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Baniasadi A, Das JP, Prendergast CM, Beizavi Z, Ma HY, Jaber MY, Capaccione KM. Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis. J Transl Med 2024; 22:567. [PMID: 38872212 PMCID: PMC11177383 DOI: 10.1186/s12967-024-05379-1] [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: 02/11/2024] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.
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Affiliation(s)
- Alireza Baniasadi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
| | - Jeeban P Das
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Conor M Prendergast
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Zahra Beizavi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Hong Y Ma
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | | | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
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Li Z, Luo Y, Jiang H, Meng N, Huang Z, Feng P, Fang T, Fu F, Li X, Bai Y, Wei W, Yang Y, Yuan J, Cheng J, Wang M. The value of diffusion kurtosis imaging, diffusion weighted imaging and 18F-FDG PET for differentiating benign and malignant solitary pulmonary lesions and predicting pathological grading. Front Oncol 2022; 12:873669. [PMID: 35965564 PMCID: PMC9373010 DOI: 10.3389/fonc.2022.873669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To explore the value of PET/MRI, including diffusion kurtosis imaging (DKI), diffusion weighted imaging (DWI) and positron emission tomography (PET), for distinguishing between benign and malignant solitary pulmonary lesions (SPLs) and predicting the histopathological grading of malignant SPLs. Material and methods Chest PET, DKI and DWI scans of 73 patients with SPL were performed by PET/MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), maximum standard uptake value (SUVmax), metabolic total volume (MTV) and total lesion glycolysis (TLG) were calculated. Student’s t test or the Mann–Whitney U test was used to analyze the differences in parameters between groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy. Logistic regression analysis was used to evaluate independent predictors. Results The MK and SUVmax were significantly higher, and the MD and ADC were significantly lower in the malignant group (0.59 ± 0.13, 10.25 ± 4.20, 2.27 ± 0.51[×10-3 mm2/s] and 1.35 ± 0.33 [×10-3 mm2/s]) compared to the benign group (0.47 ± 0.08, 5.49 ± 4.05, 2.85 ± 0.60 [×10-3 mm2/s] and 1.67 ± 0.33 [×10-3 mm2/s]). The MD and ADC were significantly lower, and the MTV and TLG were significantly higher in the high-grade malignant SPLs group (2.11 ± 0.51 [×10-3 mm2/s], 1.35 ± 0.33 [×10-3 mm2/s], 35.87 ± 42.24 and 119.58 ± 163.65) than in the non-high-grade malignant SPLs group (2.46 ± 0.46 [×10-3 mm2/s], 1.67 ± 0.33[×10-3 mm2/s], 20.17 ± 32.34 and 114.20 ± 178.68). In the identification of benign and malignant SPLs, the SUVmax and MK were independent predictors, the AUCs of the combination of SUVmax and MK, SUVmax, MK, MD, and ADC were 0.875, 0.787, 0.848, 0.769, and 0.822, respectively. In the identification of high-grade and non-high-grade malignant SPLs, the AUCs of MD, ADC, MTV, and TLG were 0.729, 0.680, 0.693, and 0.711, respectively. Conclusion DWI, DKI, and PET in PET/MRI are all effective methods to distinguish benign from malignant SPLs, and are also helpful in evaluating the pathological grading of malignant SPLs.
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Affiliation(s)
- Ziqiang Li
- Department of the Graduate Student, Xinxiang Medical University, Xinxiang, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yu Luo
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Han Jiang
- Department of the Graduate Student, Xinxiang Medical University, Xinxiang, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Ting Fang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Xiaochen Li
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Wei Wei
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jianjian Cheng
- Department of Respiratory and Critical Care Medicine, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
- *Correspondence: Jianjian Cheng, ; Meiyun Wang,
| | - Meiyun Wang
- Department of the Graduate Student, Xinxiang Medical University, Xinxiang, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
- *Correspondence: Jianjian Cheng, ; Meiyun Wang,
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Tang X, Bai G, Wang H, Guo F, Yin H. Elaboration of Multiparametric MRI-Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non-Small-Cell Lung Cancer. J Magn Reson Imaging 2022; 56:579-589. [PMID: 35040525 DOI: 10.1002/jmri.28051] [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: 09/23/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The histological grading plays an essential role in the treatment decision of lung cancer. Detected tumors are usually biopsied to confirm histologic grade. How to use MRI extracted radiomics features for accurately grading lung cancer is still challenging. PURPOSE To examine the diagnostic utility of multiparametric MRI radiomics and clinical factors for grading non-small-cell lung cancer (NSCLC). STUDY TYPE Retrospective. POPULATION A total of 148 patients (25.7% female) with postoperative pathologically confirmed NSCLC and divided into the training cohort (N = 110) and the validation cohort (N = 38). FIELD STRENGTH/SEQUENCE A 1.5 T; single-shot turbo spin-echo (TSE), T2-weighted imaging (T2WI), and integrated shimming-echo planar imaging (ISHIM-EPI) diffusion-weighted imaging (DWI). ASSESSMENT A total of 2775 radiomics features were extracted from carcinomatous regions of interest on T2WI, DWI, and the apparent diffusion coefficient (ADC) maps. The five optimal features were selected by using the Student' s t-test, the least absolute shrinkage and selection operator (LASSO) and stepwise regression. The Radscore combined with clinical factors, which selected by univariate and multivariate analyses, to develop a radiomics-clinical nomogram. Its performance was evaluated in the training cohort and the validation cohort. The potential clinical usefulness was analyzed by the receiver operating characteristic curve (ROC), area under the curve (AUC), and the Hosmer-Lemeshow test. STATISTICAL TESTS Student's t-test, univariate analyses, multivariate analyses, LASSO, ROC, AUC, and the Hosmer-Lemeshow test. P < 0.05 was considered statistically significant. RESULTS Favorable discrimination performance was obtained for five optimal features (out of the 2775 features), using the training cohorts (AUC 0.761) and validation cohorts (AUC 0.753). In addition, the radiomics-clinical nomogram significantly improved the ability to identify histological grades in the training cohort (AUC 0.814) and the validation cohort (AUC 0.767). DATA CONCLUSIONS The radiomics-clinical nomogram based on multiparametric MRI might have the potential to distinguish the histological grade of NSCLC. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xing Tang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Guoyan Bai
- Department of Clinical Laboratory, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710032, China
| | - Hong Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
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Guneyli S, Tor M, Hassoy H, Aygun MS, Altinmakas E, Dik Altintas S, Savas R. Spin-echo and diffusion-weighted MRI in differentiation between progressive massive fibrosis and lung cancer. ACTA ACUST UNITED AC 2021; 27:469-475. [PMID: 34313230 DOI: 10.5152/dir.2021.20344] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE We aimed to investigate the value of magnetic resonance imaging (MRI)-based parameters in differentiating between progressive massive fibrosis (PMF) and lung cancer. METHODS This retrospective study included 60 male patients (mean age, 67.0±9.0 years) with a history of more than 10 years working in underground coal mines who underwent 1.5 T MRI of thorax due to a lung nodule/mass suspicious for lung cancer on computed tomography. Thirty patients had PMF, and the remaining ones had lung cancer diagnosed histopathologically. The sequences were as follows: coronal single-shot turbo spin echo (SSH-TSE), axial T1- and T2-weighted spin-echo (SE), balanced turbo field echo, T1-weighted high-resolution isotropic volume excitation, free-breathing and respiratory triggered diffusion-weighted imaging (DWI). The patients' demographics, lesion sizes, and MRI-derived parameters were compared between the patients with PMF and lung cancer. RESULTS Apparent diffusion coefficient (ADC) values of DWI and respiratory triggered DWI, signal intensities on T1-weighted SE, T2-weighted SE, and SSH-TSE imaging were found to be significantly different between the groups (p < 0.001, for all comparisons). Median ADC values of free-breathing DWI in patients with PMF and cancer were 1.25 (0.93-2.60) and 0.76 (0.53-1.00) (× 10-3 mm2/s), respectively. Most PMF lesions were predominantly iso- or hypointense on T1-weighted SE, T2-weighted SE, and SSH-TSE, while most malignant ones predominantly showed high signal intensity on these sequences. CONCLUSION MRI study including SE imaging, specially T1-weighted SE imaging and ADC values of DWI can help to distinguish PMF from lung cancer.
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Affiliation(s)
- Serkan Guneyli
- Department of Radiology, Koc University, Istanbul, Turkey
| | - Meltem Tor
- Department of Pulmonology, Bulent Ecevit University, Zonguldak, Turkey
| | - Hur Hassoy
- Department of Public Health, Ege University, Izmir, Turkey
| | | | | | | | - Recep Savas
- Department of Radiology, Ege University, Izmir, Turkey
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Shen J, Xue L, Zhong Y, Wu YL, Zhang W, Yu TF. Feasibility of using dynamic contrast-enhanced MRI for differentiating thymic carcinoma from thymic lymphoma based on semi-quantitative and quantitative models. Clin Radiol 2020; 75:560.e19-560.e25. [PMID: 32197918 DOI: 10.1016/j.crad.2020.02.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 02/18/2020] [Indexed: 01/02/2023]
Abstract
AIM To evaluate the value of using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived parameters to differentiate thymic carcinoma and thymic lymphoma based on semi-quantitative and quantitative models. MATERIALS AND METHODS Twenty-nine pathologically confirmed anterior mediastinum tumours in 29 patients were enrolled in this retrospective study, including 15 thymic carcinoma and 14 lymphoma patients. All the patients underwent pre-treatment mediastinum DCE-MRI. Both semi-quantitative and quantitative parameters were calculated and the volume transfer constant Ktrans, the flux rate constant between extravascular extracellular space and plasma kep, the extravascular extracellular volume fraction ve were obtained based on a modified Tofts model. DCE-MRI derived parameters were compared between thymic carcinoma and thymic lymphoma groups. RESULTS Thymic carcinoma had significantly lower kep (p=0.040) and higher ve (p=0.018) than thymic lymphoma; however, there were no significant differences on Ktrans and semi-quantitative parameters between the two groups. ve had the highest area under the curve (cut-off value, 0.282; area under the curve, 0.748; sensitivity, 71.4%; specificity, 80%). The combination of kep and ve could increase the diagnostic performance significantly (area under the curve, 0.752; sensitivity, 57.1%; specificity, 93.3%). CONCLUSION DCE-MRI derived parameters may have value in the differentiating thymic carcinoma and thymic lymphoma.
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Affiliation(s)
- J Shen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - L Xue
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Y Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Y-L Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - W Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - T-F Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Swerkersson S, Grundberg O, Kölbeck K, Carlberg A, Nyrén S, Skorpil M. Optimizing diffusion-weighted magnetic resonance imaging for evaluation of lung tumors: A comparison of respiratory triggered and free breathing techniques. Eur J Radiol Open 2018; 5:189-193. [PMID: 30450371 PMCID: PMC6222289 DOI: 10.1016/j.ejro.2018.10.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 10/09/2018] [Accepted: 10/21/2018] [Indexed: 12/17/2022] Open
Abstract
Purpose The aim of this study was to compare respiratory-triggered (RT) and free breathing (FB) diffusion weighted imaging (DWI) techniques regarding apparent diffusion coefficient (ADC) measurements and repeatability in non-squamous non-small cell lung cancer (NSCLC) measuring the total tumor volume. Material and Methods A total of 57 magnetic resonance imaging (MRI) examinations were analyzed. DWI was obtained by a single-shot spin-echo echo-planar imaging sequence, and for each MRI examination 2 consecutive RT and 2 consecutive FB DWI sequences were performed. Two radiologists independently read the images and made measurements. For each tumor the mean ADC value of the whole tumor volume was calculated. The difference in mean ADCs between FB and RT DWI was evaluated using the paired-sample t-test. The repeatability of ADC measurements related to imaging method was evaluated by intra class correlations (ICC) for each of the FB and RT DWI pairs. Results There were no significant differences in mean ADCs between FB and RT (Reader 1 p = 0.346, Reader 2 p = 0.583). The overall repeatability of ADC measurement was good for both acquisition methods, with ICCs > 0.9. Subgroup analysis showed somewhat poorer repeatability in small tumors (50 ml or less) and tumors in the lower lung zones for the RT acquisition, with ICC as low as 0.72. Conclusions No difference in ADC measurement or repeatability between FB and RT DWI in whole lesion ADC measurements of adenocarcinomas in the lung was demonstrated. The results imply that in this setting the FB acquisition method is accurate and possibly more robust than the RT acquisition technique.
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Key Words
- ADC, apparent diffusion coefficient
- BH, breath-hold
- DWI, diffusion weighted imaging
- Diffusion weighted imaging
- FB, free breathing
- ICC, intra class correlations
- MRI, magnetic resonance imaging
- Magnetic resonance imaging
- NSCLC, non-squamous non-small cell lung cancer
- ROI, region of interest
- RT, respiratory-triggered
- SNR, signal-to-noise ratio
- non-Small cell lung cancer
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Affiliation(s)
- Signe Swerkersson
- Department of Radiology, St Göran’s Hospital, St Göransplan 1, 11281 Stockholm, Sweden
- Corresponding author.
| | - Oscar Grundberg
- Department of Respiratory Medicine & Allergology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Karl Kölbeck
- Department of Respiratory Medicine & Allergology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Andreas Carlberg
- Siemens Healthcare AB, Johanneslundsvägen 12, 194 61 Upplands Väsby, Sweden
| | - Sven Nyrén
- Department of Thoracic radiology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Mikael Skorpil
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Sweden
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Diffusion-weighted MRI in solitary pulmonary lesions: associations between apparent diffusion coefficient and multiple histopathological parameters. Sci Rep 2018; 8:11248. [PMID: 30050167 PMCID: PMC6062570 DOI: 10.1038/s41598-018-29534-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/12/2018] [Indexed: 01/20/2023] Open
Abstract
Apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) has gained wide attention as potential tool for differentiating between malignant and benign solitary pulmonary lesions (SPLs). The overall effects of multiple histopathological parameters on ADC have not been elucidated, which may help to explain the overlapping of ADC between malignant and benign SPLs. The study sought to explore associations between ADC and histopathological parameters in SPLs, and to compare diagnostic capability of ADC among different types of SPLs. Multiple histopathological parameters (cell density, nuclear-to-cytoplasm ratio, necrotic fraction, presence of mucus and grade of differentiation) were quantified in 52 malignant and 13 benign SPLs with surgical pathology available. Cell density (β = −0.40) and presence of mucus (β = 0.77) were independently correlated with ADC in malignant SPLs. The accurate diagnosis rate of squamous carcinomas, adenocarcinomas without mucus and malignant tumors with mucus was 100%, 82% and 0%, respectively. Our study suggested that cell density and presence of mucus are independently correlated with ADC in malignant SPLs. Squamous carcinoma maybe more accurately diagnosed as malignancy by ADC value. Malignant SPLs with mucus and adenocarcinomas with low cell density should be kept in mind in differentiating SPLs using ADC because of insufficient diagnostic capability.
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Montelius M, Spetz J, Jalnefjord O, Berger E, Nilsson O, Ljungberg M, Forssell-Aronsson E. Identification of Potential MR-Derived Biomarkers for Tumor Tissue Response to 177Lu-Octreotate Therapy in an Animal Model of Small Intestine Neuroendocrine Tumor. Transl Oncol 2018; 11:193-204. [PMID: 29331677 PMCID: PMC5772005 DOI: 10.1016/j.tranon.2017.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 12/04/2017] [Accepted: 12/06/2017] [Indexed: 02/08/2023] Open
Abstract
Magnetic resonance (MR) methods enable noninvasive, regional tumor therapy response assessment, but associations between MR parameters, underlying biology, and therapeutic effects must be investigated. The aim of this study was to investigate response assessment efficacy and biological associations of MR parameters in a neuroendocrine tumor (NET) model subjected to radionuclide treatment. Twenty-one mice with NETs received 177Lu-octreotate at day 0. MR experiments (day -1, 1, 3, 8, and 13) included T2-weighted, dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) and relaxation measurements (T1/T2*). Tumor tissue was analyzed using proteomics. MR-derived parameters were evaluated for each examination day and for different radial distances from the tumor center. Response assessment efficacy and biological associations were evaluated using feature selection and protein expression correlations, respectively. Reduced tumor growth rate or shrinkage was observed until day 8, followed by reestablished growth in most tumors. The most important MR parameter for response prediction was DCE-MRI-derived pretreatment signal enhancement ratio (SER) at 40% to 60% radial distance, where it correlated significantly also with centrally sampled protein CCD89 (association: DNA damage and repair, proliferation, cell cycle arrest). The second most important was changed diffusion (D) between day -1 and day 3, at 60% to 80% radial distance, where it correlated significantly also with peripherally sampled protein CATA (association: oxidative stress, proliferation, cell cycle arrest, apoptotic cell death). Important information regarding tumor biology in response to radionuclide therapy is reflected in several MR parameters, SER and D in particular. The spatial and temporal information provided by MR methods increases the sensitivity for tumor therapy response.
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Affiliation(s)
- Mikael Montelius
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Johan Spetz
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Oscar Jalnefjord
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Evelin Berger
- Proteomics Core Facility, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Ola Nilsson
- Department of Pathology, Institute of Biomedicine, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Maria Ljungberg
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
| | - Eva Forssell-Aronsson
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Cancer Center, Sahlgrenska Academy, University of Gothenburg, Sweden.
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Lin LY, Zhang Y, Suo ST, Zhang F, Cheng JJ, Wu HW. Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer. Clin Radiol 2018; 73:412.e1-412.e7. [DOI: 10.1016/j.crad.2017.11.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 11/02/2017] [Indexed: 02/07/2023]
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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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Weller A, O'Brien MER, Ahmed M, Popat S, Bhosle J, McDonald F, Yap TA, Du Y, Vlahos I, deSouza NM. Mechanism and non-mechanism based imaging biomarkers for assessing biological response to treatment in non-small cell lung cancer. Eur J Cancer 2016; 59:65-78. [PMID: 27016624 DOI: 10.1016/j.ejca.2016.02.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 02/18/2016] [Indexed: 12/18/2022]
Abstract
Therapeutic options in locally advanced non-small cell lung cancer (NSCLC) have expanded in the past decade to include a palate of targeted interventions such as high dose targeted thermal ablations, radiotherapy and growing platform of antibody and small molecule therapies and immunotherapies. Although these therapies have varied mechanisms of action, they often induce changes in tumour architecture and microenvironment such that response is not always accompanied by early reduction in tumour mass, and evaluation by criteria other than size is needed to report more effectively on response. Functional imaging techniques, which probe the tumour and its microenvironment through novel positron emission tomography and magnetic resonance imaging techniques, offer more detailed insights into and quantitation of tumour response than is available on anatomical imaging alone. Use of these biomarkers, or other rational combinations as readouts of pathological response in NSCLC have potential to provide more accurate predictors of treatment outcomes. In this article, the robustness of the more commonly available positron emission tomography and magnetic resonance imaging biomarker indices is examined and the evidence for their application in NSCLC is reviewed.
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Affiliation(s)
- A Weller
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, UK.
| | - M E R O'Brien
- Department of Medicine, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - M Ahmed
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - S Popat
- Department of Medicine, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - J Bhosle
- Department of Medicine, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - F McDonald
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - T A Yap
- Department of Medicine, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - Y Du
- Department of Nuclear Medicine, Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK
| | - I Vlahos
- Radiology Department, St George's Hospital NHS Trust, London, SW17 0QT, UK
| | - N M deSouza
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, UK
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