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Hu X, Yang L, Kang T, Yu H, Zhao T, Huang Y, Kong Y. Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence. Heliyon 2024; 10:e34863. [PMID: 39170291 PMCID: PMC11336266 DOI: 10.1016/j.heliyon.2024.e34863] [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: 02/03/2024] [Revised: 07/17/2024] [Accepted: 07/17/2024] [Indexed: 08/23/2024] Open
Abstract
Objective This study aimed to investigate the value of artificial intelligence (AI) for distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with subsolid nodules (SSNs). Materials and methods This retrospective study included 110 consecutive patients with 120 SSNs. The qualitative and quantitative imaging characteristics of SSNs were extracted automatically using an artificially intelligent assessment system. Then, radiologists had to verify these characteristics again. We split all cases into two groups: non-IA including 11 Atypical adenomatous hyperplasia (AAH) and 25 adenocarcinoma in situ (AIS) or IA including 7 minimally invasive adenocarcinoma (MIA) and 77 invasive adenocarcinoma (IAC). Variables that exhibited statistically significant differences between the non-IA and IA in the univariate analysis were included in the multivariate logistic regression analysis. Receiver operating characteristic (ROC) analyses were conducted to determine the cut-off values and their diagnostic performances. Results Multivariate logistic regression analysis showed that the major diameter (odds ratio [OR] = 1.38; 95 % confidence interval [CI], 1.02-1.87; P = 0.036) and entropy of three-dimensional(3D) CT value (OR = 3.73, 95 % CI, 1.13-2.33, P = 0.031) were independent risk factors for adenocarcinomas. The cut-off values of the major diameter and the entropy of 3D CT value for the diagnosis of invasive adenocarcinoma were 15.5 mm and 5.17, respectively. To improve the classification performance, we fused the major diameter and the entropy of 3D CT value as a combined model, and the (AUC) of the model was 0.868 (sensitivity = 0.845, specificity = 0.806). Conclusion The major diameter and entropy of 3D CT value can distinguish non-IA from IA. AI can improve performance in distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with SSNs.
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Affiliation(s)
- Xiaoqin Hu
- Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China
| | - Liu Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China
| | - Tong Kang
- Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China
| | - Hanhua Yu
- Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China
| | - Tingkuan Zhao
- Department of Pathology, Jingzhou Central Hospital, The Second Clinical Medical College, Yangtze University, Jingzhou, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Central Hospital, The Second Clinical Medical College, Yangtze University, Jingzhou, China
| | - Yuefeng Kong
- Department of Radiology, The Fourth Hospital of Wuhan, Wuhan, China
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Ping X, Jiang N, Meng Q, Hu C. Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images. Tomography 2024; 10:1042-1053. [PMID: 39058050 PMCID: PMC11280730 DOI: 10.3390/tomography10070078] [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: 05/21/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.
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Affiliation(s)
| | | | | | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou 215006, China; (X.P.); (N.J.); (Q.M.)
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Liu J, Yang X, Li Y, Xu H, He C, Zhou P, Qing H. Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort. Diagnostics (Basel) 2024; 14:147. [PMID: 38248024 PMCID: PMC10814052 DOI: 10.3390/diagnostics14020147] [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: 12/09/2023] [Revised: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.
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Affiliation(s)
| | | | | | | | | | - Peng Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China; (J.L.); (X.Y.); (Y.L.); (H.X.); (C.H.)
| | - Haomiao Qing
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China; (J.L.); (X.Y.); (Y.L.); (H.X.); (C.H.)
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Hong MP, Zhang R, Fan SJ, Liang YT, Cai HJ, Xu MS, Zhou B, Li LS. Interpretable CT radiomics model for invasiveness prediction in patients with ground-glass nodules. Clin Radiol 2024; 79:e8-e16. [PMID: 37833141 DOI: 10.1016/j.crad.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
AIM To evaluate the performance of an interpretable computed tomography (CT) radiomic model in predicting the invasiveness of ground-glass nodules (GGNs). MATERIALS AND METHODS The study was conducted retrospectively from 1 August 2017 to 1 August 2022, at three different centres. Two hundred and thirty patients with GGNs were enrolled at centre I as a training cohort. Centres II (n=157) and III (n=156) formed two external validation cohorts. Radiomics features extracted based on CT were reduced by a coarse-fine feature screening strategy. A radiomic model was developed through the use of the LASSO (least absolute shrinkage and selection operator) and XGBoost algorithms. Then, a radiological model was established through multivariate logistic regression analysis. Finally, the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). RESULTS The radiomic XGBoost model outperformed the radiomic logistic model and radiological model in assessing the invasiveness of GGNs. The area under the curve (AUC) values for the radiomic XGBoost model were 0.885 (95% confidence interval [CI] 0.836-0.923), 0.853 (95% CI 0.790-0.906), and 0.838 (95% CI 0.773-0.902) in the training and the two external validation cohorts, respectively. The SHAP method allowed for both a quantitative and visual representation of how decisions were made using a given model for each individual patient. This can provide a deeper understanding of the decision-making mechanisms within the model and the factors that contribute to its prediction effectiveness. CONCLUSIONS The present interpretable CT radiomics model has the potential to preoperatively evaluate the invasiveness of GGNs. Furthermore, it can provide personalised, image-based clinical-decision support.
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Affiliation(s)
- M P Hong
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
| | - R Zhang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - S J Fan
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Y T Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - H J Cai
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - M S Xu
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
| | - B Zhou
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
| | - L S Li
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
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Zuo Z, Zeng W, Peng K, Mao Y, Wu Y, Zhou Y, Qi W. Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study. Clin Radiol 2023; 78:e698-e706. [PMID: 37487842 DOI: 10.1016/j.crad.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 11/30/2022] [Accepted: 07/01/2023] [Indexed: 07/26/2023]
Abstract
AIM To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs). MATERIALS AND METHODS This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration. RESULTS The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration. CONCLUSION The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.
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Affiliation(s)
- Z Zuo
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China
| | - W Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China
| | - K Peng
- Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Y Mao
- Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan 410004, China
| | - Y Wu
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China
| | - Y Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China
| | - W Qi
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646100, China.
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Zhou S, Cai D, Chen C, Luo J, Wang R. Preoperative Changes of Lung Nodule on Computed Tomography and Their Relationship With Pathological Outcomes. Front Surg 2022; 9:836924. [PMID: 35372466 PMCID: PMC8965753 DOI: 10.3389/fsurg.2022.836924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundWhether changes of lung nodules on computed tomography could bring us helpful information related to their pathological outcomes remained unclear.Materials and MethodsThis retrospective study was carried out among 1,185 cases of lung nodules in Shanghai Chest Hospital from January 2015 to April 2017, which did not shrink or disappear after preoperative follow-up over three months. Their imaging features, changes, and clinical characteristics were collected. A separate analysis was performed in nodules with or without growth in long-axis diameter after follow-up, searching significant changes related to nodule malignancy and the median interval of follow-up for reference. Further study was performed similarly in malignant nodules for discrimination of malignant grading.ResultsMost nodules were stable (n = 885, 75%), whereas others grew (n = 300, 25%). For predicting nodule malignancy, increase in density (>10 Hounsfield units, median follow-up of 549 days) played an important role in growing group whereas it failed in stable group, and the increase in size was less significant in growing group. For discrimination of malignant grading, increase in density (>70 Hounsfield units, median follow-up of 366 days) showed its significance in stable group, and so did increase in size in growing group (maximum diameter growth >3.3 mm, median follow-up of 549 days, or average diameter growth >3.1 mm, median follow-up of 625 days).ConclusionsThere were significant changes of lung nodules by follow-up on computed tomography, related to their pathological outcomes. The predictive power of increase in density or size varied in different situations, whereas all referred to a long-time preoperative follow-up.
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