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Ninomiya K, Yanagawa M, Tsubamoto M, Sato Y, Suzuki Y, Hata A, Kikuchi N, Yoshida Y, Yamagata K, Doi S, Ogawa R, Tokuda Y, Kido S, Tomiyama N. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Jpn J Radiol 2024; 42:590-598. [PMID: 38413550 PMCID: PMC11139717 DOI: 10.1007/s11604-024-01534-2] [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: 11/12/2023] [Accepted: 01/13/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.
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Affiliation(s)
- Keisuke Ninomiya
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Mitsuko Tsubamoto
- Nishinomiya Municipal Central Hospital, 8-24 Hayashidacho, Nishinomiya, Hyogo, 663-8014, Japan
| | - Yukihisa Sato
- Suita Municipal Hospital, 5-7 Kishibeshinmachi, Suita, Osaka, 564-0018, Japan
| | - Yuki Suzuki
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Akinori Hata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriko Kikuchi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuriko Yoshida
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kazuki Yamagata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shuhei Doi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Ryo Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yukiko Tokuda
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shoji Kido
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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Zhang X, Tong X, Chen Y, Chen J, Li Y, Ding C, Ju S, Zhang Y, Zhang H, Zhao J. A metabolomics study on carcinogenesis of ground-glass nodules. Cytojournal 2024; 21:12. [PMID: 38628288 PMCID: PMC11021118 DOI: 10.25259/cytojournal_68_2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/03/2023] [Indexed: 04/19/2024] Open
Abstract
Objective This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer. Material and Methods Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure. Results Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples. Conclusion Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.
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Affiliation(s)
- Xiaomiao Zhang
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xin Tong
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuan Chen
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jun Chen
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Li
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Cheng Ding
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Sheng Ju
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi Zhang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hang Zhang
- Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jun Zhao
- Department of Thoracic Surgery, Institute of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, China
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Crombé A, Lafon M, Nougaret S, Kind M, Cousin S. Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. Eur J Radiol 2022; 155:110472. [PMID: 35985090 DOI: 10.1016/j.ejrad.2022.110472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/09/2022] [Accepted: 08/09/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. METHODS This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. RESULTS 101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. CONCLUSION Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France; Department of Imaging, Pellegrin University Hospital, F-33300 Bordeaux, France; University of Bordeaux, UMR CNRS 5251, INRIA Project team Models in Oncology (Monc), F-33400 Talence, France.
| | - Mathilde Lafon
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Stéphanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, F-34295 Montpellier, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
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Shi L, Zhao J, Peng X, Wang Y, Liu L, Sheng M. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol 2021; 144:109956. [PMID: 34563797 DOI: 10.1016/j.ejrad.2021.109956] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research. METHOD PubMed, Embase, Web of Science Core Collection were searched for relevant studies. The studies differentiating IAC from indolent lung adenocarcinomas appearing as GGNs based on CT radiomics features were included. Basic information, patient information, CT-scanner information, technique information and performance information were extracted for each included study. The quality of each study was assessed using the Radiomic Quality Score (RQS) and the Prediction model Risk of Bias Assessment Tool (PROBAST). RESULTS Twenty-eight studies were included with patients ranging from 34 to 794. All of them were retrospective. Patients in three studies were from multiple centers. Most studies segmented regions of interest manually. Pyradiomics and AK software were the most frequently used for features extraction. The number of radiomics features extracted varied from 7 to 10329. Logistic regression was the most frequently chosen model. Entropy was identified as radiomics signature in seven studies. The AUC of included studies ranged from 0.77 to 0.98 in 15 validation sets. The percentage RQS ranged from 3% to 50%. According to PROBAST, the overall risk of bias (ROB) was high in 89.3% (25/28) of included studies, unclear in 7.1% (2/28) of included studies, and low in 3.6% (1/28) of included studies. All studies were low concern regarding the applicability of primary studies to the review question. CONCLUSION CT radiomics-based model is promising and encouraging in differentiating IAC from indolent lung adenocarcinomas, though they require methodological rigor. Well-designed studies are necessary to demonstrate their validity and standardization of methods and results can prompt their use in daily clinical practice.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China; Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Xueqing Peng
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China; School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China.
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China.
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