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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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Eguchi T, Matsuoka S, Iwaya M, Kobayashi S, Seshimoto M, Mishima S, Hara D, Kumeda H, Miura K, Hamanaka K, Uehara T, Shimizu K. Improving intraoperative diagnosis of spread through air spaces: A cryo-embedding-medium inflation method for frozen section analysis. JTCVS Tech 2024; 25:170-176. [PMID: 38899076 PMCID: PMC11184482 DOI: 10.1016/j.xjtc.2024.02.014] [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: 09/03/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 06/21/2024] Open
Abstract
Objective Accurate intraoperative diagnosis of spread through air spaces (STAS), a known poor prognostic factor in lung cancer, is crucial for guiding surgical decision-making during sublobar resections. This study aimed to evaluate the diagnostic sensitivity of STAS using frozen section (FS) slides prepared with the cryo-embedding medium inflation technique. Methods In this prospective study at Shinshu University Hospital, 99 patients undergoing lung resection for tumors <3 cm in size were included, a total of 114 lesions. FS slides were prepared with injecting diluted cryo-embedding medium into the lung parenchyma of resected specimens. The diagnostic performance of these FS slides for STAS detection was evaluated by comparing FS-STAS results with the gold-standard STAS status. Results The incidence of STAS, determined by the gold standard, was 43 (38%) of 114 lesions, including 31 (37%) of 84 primary lung cancers and 12 (40%) of 30 metastatic lung tumors. The sensitivity, specificity, positive and negative predictive values, and accuracy of FS slides for STAS detection were 81%, 89%, 81%, 89%, and 86%, respectively. Specifically, in primary lung cancers, these values were 90%, 89%, 82%, 94%, and 89%, respectively. Regarding metastatic lung tumors, the corresponding values were 58%, 89%, 78%, 76%, and 77%, respectively. Conclusions Our adapted cryo-embedding medium inflation method has demonstrated enhanced sensitivity in detecting STAS on FS slides, providing results similar to the gold-standard STAS detection. Compared with historical benchmarks, this technique could show excellent performance and be readily incorporated into clinical practice without requiring additional resources beyond those used for standard FS analysis.
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Affiliation(s)
- Takashi Eguchi
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Shunichiro Matsuoka
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Mai Iwaya
- Department of Laboratory Medicine, Shinshu University Hospital, Matsumoto, Japan
| | - Shota Kobayashi
- Department of Laboratory Medicine, Shinshu University Hospital, Matsumoto, Japan
| | - Maho Seshimoto
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Shuji Mishima
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Daisuke Hara
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Hirotaka Kumeda
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Kentaro Miura
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Kazutoshi Hamanaka
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
| | - Takeshi Uehara
- Department of Laboratory Medicine, Shinshu University Hospital, Matsumoto, Japan
| | - Kimihiro Shimizu
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan
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Wang S, Liu X, Jiang C, Kang W, Pan Y, Tang X, Luo Y, Gong J. CT-Based Super-Resolution Deep Learning Models with Attention Mechanisms for Predicting Spread Through Air Spaces of Solid or Part-Solid Lung Adenocarcinoma. Acad Radiol 2024; 31:2601-2609. [PMID: 38184418 DOI: 10.1016/j.acra.2023.12.034] [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] [Received: 11/07/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/08/2024]
Abstract
RATIONALE AND OBJECTIVES Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD), and preoperative knowledge of STAS status is helpful in choosing an appropriate surgical approach. MATERIALS AND METHODS This retrospective study collected and analyzed 602 patients diagnosed with LUAD from two medical centers: center 1 was randomly partitioned into training (n = 358) and validation cohorts (n = 154) at a 7:3 ratio; and center 2 was the external test cohort (n = 90). Super resolution was performed on all images to acquire high-resolution images, which were used to train the SE-ResNet50 model, before creating an equivalent parameter ResNet50 model. Disparities were compared between the two models using receiver operating characteristic curves, area under the curve, accuracy, precision, sensitivity, and specificity. RESULTS In this study, 512 and 90 patients with LUAD were enrolled from centers 1 and 2, respectively. The curve values of the SE-ResNet50 and ResNet50 models were compared for training, validation, and test cohorts, resulting in values of 0.933 vs 0.909, 0.783 vs 0.728, and 0.806 vs 0.695, respectively. In the external test cohort, the accuracy of the SE-ResNet50 model demonstrated a 10% improvement over the ResNet50 model (82.2% vs 72.2%). CONCLUSION The SE-ResNet50 model based on computed tomography super-resolution has great potential for predicting STAS status in patients with solid or partially solid LUAD, with superior predictive performance compared to traditional deep learning models.
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Affiliation(s)
- Shuxing Wang
- The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, China (S.W., X.L., Y.P., J.G.)
| | - Xiaowen Liu
- The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, China (S.W., X.L., Y.P., J.G.)
| | - Changsi Jiang
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Floor 1 Bldg 4, Dongbeilu 1017, Shenzhen 518020, Guangdong, China (C.J., X.T., Y.L., J.G.)
| | - Wenyan Kang
- Department of Radiology, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China (W.K.)
| | - Yudie Pan
- The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, China (S.W., X.L., Y.P., J.G.)
| | - Xue Tang
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Floor 1 Bldg 4, Dongbeilu 1017, Shenzhen 518020, Guangdong, China (C.J., X.T., Y.L., J.G.)
| | - Yan Luo
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Floor 1 Bldg 4, Dongbeilu 1017, Shenzhen 518020, Guangdong, China (C.J., X.T., Y.L., J.G.)
| | - Jingshan Gong
- The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong, China (S.W., X.L., Y.P., J.G.); Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Floor 1 Bldg 4, Dongbeilu 1017, Shenzhen 518020, Guangdong, China (C.J., X.T., Y.L., J.G.).
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Li H, Li L, Liu Y, Deng Y, Zhu Y, Huang L, Long T, Zeng L, Shu Y, Peng D. Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma. BMC Cancer 2024; 24:434. [PMID: 38589832 PMCID: PMC11003164 DOI: 10.1186/s12885-024-12220-x] [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: 01/19/2024] [Accepted: 04/02/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Lung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making. METHODS Between February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value. RESULTS Sixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter. CONCLUSION The radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.
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Affiliation(s)
- Haijun Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
- PET Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Lifeng Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
- Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan Province, China
| | - Yumeng Liu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Yingke Deng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Yu Zhu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Ling Huang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Ting Long
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Li Zeng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China
| | - Yongqiang Shu
- PET Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Dechang Peng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.17 Yongwai Zheng Street, Donghu District, Nanchang City, 330006, Jiangxi Province, China.
- PET Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
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Bassi M, Vaz Sousa R, Zacchini B, Centofanti A, Ferrante F, Poggi C, Carillo C, Pecoraro Y, Amore D, Diso D, Anile M, De Giacomo T, Venuta F, Vannucci J. Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment. Healthcare (Basel) 2024; 12:803. [PMID: 38610225 PMCID: PMC11011722 DOI: 10.3390/healthcare12070803] [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: 01/07/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.
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Affiliation(s)
- Massimiliano Bassi
- Division of Thoracic Surgery, Department of General Surgery and Surgical Specialties “Paride Stefanini”, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
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Wang Y, Lyu D, Hu L, Wu J, Duan S, Zhou T, Tu W, Xiao Y, Fan L, Liu S. CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:520-535. [PMID: 38343212 PMCID: PMC11031508 DOI: 10.1007/s10278-023-00939-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 04/20/2024]
Abstract
The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.
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Affiliation(s)
- Yun Wang
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Deng Lyu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Lei Hu
- Department of Radiology Medicine, The People's Hospital of Chizhou, Chizhou, Anhui, 247100, China
| | - Junhong Wu
- Department of Radiology Medicine, The People's Hospital of Guigang, Guigang, Guangxi Zhuang Autonomous Region, 537100, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Wenting Tu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Yi Xiao
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China.
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China.
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Lin MW, Chen LW, Yang SM, Hsieh MS, Ou DX, Lee YH, Chen JS, Chang YC, Chen CM. CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma. Ann Surg Oncol 2024; 31:1536-1545. [PMID: 37957504 DOI: 10.1245/s10434-023-14565-2] [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: 08/28/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This study aimed to develop a STAS deep-learning (STAS-DL) prediction model in lung adenocarcinoma with tumor smaller than 3 cm and a consolidation-to-tumor (C/T) ratio less than 0.5. METHODS The study retrospectively enrolled of 581 patients from two institutions between 2015 and 2019. The STAS-DL model was developed to extract the feature of solid components through solid components gated (SCG) for predicting STAS. The STAS-DL model was assessed with external validation in the testing sets and compared with the deep-learning model without SCG (STAS-DLwoSCG), the radiomics-based model, the C/T ratio, and five thoracic surgeons. The performance of the models was evaluated using area under the curve (AUC), accuracy and standardized net benefit of the decision curve analysis. RESULTS The study evaluated 458 patients (institute 1) in the training set and 123 patients (institute 2) in the testing set. The proposed STAS-DL yielded the best performance compared with the other methods in the testing set, with an AUC of 0.82 and an accuracy of 74%, outperformed the STAS-DLwoSCG with an accuracy of 70%, and was superior to the physicians with an AUC of 0.68. Moreover, STAS-DL achieved the highest standardized net benefit compared with the other methods. CONCLUSION The proposed STAS-DL model has great potential for the preoperative prediction of STAS and may support decision-making for surgical planning in early-stage, ground glass-predominant lung adenocarcinoma.
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Affiliation(s)
- Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Shun-Mao Yang
- Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Hsinchu County, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - De-Xiang Ou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Hsuan Lee
- Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Hsinchu County, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
- Department of Surgical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Radiology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.
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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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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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