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Tian C, Hu Y, Li S, Zhang X, Wei Q, Li K, Chen X, Zheng L, Yang X, Qin Y, Bian Y. Peri- and intra-nodular radiomic features based on 18F-FDG PET/CT to distinguish lung adenocarcinomas from pulmonary granulomas. Front Med (Lausanne) 2024; 11:1453421. [PMID: 39175818 PMCID: PMC11339787 DOI: 10.3389/fmed.2024.1453421] [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: 06/23/2024] [Accepted: 07/23/2024] [Indexed: 08/24/2024] Open
Abstract
Objective To compare the effectiveness of radiomic features based on 18F-FDG PET/CT images within (intranodular) and around (perinodular) lung nodules/masses in distinguishing between lung adenocarcinoma and pulmonary granulomas. Methods For this retrospective study, 18F-FDG PET/CT images were collected for 228 patients. Patients diagnosed with lung adenocarcinoma (n = 156) or granulomas (n = 72) were randomly assigned to a training (n = 159) and validation (n = 69) groups. The volume of interest (VOI) of intranodular, perinodular (1-5 voxels, termed Lesion_margin1 to Lesion_margin5) and total area (intra- plus perinodular region, termed Lesion_total1 to Lesion_total5) on PET/CT images were delineated using PETtumor and Marge tool of segmentation editor. A total of 1,037 radiomic features were extracted separately from PET and CT images, and the optimal features were selected to develop radiomic models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results Good and acceptable performance was, respectively, observed in the training (AUC = 0.868, p < 0.001) and validation (AUC = 0.715, p = 0.004) sets for the intranodular radiomic model. Among the perinodular models, the Lesion_margin2 model demonstrated the highest AUC in both sets (0.883 and 0.616, p < 0.001 and p = 0.122). Similarly, in terms of total models, Lesion_total2 model was found to outperform others in the training (AUC = 0.879, p < 0.001) and validation (AUC = 0.742, p = 0.001) sets, slightly surpassing the intranodular model. Conclusion When intra- and perinodular radiomic features extracted from the immediate vicinity of the nodule/mass up to 2 voxels distance on 18F-FDG PET/CT imaging are combined, improved differential diagnostic performance in distinguishing between lung adenocarcinomas and granulomas is achieved compared to the intra- and perinodular radiomic features alone.
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Affiliation(s)
- Congna Tian
- Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Shuheng Li
- Department of Nuclear Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, China
| | - Xinchao Zhang
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Qiang Wei
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Kang Li
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Lu Zheng
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Xin Yang
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Yanan Qin
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Yanzhu Bian
- Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, Hebei, 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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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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Tang Y, Li M, Lin B, Tao X, Shi Z, Jin X, Bongiolatti S, Ricciardi S, Divisi D, Durand M, Youness HA, Shinohara S, Zhu C, Liu Y. Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules. Transl Lung Cancer Res 2023; 12:2494-2504. [PMID: 38205216 PMCID: PMC10775010 DOI: 10.21037/tlcr-23-666] [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: 10/16/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Background The prediction of the persistent pure ground-glass nodule (pGGN) growth is challenging and limited by subjective assessment and variation across radiologists. A chest computed tomography (CT) image-based deep learning classification model (DLCM) may provide a more accurate growth prediction. Methods This retrospective study enrolled consecutive patients with pGGNs from January 2010 to December 2020 from two independent medical institutions. Four DLCM algorithms were built to predict the growth of pGGNs, which were extracted from the nodule areas of chest CT images annotated by two radiologists. All nodules were assigned to either the study, the inner validation, or the external validation cohort. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUROCs) were analyzed to evaluate our models. Results A total of 286 patients were included, with 419 pGGN. In total, 197 (68.9%) of the patients were female and the average age was 59.5±12.0 years. The number of pGGN assigned to the study, the inner validation, and the external validation cohort were 193, 130, and 96, respectively. The follow-up time of stable pGGNs for the primary and external validation cohorts were 3.66 (range, 2.01-10.08) and 4.63 (range, 2.00-9.91) years, respectively. Growth of the pGGN occurred in 166 nodules [83 (43%), 39 (30%), and 44 (45%) in the study, inner and external validation cohorts respectively]. The best-performing DLCM algorithm was DenseNet_DR, which achieved AUROCs of 0.79 [95% confidence interval (CI): 0.70, 0.86] in predicting pGGN growth in the inner validation cohort and 0.70 (95% CI: 0.60, 0.79) in the external validation cohort. Conclusions DLCM algorithms that use chest CT images can help predict the growth of pGGNs.
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Affiliation(s)
- Yanhua Tang
- Department of Radiology, Beijing Chaoyang Hospital, Beijing, China
| | - Minzhen Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
| | - Benke Lin
- Department of Surgical Oncology, Qinghai Provincial People’s Hospital, Xining, China
| | - Xuemin Tao
- Department of Radiology, Beijing Electric Power Hospital, Beijing, China
- Department of Radiology, People’s Liberation Army General Hospital, Beijing, China
| | - Zhongyue Shi
- Department of Pathology, Beijing Chaoyang Hospital, Beijing, China
| | - Xin Jin
- Department of Radiology, People’s Liberation Army General Hospital, Beijing, China
- Department of Radiology, Peking University Cancer Hospital, Beijing, China
| | | | - Sara Ricciardi
- Unit of Thoracic Surgery, San Camillo Forlanini Hospital, Rome, Italy
- PhD Program University of Bologna, Bologna, Italy
| | - Duilio Divisi
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Thoracic Surgery Unit, “Giuseppe Mazzini” Hospital of Teramo, Teramo, Italy
| | - Marion Durand
- Groupe Hospitalier Privé Ambroise Paré Hartmann, Thoracic Unit, Neuilly-Sur-Seine, France
| | - Houssein A. Youness
- Interventional Pulmonary Program, Section of Pulmonary, Critical Care and Sleep Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Shuichi Shinohara
- Department of Thoracic Surgery, Anjo Kosei Hospital, Anjo, Aichi, Japan
| | - Chuang Zhu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
| | - Yi Liu
- Department of Thoracic Surgery, Beijing Chaoyang Hospital, Beijing, China
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Laqua FC, Woznicki P, Bley TA, Schöneck M, Rinneburger M, Weisthoff M, Schmidt M, Persigehl T, Iuga AI, Baeßler B. Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer. Cancers (Basel) 2023; 15:2850. [PMID: 37345187 DOI: 10.3390/cancers15102850] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/06/2023] [Accepted: 05/19/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVES Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. METHODS In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional "hand-crafted" radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). RESULTS In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865-0.878), SBS 35.8 (34.2-37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). CONCLUSION Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.
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Affiliation(s)
- Fabian Christopher Laqua
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany
| | - Piotr Woznicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany
| | - Thorsten A Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany
| | - Mirjam Schöneck
- Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Miriam Rinneburger
- Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Mathilda Weisthoff
- Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Matthias Schmidt
- Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Thorsten Persigehl
- Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Andra-Iza Iuga
- Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany
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