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Wang K, Wu G. Whole-volume diffusion kurtosis magnetic resonance (MR) imaging histogram analysis of non-small cell lung cancer: correlation with histopathology and degree of tumor differentiation. Clin Radiol 2024; 79:e1072-e1080. [PMID: 38816262 DOI: 10.1016/j.crad.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 06/01/2024]
Abstract
AIMS To evaluate the role of diffusion kurtosis imaging (DKI) histogram analysis in the characterization of non-small cell lung cancer (NSCLC) and to correlate DKI parameters with tumor cellularity. MATERIALS AND METHODS Sixty-four patients with pathologically diagnosed NSCLCs were evaluated by DKI on a 3-T scanner. Regions of interest (ROIs) were drawn on the map of b1000 manually. All NSCLCs were histologically graded according to the degree of tumor differentiation. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN), the expression of Ki-67 was detected using the streptavidin-peroxidase method. Histogram analysis was performed using voxel-based on raw data from each ROI. RESULTS NSCLCs were classified as grades 1, 2, and 3 according to differentiation degree. Histogram parameters of apparent diffusion coefficient (ADC) and DKI could discriminate between different grades of tumors (p<0.001). Receiver operating characteristic (ROC) curve analysis showed that Kapp 75th exhibited the best performance with an AUC of 0.936 and sensitivity/specificity of 95.74%/80% (p<0.001) in distinguishing grade 1 from grade 2, ADC mean exhibited the best performance with an AUC of 0.923 and sensitivity/specificity of 92.33%/86.67% (p<0.001) in distinguishing grade 2 from 3. N/C ratio and Ki-67 changed significantly with grade (p<0.01). Negative correlations were found between the ADC mean and the N/C ratio, Ki-67, Dapp mean and N/C ratio, whereas Kapp mean and N/C ratio, Ki-67 were positively correlated. CONCLUSIONS DKI histogram analysis could quantitatively characterize NSCLC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor.
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Affiliation(s)
- K Wang
- PET-CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China.
| | - G Wu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430000, Hubei, China
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Liu F, Xiang Z, Li Q, Fang X, Zhou J, Yang X, Lin H, Yang Q. 18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study. Clin Radiol 2024; 79:e147-e155. [PMID: 37884401 DOI: 10.1016/j.crad.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023]
Abstract
AIM To explore the value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics model for predicting the degree of pathological differentiation in non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS Clinical characteristics of 182 NSCLC patients from four centres were collected, and radiomics features were extracted from 18F-FDG PET/CT images. Three logistic regression prediction models were established: clinical model; radiomics model; and nomogram combining radiomics signatures and clinical features. The predictive ability of the models was assessed using receiver operating characteristics curve analysis. RESULTS Patients from centre 1 were assigned randomly to the training and internal validation cohorts (7:3 ratio); patients from centres 2-4 served as the external validation cohort. The area under the curve (AUC) values for the clinical model in the training, internal validation, and external validation cohort were 0.74 (95% confidence interval [CI] = 0.64-0.84), 0.64 (95% CI = 0.46-0.81), and 0.74 (95% CI = 0.60-0.88), respectively. In the training (AUC: 0.84 [95% CI = 0.77-0.92]), internal validation (AUC: 0.81 [95% CI = 0.67-0.95]), and external validation cohorts (AUC: 0.74 [95% CI = 0.58-0.89]), the radiomics model showed good predictive ability for differentiation. Compared to the clinical and radiomics models, the nomogram has relatively better diagnostic performance, and the AUC values for nomogram in the training, internal validation, and external validation cohort were 0.86 (95% CI = 0.78-0.93), 0.83 (95% CI = 0.70-0.96), and 0.77 (95% CI = 0.62-0.92), respectively. CONCLUSIONS The 18F-FDG PET/CT-based radiomics model showed good ability for predicting the degree of differentiation of NSCLC. The nomogram combining the radiomics signature and clinical features has relatively better diagnostic performance.
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Affiliation(s)
- F Liu
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Z Xiang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Q Li
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - X Fang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
| | - J Zhou
- The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - X Yang
- Sichuan Science City Hospital, Mianyang, Sichuan 621000, China
| | - H Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Q Yang
- Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
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Broncano J, Steinbrecher K, Marquis KM, Raptis CA, Royuela Del Val J, Vollmer I, Bhalla S, Luna A. Diffusion-weighted Imaging of the Chest: A Primer for Radiologists. Radiographics 2023; 43:e220138. [PMID: 37347699 DOI: 10.1148/rg.220138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Diffusion-weighted imaging (DWI) is a fundamental sequence not only in neuroimaging but also in oncologic imaging and has emerging applications for MRI evaluation of the chest. DWI can be used in clinical practice to enhance lesion conspicuity, tissue characterization, and treatment response. While the spatial resolution of DWI is in the order of millimeters, changes in diffusion can be measured on the micrometer scale. As such, DWI sequences can provide important functional information to MRI evaluation of the chest but require careful optimization of acquisition parameters, notably selection of b values, application of parallel imaging, fat saturation, and motion correction techniques. Along with assessment of morphologic and other functional features, evaluation of DWI signal attenuation and apparent diffusion coefficient maps can aid in tissue characterization. DWI is a noninvasive noncontrast acquisition with an inherent quantitative nature and excellent reproducibility. The outstanding contrast-to-noise ratio provided by DWI can be used to improve detection of pulmonary, mediastinal, and pleural lesions, to identify the benign nature of complex cysts, to characterize the solid portions of cystic lesions, and to classify chest lesions as benign or malignant. DWI has several advantages over fluorine 18 (18F)-fluorodeoxyglucose PET/CT in the assessment, TNM staging, and treatment monitoring of lung cancer and other thoracic neoplasms with conventional or more recently developed therapies. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.
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Affiliation(s)
- Jordi Broncano
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Kacie Steinbrecher
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Kaitlin M Marquis
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Constantin A Raptis
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Javier Royuela Del Val
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Ivan Vollmer
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Sanjeev Bhalla
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
| | - Antonio Luna
- From the Cardiothoracic Imaging Unit (J.B.) and Department of Radiology (J.B., J.R.d.V.), Hospital San Juan de Dios, HT-RESSALTA, HT Médica, Avenida el Brillante No. 36, 14012 Córdoba, Spain; Cardiothoracic Imaging Section, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.S., K.M.M., C.A.R., S.B.); Cardiothoracic Imaging Section, Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (I.V.); and MRI Section, Department of Radiology, Clínica Las Nieves, HT-SERCOSA, HT Médica, Jaén, Spain (A.L.)
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Zhang A, Meng X, Yao Y, Zhou X, Yan S, Fei W, Zhou N, Zhang Y, Kong H, Li N. Predictive Value of 18 F-FDG PET/MRI for Pleural Invasion in Solid and Subsolid Lung Adenocarcinomas Smaller Than 3 cm. J Magn Reson Imaging 2022; 57:1367-1375. [PMID: 36066210 DOI: 10.1002/jmri.28422] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Positron emission tomography (PET)/MRI combines the characteristics of metabolism imaging and high soft tissue resolution, and could provide high diagnostic efficacy for assessment of pleural invasion (PI) of lung cancer. PURPOSE To investigate the application of 18 F-fluorodeoxyglucose (FDG) PET/MRI for predicting PI of lung cancer with the maximum diameter ≤3 cm. STUDY TYPE Prospective. POPULATION A total of 44 patients with non-small cell lung cancer (NSCLC), age from 39 to 79 years old, including 19 (56.82%) females. FIELD STRENGTH/SEQUENCE A 3-T, hybrid PET/MRI including axial fast spin echo respiratory-triggered T2 fat-suppressed imaging (T2FS) and echo planar imaging diffusion-weighted imaging (DWI). ASSESSMENT The maximum standardized uptake value (SUVmax) of all lesions was measured on PET images. Localized effusion outside the contact between the nodules and the pleura on T2FS and signal at the contact between the nodules and the pleura on DWI were evaluated by experienced physicians through visual assessment of the MRI sequences. STATISTICAL TESTS Three models (models 1-3) were developed, incorporating CT, CT and PET, PET and MRI features, and Lasso regression was used in feature selection. The receiver operating characteristic (ROC) curve for PI diagnosis was visualized for each model, and the area under the curve (AUC) was calculated. The DeLong test was used to compare the different AUCs. A P value < 0.05 was considered statistically significant. RESULTS The AUC of models 1-3 was 0.762, 0.829, and 0.915, respectively. The DeLong test showed a statistically significant difference between the AUCs of model 1 vs. model 3, while the differences between the AUCs of model 1 vs. model 2 (P = 0.253) and model 2 vs. model 3 (P = 0.075) were not statistically significant. DATA CONCLUSION 18 F-FDG PET/MRI might show high predictive value for lung adenocarcinoma smaller than 3 cm with PI. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Annan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Xiangxi Meng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Yuan Yao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Xin Zhou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Shuo Yan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Wang Fei
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Nina Zhou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
| | - Hanjing Kong
- Beijing United Imaging Research Institute of Intelligent Imaging, UIH Group, Beijing, China
| | - Nan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Haidian, Beijing, China
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5
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Volumetric analysis of intravoxel incoherent motion diffusion-weighted imaging in preoperative assessment of non-small cell lung cancer. Jpn J Radiol 2022; 40:903-913. [PMID: 35507139 DOI: 10.1007/s11604-022-01279-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 04/05/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE To evaluate the potential of intravoxel incoherent motion (IVIM) and apparent diffusion coefficient (ADC) in the prediction of tumor grade, lymph node metastasis and pleural invasion of non-small cell lung cancer (NSCLC) before surgery. MATERIALS AND METHODS 65 patients diagnosed with NSCLC by surgery were enrolled. IVIM-DWI (10 b-values, 0-1000 s/mm2) was performed before surgery. The mean and minimum ADC (ADCmean, ADCmin) and IVIM parameters D, D* and f were independently measured and calculated by 2 radiologists by drawing regions of interest (ROIs) including the solid component of the whole tumor. Intraclass correlation coefficients (ICCs) were analysed. Spearman analysis was used to determine the correlation between IVIM parameters and tumor differentiation. Independent sample t-tests (normal distribution) or Mann-Whitney U tests (non-normal distribution) were used to compare the differences between the parameters in moderately-well and poorly differentiated groups, with and without lymph node metastasis and pleural invasion groups. Receiver operating characteristic (ROC) curves were generated. RESULTS The ADCmean, ADCmin, D and f values were negatively correlated with the pathological grades of tumor (P < 0.05). The ADCmean and D values of patients with poor differentiation and lymph node metastasis were significantly lower than that of patients with moderately-well differentiation and without lymph node metastasis (P < 0.001-0.012). The D value was significantly lower and f value was significantly higher among patients with pleural invasion than those without (P = 0.033 and < 0.001). ROC analysis showed that the area under the ROC curve (AUC) was larger for D in predicting the degree of differentiation (0.832) and lymph node metastasis (0.806), and higher for f in predicting pleural invasion (0.832). CONCLUSIONS IVIM is useful for predicting the tumor differentiation, lymph node metastasis and pleural invasion in NSCLC patients before surgery.
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Basson L, Jarraya H, Escande A, Cordoba A, Daghistani R, Pasquier D, Lacornerie T, Lartigau E, Mirabel X. Chest Magnetic Resonance Imaging Decreases Inter-observer Variability of Gross Target Volume for Lung Tumors. Front Oncol 2019; 9:690. [PMID: 31456936 PMCID: PMC6700272 DOI: 10.3389/fonc.2019.00690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 07/12/2019] [Indexed: 12/14/2022] Open
Abstract
Purpose: PET/CT is a standard medical imaging used in the delineation of gross tumor volume (GTV) in case of radiation therapy for lung tumors. However, PET/CT could present some limitations such as resolution and standardized uptake value threshold. Moreover, chest MRI has shown good potential in diagnosis for thoracic oncology. Therefore, we investigated the influence of chest MRI on inter-observer variability of GTV delineation. Methods and Materials: Five observers contoured the GTV on CT for 14 poorly defined lung tumors during three contouring phases based on true daily clinical routine and acquisition: CT phase, with only CT images; PET phase, with PET/CT; and MRI phase, with both PET/CT and MRI. Observers waited at least 1 week between each phases to decrease memory bias. Contours were compared using descriptive statistics of volume, coefficient of variation (COV), and Dice similarity coefficient (DSC). Results: MRI phase volumes (median 4.8 cm3) were significantly smaller than PET phase volumes (median 6.4 cm3, p = 0.015), but not different from CT phase volumes (median 5.7 cm3, p = 0.30). The mean COV was improved for the MRI phase (0.38) compared to the CT (0.58, p = 0.024) and PET (0.53, p = 0.060) phases. The mean DSC of the MRI phase (0.67) was superior to those of the CT and PET phases (0.56 and 0.60, respectively; p < 0.001 for both). Conclusions: The addition of chest MRI seems to decrease inter-observer variability of GTV delineation for poorly defined lung tumors compared to PET/CT alone and should be explored in further prospective studies.
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Affiliation(s)
- Laurent Basson
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France.,University of Lille, Lille, France
| | - Hajer Jarraya
- Medical Imaging Department, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - Alexandre Escande
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France.,University of Lille, Lille, France
| | - Abel Cordoba
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - Rayyan Daghistani
- University of Lille, Lille, France.,Medical Imaging Department, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - David Pasquier
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France.,University of Lille, Lille, France
| | - Thomas Lacornerie
- Department of Medical Physics, Oscar Lambret Comprehensive Cancer Center, Lille, France
| | - Eric Lartigau
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France.,University of Lille, Lille, France
| | - Xavier Mirabel
- Universitary Radiation Oncology Department, Oscar Lambret Comprehensive Cancer Center, Lille, France
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Chen X, Fang M, Dong D, Wei X, Liu L, Xu X, Jiang X, Tian J, Liu Z. A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non-small Cell Lung Cancer. Acad Radiol 2018; 25:1548-1555. [PMID: 29572049 DOI: 10.1016/j.acra.2018.02.019] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 02/18/2018] [Accepted: 02/25/2018] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Poorly differentiated non-small cell lung cancer (NSCLC) indicated a poor prognosis and well-differentiated NSCLC indicates a noninvasive nature and good prognosis. The purpose of this study was to build and validate a radiomics signature to predict the degree of tumor differentiation (DTD) for patients with NSCLC. MATERIALS AND METHODS A total of 487 patients with pathologically diagnosed NSCLC were retrospectively included in our study. Five hundred ninety-one radiomics features were extracted from each tumor from the contrast-enhanced computed tomography images. A minimum redundancy maximum relevance algorithm and a logistic regression model were used for dimension reduction, feature selection, and radiomics signature building. The performance of the radiomics signature was assessed using receiver operating characteristic analysis, and the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to quantify the association between a signature and DTD. An independent validation set contained 184 consecutive patients with NSCLC. RESULTS A nine-radiomics-feature-based signature was built and it could differentiate low and high DTDs in the training set (AUC = 0.763, sensitivity = 0.750, specificity = 0.665, and accuracy = 0.687), and the radiomics signature had good discrimination performance in the validation set (AUC = 0.782, sensitivity = 0.608, specificity = 0.752, and accuracy = 0.712). CONCLUSIONS A radiomics signature based on contrast-enhanced computed tomography imaging is a potentially useful imaging biomarker for differentiating low from high DTD in patients with NSCLC.
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Affiliation(s)
- Xin Chen
- The Second School of Clinical Medicine, Southern Medical University, 1023 Shatai Nan Road, Guangzhou, 510515, China; Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China; Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, 1 Panfu Road, Guangzhou, China
| | - Mengjie Fang
- University of Chinese Academy of Sciences, 95 Zhongguancun Dong Road, Beijing, 100190, China
| | - Di Dong
- University of Chinese Academy of Sciences, 95 Zhongguancun Dong Road, Beijing, 100190, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, 1 Panfu Road, Guangzhou, China
| | - Lingling Liu
- Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, 1 Panfu Road, Guangzhou, China
| | - Xiangdong Xu
- Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, 1 Panfu Road, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, 1 Panfu Road, Guangzhou, China
| | - Jie Tian
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China; University of Chinese Academy of Sciences, 95 Zhongguancun Dong Road, Beijing, 100190, China.
| | - Zaiyi Liu
- The Second School of Clinical Medicine, Southern Medical University, 1023 Shatai Nan Road, Guangzhou, 510515, China; Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
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Usuda K, Funazaki A, Maeda R, Sekimura A, Motono N, Matoba M, Uramoto H. Economic Benefits and Diagnostic Quality of Diffusion-Weighted Magnetic Resonance Imaging for Primary Lung Cancer. Ann Thorac Cardiovasc Surg 2017; 23:275-280. [PMID: 28978865 DOI: 10.5761/atcs.ra.17-00097] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
This paper focuses on the latest research of diffusion-weighted magnetic resonance imaging (DWI), and deals with economic benefits, diagnostic benefits, and prospects of DWI for lung cancer. The medical cost of a magnetic resonance imaging (MRI) is 81%-84% cheaper than that of 18-fluoro-2-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT). DWI is reported to be useful for differential diagnosis of malignancy or benignity for neoplasm in various organs. Diagnostic efficacy by DWI for pulmonary nodules and masses and the evaluation of N factor and M factor in lung cancer are equivalent to or more than that of FDG-PET/CT. The diagnostic capability of whole-body DWI (WB-DWI) for the staging of clinically operable lung cancers is equivalent to that of FDG-PET/CT and brain MRI, and WB-DWI is now becoming a more main stream procedure. Although the diagnostic performance of DWI for lung cancer may be equivalent to that of FDG-PET/CT, prospective randomized controlled trial for comparison of diagnostic efficacy between FDG-PET/CT and DWI for lung cancer is necessary for an accurate comparison. DWI may have an advantage in the aspect of the cost and diagnostic efficacy in lung cancer management.
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Affiliation(s)
- Katsuo Usuda
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Aika Funazaki
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Ryo Maeda
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Atsushi Sekimura
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Nozomu Motono
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Munetaka Matoba
- Department of Radiology, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
| | - Hidetaka Uramoto
- Department of Thoracic Surgery, Kanazawa Medical University, Kahoku-gun, Ishikawa, Japan
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Tsuchiya N, Doai M, Usuda K, Uramoto H, Tonami H. Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion. PLoS One 2017; 12:e0172433. [PMID: 28207858 PMCID: PMC5313135 DOI: 10.1371/journal.pone.0172433] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Accepted: 02/04/2017] [Indexed: 12/30/2022] Open
Abstract
PURPOSE Investigating the diagnostic accuracy of histogram analyses of apparent diffusion coefficient (ADC) values for determining non-small cell lung cancer (NSCLC) tumor grades, lymphovascular invasion, and pleural invasion. MATERIALS AND METHODS We studied 60 surgically diagnosed NSCLC patients. Diffusion-weighted imaging (DWI) was performed in the axial plane using a navigator-triggered single-shot, echo-planar imaging sequence with prospective acquisition correction. The ADC maps were generated, and we placed a volume-of-interest on the tumor to construct the whole-lesion histogram. Using the histogram, we calculated the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC, skewness, and kurtosis. Histogram parameters were correlated with tumor grade, lymphovascular invasion, and pleural invasion. We performed a receiver operating characteristics (ROC) analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathologic features. RESULTS The ADC mean, 10th, 25th, 50th, 75th, 90th, and 95th percentiles showed significant differences among the tumor grades. The ADC mean, 25th, 50th, 75th, 90th, and 95th percentiles were significant histogram parameters between high- and low-grade tumors. The ROC analysis between high- and low-grade tumors showed that the 95th percentile ADC achieved the highest area under curve (AUC) at 0.74. Lymphovascular invasion was associated with the ADC mean, 50th, 75th, 90th, and 95th percentiles, skewness, and kurtosis. Kurtosis achieved the highest AUC at 0.809. Pleural invasion was only associated with skewness, with the AUC of 0.648. CONCLUSIONS ADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion.
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Affiliation(s)
- Naoko Tsuchiya
- Department of Radiology, Kanazawa Medical University, Uchinada, Ishikawa, Japan
| | - Mariko Doai
- Department of Radiology, Kanazawa Medical University, Uchinada, Ishikawa, Japan
| | - Katsuo Usuda
- Department of Thoracic Surgery, Kanazawa Medical University, Uchinada, Ishikawa, Japan
| | - Hidetaka Uramoto
- Department of Thoracic Surgery, Kanazawa Medical University, Uchinada, Ishikawa, Japan
| | - Hisao Tonami
- Department of Radiology, Kanazawa Medical University, Uchinada, Ishikawa, Japan
- * E-mail:
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Chen GX, Wang MH, Zheng T, Tang GC, Han FG, Tu GJ. Diffusion-weighted magnetic resonance imaging for the detection of metastatic lymph nodes in patients with lung cancer: A meta-analysis. Mol Clin Oncol 2017; 6:344-354. [PMID: 28451411 PMCID: PMC5403316 DOI: 10.3892/mco.2017.1153] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 12/06/2016] [Indexed: 12/30/2022] Open
Abstract
The aim of the present meta-analysis was to evaluate the diagnostic value of diffusion-weighted imaging (DWI) in differentiating metastatic from non-metastatic lymph nodes in patients with lung cancer. A systematic literature search was performed to identify eligible original studies. The quality of included studies was assessed using ‘quality assessment of diagnostic accuracy studies’ (QUADAS-2). Meta-analysis was performed to pool sensitivity and specificity, to calculate the positive likelihood ratio (PLR), the negative likelihood ratio (NLR) and the diagnostic odds ratio (DOR), and to construct the summary receiver operating characteristic (SROC) curve. The homogeneity, threshold effect and publication bias were also investigated. Meta-regression analysis was performed to identify the sources of heterogeneity. A total of 10 studies with 11 datasets met the inclusion criteria, which comprised 796 patients with a total of 2,433 lymph nodes. The pooled diagnostic sensitivity was 0.78 [95% confidence interval (CI): 0.74–0.81] and the pooled diagnostic specificity was 0.88 (95% CI: 0.86–0.89). The PLR, NLR, and DOR were 7.11 (95% CI: 4.39–11.52), 0.24 (95% CI: 0.18–0.33), and 31.14 (95% CI: 17.32–55.98), respectively. The area under the SROC curve was 0.90. No publication bias was found (bias=−0.15, P=0.887). Notable heterogeneity was, however, observed, and patient selection, type of lung cancer, number of enrolled lymph nodes, reference standard, B-value and the type of scanner were the sources of heterogeneity (P<0.05). No significant threshold effect was identified (P=0.537). In conclusion, DWI has been revealed to be a valuable magnetic resonance imaging (MRI) modality, with good diagnostic performance for distinguishing metastatic from non-metastatic lymph nodes in patients with lung cancer. Therefore, DWI may be a useful supplement to conventional MRI techniques.
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Affiliation(s)
- Guang-Xiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
| | - Mao-Hua Wang
- Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
| | - Ting Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
| | - Guang-Cai Tang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
| | - Fu-Gang Han
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
| | - Guo-Jian Tu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
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Zhao D, Hu Q, Qi L, Wang J, Wu H, Zhu G, Yu H. Magnetic resonance (MR) imaging for tumor staging and definition of tumor volumes on radiation treatment planning in nonsmall cell lung cancer: A prospective radiographic cohort study of single center clinical outcome. Medicine (Baltimore) 2017; 96:e5943. [PMID: 28225485 PMCID: PMC5569433 DOI: 10.1097/md.0000000000005943] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
We investigate the impact of magnetic resonance (MR) on the staging and radiotherapy planning for patients with nonsmall cell lung cancer (NSCLC).A total of 24 patients with NSCLC underwent MRI, which was fused with radiotherapy planning CT using rigid registration. Gross tumor volume (GTV) was delineated not only according to CT image alone (GTVCT), but also based on both CT and MR image (GTVCT/MR). For each patient, 2 conformal treatment plans were made according to GTVCT and GTVCT/MR, respectively. Dose-volume histograms (DVH) for lesion and normal organs were generated using both GTVCT and GTVCT/MR treatment plans. All patients were irradiated according to GTVCT/MR plan.Median volume of the GTVCT/MR and GTVCT were 105.42 cm and 124.45 cm, respectively, and the mean value of GTVCT/MR was significantly smaller than that of GTVCT (145.71 ± 145.04 vs 174.30 ± 150.34, P < 0.01). Clinical stage was modified in 9 patients (37.5%). The objective response rate (ORR) was 83.3% and the l-year overall survival (OS) was 87.5%.MR is a useful tool in radiotherapy treatment planning for NSCLC, which improves the definition of tumor volume, reduces organs at risk dose and does not increase the local recurrence rate.
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MESH Headings
- Adenocarcinoma/diagnostic imaging
- Adenocarcinoma/pathology
- Adenocarcinoma/radiotherapy
- Adenocarcinoma of Lung
- Carcinoma, Non-Small-Cell Lung/diagnostic imaging
- Carcinoma, Non-Small-Cell Lung/pathology
- Carcinoma, Non-Small-Cell Lung/radiotherapy
- Carcinoma, Squamous Cell/diagnostic imaging
- Carcinoma, Squamous Cell/pathology
- Carcinoma, Squamous Cell/radiotherapy
- Female
- Follow-Up Studies
- Humans
- Lung/diagnostic imaging
- Lung Neoplasms/diagnostic imaging
- Lung Neoplasms/pathology
- Lung Neoplasms/radiotherapy
- Magnetic Resonance Imaging/methods
- Male
- Middle Aged
- Neoplasm Staging
- Pilot Projects
- Prospective Studies
- Radiotherapy Planning, Computer-Assisted/methods
- Radiotherapy, Conformal/methods
- Radiotherapy, Image-Guided/methods
- Survival Analysis
- Tomography, X-Ray Computed/methods
- Treatment Outcome
- Tumor Burden
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Affiliation(s)
- Dan Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology
| | - Qiaoqiao Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology
| | - Liping Qi
- Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Juan Wang
- Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology
| | - Guangying Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology
| | - Huiming Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology
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Baliyan V, Das CJ, Sharma R, Gupta AK. Diffusion weighted imaging: Technique and applications. World J Radiol 2016; 8:785-798. [PMID: 27721941 PMCID: PMC5039674 DOI: 10.4329/wjr.v8.i9.785] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 06/11/2016] [Accepted: 08/15/2016] [Indexed: 02/06/2023] Open
Abstract
Diffusion weighted imaging (DWI) is a method of signal contrast generation based on the differences in Brownian motion. DWI is a method to evaluate the molecular function and micro-architecture of the human body. DWI signal contrast can be quantified by apparent diffusion coefficient maps and it acts as a tool for treatment response evaluation and assessment of disease progression. Ability to detect and quantify the anisotropy of diffusion leads to a new paradigm called diffusion tensor imaging (DTI). DTI is a tool for assessment of the organs with highly organised fibre structure. DWI forms an integral part of modern state-of-art magnetic resonance imaging and is indispensable in neuroimaging and oncology. DWI is a field that has been undergoing rapid technical evolution and its applications are increasing every day. This review article provides insights in to the evolution of DWI as a new imaging paradigm and provides a summary of current role of DWI in various disease processes.
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Broncano J, Luna A, Sánchez-González J, Alvarez-Kindelan A, Bhalla S. Functional MR Imaging in Chest Malignancies. Magn Reson Imaging Clin N Am 2016; 24:135-155. [DOI: 10.1016/j.mric.2015.08.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Nomori H, Cong Y, Sugimura H, Kato Y. Diffusion-weighted imaging can correctly identify false-positive lymph nodes on positron emission tomography in non-small cell lung cancer. Surg Today 2015; 46:1146-51. [DOI: 10.1007/s00595-015-1285-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 11/10/2015] [Indexed: 01/18/2023]
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15
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Hagen JA. Diffusion-weighted magnetic resonance imaging may identify candidates for alternatives to lobectomy for early stage lung cancer. J Thorac Cardiovasc Surg 2015; 149:997. [PMID: 25906714 DOI: 10.1016/j.jtcvs.2015.02.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 02/12/2015] [Indexed: 10/23/2022]
Affiliation(s)
- Jeffrey A Hagen
- Division of Thoracic Surgery, Keck School of Medicine, University of Southern California, Los Angeles, Calif.
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