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Kifjak D, Hochmair M, Sobotka D, Haug AR, Ambros R, Prayer F, Heidinger BH, Roehrich S, Milos RI, Wadsak W, Fuereder T, Krenbek D, Fazekas A, Meilinger M, Mayerhoefer ME, Langs G, Herold C, Prosch H, Beer L. Metabolic tumor volume and sites of organ involvement predict outcome in NSCLC immune-checkpoint inhibitor therapy. Eur J Radiol 2024; 170:111198. [PMID: 37992608 DOI: 10.1016/j.ejrad.2023.111198] [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: 08/20/2023] [Revised: 10/13/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
PURPOSE The purpose of this study was to assess the ability of pretreatment PET parameters and peripheral blood biomarkers to predict progression-free survival (PFS) and overall survival (OS) in NSCLC patients treated with ICIT. METHODS We prospectively included 87 patients in this study who underwent pre-treatment [18F]-FDG PET/CT. Organ-specific and total metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured using a semiautomatic software. Sites of organ involvement (SOI) were assessed by PET/CT. The log-rank test and Cox-regression analysis were used to assess associations between clinical, laboratory, and imaging parameters with PFS and OS. Time dependent ROC were calculated and model performance was evaluated in terms of its clinical utility. RESULTS MTV increased with the number of SOI and was correlated with neutrophil and lymphocyte cell count (Spearman's rho = 0.27 or 0.32; p =.02 or 0.003; respectively). Even after adjustment for known risk factors, such as PD-1 expression and neutrophil cell count, the MTV and the number of SOI were independent risk factors for progression (per 100 cm3; adjusted hazard ratio [aHR]: 1.13; 95% confidence interval [95%CI]: 1.01-1.28; p =.04; single SOI vs. ≥ 4 SOI: aHR: 2.26, 95%CI: 1.04-4.94; p =.04). MTV and the number of SOI were independent risk factors for overall survival (per 100 cm3 aHR: 1.11, 95%CI: 1.01-1.23; p =.03; single SOI vs. ≥ 4 SOI: aHR: 4.54, 95%CI: 1.64-12.58; p =.04). The combination of MTV and the number of SOI improved the risk stratification for PFS and OS (log-rank test p <.001; C-index: 0.64 and 0.67). CONCLUSION The MTV and the number of SOI are simple imaging markers that provide complementary information to facilitate risk stratification in NSCLC patients scheduled for ICIT.
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Affiliation(s)
- Daria Kifjak
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Radiology, UMass Memorial Medical Center and University of Massachusetts Chan Medical School, Worcester, MA, USA; Christian Doppler Laboratory for Machine Learning Driven Precision, Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Maximilian Hochmair
- Department of Respiratory and Critical Care Medicine, Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna, Austria
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander R Haug
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Raphael Ambros
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Florian Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Benedikt H Heidinger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sebastian Roehrich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ruxandra-Iulia Milos
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Center for Biomarker Research in Medicine, CBmed, Graz, Austria
| | - Thorsten Fuereder
- Department of Internal Medicine I & Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Dagmar Krenbek
- Department of Pathology and Bacteriology, Klinik Floridsdorf, Brünner Strasse 68, 1210 Vienna, Austria
| | - Andreas Fazekas
- Department of Respiratory and Critical Care Medicine, Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna, Austria
| | - Michael Meilinger
- Department of Respiratory and Critical Care Medicine, Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna, Austria
| | - Marius E Mayerhoefer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Christian Doppler Laboratory for Machine Learning Driven Precision, Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christian Herold
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Machine Learning Driven Precision, Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria.
| | - Lucian Beer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Machine Learning Driven Precision, Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
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Wang D, Qiu B, Liu Q, Xia L, Liu S, Zheng C, Liu H, Mo Y, Zhang X, Hu Y, Zheng S, Zhou Y, Fu J, Chen N, Liu F, Zhou R, Guo J, Fan W, Liu H. Patlak-Ki derived from ultra-high sensitivity dynamic total body [ 18F]FDG PET/CT correlates with the response to induction immuno-chemotherapy in locally advanced non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging 2023; 50:3400-3413. [PMID: 37310427 DOI: 10.1007/s00259-023-06298-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
PURPOSE This study aimed to investigate the predictive value of metabolic features in response to induction immuno-chemotherapy in patients with locally advanced non-small cell cancer (LA-NSCLC), using ultra-high sensitivity dynamic total body [18F]FDG PET/CT. METHODS The study analyzed LA-NSCLC patients who received two cycles of induction immuno-chemotherapy and underwent a 60-min dynamic total body [18F]FDG PET/CT scan before treatment. The primary tumors (PTs) were manually delineated, and their metabolic features, including the Patlak-Ki, Patlak-Intercept, maximum SUV (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were evaluated. The overall response rate (ORR) to induction immuno-chemotherapy was evaluated according to RECIST 1.1 criteria. The Patlak-Ki of PTs was calculated from the 20-60 min frames using the Patlak graphical analysis. The best feature was selected using Laplacian feature importance scores, and an unsupervised K-Means method was applied to cluster patients. ROC curve was used to examine the effect of selected metabolic feature in predicting tumor response to treatment. The targeted next generation sequencing on 1021 genes was conducted. The expressions of CD68, CD86, CD163, CD206, CD33, CD34, Ki67 and VEGFA were assayed through immunohistochemistry. The independent samples t test and the Mann-Whitney U test were applied in the intergroup comparison. Statistical significance was considered at P < 0.05. RESULTS Thirty-seven LA-NSCLC patients were analyzed between September 2020 and November 2021. All patients received two cycles of induction chemotherapy combined with Nivolumab/ Camrelizumab. The Laplacian scores showed that the Patlak-Ki of PTs had the highest importance for patient clustering, and the unsupervised K-Means derived decision boundary of Patlak-Ki was 2.779 ml/min/100 g. Patients were categorized into two groups based on their Patlak-Ki values: high FDG Patlak-Ki (H-FDG-Ki, Patlak-Ki > 2.779 ml/min/100 g) group (n = 23) and low FDG Patlak-Ki (L-FDG-Ki, Patlak-Ki ≤ 2.779 ml/min/100 g) group (n = 14). The ORR to induction immuno-chemotherapy was 67.6% (25/37) in the whole cohort, with 87% (20/23) in H-FDG-Ki group and 35.7% (5/14) in L-FDG-Ki group (P = 0.001). The sensitivity and specificity of Patlak-Ki in predicting the treatment response were 80% and 75%, respectively [AUC = 0.775 (95%CI 0.605-0.945)]. The expression of CD3+/CD8+ T cells and CD86+/CD163+/CD206+ macrophages were higher in the H-FDG-Ki group, while Ki67, CD33+ myeloid cells, CD34+ micro-vessel density (MVD) and tumor mutation burden (TMB) were comparable between the two groups. CONCLUSIONS The total body [18F]FDG PET/CT scanner performed a dynamic acquisition of the entire body and clustered LA-NSCLC patients into H-FDG-Ki and L-FDG-Ki groups based on the Patlak-Ki. Patients with H-FDG-Ki demonstrated better response to induction immuno-chemotherapy and higher levels of immune cell infiltration in the PTs compared to those with L-FDG-Ki. Further studies with a larger patient cohort are required to validate these findings.
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Affiliation(s)
- DaQuan Wang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Bo Qiu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - QianWen Liu
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - LiangPing Xia
- Department of VIP, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - SongRan Liu
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | | | - Hui Liu
- United Imaging Healthcare, Shanghai, China
| | - YiWen Mo
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Xu Zhang
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - YingYing Hu
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - ShiYang Zheng
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Yin Zhou
- SuZhou TongDiao Company, Suzhou, China
| | - Jia Fu
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - NaiBin Chen
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - FangJie Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Rui Zhou
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - JinYu Guo
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Wei Fan
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
| | - Hui Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
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Kudura K, Ritz N, Templeton AJ, Kutzker T, Foerster R, Antwi K, Kreissl MC, Hoffmann MHK. Predictive Value of Total Metabolic Tumor Burden Prior to Treatment in NSCLC Patients Treated with Immune Checkpoint Inhibition. J Clin Med 2023; 12:jcm12113725. [PMID: 37297920 DOI: 10.3390/jcm12113725] [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: 03/29/2023] [Revised: 04/29/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVES We aimed to assess the predictive value of the total metabolic tumor burden prior to treatment in patients with advanced non-small-cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). METHODS Pre-treatment 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (PET/CT) scans performed in two consecutive years for staging in adult patients with confirmed NSCLC were considered. Volume, maximum/mean standardized uptake value (SUVmax/SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were assessed per delineated malignant lesion (including primary tumor, regional lymph nodes and distant metastases) in addition to the morphology of the primary tumor and clinical data. Total metabolic tumor burden was captured by totalMTV and totalTLG. Overall survival (OS), progression-free survival (PFS) and clinical benefit (CB) were used as endpoints for response to treatment. RESULTS A total of 125 NSCLC patients were included. Osseous metastases were the most frequent distant metastases (n = 17), followed by thoracal distant metastases (pulmonal = 14 and pleural = 13). Total metabolic tumor burden prior to treatment was significantly higher in patients treated with ICIs (mean totalMTV ± standard deviation (SD) 72.2 ± 78.7; mean totalTLG ± SD 462.2 ± 538.9) compared to those without ICI treatment (mean totalMTV ± SD 58.1 ± 233.8; mean totalTLG ± SD 290.0 ± 784.2). Among the patients who received ICIs, a solid morphology of the primary tumor on imaging prior to treatment was the strongest outcome predictor for OS (Hazard ratio HR 28.04, p < 0.01), PFS (HR 30.89, p < 0.01) and CB (parameter estimation PE 3.46, p < 0.01), followed by the metabolic features of the primary tumor. Interestingly, total metabolic tumor burden prior to immunotherapy showed a negligible impact on OS (p = 0.04) and PFS (p = 0.01) after treatment given the hazard ratios of 1.00, but also on CB (p = 0.01) given the PE < 0.01. Overall, biomarkers on pre-treatment PET/CT scans showed greater predictive power in patients receiving ICIs, compared to patients without ICI treatment. CONCLUSIONS Morphological and metabolic properties of the primary tumors prior to treatment in advanced NSCLC patients treated with ICI showed great outcome prediction performances, as opposed to the pre-treatment total metabolic tumor burdens, captured by totalMTV and totalTLG, both with negligible impact on OS, PFS and CB. However, the outcome prediction performance of the total metabolic tumor burden might be influenced by the value itself (e.g., poorer prediction performance at very high or very low values of total metabolic tumor burden). Further studies including subgroup analysis with regards to different values of total metabolic tumor burden and their respective outcome prediction performances might be needed.
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Affiliation(s)
- Ken Kudura
- Department of Nuclear Medicine, Sankt Clara Hospital, 4058 Basel, Switzerland
- Department of Radiology, Sankt Clara Hospital, 4058 Basel, Switzerland
- Sankt Clara Research, 4002 Basel, Switzerland
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Nando Ritz
- Faculty of Medicine, University of Basel, 4001 Basel, Switzerland
| | - Arnoud J Templeton
- Sankt Clara Research, 4002 Basel, Switzerland
- Faculty of Medicine, University of Basel, 4001 Basel, Switzerland
| | - Tim Kutzker
- Faculty of Applied Statistics, Humboldt University, 10117 Berlin, Germany
| | - Robert Foerster
- Department of Radiooncology, Cantonal Hospital Winterthur, 8400 Winterthur, Switzerland
| | - Kwadwo Antwi
- Department of Nuclear Medicine, Sankt Clara Hospital, 4058 Basel, Switzerland
- Department of Radiology, Sankt Clara Hospital, 4058 Basel, Switzerland
| | - Michael C Kreissl
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Martin H K Hoffmann
- Department of Nuclear Medicine, Sankt Clara Hospital, 4058 Basel, Switzerland
- Department of Radiology, Sankt Clara Hospital, 4058 Basel, Switzerland
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Kudura K, Ritz N, Kutzker T, Hoffmann MHK, Templeton AJ, Foerster R, Kreissl MC, Antwi K. Predictive Value of Baseline FDG-PET/CT for the Durable Response to Immune Checkpoint Inhibition in NSCLC Patients Using the Morphological and Metabolic Features of Primary Tumors. Cancers (Basel) 2022; 14:cancers14246095. [PMID: 36551581 PMCID: PMC9776660 DOI: 10.3390/cancers14246095] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/01/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Objectives: We aimed to investigate the predictive value of baseline 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (FDG-PET/CT) for durable responses to immune checkpoint inhibitors (ICIs) by linking the morphological and metabolic features of primary tumors (PTs) in nonsmall cell lung cancer (NSCLC) patients. Methods: For the purpose of this single-center study, the imaging data of the patients with a first diagnosis of NSCLC and an available baseline FDG-PET/CT between 2020 and 2021 were retrospectively assessed. The baseline characteristics were collected based on clinical reports and interdisciplinary tumor board documentation. The metabolic (such as standardized uptake value SUV maximum and mean (SUVmax, SUV mean), metabolic tumor volume (MTV), total lesion glycolysis (TLG)) and morphological (such as volume, morphology, margin, and presence of lymphangiosis through imaging) features of all the PTs were retrospectively assessed using FDG-PET/CT. Overall survival (OS), progression-free survival (PFS), clinical benefit (CB) and mortality rate were used as endpoints to define the long-term response to therapy. A backward, stepwise logistic regression analysis was performed in order to define the best model for predicting lasting responses to treatment. Statistical significance was assumed at p < 0.05. Results: A total of 125 patients (median age ± standard deviation (SD) 72.0 ± 9.5 years) were enrolled: 64 men (51.2%) and 61 women (48.8%). Adenocarcinoma was by far the most common histological subtype of NSCLC (47.2%). At the initial diagnosis, the vast majority of all the included patients showed either locally advanced disease (34.4%) or metastatic disease (36.8%). Fifty patients were treated with ICIs either as a first-line (20%) or second-line (20%) therapy, while 75 patients did not receive ICIs. The median values ± SD of PT SUVmax, mean, MTV, and TLG were respectively 10.1 ± 6.0, 6.1 ± 3.5, 13.5 ± 30.7, and 71.4 ± 247.7. The median volume of PT ± SD was 13.7 ± 30.7 cm3. The PTs were most frequently solid (86.4%) with irregular margins (76.8%). Furthermore, in one out of five cases, the morphological evidence of lymphangiosis was seen through imaging (n = 25). The median follow-up ± SD was 18.93 ± 6.98 months. The median values ± SD of OS and PFS were, respectively, 14.80 ± 8.68 months and 14.03 ± 9.02 months. Age, PT volume, SUVmax, TLG, the presence of lymphangiosis features through imaging, and clinical stage IV were very strong long-term outcome predictors of patients treated with ICIs, while no significant outcome predictors could be found for the cohort with no ICI treatment. The optimal cut-off values were determined for PT volume (26.94 cm3) and SUVmax (15.05). Finally, 58% of NSCLC patients treated with ICIs had a CB vs. 78.7% of patients in the cohort with no ICI treatment. However, almost all patients treated with ICIs and with disease progression over time died (mortality in the case of disease progression 95% vs. 62.5% in the cohort without ICIs). Conclusion: Baseline FDG-PET/CT could be used to predict a durable response to ICIs in NSCLC patients. Age, clinical stage IV, lymphangiosis features through imaging, PT volume (thus PT MTV due to a previously demonstrated linear correlation), PT SUVmax, and TLG were very strong long-term outcome predictors. Our results highlight the importance of linking clinical data, as much as morphological features, to the metabolic parameters of primary tumors in a multivariate outcome-predicting model using baseline FDG-PET/CT.
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Affiliation(s)
- Ken Kudura
- Department of Nuclear Medicine, Sankt Clara Hospital, 4058 Basel, Switzerland
- Correspondence:
| | - Nando Ritz
- Faculty of Medicine, University of Basel, 4058 Basel, Switzerland
| | - Tim Kutzker
- Faculty of Applied Statistics, Humboldt University, 10 117 Berlin, Germany
| | | | - Arnoud J. Templeton
- Faculty of Medicine, University of Basel, 4058 Basel, Switzerland
- Sankt Clara Research, 4002 Basel, Switzerland
| | - Robert Foerster
- Department of Radiooncology, Cantonal Hospital Winterthur, 8400 Winterthur, Switzerland
| | - Michael C. Kreissl
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Kwadwo Antwi
- Department of Nuclear Medicine, Sankt Clara Hospital, 4058 Basel, Switzerland
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Ling T, Zhang L, Peng R, Yue C, Huang L. Prognostic value of 18F-FDG PET/CT in patients with advanced or metastatic non-small-cell lung cancer treated with immune checkpoint inhibitors: A systematic review and meta-analysis. Front Immunol 2022; 13:1014063. [PMID: 36466905 PMCID: PMC9713836 DOI: 10.3389/fimmu.2022.1014063] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/20/2022] [Indexed: 08/30/2023] Open
Abstract
PURPOSE This study aimed to investigate the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting early immunotherapy response of immune checkpoint inhibitors (ICIs) in patients with advanced or metastatic non-small-cell lung cancer (NSCLC). METHODS A comprehensive search of PubMed, Web of science, Embase and the Cochrane library was performed to examine the prognostic value of 18F-FDG PET/CT in predicting early immunotherapy response of ICIs in patients with NSCLC. The main outcomes for evaluation were overall survival (OS) and progression-free survival (PFS). Detailed data from each study were extracted and analyzed using STATA 14.0 software. RESULTS 13 eligible articles were included in this systematic review. Compared to baseline 18F-FDG PET/CT imaging, the pooled hazard ratios (HR) of maximum and mean standardized uptake values SUVmax, SUVmean, MTV and TLG for OS were 0.88 (95% CI: 0.69-1.12), 0.79 (95% CI: 0.50-1.27), 2.10 (95% CI: 1.57-2.82) and 1.58 (95% CI: 1.03-2.44), respectively. The pooled HR of SUVmax, SUVmean, MTV and TLG for PFS were 1.06 (95% CI: 0.68-1.65), 0.66 (95% CI: 0.48-0.90), 1.50 (95% CI: 1.26-1.79), 1.27 (95% CI: 0.92-1.77), respectively. Subgroup analysis showed that high MTV group had shorter OS than low MTV group in both first line group (HR: 1.97, 95% CI: 1.39-2.79) and undefined line group (HR: 2.11, 95% CI: 1.61-2.77). High MTV group also showed a shorter PFS in first line group (HR: 1.85, 95% CI: 1.28-2.68), and low TLG group had a longer OS in undefined group (HR: 1.37, 95% CI: 1.00-1.86). No significant differences were in other subgroup analysis. CONCLUSION Baseline MTV and TLG may have predictive value and should be prospectively studied in clinical trials. Baseline SUVmax and SUVmean may not be appropriate prognostic markers in advanced or metastatic NSCLC patients treated with ICIs. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=323906, identifier CRD42022323906.
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Affiliation(s)
- Tao Ling
- Department of Pharmacy, Suqian First Hospital, Suqian, China
| | - Lianghui Zhang
- Department of Oncology, Changzhou Traditional Chinese Medicine Hospital, Changzhou, China
| | - Rui Peng
- Department of General Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Yue
- Department of General Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Lingli Huang
- Department of Pharmacy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
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Gao Y, Wu C, Chen X, Ma L, Zhang X, Chen J, Liao X, Liu M. PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy. Front Immunol 2022; 13:1049043. [PMID: 36341331 PMCID: PMC9630646 DOI: 10.3389/fimmu.2022.1049043] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/10/2022] [Indexed: 04/24/2024] Open
Abstract
Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.
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Ter Maat LS, van Duin IAJ, Elias SG, van Diest PJ, Pluim JPW, Verhoeff JJC, de Jong PA, Leiner T, Veta M, Suijkerbuijk KPM. Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review. Eur J Cancer 2022; 175:60-76. [PMID: 36096039 DOI: 10.1016/j.ejca.2022.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. METHODS PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. RESULTS In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. CONCLUSIONS There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.
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Affiliation(s)
- Laurens S Ter Maat
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Josien P W Pluim
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Utrecht University, Utrecht, the Netherlands; Department of Radiology, Mayo Clinical, Rochester, MN, USA
| | - Mitko Veta
- Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands.
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8
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Zhu K, Su D, Wang J, Cheng Z, Chin Y, Chen L, Chan C, Zhang R, Gao T, Ben X, Jing C. Predictive value of baseline metabolic tumor volume for non-small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis. Front Oncol 2022; 12:951557. [PMID: 36147904 PMCID: PMC9487526 DOI: 10.3389/fonc.2022.951557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Immune checkpoint inhibitors (ICIs) have emerged as a promising treatment option for advanced non-small-cell lung cancer (NSCLC) patients, highlighting the need for biomarkers to identify responders and predict the outcome of ICIs. The purpose of this study was to evaluate the predictive value of baseline standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) derived from 18F-FDG-PET/CT in advanced NSCLC patients receiving ICIs. Methods PubMed and Web of Science databases were searched from January 1st, 2011 to July 18th, 2022, utilizing the search terms “non-small-cell lung cancer”, “PET/CT”, “standardized uptake value”, “metabolic tumor volume”, “ total lesion glycolysis”, and “immune checkpoint inhibitors”. Studies that analyzed the association between PET/CT parameters and objective response, immune-related adverse events (irAEs) and prognosis of NSCLC patients treated with ICIs were included. We extracted the hazard ratio (HR) with a 95% confidence interval (CI) for progression-free survival (PFS) and overall survival (OS). We performed a meta-analysis of HR using Review Manager v.5.4.1. Results Sixteen studies were included for review and thirteen for meta-analysis covering 770 patients. As for objective response and irAEs after ICIs, more studies with consistent assessment methods are needed to determine their relationship with MTV. In the meta-analysis, low SUVmax corresponded to poor PFS with a pooled HR of 0.74 (95% CI, 0.57-0.96, P=0.02). And a high level of baseline MTV level was related to shorter PFS (HR=1.45, 95% CI, 1.11-1.89, P<0.01) and OS (HR, 2.72; 95% CI, 1.97-3.73, P<0.01) especially when the cut-off value was set between 50-100 cm3. SUVmean and TLG were not associated with the prognosis of NSCLC patients receiving ICIs. Conclusions High level of baseline MTV corresponded to shorter PFS and OS, especially when the cut-off value was set between 50-100 cm3. MTV is a potential predictive value for the outcome of ICIs in NSCLC patients.
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Affiliation(s)
- Ke Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Danqian Su
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Jianing Wang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Zhouen Cheng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Yiqiao Chin
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Luyin Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Chingtin Chan
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Rongcai Zhang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- International School, Jinan University, Guangzhou, China
| | - Tianyu Gao
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Xiaosong Ben
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xiaosong Ben, ; Chunxia Jing,
| | - Chunxia Jing
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, Guangzhou, China
- *Correspondence: Xiaosong Ben, ; Chunxia Jing,
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9
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Saadani H, Aalbersberg EA, Schats W, Hoekstra OS, Stokkel MPM, de Vet HCW. Comparing [18F]FDG PET/CT response criteria in melanoma and lung cancer patients treated with immunotherapy: a systematic review. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Liu WL, Zhang YQ, Li LT, Zhu YY, Ming ZH, Chen WL, Yang RQ, Li RH, Chen M, Zhang GJ. Application of molecular imaging in immune checkpoints therapy: From response assessment to prognosis prediction. Crit Rev Oncol Hematol 2022; 176:103746. [PMID: 35752425 DOI: 10.1016/j.critrevonc.2022.103746] [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: 01/27/2022] [Revised: 05/30/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022] Open
Abstract
Recently, immune checkpoint therapy (ICT) represented by programmed cell death1 (PD-1) and its major ligands, programmed death ligand 1 (PD-L1), has achieved significant success. Detection of PD-L1 by immunohistochemistry (IHC) is a classic method to guide the treatment of ICT patients. However, PD-L1 expression in the tumor microenvironment is highly complex. Thus, PD-L1 IHC is inadequate to fully understand the relevance of PD-L1 levels in the whole body and their dynamics to improve therapeutic outcomes. Intriguingly, numerous studies have revealed that molecular imaging technologies could potentially meet this need. Therefore, the purpose of this narrative review is to summarize the preclinical and clinical application of ICT guided by molecular imaging technology, and to explore the future opportunities and practical difficulties of these innovations.
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Affiliation(s)
- Wan-Ling Liu
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Yong-Qu Zhang
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Liang-Tao Li
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Yuan-Yuan Zhu
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Zi-He Ming
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Wei-Ling Chen
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Rui-Qin Yang
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China
| | - Rong-Hui Li
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Department of Medical Oncology, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China
| | - Min Chen
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China.
| | - Guo-Jun Zhang
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer (Xiang'an Hospital of Xiamen University), 2000 East Xiang'an Road, Xiamen, China; Xiamen Key Laboratory for Endocrine Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, 2000 East Xiang'an Road, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, 2000 East Xiang'an Road, Xiamen, China; Cancer Research Center, School of Medicine, Xiamen University, 4221 South Xiang'an Road, Xiamen, China.
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11
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Kameyama K, Imai K, Ishiyama K, Takashima S, Kuriyama S, Atari M, Ishii Y, Kobayashi A, Takahashi S, Kobayashi M, Harata Y, Sato Y, Motoyama S, Hashimoto M, Nomura K, Minamiya Y. New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes. Thorac Cancer 2022; 13:708-715. [PMID: 35048499 PMCID: PMC8888156 DOI: 10.1111/1759-7714.14302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/16/2021] [Accepted: 12/18/2021] [Indexed: 11/30/2022] Open
Abstract
Background The aim of the present study was to use surgical and histological results to develop a simple noninvasive technique to improve nodal staging using preoperative PET/CT in patients with resectable lung cancer. Methods Preoperative PET/CT findings (pStage IB–III 182 patients) and pathological diagnoses after surgical resection were evaluated. Using PET/CT images to determine the standardized uptake value (SUV) ratio, the SUVmax of a contralateral hilar lymph node (on the side of the chest opposite to the primary tumor) was measured simultaneously. The I/C‐SUV ratio was calculated as ipsilateral hilar node SUV/contralateral hilar node SUV. Receiver operating characteristic (ROC) curves were then used to analyze those data. Results Based on ROC analyses, the cutoff I/C‐SUV ratio for diagnosis of lymph node metastasis was 1.34. With a tumor ipsilateral lymph node SUVmax ≥2.5, an IC‐SUV ratio ≥1.34 had the highest accuracy for predicting N1/N2 metastasis; the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of nodal staging were 60.66, 85.11, 84.09, 62.5 and 71.29%, respectively. Conclusions When diagnosing nodal stage, a lymph node I/C‐SUV ratio ≥1.34 can be an effective criterion for determining surgical indications in advanced lung cancer.
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Affiliation(s)
- Komei Kameyama
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Kazuhiro Imai
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Koichi Ishiyama
- Department of Radiology, Akita University Graduate School of Medicine, Akita, Japan
| | - Shinogu Takashima
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Shoji Kuriyama
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Maiko Atari
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Yoshiaki Ishii
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Akihito Kobayashi
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Shugo Takahashi
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Mirai Kobayashi
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Yuzu Harata
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Yusuke Sato
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Satoru Motoyama
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
| | - Manabu Hashimoto
- Department of Radiology, Akita University Graduate School of Medicine, Akita, Japan
| | - Kyoko Nomura
- Department of Health Environmental Science and Public Health, Akita University Graduate School of Medicine, Akita, Japan
| | - Yoshihiro Minamiya
- Department of Thoracic Surgery, Akita University Graduate School of Medicine, Akita, Japan
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12
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Deboever N, McGrail DJ, Lee Y, Tran HT, Mitchell KG, Antonoff MB, Hofstetter WL, Mehran RJ, Rice DC, Roth JA, Swisher SG, Vaporciyan AA, Walsh GL, Bernatchez C, Vailati Negrao M, Zhang J, Wistuba II, Heymach JV, Cascone T, Gibbons DL, Haymaker CL, Sepesi B. Surgical approach does not influence changes in circulating immune cell populations following lung cancer resection. Lung Cancer 2022; 164:69-75. [PMID: 35038676 DOI: 10.1016/j.lungcan.2022.01.001] [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: 11/04/2021] [Revised: 12/27/2021] [Accepted: 01/02/2022] [Indexed: 10/19/2022]
Abstract
INTRODUCTION The multimodal management of operable non-small cell lung cancer (NSCLC) continues to evolve rapidly. The immune milieu allowing for immunotherapeutic benefit can be affected by multiple parameters including clinicopathologic and genetic. Surgery induced physiological changes has received attention for modulating and affecting post-operative oncotaxis and immunosuppression. Here, we sought to investigate how surgical stress influences phenotype of peripheral blood mononuclear cells (PBMCs) in patients with NSCLC who underwent lobectomy. METHODS Blood was prospectively collected from patients with Stage IA-IIIA NSCLC undergoing lung resection between 2016 and 2018. Samples were obtained pre-operatively, 24 h and 4 weeks after surgery. PBMCs were isolated and subject to high-dimensional flow cytometry, analyzing a total of 115 cell populations with a focus on myeloid cells, T cell activation, and T cell trafficking. We further evaluated how surgical approach influenced post-operative PBMC changes, whether the operation was conducted in an open fashion with thoracotomy, or with minimally invasive Video Assisted Thoracoscopic Surgery (VATS). RESULTS A total of 76 patients met the inclusion criteria (Open n = 55, VATS n = 21). Surgical resection coincided with a decrease in T lymphocyte populations, including total CD3+ T cells, CD8+ T cells, and T effector memory cells, as well as an increase in monocytic myeloid-derived suppressor cells (mMDSC). Post-operative changes in PBMC populations were resolved after 4 weeks. Surgical-induced changes in immune populations were equivalent in patients undergoing open thoracotomy and VATS. DISCUSSION Surgical stress resulted in transient reduction in T cells and T effector memory cells, and increase of mMDSC following resection in NSCLC patients. The immune profile modulation was similar regardless of surgical approach. These findings suggest that surgical approach does not seem to affect mononuclear cell lines obtained from peripheral blood. Thus, the decision regarding surgical approach should be patient centered, rather than based on post-operative treatment response optimization.
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Affiliation(s)
- Nathaniel Deboever
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Daniel J McGrail
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Younghee Lee
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hai T Tran
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kyle G Mitchell
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Wayne L Hofstetter
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Reza J Mehran
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David C Rice
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jack A Roth
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stephen G Swisher
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ara A Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Garrett L Walsh
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Chantale Bernatchez
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Marcelo Vailati Negrao
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cara L Haymaker
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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13
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Qiu B, Cai K, Chen C, Chen J, Chen KN, Chen QX, Cheng C, Dai TY, Fan J, Fan Z, Hu J, Hu WD, Huang YC, Jiang GN, Jiang J, Jiang T, Jiao WJ, Li HC, Li Q, Liao YD, Liu HX, Liu JF, Liu L, Liu Y, Long H, Luo QQ, Ma HT, Mao NQ, Pan XJ, Tan F, Tan LJ, Tian H, Wang D, Wang WX, Wei L, Wu N, Wu QC, Xiang J, Xu SD, Yang L, Zhang H, Zhang L, Zhang P, Zhang Y, Zhang Z, Zhu K, Zhu Y, Um SW, Oh IJ, Tomita Y, Watanabe S, Nakada T, Seki N, Hida T, Sasada S, Uchino J, Sugimura H, Dermime S, Cappuzzo F, Rizzo S, Cho WCS, Crucitti P, Longo F, Lee KY, De Ruysscher D, Vanneste BGL, Furqan M, Sieren JC, Yendamuri S, Merrell KW, Molina JR, Metro G, Califano R, Bongiolatti S, Provencio M, Hofman P, Gao S, He J. Expert consensus on perioperative immunotherapy for local advanced non-small cell lung cancer. Transl Lung Cancer Res 2021; 10:3713-3736. [PMID: 34733623 PMCID: PMC8512472 DOI: 10.21037/tlcr-21-634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/18/2021] [Indexed: 02/05/2023]
Affiliation(s)
- Bin Qiu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kaican Cai
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jun Chen
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Ke-Neng Chen
- Department of Thoracic Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Qi-Xun Chen
- Department of Thoracic Surgery, Cancer Hospital of University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Science, Hangzhou, China
| | - Chao Cheng
- Department of Thoracic Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tian-Yang Dai
- Department of Thoracic Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Junqiang Fan
- Department of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Zhaohui Fan
- Department of Thoracic Surgery, Jiangsu Cancer Hospital (Nanjing Medical University Affiliated Cancer Hospital) and Jiangsu Institute of Cancer Research, Nanjing, China
| | - Jian Hu
- Department of Thoracic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wei-Dong Hu
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yun-Chao Huang
- Department of Thoracic Surgery, Yunnan Cancer Hospital, Kunming, China
| | - Ge-Ning Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jie Jiang
- Department of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Tao Jiang
- Department of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Wen-Jie Jiao
- Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - He-Cheng Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Li
- Department of Thoracic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong-De Liao
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong-Xu Liu
- Department of Thoracic Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Jun-Feng Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Lunxu Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Liu
- Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, China
| | - Hao Long
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qing-Quan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hai-Tao Ma
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Nai-Quan Mao
- Department of Thoracic Surgery, Tumor Hospital Affiliated to Guangxi Medical University, Nanning, China
| | - Xiao-Jie Pan
- Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li-Jie Tan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dong Wang
- Department of Cardiothoracic Surgery, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wen-Xiang Wang
- Department of Thoracic Surgery II, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Li Wei
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Qing-Chen Wu
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiaqing Xiang
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Shi-Dong Xu
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lin Yang
- Department of Thoracic Surgery, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, China
| | - Hao Zhang
- Department of Thoracic Cardiovascular Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Lanjun Zhang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangdong Esophageal Cancer Institute, Guangzhou, China
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Zhang
- Department of Thoracic Surgery, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zhenfa Zhang
- Department of Lung Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, China
| | - Kunshou Zhu
- Department of Thoracic Surgery, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - In-Jae Oh
- Department of Internal Medicine, Chonnam National University Medical School and Hwasun Hospital, Jeonnam, Korea
| | - Yusuke Tomita
- Department of Respiratory Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Satoshi Watanabe
- Department of Respiratory Medicine and Infectious Diseases, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takeo Nakada
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Nobuhiko Seki
- Division of Medical Oncology, Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan
| | - Toyoaki Hida
- Lung Cancer Center, Central Japan International Medical Center, Gifu, Japan
| | - Shinji Sasada
- Department of Respiratory Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Junji Uchino
- Department of Pulmonary Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Haruhiko Sugimura
- Department of Tumor Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Said Dermime
- Department of Medical Oncology and Translational Research Institute, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Federico Cappuzzo
- Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Stefania Rizzo
- Imaging Institute of the Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Università della Svizzera Italiana, Lugano, Switzerland
| | | | | | - Filippo Longo
- Department of Thoracic Surgery, University Campus Bio-Medico, Rome, Italy
| | - Kye Young Lee
- Precision Medicine Lung Cancer Center, Konkuk University Medical Center, Seoul, Korea
| | - Dirk De Ruysscher
- Department of Radiation Oncology, MAASTRO Clinic, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ben G L Vanneste
- Department of Radiation Oncology, MAASTRO Clinic, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Muhammad Furqan
- Division of Hematology, Oncology and Blood and Marrow Transplantation, Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
| | - Jessica C Sieren
- Department of Radiology and Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Sai Yendamuri
- Department of Thoracic Surgery, Roswell Park Cancer Institute, Buffalo, NY, USA
| | | | - Julian R Molina
- Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Giulio Metro
- Medical Oncology, Santa Maria della Misericordia Hospital, Azienda Ospedaliera di Perugia, Perugia, Italy
| | - Raffaele Califano
- Department of Medical Oncology, The Christie NHS Foundation Trust and Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | | | - Mariano Provencio
- Medical Oncology Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
| | - Paul Hofman
- Laboratory of Clinical and Experimental Pathology, FHU OncoAge, Pasteur Hospital, BB-0033-00025, CHU Nice, Université Côte d'Azur, Nice, France
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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14
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Lopci E. Immunotherapy Monitoring with Immune Checkpoint Inhibitors Based on [ 18F]FDG PET/CT in Metastatic Melanomas and Lung Cancer. J Clin Med 2021; 10:jcm10215160. [PMID: 34768681 PMCID: PMC8584484 DOI: 10.3390/jcm10215160] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 12/15/2022] Open
Abstract
Immunotherapy with checkpoint inhibitors has prompted a major change not only in cancer treatment but also in medical imaging. In parallel with the implementation of new drugs modulating the immune system, new response criteria have been developed, aiming to overcome clinical drawbacks related to the new, unusual, patterns of response characterizing both solid tumors and lymphoma during the course of immunotherapy. The acknowledgement of pseudo-progression, hyper-progression, immune-dissociated response and so forth, has become mandatory for all imagers dealing with this clinical scenario. A long list of acronyms, i.e., irRC, iRECIST, irRECIST, imRECIST, PECRIT, PERCIMT, imPERCIST, iPERCIST, depicts the enormous effort made by radiology and nuclear medicine physicians in the last decade to optimize imaging parameters for better prediction of clinical benefit in immunotherapy regimens. Quite frequently, a combination of clinical-laboratory data with imaging findings has been tested, proving the ability to stratify patients into various risk groups. The next steps necessarily require a large scale validation of the most robust criteria, as well as the clinical implementation of immune-targeting tracers for immuno-PET or the exploitation of radiomics and artificial intelligence as complementary tools during the course of immunotherapy administration. For the present review article, a summary of PET/CT role for immunotherapy monitoring will be provided. By scrolling into various cancer types and applied response criteria, the reader will obtain necessary information for better understanding the potentials and limitations of the modality in the clinical setting.
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Affiliation(s)
- Egesta Lopci
- Nuclear Medicine Unit, IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, MI, Italy
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