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Azour L, Oh AS, Prosper AE, Toussie D, Villasana-Gomez G, Pourzand L. Subsolid Nodules: Significance and Current Understanding. Clin Chest Med 2024; 45:263-277. [PMID: 38816087 DOI: 10.1016/j.ccm.2024.02.003] [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] [Indexed: 06/01/2024]
Abstract
Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.
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Affiliation(s)
- Lea Azour
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA.
| | - Andrea S Oh
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Ashley E Prosper
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
| | - Danielle Toussie
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Geraldine Villasana-Gomez
- Department of Radiology, New York University Grossman School of Medicine, NYU Langone Health, 660 1st Avenue, New York, NY 10016, USA
| | - Lila Pourzand
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 957437, 757 Westwood Plaza, Los Angeles, CA 90095-7437, USA
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Liu D, Lv W, Liu WV, Tian T, Qin Y, Li Y, Liu Q, Cai J, Gao S, Ding G, Zhao Y, Zhou Y, Xie Y, Zhu W. MRI Radiomics Features of Adenohypophysis Determine the Activation of Hypothalamic-Pituitary-Gonadal Axis in Peri-Puberty Children. J Magn Reson Imaging 2024; 59:1769-1776. [PMID: 37501392 DOI: 10.1002/jmri.28914] [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: 04/06/2023] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND The status of the hypothalamic-pituitary-gonadal (HPG) axis is important for assessing the onset of physiological or pathological puberty. The reference standard gonadotropin-releasing hormone (GnRH) stimulation test requires hospital admission and repeated blood samples. A simple noninvasive method would be beneficial. OBJECTIVES To explore a noninvasive method for evaluating HPG axis activation in children using an MRI radiomics model. STUDY TYPE Retrospective. POPULATION Two hundred thirty-nine children (83 male; 3.6-14.6 years) with hypophysial MRI and GnRH stimulation tests, randomly divided a training set (168 children) and a test set (71 children). FIELD STRENGTH/SEQUENCE 3.0 T, 3D isotropic fast spin echo (CUBE) T1-weighted imaging (T1WI) sequences. ASSESSMENT Radiomics features were extracted from sagittal 3D CUBE T1WI, and imaging signatures were generated using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Diagnostic performance for differential diagnosis of HPG status was compared between a radiomics model and MRI features (adenohypophyseal height [aPH] and volume [aPV]). STATISTICAL TESTS Receiver operating characteristic (ROC) and decision curve analysis (DCA). A P value <0.05 was considered statistically significant. RESULTS Eight hundred fifty-one radiomics features were extracted and reduced to 10 by the LASSO method in the training cohort. The radiomics model based on CUBE T1WI showed good performance in assessment of HPG axis activation with an area under the ROC curve (AUC) of 0.81 (95% CI: 0.71, 0.91) in the test set. The AUC of the radiomics model was significantly higher than that of aPH (0.81 vs. 0.65) but there was no significant difference compared to aPV (0.81 vs. 0.78, P = 0.58). In DCA analysis, the radiomics signature showed higher net benefit over the aPV and aPH models. DATA CONCLUSIONS The MRI radiomics model has potential to assess HPG axis activation status noninvasively, potentially providing valuable information in the diagnosis of patients with pathological puberty onset. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Dong Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, Hubei, China
| | | | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yakun Li
- Department of Endocrinology and Metabolism, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qin Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianjian Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sikang Gao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guojun Ding
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunyun Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiran Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Chen H, Song A, Ul Rehman F, Han D. Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer. ENVIRONMENTAL TOXICOLOGY 2024; 39:890-904. [PMID: 37956258 DOI: 10.1002/tox.24018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 11/15/2023]
Abstract
Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.
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Affiliation(s)
- Hua Chen
- Department of Research and Development, Qingdao Bioman Biomedical Technology Co., LTD, Qingdao, China
- Department of Research and Development, Shanghai life Biomedical Technology Co., LTD, Shanghai, China
| | - Anqi Song
- Department of Student Affairs, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Faisal Ul Rehman
- Precision Medicine Center of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Dan Han
- Department of Emergency Medicine and Intensive Care, Shanghai Songjiang District Central Hospital, Shanghai, China
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Koike H, Ashizawa K, Tsutsui S, Kurohama H, Okano S, Nagayasu T, Kido S, Uetani M, Toya R. Differentiation Between Heterogeneous GGN and Part-Solid Nodule Using 2 D Grayscale Histogram Analysis of Thin-Section CT Image. Clin Lung Cancer 2023; 24:541-550. [PMID: 37407293 DOI: 10.1016/j.cllc.2023.06.001] [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: 04/27/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Abstract
INTRODUCTION/BACKGROUND To evaluate cases of surgically resected pulmonary adenocarcinoma (Ad) with heterogenous ground-glass nodules (HGGNs) or part-solid nodules (PSNs) and to clarify the differences between them, and between invasive adenocarcinoma (IVA) and minimally invasive adenocarcinoma (MIA) + adenocarcinoma in situ (AIS) using grayscale histogram analysis of thin-section computed tomography (TSCT). MATERIALS AND METHODS 241 patients with pulmonary Ad were retrospectively classified into HGGNs and PSNs on TSCT by three thoracic radiologists. Sixty HGGNs were classified into 17 IVAs, 26 MIAs, and 17 AISs. 181 PSNs were classified into 114 IVAs, 55 MIAs, and 12 AISs. RESULTS We found significant differences in area (P = 0.0024), relative size of solid component (P <0.0001), circumference (P <0.0001), mean CT value (P <0.0001), standard deviation of the CT value (P <0.0001), maximum CT value (P <0.0001), skewness (P <0.0001), kurtosis (P <0.0001), and entropy (P <0.0001) between HGGNs and PSNs. In HGGNs, we found significant differences in relative size of solid component (P <0.0001), mean CT value (P = 0.0005), standard deviation of CT value (P = 0.0071), maximum CT value (P = 0.0237), and skewness (P = 0.0027) between IVAs and MIA+AIS lesions. In PSNs, we found significant differences in area (P = 0.0029), relative size of solid component (P = 0.0003), circumference (P = 0.0004), mean CT value (P = 0.0011), skewness (P = 0.0009), and entropy (P = 0.0002) between IVAs and the MIA+AIS lesions. CONCLUSION Quantitative evaluations using grayscale histogram analysis can clearly distinguish between HGGNs and PSNs, and may be useful for estimating the pathology of such lesions.
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Affiliation(s)
- Hirofumi Koike
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Kazuto Ashizawa
- Departments of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
| | - Shin Tsutsui
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hirokazu Kurohama
- Department of Pathology, Nagasaki University Hospital, Nagasaki, Japan
| | - Shinji Okano
- Department of Pathology, Nagasaki University Hospital, Nagasaki, Japan
| | - Takeshi Nagayasu
- Departments of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shoji Kido
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Masataka Uetani
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Ryo Toya
- Departments of Radiology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Sun Y, Salerno S, He X, Pan Z, Yang E, Sujimongkol C, Song J, Wang X, Han P, Kang J, Sjoding MW, Jolly S, Christiani DC, Li Y. Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Sci Rep 2023; 13:7318. [PMID: 37147440 PMCID: PMC10161188 DOI: 10.1038/s41598-023-34559-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 05/03/2023] [Indexed: 05/07/2023] Open
Abstract
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.
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Affiliation(s)
- Yuming Sun
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Stephen Salerno
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinwei He
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Ziyang Pan
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Eileen Yang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Chinakorn Sujimongkol
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jiyeon Song
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinan Wang
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Peisong Han
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Michael W Sjoding
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan Rogel Cancer Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - David C Christiani
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care, Department of Internal Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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Chen C, Geng Q, Song G, Zhang Q, Wang Y, Sun D, Zeng Q, Dai Z, Wang G. A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules. Front Oncol 2023; 13:1066360. [PMID: 37007065 PMCID: PMC10064794 DOI: 10.3389/fonc.2023.1066360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.
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Affiliation(s)
- Chengyu Chen
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qun Geng
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Qian Zhang
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Youruo Wang
- Elite Class of 2017, Shandong First Medical University, Jinan, China
| | - Dongfeng Sun
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Gongchao Wang
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Gongchao Wang,
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Zhao W, Xiong Z, Tian D, Wang K, Zhao M, Lu X, Qin D, Li Z. The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses. Front Public Health 2022; 10:1018527. [PMID: 36267999 PMCID: PMC9577178 DOI: 10.3389/fpubh.2022.1018527] [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: 08/13/2022] [Accepted: 09/20/2022] [Indexed: 01/28/2023] Open
Abstract
Purpose To compare the value of contrast-enhanced CT (CECT) and non-contrast-enhanced CT (NCECT) radiomics models in differentiating tuberculosis (TB) from non-tuberculous infectious lesions (NTIL) presenting as solid pulmonary nodules or masses, and develop a combine radiomics model (RM). Materials and methods This study was a retrospective analysis of 101 lesions in 95 patients, including 49 lesions (from 45 patients) in the TB group and 52 lesions (from 50 patients) in the NTIL group. Lesions were randomly divided into training and test sets in the ratio of 7:3. Conventional imaging features were used to construct a conventional imaging model (IM). Radiomics features screening and NCECT or CECT RM construction were carried out by correlation analysis and gradient boosting decision tree, and logistic regression. Finally, conventional IM, NCECT RM, and CECT RM were used for combine RM construction. Additionally, we recruited three radiologists for independent diagnosis. The differential diagnostic performance of each model was assessed using the areas under the receiver operating characteristic curve (AUCs). Results The CECT RM (training AUC, 0.874; test AUC, 0.796) outperformed the conventional IM (training AUC, 0.792; test AUC, 0.708), the NCECT RM (training AUC, 0.835; test AUC, 0.704), and three radiologists. The diagnostic efficacy of the combine RM (training AUC, 0.922; test AUC, 0.833) was best in the training and test sets. Conclusions The diagnostic efficacy of the CECT RM was superior to that of the NCECT RM in identifying TB from NTIL presenting as solid pulmonary nodules or masses. The combine RM had the best performance and may outperform expert radiologists.
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Affiliation(s)
- Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Di Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Kunpeng Wang
- Department of Radiology, Dalian Public Health Clinical Center, Dalian, China
| | | | - Xiwei Lu
- Department of Tuberculosis, Dalian Public Health Clinical Center, Dalian, China
| | - Dongxue Qin
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China,*Correspondence: Dongxue Qin
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China,Zhiyong Li
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Jiang Y, Xiong Z, Zhao W, Zhang J, Guo Y, Li G, Li Z. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs. Gan To Kagaku Ryoho 2022; 70:880-890. [PMID: 35301662 DOI: 10.1007/s11748-022-01801-x] [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: 12/17/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.
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Affiliation(s)
- Yining Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. .,Dalian Engineering Research Centre for Artificial Intelligence in Medical Imaging, Dalian, China.
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Chen N, Li R, Jiang M, Guo Y, Chen J, Sun D, Wang L, Yao X. Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT. Front Med (Lausanne) 2022; 9:833283. [PMID: 35280863 PMCID: PMC8911879 DOI: 10.3389/fmed.2022.833283] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Purposes and Objectives The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT). Methods A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively. Then, the prognostic and robust features were selected by machine learning methods, such as (1) univariate analysis based on a Cox proportional hazard (CPH) model, (2) redundancy removing using the variance inflation factor (VIF), and (3) multivariate importance analysis based on random survival forests (RSF). Finally, PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic area under the curve (C/D AUC). Results In total, 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964). Conclusion The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict the PFS for patients with SCLC by a high accuracy, which could be used as a useful tool to support the personalized clinical decision for the diagnosis and patient management of patients with SCLC.
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Affiliation(s)
- Ningxin Chen
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Ruikun Li
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Mengmeng Jiang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Yixian Guo
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Jiejun Chen
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Dazhen Sun
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Lisheng Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Xiuzhong Yao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
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Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:3953442. [PMID: 34976107 PMCID: PMC8716224 DOI: 10.1155/2021/3953442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 12/09/2022]
Abstract
This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer's cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image's slope and the other CT fine texture image's value of entropy, the medium texture image's slope and kurtosis, and the medium texture image's mean and the other CT fine texture image's value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.
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CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation. J Clin Med 2021; 10:jcm10235571. [PMID: 34884272 PMCID: PMC8658090 DOI: 10.3390/jcm10235571] [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: 10/18/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, "S(1,1)SumEntrp" was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed "S(4,4)InvDfMom" (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. "Sumentrp" might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.
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Aydos U, Ünal ER, Özçelik M, Akdemir D, Ekinci Ö, Taştepe Aİ, Memiş L, Atay LÖ, Akdemir ÜÖ. Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters. Rev Esp Med Nucl Imagen Mol 2021; 40:343-350. [PMID: 34752367 DOI: 10.1016/j.remnie.2020.09.012] [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: 05/04/2020] [Accepted: 06/19/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients. METHODS PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, MTV, TLG) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded. RESULTS Among the two histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis. CONCLUSIONS Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.
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Affiliation(s)
- Uğuray Aydos
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey.
| | - Emel Rodoplu Ünal
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Mahsun Özçelik
- Yüzüncü Yıl University, Faculty of Medicine, Department of Nuclear Medicine, Van, Turkey
| | - Deniz Akdemir
- Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI, USA
| | - Özgür Ekinci
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Abdullah İrfan Taştepe
- Gazi University, Faculty of Medicine, Department of Thoracic Surgery, Beşevler/Ankara, Turkey
| | - Leyla Memiş
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turkey
| | - Lütfiye Özlem Atay
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey
| | - Ümit Özgür Akdemir
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turkey
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Kim TY, Lee JY, Lee YJ, Park DW, Tae K, Choi YY. CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils. PLoS One 2021; 16:e0255835. [PMID: 34379652 PMCID: PMC8357133 DOI: 10.1371/journal.pone.0255835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/23/2021] [Indexed: 11/18/2022] Open
Abstract
The purposes of the study were to determine whether there are differences in texture analysis parameters between tonsil cancers and normal tonsils, and to correlate texture analysis with 18F-FDG PET/CT to investigate the relationship between texture analysis and metabolic parameters. Sixty-four patients with squamous cell carcinoma of the palatine tonsil were included. A ROI was drawn, including all slices, to involve the entire tumor. The contralateral normal tonsil was used for comparison with the tumors. Texture analysis parameters, mean, standard deviation (SD), entropy, mean positive pixels, skewness, and kurtosis were obtained using commercially available software. Parameters were compared between the tumor and the normal palatine tonsils. Comparisons were also performed among early tonsil cancer, advanced tonsil cancer, and normal tonsils. An ROC curve analysis was performed to assess discrimination of tumor from normal tonsils. Correlation between texture analysis and 18F-FDG PET/CT was performed. Compared to normal tonsils, the tumors showed a significantly lower mean, higher SD, higher entropy, lower skewness, and higher kurtosis on most filters (p<0.001). On comparisons among normal tonsils, early cancers, and advanced tonsil cancers, SD and entropy showed significantly higher values on all filters (p<0.001) between early cancers and normal tonsils. The AUC from the ROC analysis was 0.91, obtained from the entropy. A mild correlation was shown between texture parameters and metabolic parameters. The texture analysis parameters, especially entropy, showed significant differences in contrast-enhanced CT results between tumor and normal tonsils, and between early tonsil cancers and normal tonsils. Texture analysis can be useful as an adjunctive tool for the diagnosis of tonsil cancers.
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Affiliation(s)
- Tae-Yoon Kim
- Department of Radiology, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University Hospital, Seoul, Republic of Korea
- * E-mail: (JYL); (YJL)
| | - Young-Jun Lee
- Department of Radiology, Hanyang University Hospital, Seoul, Republic of Korea
- * E-mail: (JYL); (YJL)
| | - Dong Woo Park
- Department of Radiology, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Kyung Tae
- Department of Otolaryngology-Head and Neck Surgery, Hanyang University Hospital, Seoul, Republic of Korea
| | - Yun Young Choi
- Department of Nuclear Medicine, Hanyang University Hospital, Seoul, Republic of Korea
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Mottola M, Ursprung S, Rundo L, Sanchez LE, Klatte T, Mendichovszky I, Stewart GD, Sala E, Bevilacqua A. Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients. Sci Rep 2021; 11:11542. [PMID: 34078993 PMCID: PMC8172898 DOI: 10.1038/s41598-021-90985-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 05/10/2021] [Indexed: 12/19/2022] Open
Abstract
Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.
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Affiliation(s)
- Margherita Mottola
- Department of Electrical, Electronic, and Information Engineering (DEI), University of Bologna, 40136, Bologna, Italy
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40125, Bologna, Italy
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Tobias Klatte
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Urology, Royal Bournemouth Hospital, Bournemouth, BH7 7DW, UK
| | | | - Grant D Stewart
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Alessandro Bevilacqua
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40125, Bologna, Italy.
- Department of Computer Science and Engineering (DISI), University of Bologna, 40136, Bologna, Italy.
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Aydos U, Ünal ER, Özçelik M, Akdemir D, Ekinci Ö, Taştepe AI, Memiş L, Atay LÖ, Akdemir ÜÖ. Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters. Rev Esp Med Nucl Imagen Mol 2021; 40:S2253-654X(20)30134-7. [PMID: 33785321 DOI: 10.1016/j.remn.2020.06.025] [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: 05/04/2020] [Revised: 06/16/2020] [Accepted: 06/19/2020] [Indexed: 10/21/2022]
Abstract
OBJECTIVES The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients. MATERIAL AND METHODS PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, metabolic tumor volume [MTV] and total lesion glycolysis [TLG]) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded. RESULTS Among the 2histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis. CONCLUSIONS Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.
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Affiliation(s)
- U Aydos
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía.
| | - E R Ünal
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - M Özçelik
- Yüzüncü Yıl University, Faculty of Medicine, Department of Nuclear Medicine, Van, Turquía
| | - D Akdemir
- Michigan State University, Department of Epidemiology and Biostatistics, East Lansing, MI, Estados Unidos
| | - Ö Ekinci
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - A I Taştepe
- Gazi University, Faculty of Medicine, Department of Thoracic Surgery, Beşevler/Ankara, Turquía
| | - L Memiş
- Gazi University, Faculty of Medicine, Department of Pathology, Beşevler/Ankara, Turquía
| | - L Ö Atay
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía
| | - Ü Ö Akdemir
- Gazi University, Faculty of Medicine, Department of Nuclear Medicine, Beşevler/Ankara, Turquía
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 216] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Hu H, Li C, Lv T, Li H, Hu Y, Shen Q, Mino-Kenudson M, Bertolaccini L, Rocco G, Zarogoulidis P, Zhang F, Lin D, Liu H, Song Y. Contrast-enhanced computed tomography prior to percutaneous transthoracic needle biopsy reduces the incidence of hemorrhage. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:288. [PMID: 33708915 PMCID: PMC7944326 DOI: 10.21037/atm-20-4384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Background Hemorrhage is the second most common complication of percutaneous transthoracic needle biopsy (PTNB), and at present, there is no effective prevention strategy. Contrast-enhanced computed tomography (CECT) has the advantage of clearly visualizing blood supply within the lesion and aiding in the imaging of blood vessels, which can reduce hemorrhage complicating PTNB. As no large-sample studies were evaluating whether CECT could reduce hemorrhage, we conducted the present retrospective study. Methods From November 2011 to February 2016, 1,282 biopsies at Jinling Hospital were retrospectively reviewed; 555 underwent CECT, and 727 underwent non-contrast computed tomography (CT). Factors associated with hemorrhage were defined, and hemorrhage rates were compared between the 2 groups. Results We found that pre-biopsy CECT was associated with a reduced incidence of biopsy-related hemorrhage compared to non-contrast CT (16.4% vs. 23.1%, P=0.003). Propensity score matching (PSM) analysis also showed that the incidence of hemorrhage in the CECT group was lower than that of the non-contrast CT group at a ratio of 1:1 (P=0.039), 1:2 (P=0.028), or 1:3 (P=0.013). In the multivariate analysis, CECT before PTNB was found to be significantly associated with a reduced risk of hemorrhage [odds ratio (OR): 0.671, 95% confidence interval (CI): 0.499–0.902, P=0.008]. Puncture position, lesion size, depth of needle tract, and the number of punctures were also found to be associated with hemorrhage (all P<0.05). Conclusions Compared with non-contrast CT, CECT significantly reduced the risk of post-biopsy pulmonary hemorrhage, which suggests that CECT should be performed before PTNB.
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Affiliation(s)
- Huan Hu
- Department of Respiratory Medicine, Suzhou Hospital Affiliated Nanjing Medical University, Suzhou, China
| | - Chuling Li
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Tangfeng Lv
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Huijuan Li
- Department of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China
| | - Yangbo Hu
- Department of Respiratory Medicine, Jinling Hospital, Medical School of Southeast University, Nanjing, China
| | - Qin Shen
- Department of Pathology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Luca Bertolaccini
- Division of Thoracic Surgery IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gaetano Rocco
- Department of Thoracic Surgery and Oncology, National Cancer Institute, Pascale Foundation, Naples, Italy
| | - Pavlos Zarogoulidis
- Pulmonary Department-Oncology Unit, "G. Papanikolaou" General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Fang Zhang
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Dang Lin
- Department of Respiratory Medicine, Suzhou Hospital Affiliated Nanjing Medical University, Suzhou, 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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Ohki K, Igarashi T, Ashida H, Takenaga S, Shiraishi M, Nozawa Y, Ojiri H. Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps. Jpn J Radiol 2020; 39:66-75. [PMID: 32885378 DOI: 10.1007/s11604-020-01038-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/21/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET). MATERIALS AND METHODS Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs). RESULTS G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively. CONCLUSION Texture analysis may aid in differentiating between G1 and G2-3 PNET.
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Affiliation(s)
- Kazuyoshi Ohki
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan.
| | - Takao Igarashi
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Hirokazu Ashida
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Shinsuke Takenaga
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Megumi Shiraishi
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Yosuke Nozawa
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
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Wang X, Li Q, Cai J, Wang W, Xu P, Zhang Y, Fang Q, Fu C, Fan L, Xiao Y, Liu S. Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics. Transl Lung Cancer Res 2020; 9:1397-1406. [PMID: 32953512 PMCID: PMC7481614 DOI: 10.21037/tlcr-20-370] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs. Methods This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance. Results The AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05). Conclusions Deep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.
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Affiliation(s)
- Xiang Wang
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Qingchu Li
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Jiali Cai
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Wei Wang
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Peng Xu
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yiqian Zhang
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Qu Fang
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Chicheng Fu
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital of the Second Military Medical University, Shanghai, China
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Chen BT, Chen Z, Ye N, Mambetsariev I, Fricke J, Daniel E, Wang G, Wong CW, Rockne RC, Colen RR, Nasser MW, Batra SK, Holodny AI, Sampath S, Salgia R. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Front Oncol 2020; 10:593. [PMID: 32391274 PMCID: PMC7188953 DOI: 10.3389/fonc.2020.00593] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 03/31/2020] [Indexed: 01/06/2023] Open
Abstract
Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.
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Affiliation(s)
- Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Zikuan Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ningrong Ye
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Isa Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Jeremy Fricke
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Ebenezer Daniel
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - George Wang
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Rivka R Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.,Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Mohd W Nasser
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Sagus Sampath
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
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Ye J, Ling J, Lv Y, Chen J, Cai J, Chen M. Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis. Exp Ther Med 2020; 19:2483-2490. [PMID: 32256725 PMCID: PMC7086215 DOI: 10.3892/etm.2020.8511] [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: 07/29/2019] [Accepted: 12/04/2019] [Indexed: 11/06/2022] Open
Abstract
The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.
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Affiliation(s)
- Jing Ye
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
| | - Jun Ling
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
| | - Yan Lv
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
| | - Juan Chen
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
| | - Junhui Cai
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
| | - Mingxiang Chen
- Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
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22
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Nardone V, Tini P, Pastina P, Botta C, Reginelli A, Carbone SF, Giannicola R, Calabrese G, Tebala C, Guida C, Giudice A, Barbieri V, Tassone P, Tagliaferri P, Cappabianca S, Capasso R, Luce A, Caraglia M, Mazzei MA, Pirtoli L, Correale P. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol Lett 2019; 19:1559-1566. [PMID: 31966081 DOI: 10.3892/ol.2019.11220] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 08/13/2019] [Indexed: 12/27/2022] Open
Abstract
Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.
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Affiliation(s)
- Valerio Nardone
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Pastina
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Cirino Botta
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Salvatore Francesco Carbone
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Rocco Giannicola
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Grazia Calabrese
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Carmela Tebala
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Cesare Guida
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Aldo Giudice
- Epidemiology Unit, IRCCS Istituto Nazionale Tumori 'Fondazione G. Pascale', I-80131 Naples, Italy
| | - Vito Barbieri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierfrancesco Tassone
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierosandro Tagliaferri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Rosanna Capasso
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Amalia Luce
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Maria Antonietta Mazzei
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Luigi Pirtoli
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Correale
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
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Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer. Sci Rep 2019; 9:17389. [PMID: 31757989 PMCID: PMC6874598 DOI: 10.1038/s41598-019-53831-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 11/06/2019] [Indexed: 12/11/2022] Open
Abstract
Pancreatic cancer is a lethal disease, and resistance to chemotherapy is a critical factor influencing the postoperative prognosis. Tumour heterogeneity is an important indicator of chemoresistance. Therefore, we analysed tumour heterogeneity in preoperative computed tomography scans by performing texture analysis using the grey-level run-length matrix and analysed the correlation of survival with the value obtained in these analyses. We analysed 116 consecutive patients who underwent curative resection and had preoperative contrast-enhanced computed tomography data available for analysis. A region of interest was drawn on all slices with a visible tumour and normal pancreas on the arterial phase computed tomography scans; the correlation of pathological characteristics with grey-level run-length matrix features was analysed. We then performed Kaplan–Meier survival curve analysis among pancreatic cancer patients. The grey-level non-uniformity values in grey-level run-length matrix features for tumours were higher than those for normal pancreas. High grey-level non-uniformity values represent a non-uniform texture, i.e., heterogeneity. Grey-level run-length matrix features showed that recurrence-free survival was shorter in the group with high grey-level non-uniformity 135 values (p = 0.025). Our analyses of the correlation between pathological outcomes and grey-level run-length matrix features in pancreatic cancer patients showed that grey-level non-uniformity values were powerful prognostic indicators.
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24
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E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Acad Radiol 2019; 26:1245-1252. [PMID: 30502076 DOI: 10.1016/j.acra.2018.10.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/27/2018] [Accepted: 10/04/2018] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images. MATERIALS AND METHODS 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC). In total, 1695 quantitative radiomic features (QRF) were calculated from the primary lung cancer tumor in each patient. To build radiomic classification model based on the extracted QRFs, several machine-learning algorithms were applied sequentially. First, unsupervised hierarchical clustering was used to exclude highly correlated QRFs; second, the minimum Redundancy Maximum Relevance feature selection algorithm was employed to select informative and nonredundant QRFs; finally, the Incremental Forward Search and Support Vector Machine classification algorithms were used to combine the selected QRFs and build the model. In our work, to study the phenotypic differences among lung cancer histological subtypes, four classification models were built. They were models of SCLC vs NSCLC, SCLC vs AD, SCLC vs SCC, and AD vs SCC. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC) estimated by three-fold cross-validation. RESULTS The AUC (95% confidence interval) for the model of SCLC vs NSCLC was 0.741(0.678, 0.795). For the models of SCLC vs AD and SCLC vs SCC, the AUCs were 0.822(0.755, 0.875) and 0.665(0.583, 0.738), respectively. The AUC for the model of AD vs SCC was 0.655(0.570, 0.731). Several QRFs ("Law_15," "LoG_Uniformity," "GLCM_Contrast," and "Compactness Factor") that characterize tumor heterogeneity and shape were selected as the significant features to build the models. CONCLUSION Our results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image.
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Affiliation(s)
- Linning E
- Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA.
| | - Li Li
- Department of Pathology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Hao Yang
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032, USA
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25
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Meyer HJ, Hamerla G, Höhn AK, Surov A. CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas. Front Oncol 2019; 9:444. [PMID: 31192138 PMCID: PMC6546809 DOI: 10.3389/fonc.2019.00444] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 05/10/2019] [Indexed: 12/26/2022] Open
Abstract
Introduction: Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC). Materials and Methods: Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests. Results: Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (ρ = −0.60 and ρ = −0.50, respectively). Conclusions: CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | | | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, Duan SF, Wang ZQ. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res 2019; 11:1933-1944. [PMID: 30881119 PMCID: PMC6407516 DOI: 10.2147/cmar.s195376] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance. Patients and methods PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades. Results There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989. Conclusion Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.
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Affiliation(s)
- Chuan-Gen Guo
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Shuai Ren
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Qi-Dong Wang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Wen-Bo Xiao
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Jing-Feng Zhang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | | | - Zhong-Qiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
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D'Onofrio M, Ciaravino V, Cardobi N, De Robertis R, Cingarlini S, Landoni L, Capelli P, Bassi C, Scarpa A. CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms. Sci Rep 2019; 9:2176. [PMID: 30778137 PMCID: PMC6379382 DOI: 10.1038/s41598-018-38459-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 12/28/2018] [Indexed: 12/13/2022] Open
Abstract
To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.
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Affiliation(s)
- Mirko D'Onofrio
- Department of Radiology, G.B. Rossi Hospital - University of Verona, Verona, Italy.
| | - Valentina Ciaravino
- Department of Radiology, G.B. Rossi Hospital - University of Verona, Verona, Italy
| | - Nicolò Cardobi
- Department of Radiology, Ospedale Civile Maggiore, Verona, Italy
| | | | - Sara Cingarlini
- Department of Oncology, G.B. Rossi Hospital - University of Verona, Verona, Italy
| | - Luca Landoni
- Department of General and Pancreatic Surgery, Pancreas Institute, G.B. Rossi Hospital - University of Verona, Verona, Italy
| | - Paola Capelli
- Department of Pathology, Pancreas Institute, G.B. Rossi Hospital - University of Verona, Verona, Italy
| | - Claudio Bassi
- Department of General and Pancreatic Surgery, Pancreas Institute, G.B. Rossi Hospital - University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Pathology, Pancreas Institute, G.B. Rossi Hospital - University of Verona, Verona, Italy
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28
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Giannini V, Mazzetti S, Bertotto I, Chiarenza C, Cauda S, Delmastro E, Bracco C, Di Dia A, Leone F, Medico E, Pisacane A, Ribero D, Stasi M, Regge D. Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging 2019; 46:878-888. [PMID: 30637502 DOI: 10.1007/s00259-018-4250-6] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/26/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment of 18F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC. METHODS Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. RESULTS In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7% and specificity = 76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86%, and specificity = 83% at the Youden index). CONCLUSIONS If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.
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Affiliation(s)
- V Giannini
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy. .,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy.
| | - S Mazzetti
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy.,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy
| | - I Bertotto
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy
| | - C Chiarenza
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy
| | - S Cauda
- Nuclear Medicine Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - E Delmastro
- Radiation Therapy Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - C Bracco
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - A Di Dia
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - F Leone
- Medical Oncology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - E Medico
- Laboratory of Oncogenomics, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - A Pisacane
- Pathology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - D Ribero
- Hepatobilio-Pancreatic and Colorectal Surgery Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - M Stasi
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - D Regge
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy.,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy
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HRCT texture analysis for pure or part-solid ground-glass nodules: distinguishability of adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma. Jpn J Radiol 2017; 36:113-121. [PMID: 29273964 DOI: 10.1007/s11604-017-0711-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 11/23/2017] [Indexed: 12/23/2022]
Abstract
PURPOSE To distinguish between adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) showing pure or part-solid ground-glass nodules (GGNs) by high-resolution computed tomography (HRCT) texture analysis. MATERIALS AND METHODS This retrospective study included 101 consecutive patients with 115 pure or part-solid GGNs ≤ 3 cm diameter, which were surgically resected and pathologically diagnosed with AIS, MIA, or IAC (48 AIS-MIA and 67 IAC) between April 2011 and March 2015. Each tumor was manually segmented on axial CT images, and the following texture features were calculated: volume, mass, mean CT value, variance, skewness, kurtosis, entropy, uniformity, and percentile CT numbers (10th, 25th, 50th, 75th, 90th, 95th percentiles). The differences between AIS-MIA and IAC were statistically evaluated using univariate, multivariate, and receiver operating characteristic analysis. RESULTS Compared with IAC, AIS-MIA had significantly greater skewness, kurtosis, and uniformity, whereas in the other parameters, AIS-MIA demonstrated significantly lower values than those of IAC. Multivariate analysis revealed that independent differentiators were the 90th percentile CT numbers (P < 0.001) and entropy (P = 0.005) with an excellent accuracy (area under the curve, 0.90). CONCLUSIONS The 90th percentile CT numbers and entropy can accurately distinguish AIS-MIA from IAC.
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Chaddad A, Desrosiers C, Toews M, Abdulkarim B. Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget 2017; 8:104393-104407. [PMID: 29262648 PMCID: PMC5732814 DOI: 10.18632/oncotarget.22251] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/02/2017] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data. MATERIALS AND METHODS Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons. RESULTS Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%). CONCLUSION Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).
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Affiliation(s)
- Ahmad Chaddad
- Division of Radiation Oncology, McGill University, Montréal, Canada
- The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Supérieure, Montréal, Canada
| | - Christian Desrosiers
- The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Supérieure, Montréal, Canada
| | - Matthew Toews
- The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Supérieure, Montréal, Canada
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