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Meng N, Jiang H, Sun J, Shen L, Wang X, Zhou Y, Wu Y, Fu F, Yuan J, Yang Y, Wang Z, Wang M. Amide Proton Transfer-Weighted Imaging and Multiple Models Intravoxel Incoherent Motion-Based 18F-FDG PET/MRI for Predicting Progression-Free Survival in Non-Small Cell Lung Cancer. J Magn Reson Imaging 2024; 60:125-135. [PMID: 37850873 DOI: 10.1002/jmri.29037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Amide proton transfer-weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18F-FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored. PURPOSE To determine whether pretreatment APTWI, mono-, bi-, and stretched-exponential model IVIM, and 18F-FDG PET-derived parameters of the primary lesion may be associated with progression-free survival (PFS) in NSCLC. STUDY TYPE Prospective. POPULATION Seventy-seven patients (mean age, 62 years, range, 20-81 years) with 37 men and 40 women were included. FIELD STRENGTH/SEQUENCE 3.0 T 18F-FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin-echo sequences with magnetization transfer pulses for APTWI. ASSESSMENT Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow-up was 18 months, range, 4-27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand, D, D*, f, DDC, and α), and 18F-FDG PET (SUVmax, MTV, and TLG) parameters were recorded. STATISTICAL TESTS Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log-rank test. A P value <0.05 was considered statistically significant. RESULTS Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C-index of 0.895 (95% CI: 0.839-0.951), which was significantly superior to each of the above predictors alone (C-index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log-rank test results showed a significant difference in PFS between the high- and low-risk groups. DATA CONCLUSION APTWI and multiple models IVIM based 18F-FDG PET/MRI can be used for PFS assessment in NSCLC. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
- Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Jing Sun
- Department of Pediatrics, Zhengzhou Central Hospital Affiliated to Zhengzhou University & Zhengzhou Central Hospital, Zhengzhou, China
| | - Lei Shen
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Xinhui Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Yihang Zhou
- Department of Medical Imaging, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Group, Beijing, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
- Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
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Wang D, Liu S, Fu J, Zhang P, Zheng S, Qiu B, Liu H, Ye Y, Guo J, Zhou Y, Jiang H, Yin S, He H, Xie C, Liu H. Correlation of K trans derived from dynamic contrast-enhanced MRI with treatment response and survival in locally advanced NSCLC patients undergoing induction immunochemotherapy and concurrent chemoradiotherapy. J Immunother Cancer 2024; 12:e008574. [PMID: 38910009 PMCID: PMC11328668 DOI: 10.1136/jitc-2023-008574] [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] [Accepted: 05/30/2024] [Indexed: 06/25/2024] Open
Abstract
PURPOSE This study aimed to investigate the prognostic significance of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters concerning tumor response following induction immunochemotherapy and survival outcomes in patients with locally advanced non-small cell lung cancer (NSCLC) who underwent immunotherapy-based multimodal treatments. MATERIAL AND METHODS Unresectable stage III NSCLC patients treated by induction immunochemotherapy, concurrent chemoradiotherapy (CCRT) with or without consolidative immunotherapy from two prospective clinical trials were screened. Using the two-compartment Extend Tofts model, the parameters including Ktrans, Kep, Ve, and Vp were calculated from DCE-MRI data. The apparent diffusion coefficient was calculated from diffusion-weighted-MRI data. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to assess the predictive performance of MRI parameters. The Cox regression model was used for univariate and multivariate analysis. RESULTS 111 unresectable stage III NSCLC patients were enrolled. Patients received two cycles of induction immunochemotherapy and CCRT, with or without consolidative immunotherapy. With the median follow-up of 22.3 months, the median progression-free survival (PFS) and overall survival (OS) were 16.3 and 23.8 months. The multivariate analysis suggested that Eastern Cooperative Oncology Group score, TNM stage and the response to induction immunochemotherapy were significantly related to both PFS and OS. After induction immunochemotherapy, 67 patients (59.8%) achieved complete response or partial response and 44 patients (40.2%) had stable disease or progressive disease. The Ktrans of primary lung tumor before induction immunochemotherapy yielded the best performance in predicting the treatment response, with an AUC of 0.800. Patients were categorized into two groups: high-Ktrans group (n=67, Ktrans>164.3×10-3/min) and low-Ktrans group (n=44, Ktrans≤164.3×10-3/min) based on the ROC analysis. The high-Ktrans group had a significantly higher objective response rate than the low-Ktrans group (85.1% (57/67) vs 22.7% (10/44), p<0.001). The high-Ktrans group also presented better PFS (median: 21.1 vs 11.3 months, p=0.002) and OS (median: 34.3 vs 15.6 months, p=0.035) than the low-Ktrans group. CONCLUSIONS Pretreatment Ktrans value emerged as a significant predictor of the early response to induction immunochemotherapy and survival outcomes in unresectable stage III NSCLC patients who underwent immunotherapy-based multimodal treatments. Elevated Ktrans values correlated positively with enhanced treatment response, leading to extended PFS and OS durations.
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Affiliation(s)
- DaQuan Wang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - SongRan Liu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Jia Fu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - PengXin Zhang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - ShiYang Zheng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Bo Qiu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Hui Liu
- United Imaging Healthcare, ShangHai, China
| | - YongQuan Ye
- United Imaging of Healthcare America, Houston, Texas, USA
| | - JinYu Guo
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Yin Zhou
- SuZhou TongDiao Company, Suzhou, China
| | | | - ShaoHan Yin
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - HaoQiang He
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - ChuanMiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
| | - Hui Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, China
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Zheng Y, Zhou L, Huang W, Han N, Zhang J. Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy. Cancer Imaging 2024; 24:71. [PMID: 38863062 PMCID: PMC11167789 DOI: 10.1186/s40644-024-00713-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC. METHODS Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model. RESULTS ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively. CONCLUSIONS Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.
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Affiliation(s)
- Yu Zheng
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Liang Zhou
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Wenjing Huang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Na Han
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China.
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China.
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Yin M, Cao G, Lv S, Sun Z, Li M, Wang H, Yue X. Intravoxel incoherent motion diffusion-weighted imaging of solitary pulmonary lesions: initial study with gradient- and spin-echo sequences. Clin Radiol 2024; 79:296-302. [PMID: 38307815 DOI: 10.1016/j.crad.2024.01.003] [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: 03/27/2023] [Revised: 11/15/2023] [Accepted: 01/02/2024] [Indexed: 02/04/2024]
Abstract
AIM To evaluate the feasibility and image quality of intravoxel incoherent motion diffusion-weighted imaging (IVIM) using gradient- and spin-echo (GRASE) in solitary pulmonary lesions (SPLs) compared to echo planar imaging (EPI) and turbo spin-echo (TSE) at 3 T. MATERIALS AND METHODS Forty-two patients with SPLs underwent lung magnetic resonance imaging (MRI) using TSE-IVIM, GRASE-IVIM, and EPI-IVIM at 3 T. Signal ratio (SR), contrast ratio (CR), and image distortion ratio (DR) of three sequences were compared. The reproducibility and repeatability of the apparent diffusion coefficient (ADC) and IVIM-derived parameters were assessed using the interclass correlation coefficient (ICC) and coefficient of variation (CV). The repeatability of the ADC and IVIM-derived parameters between all sequences was evaluated using the Bland-Altman method. RESULTS EPI-IVIM had a higher SR, lower CR, and higher DR (p<0.05); however, there was no significant difference between TSE-IVIM and GRASE-IVIM (p>0.05). Compared to the D and f values of TSE-IVIM (ICC lower limit >0.90), GRASE-IVIM and EPI-IVIM showed poor reproducibility (ICC lower limit<0.90). The repeatability of the ADC and D values obtained by TSE-IVIM (CV, 1.93-2.96% and 2.44-3.18%, respectively) and GRASE-IVIM (CV, 2.56-3.12% and 3.21-3.51%, respectively) were superior to those of EPI-IVIM (CV, 10.03-10.2% and 11.30-11.57%). The repeatability of D∗ and f values for all sequences was poor. Bland-Altman analysis showed wide limits of agreement between the ADC and IVIM-derived parameters for all sequences. CONCLUSION GRASE-IVIM reduced the DR, improved the stability of the ADC and D values on repeated scans, and had the shortest scanning time.
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Affiliation(s)
- M Yin
- Clinical Medical College of Jining Medical University, Jining 272000, China
| | - Guanjie Cao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China
| | - S Lv
- Clinical Medical College of Jining Medical University, Jining 272000, China.
| | - Z Sun
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China
| | - M Li
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China
| | - H Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China
| | - X Yue
- Philips Healthcare, Beijing 100600, China
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Bao X, Bian D, Yang X, Wang Z, Shang M, Jiang G, Shi J. Multiparametric MRI for evaluation of pathological response to the neoadjuvant chemo-immunotherapy in resectable non-small-cell lung cancer. Eur Radiol 2023; 33:9182-9193. [PMID: 37382618 DOI: 10.1007/s00330-023-09813-8] [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: 11/14/2022] [Revised: 03/26/2023] [Accepted: 03/28/2023] [Indexed: 06/30/2023]
Abstract
OBJECTIVES This study aimed to explore the predictive value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion kurtosis imaging (DKI) quantitative parameters for the response to neoadjuvant chemo-immunotherapy (NCIT) in resectable non-small-cell lung cancer (NSCLC) patients, so as to provide a basis for clinical individualized precision treatment. METHODS Treatment naive locally advanced NSCLC patients who enrolled in 3 prospective, open-label, and single-arm clinical trials and received NCIT were retrospectively analyzed in this study. Functional MRI imaging was performed at baseline and following 3 weeks of treatment as an exploratory endpoint to evaluate treatment efficacy. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCIT response. Prediction models were built with statistically significant quantitative parameters and their combinations. RESULTS In total of 32 patients, 13 were classified as complete pathological response (pCR) and 19 were non-pCR. Post-NCIT ADC, ΔADC, and ΔD values in the pCR group were significantly higher than those in the non-pCR group, while the pre-NCIT D, post-NCIT Kapp, and ΔKapp were significantly lower than those in non-pCR group. Multivariate logistic regression analysis demonstrated that pre-NCIT D and post-NCIT Kapp values were independent predictors for NCIT response. The combined predictive model, which consisted of IVIM-DWI and DKI, showed the best prediction performance with AUC of 0.889. CONCLUSIONS The pre-NCIT D, post-NCIT parameters (ADC and Kapp) and Δ parameters (ΔADC, ΔD, and ΔKapp) were effective biomarkers for predicting pathologic response, and pre-NCIT D and post-NCIT Kapp values were independent predictors of NCIT response for NSCLC patients. CLINICAL RELEVANCE STATEMENT This exploratory study indicated that IVIM-DWI and DKI MRI imaging would predict pathologic response of neoadjuvant chemo-immunotherapy in locally advanced NSCLC patients at initial state and early treatment, which could help make clinical individualized treatment strategies. KEY POINTS • Effective NCIT treatment resulted in increased ADC and D values for NSCLC patients. • The residual tumors in non-pCR group tend to have higher microstructural complexity and heterogeneity, as measured by Kapp. • Pre-NCIT D and post-NCIT Kapp values were independent predictors of NCIT response.
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Affiliation(s)
- Xiao Bao
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Dongliang Bian
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Xing Yang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Zheming Wang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Mingdong Shang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.
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Bai JW, Qiu SQ, Zhang GJ. Molecular and functional imaging in cancer-targeted therapy: current applications and future directions. Signal Transduct Target Ther 2023; 8:89. [PMID: 36849435 PMCID: PMC9971190 DOI: 10.1038/s41392-023-01366-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/19/2023] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
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Affiliation(s)
- Jing-Wen Bai
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Medical Oncology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
| | - Si-Qi Qiu
- Diagnosis and Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, 515041, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Shantou University Medical College, 515041, Shantou, China
| | - Guo-Jun Zhang
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
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Volumetric Analysis of Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting the Response to Chemotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer. J Comput Assist Tomogr 2022; 46:406-412. [PMID: 35405718 DOI: 10.1097/rct.0000000000001282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We aimed to prospectively investigate intravoxel incoherent motion parameters to predict the response to chemotherapy in locally advanced non-small cell lung cancer (NSCLC) patients. METHODS From July 2016 to March 2018, 30 advanced NSCLC patients were enrolled and underwent chest intravoxel incoherent motion-diffusion-weighted imaging at Siemens 3T magnetic resonance imaging before and at the end of the first cycle of chemotherapy. Regions of interest were drawn including the whole tumor volume to derive the apparent diffusion coefficient value, D, D*, and f, respectively. Time-dependent receiver operating characteristic curves were generated to evaluate the cutoff values of continuous variables. A Cox proportional hazards model was used to assess the independent predictors of progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and log-rank test were generated. RESULTS Among the 30 patients, 28 cases (93.3%) died and 2 cases (6.7%) survived till the closeout date. Univariate Cox regression analyses revealed that the significant predictors of PFS and OS were the tumor size reduction rate, the change rates of D and apparent diffusion coefficient values, and the D value before therapy (PFS: P = 0.015, hazard ratio [HR] = 2.841; P < 0.001, HR = 5.840; P = 0.044, HR = 2.457; and P = 0.027, HR = 2.715; OS: P = 0.008, HR = 2.987; P < 0.001, HR = 4.357; P = 0.006, HR = 3.313; and P = 0.013, HR = 2.941, respectively). Multivariate Cox regression analysis suggested that △D% was identified as independent predictors of both PFS and OS (P = 0.003, HR = 9.200 and P = 0.016, HR = 4.617). In addition, the cutoff value of △D% was 21.06% calculated by receiver operating characteristic curve analysis. In the Kaplan-Meier analysis, the PFS and OS were significantly greater in the group of patients with △D% larger than 21.06% (log-rank test, χ2 = 16.453, P < 0.001; χ2 = 13.952, P < 0.001). CONCLUSIONS Intravoxel incoherent motion-diffusion-weighted imaging was preferred for predicting the prognosis of advanced NSCLC patients treated with chemotherapy. A D increase more than 21.06% at 1 month was associated with a lower rate of disease progression and death.
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