li XF. Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis. Oncol Lett 2024; 27:122. [PMID: 38348387 PMCID: PMC10859825 DOI: 10.3892/ol.2024.14255] [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: 07/01/2023] [Accepted: 01/03/2024] [Indexed: 02/15/2024] Open
Abstract
Spread Through Air Spaces (STAS) is involved in lung adenocarcinoma (LUAD) recurrence, where cancer cells spread into adjacent lung tissue, impacting surgical planning and prognosis assessment. Radiomics-based models show promise in predicting STAS preoperatively, enhancing surgical precision and prognostic evaluations. The present study performed network meta-analysis to assess the predictive efficacy of imaging models for STAS in LUAD. Data were systematically sourced from PubMed, Embase, Scopus, Wiley and Web of Science, according to the Cochrane Handbook for Systematic Reviews of Interventions) and A Measurement Tool to Assess systematic Reviews 2. Using Stata software v17.0 for meta-analysis, surface under the cumulative ranking area (SUCRA) was applied to identify the most effective diagnostic method. Quality assessments were performed using Cochrane Collaboration's risk-of-bias tool and publication bias was assessed using Deeks' funnel plot. The analysis encompassed 14 articles, involving 3,734 patients, and assessed 17 predictive models for STAS in LUAD. According to comprehensive analysis of SUCRA, the machine learning (ML)_Peri_tumour model had the highest accuracy (56.5), the Features_computed tomography (CT) model had the highest sensitivity (51.9) and the positron emission tomography (pet)_CT model had the highest specificity (53.9). ML_Peri_tumour model had the highest predictive performance. The accuracy was as follows: ML_Peri_tumour vs. Features_CT [relative risk (RR)=1.14; 95% confidence interval (CI), 0.99-1.32]; ML_Peri_tumour vs. ML_Tumour (RR=1.04; 95% CI, 0.83-1.30) and ML_Peri_tumour vs. pet_CT (RR=1.04; 95% CI, 0.84-1.29). Comparative analyses revealed heightened predictive accuracy of the ML_Peri_tumour compared with other models. Nonetheless, the field of radiological feature analysis for STAS prediction remains nascent, necessitating improvements in technical reproducibility and comprehensive model evaluation.
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Affiliation(s)
- Cong Liu
- Department of Minimally Invasive Oncology, Xuzhou New Health Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Yu-Feng Wang
- Department of Nuclear Medicine, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Peng Wang
- Department of Nuclear Medicine, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Feng Guo
- Department of Medical Oncology, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Hong-Ying Zhao
- Department of Radiotherapy, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Qiang Wang
- Department of Radiotherapy, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Zhi-Wei Shi
- Department of Radiology, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
| | - Xiao-Feng Li
- Department of Radiology, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China
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Suh YJ, Han K, Kwon Y, Kim H, Lee S, Hwang SH, Kim MH, Shin HJ, Lee CY, Shim HS. Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas. Yonsei Med J 2024; 65:163-173. [PMID: 38373836 PMCID: PMC10896671 DOI: 10.3349/ymj.2023.0368] [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: 09/11/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 02/21/2024] Open
Abstract
PURPOSE To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.
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Affiliation(s)
- Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yonghan Kwon
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
| | - Hwiyoung Kim
- Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Myung Hyun Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Hyun Joo Shin
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Chang Young Lee
- Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyo Sup Shim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Jin W, Shen L, Tian Y, Zhu H, Zou N, Zhang M, Chen Q, Dong C, Yang Q, Jiang L, Huang J, Yuan Z, Ye X, Luo Q. Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study. Biomark Res 2023; 11:102. [PMID: 37996894 PMCID: PMC10668492 DOI: 10.1186/s40364-023-00539-9] [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: 04/25/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer. METHOD Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features. RESULTS The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps. CONCLUSION The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.
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Affiliation(s)
- Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Leilei Shen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yu Tian
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Hongda Zhu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Ningyuan Zou
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Mengwei Zhang
- School of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Changzi Dong
- Department of Bioengineering, School of Engineering and Science, University of Pennsylvania, Philadelphia, 19104, USA
| | - Qisheng Yang
- School of Integrated Circuits & Beijing National Research On Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Long Jiang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Jia Huang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Zheng Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
| | - Xiaodan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
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Zeng H, Tohidinezhad F, De Ruysscher DKM, Willems YCP, Degens JHRJ, van Kampen-van den Boogaart VEM, Pitz C, Cortiula F, Brandts L, Hendriks LEL, Traverso A. The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15113010. [PMID: 37296973 DOI: 10.3390/cancers15113010] [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: 05/10/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC). METHODS Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance. RESULTS Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%). CONCLUSION Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.
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Affiliation(s)
- Haiyan Zeng
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Dirk K M De Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Yves C P Willems
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Juliette H R J Degens
- Department of Respiratory Medicine, Zuyderland Medical Center, 6419 PC Heerlen, The Netherlands
| | | | - Cordula Pitz
- Department of Pulmonary Diseases, Laurentius Hospital, 6043 CV Roermond, The Netherlands
| | - Francesco Cortiula
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- Department of Medical Oncology, University Hospital of Udine, 33100 Udine, Italy
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, Maastricht, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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8
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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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