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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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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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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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Song H, Cui S, Zhang L, Lou H, Yang K, Yu H, Lin J. Preliminary exploration of the correlation between spectral computed tomography quantitative parameters and spread through air spaces in lung adenocarcinoma. Quant Imaging Med Surg 2024; 14:386-396. [PMID: 38223127 PMCID: PMC10784001 DOI: 10.21037/qims-23-984] [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: 07/06/2023] [Accepted: 10/16/2023] [Indexed: 01/16/2024]
Abstract
Background The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD. Methods We retrospectively collected consecutive patients with LUAD who underwent surgical resection and preoperative spectral CT scan at our institution. The quantitative parameters included CT values at 40, 70, and 100 keV [CT40keVa/v, CT70keVa/v, and CT100keVa/v (a: arterial; v: venous)]; iodine concentration (ICa/ICv); normalized iodine concentration (NICa/NICv); and slope λHU of the spectral curve (λHUa/λHUv). Clinical and CT features of the patients were also collected. Statistical analysis was performed to identify the quantitative parameters, clinical and CT features that were significantly correlated with STAS status. We evaluated the diagnostic performance of significant factors or models which combined quantitative parameters and CT features, using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results We enrolled a total of 47 patients, with 32 positive and 15 negative for STAS. The results revealed that CT100keVa (P=0.002), CT100keVv (P=0.007), pathologic stage (P=0.040), tumor density (P<0.001), spiculation (P=0.003), maximum solid component diameter (P=0.008), and the consolidation/tumor ratio (CTR) (P=0.001) were significantly correlated with STAS status. The tumor density demonstrated a superior diagnostic capability [AUC =0.824, 95% confidence interval (CI): 0.709-0.939, sensitivity =59.4%, specificity =100.0%] compared to other variables. CT100keVa exhibited the best diagnostic performance (AUC =0.779, 95% CI: 0.633-0.925, sensitivity =78.1%, specificity =80.0%) among the quantitative parameters. Combination models were then constructed by combining the quantitative parameters with CT features. The total combined model showed the highest diagnostic efficiency (AUC =0.952, 95% CI: 0.894-1.000, sensitivity =90.6%, specificity =86.7%). Conclusions Spectral CT quantitative parameters CT100keVa and CT100keVv may be potentially useful parameters in distinguishing the STAS status in LUAD.
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Affiliation(s)
- Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shiyu Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Liang Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Henan Lou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Kai Yang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hualong Yu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jizheng Lin
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Gong J, Yin R, Sun L, Gao N, Wang X, Zhang L, Zhang Z. CT-based radiomics model to predict spread through air space in resectable lung cancer. Cancer Med 2023; 12:18755-18766. [PMID: 37676092 PMCID: PMC10557899 DOI: 10.1002/cam4.6496] [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: 05/09/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Spread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS. METHOD There were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation. RESULTS The incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834. CONCLUSION CT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.
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Affiliation(s)
- Jialin Gong
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Rui Yin
- School of Biomedical Engineering & TechnologyTianjin Medical UniversityTianjinChina
| | - Leina Sun
- Department of Pathology, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Na Gao
- Department of Pathology, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Xiaofei Wang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Lianmin Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Zhenfa Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
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