1
|
Hui YM, Guo Y, Li B, Meng YQ, Feng HM, Su ZP, Lin MZ, Chen YZ, Zheng ZZ, Li HT. Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules. Clin Radiol 2024:S0009-9260(24)00341-6. [PMID: 39068114 DOI: 10.1016/j.crad.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
AIM To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness. MATERIALS AND METHODS A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test. RESULTS The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05). CONCLUSION The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.
Collapse
Affiliation(s)
- Y-M Hui
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y Guo
- Department of Radiology, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - B Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Q Meng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-M Feng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-P Su
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - M-Z Lin
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Y-Z Chen
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - Z-Z Zheng
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| | - H-T Li
- Department of Thoracic Surgery, The Second Hospital & Clinical Medical School, Lanzhou University, LanZhou, Gansu Province, China.
| |
Collapse
|
2
|
Yang H, Liu X, Wang L, Zhou W, Tian Y, Dong Y, Zhou K, Chen L, Wang M, Wu H. 18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model. Nucl Med Commun 2024; 45:338-346. [PMID: 38312089 DOI: 10.1097/mnm.0000000000001819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
OBJECTIVE This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor. MATERIALS AND METHODS We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness. RESULTS Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively. CONCLUSION Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.
Collapse
Affiliation(s)
- Hanyun Yang
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | | | | | | | | | | | | | | | | | | |
Collapse
|
3
|
Deng L, Tang HZ, Luo YW, Feng F, Wu JY, Li Q, Qiang JW. Preoperative CT Radiomics Nomogram for Predicting Microvascular Invasion in Stage I Non-Small Cell Lung Cancer. Acad Radiol 2024; 31:46-57. [PMID: 37331866 DOI: 10.1016/j.acra.2023.05.015] [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: 03/03/2023] [Revised: 05/08/2023] [Accepted: 05/15/2023] [Indexed: 06/20/2023]
Abstract
RATIONALE AND OBJECTIVES: This study aims to develop and validate a nomogram integrating clinical-CT and radiomic features for preoperative prediction of microvascular invasion (MVI) in patients with stage I non‑small cell lung cancer (NSCLC). MATERIALS AND METHODS This retrospective study analyzed 188 cases of stage I NSCLC (63 MVI positives and 125 negatives), which were randomly assigned to training (n = 133) and validation cohorts (n = 55) at a ratio of 7:3. Preoperative non-contrast and contrast-enhanced CT (CECT) images were used to analyze computed tomography (CT) features and extract radiomics features. The student's t-test, the Mann-Whitney-U test, the Pearson correlation, the least absolute shrinkage and selection operator, and multivariable logistic analysis were used to select the significant CT and radiomics features. Multivariable logistic regression analysis was performed to build the clinical-CT, radiomics, and integrated models. The predictive performances were evaluated through the receiver operating characteristic curve and compared with the DeLong test. The integrated nomogram was analyzed regarding discrimination, calibration, and clinical significance. RESULTS The rad-score was developed with one shape and four textural features. The integrated nomogram incorporating radiomics score, spiculation, and the number of tumor-related vessels (TVN) demonstrated better predictive efficacy than the radiomics and clinical-CT models in the training cohort (area under the curve [AUC], 0.893 vs 0.853 and 0.828, and p = 0.043 and 0.027, respectively) and validation cohort (AUC, 0.887 vs 0.878 and 0.786, and p = 0.761 and 0.043, respectively). The nomogram also demonstrated good calibration and clinical usefulness. CONCLUSION The radiomics nomogram integrating the radiomics with clinical-CT features demonstrated good performance in predicting MVI status in stage I NSCLC. The nomogram may be a useful tool for physicians in improving personalized management of stage I NSCLC.
Collapse
Affiliation(s)
- Lin Deng
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China (L.D., H.Z.T., J.Y.W., J.W.Q.)
| | - Han Zhou Tang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China (L.D., H.Z.T., J.Y.W., J.W.Q.)
| | - Ying Wei Luo
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center/Cancer Hospital, Guangzhou, China (Y.W.L., Q.L.)
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China (F.F.)
| | - Jing Yan Wu
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China (L.D., H.Z.T., J.Y.W., J.W.Q.)
| | - Qiong Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center/Cancer Hospital, Guangzhou, China (Y.W.L., Q.L.)
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, China (L.D., H.Z.T., J.Y.W., J.W.Q.).
| |
Collapse
|
4
|
Zhu H, Zhang L, Huang Z, Chen J, Sun L, Chen Y, Huang G, Chen Q, Yu H. Lung adenocarcinoma associated with cystic airspaces: Predictive value of CT features in assessing pathologic invasiveness. Eur J Radiol 2023; 165:110947. [PMID: 37392546 DOI: 10.1016/j.ejrad.2023.110947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/10/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES Lung adenocarcinoma associated with cystic airspaces (LACA) is a unique entity with limited understanding. Our aim was to evaluate the radiological characteristics of LACA and to study which criteria were predictive of invasiveness. METHODS A retrospective monocentric analysis of consecutive patients with pathologically confirmed LACA was performed. The diagnosed adenocarcinomas were classified into preinvasive (atypical adenomatous hyperplasia, adenocarcinoma in situ, or minimally invasive adenocarcinoma) and invasive adenocarcinomas. Eight clinical features and twelve CT features were evaluated. Univariable and multivariable analyses were performed to analyse the correlation between invasiveness, and CT and clinical features. The inter-observer agreement was evaluated using κ statistics and intraclass correlation coefficients. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 252 patients with 265 lesions (128 men and 124 women; mean age, 58.0 ± 11.1 years) were enrolled. Multivariable logistic regression indicated that multiple cystic airspaces (OR, 5.599; 95 % CI, 1.865-16.802), irregular shape of cystic airspace (OR, 3.236; 95 % CI, 1.073-9.761), entire tumour size (OR, 1.281; 95 % CI, 1.075-1.526), and attenuation (OR, 1.007; 95 % CI, 1.005-1.010) were independent risk factors for invasive LACA. The AUC of the logistic regression model was 0.964 (95 % CI, 0.944-0.985). CONCLUSION Multiple cystic airspaces, irregular shape of cystic airspace, entire tumour size, and attenuation were identified as independent risk factors for invasive LACA. The prediction model gives a good predictive performance, providing additional diagnostic information.
Collapse
Affiliation(s)
- Huiyuan Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lian Zhang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Zike Huang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linlin Sun
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinan Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Huang
- Shanghai University of Traditional Chinese Medicine, Shanghai, China; Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China; Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| |
Collapse
|
5
|
Computed tomography of ground glass nodule image based on fuzzy C-means clustering algorithm to predict invasion of pulmonary adenocarcinoma. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
6
|
CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8967643. [PMID: 35035526 PMCID: PMC8759914 DOI: 10.1155/2022/8967643] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 11/17/2022]
Abstract
Objectives. The intraoperative frozen section examination (IFSE) of pulmonary ground-glass density nodules (GGNs) is a great challenge. In the present study, through comparing the correlation between the computed tomography (CT) findings and pathological diagnosis of GGNs, the CT features as independent risk factors affecting the examination were defined, and their value in the rapid intraoperative examination of GGNs was explored. Methods. The relevant clinical data of 90 patients with GGNs on CT were collected, and all CT findings of GGNs, including the maximum transverse diameter, average CT value, spiculation, solid component, vascular sign, air sign, bronchus sign, lobulation, and pleural indentation, were recorded. All the cases received thoracoscopic surgery, and final pathological results were obtained. The cases were divided into three groups on the basis of pathological diagnosis: benign/atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). The CT findings were analyzed statistically, the independent risk factors were identified through the intergroup bivariate logistic regression analysis on variables with statistically significant differences, and a receiver operating curve (ROC) was plotted to establish a logistic regression model for diagnosing GGNs. A retrospective analysis was conducted on the coincidence rate of the rapid intraoperative and routine postoperative pathological examinations of the 90 cases with GGNs. The relevant clinical data of 49 cases with GGNs were collected. Conventional rapid intraoperative examination and CT-assisted rapid intraoperative examination were performed, and their coincidence rates with routine postoperative pathological examinations were compared. Results. No statistical differences in the onset age, gender, smoking history, and family history of malignant tumors were found among cases with GGNs in the identification of benign/AAH, AIS/MIA, and IAC (
,
,
,
). No statistically significant difference was found among the three groups in terms of CT findings, such as lobulation, bronchus sign, pleural indentation, spiculation, vascular sign, and solid component (
). The air sign, the maximum transverse diameter of GGNs, and average CT value showed statistically significant differences among the groups (
,
,
). Bivariate logistic regression analysis was performed on three risk factors, and the predicted probability value was obtained. A ROC curve was plotted by using the maximum transverse diameter as a predictor for analysis between the groups with benign/AAH and AIS/MIA, and the results demonstrated that the area under the curve (AUC) was 0.692. A ROC curve was plotted by using the predicted probability value, maximum transverse diameter, and average CT value as predictors for distinguishing between the groups with AIS/MIA and IAC, and the results showed that the AUC values of the predicted probability value, maximum transverse diameter, and CT value were 0.920, 0.816, and 0.772, respectively. A regression model
was established to identify GGNs as IAC, obtaining AUC values of up to 0.920 for the groups with AIS/MIA and IAC, the sensitivity of 0.821, and the specificity of 0.894. The coincidence rate of rapid intraoperative and routine postoperative pathological examinations taken for modeling was 79.3%, that of conventional IFSE and postoperative pathological examination in prospective studies was 83.7%, and that of CT-assisted rapid intraoperative and postoperative pathological examinations was 98.0%. The former two were statistically different from the last one (
and
, respectively). Conclusion. The air sign, maximum transverse diameter, and average CT value of the CT findings of GGNs had superior capabilities to enhance the pathologic classification of GGNs. The auxiliary function of the comprehensive multifactor analysis of GGNs was better than that of single-factor analysis. CT-assisted diagnosis can improve the accuracy of rapid intraoperative examination, thereby increasing the accuracy of the selection of operative approaches in clinical practice.
Collapse
|
7
|
Virtual special issue: pulmonary nodules. Clin Radiol 2021; 76:916-917. [PMID: 34565526 DOI: 10.1016/j.crad.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 11/22/2022]
|
8
|
Saeki Y, Kitazawa S, Yanagihara T, Kobayashi N, Kikuchi S, Goto Y, Ichimura H, Sato Y. Consolidation volume and integration of computed tomography values on three-dimensional computed tomography may predict pathological invasiveness in early lung adenocarcinoma. Surg Today 2021; 51:1320-1327. [PMID: 33547958 DOI: 10.1007/s00595-021-02231-7] [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: 08/24/2020] [Accepted: 12/10/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE To investigate the relationship between three-dimensional computed tomography (3D-CT) findings and pathological invasiveness in lung adenocarcinoma. METHODS We retrospectively evaluated 95 patients who underwent surgical resection of lung adenocarcinoma of ≤ 20 mm. The diameters, volumes, and CT values of tumor consolidation were analyzed. We defined the modified CT value by setting air as 0 and water as 1000 and assumed a correlation with pathological invasiveness. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. We compared the clinico-radiological features with pathological invasiveness. Receiver operator characteristic (ROC) curves and recurrence-free survival curves were constructed. RESULTS Twenty-six non-invasive adenocarcinomas and 69 invasive adenocarcinomas were evaluated. The multivariate analysis revealed that the consolidation volume and the integration of modified CT values were the most important predictors of pathological invasion. The area under the ROC curve and the cut-off values of the consolidation volume were 0.868 and 75 mm3, respectively. The area under the ROC curve and the cut-off values of the integration of modified CT values were 0.871 and 80,000, respectively. There was no recurrence in cases with values below the cut-off across all parameters. CONCLUSION The consolidation volume and integration of modified CT values were shown to be highly predictive of pathological invasiveness.
Collapse
Affiliation(s)
- Yusuke Saeki
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Shinsuke Kitazawa
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Takahiro Yanagihara
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Naohiro Kobayashi
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Shinji Kikuchi
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Yukinobu Goto
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Hideo Ichimura
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Yukio Sato
- Department of General Thoracic Surgery, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
| |
Collapse
|
9
|
Dyer SC, Bartholmai BJ, Koo CW. Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening. J Thorac Dis 2020; 12:6966-6977. [PMID: 33282402 PMCID: PMC7711402 DOI: 10.21037/jtd-2019-cptn-02] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Lung cancer remains the leading cause of cancer death in the United States. Screening with low-dose computed tomography (LDCT) has been proven to aid in early detection of lung cancer and reduce disease specific mortality. In 2014, the American College of Radiology (ACR) released version 1.0 of the Lung CT Screening Reporting and Data System (Lung-RADS) as a quality tool to standardize the reporting of lung cancer screening LDCT. In 2019, 5 years after the implementation of Lung-RADS version 1.0 the ACR released the updated Lung-RADS version 1.1 which incorporates initial experience with lung cancer screening. In this review, we outline the implications of the changes and additions in Lung-RADS version 1.1 and examine relevant literature for many of the updates. We also highlight several challenges and opportunities as Lung-RADS version 1.1 is implemented in lung cancer screening programs.
Collapse
Affiliation(s)
- Spencer C Dyer
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
10
|
Wu L, Gao C, Xiang P, Zheng S, Pang P, Xu M. CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features. Front Oncol 2020; 10:838. [PMID: 32537436 PMCID: PMC7267037 DOI: 10.3389/fonc.2020.00838] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 04/28/2020] [Indexed: 12/19/2022] Open
Abstract
Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra- and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or peri-nodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Conclusions: The gross radiomic signature of intra- and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.
Collapse
Affiliation(s)
- Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Sisi Zheng
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.,Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| |
Collapse
|