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McGale JP, Chen DL, Trebeschi S, Farwell MD, Wu AM, Cutler CS, Schwartz LH, Dercle L. Artificial intelligence in immunotherapy PET/SPECT imaging. Eur Radiol 2024:10.1007/s00330-024-10637-3. [PMID: 38355986 DOI: 10.1007/s00330-024-10637-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/12/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
OBJECTIVE Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. METHODS We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. RESULTS Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. CONCLUSION Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. CLINICAL RELEVANCE STATEMENT This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. KEY POINTS • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.
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Affiliation(s)
- Jeremy P McGale
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Delphine L Chen
- Department of Molecular Imaging and Therapy, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School of Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Michael D Farwell
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna M Wu
- Department of Immunology and Theranostics, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Cathy S Cutler
- Collider Accelerator Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laurent Dercle
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
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Filippi L, Proietti I, Petrozza V, Potenza C, Bagni O, Schillaci O. The Prognostic Role of [ 18F]FDG PET/CT in Patients with Advanced Cutaneous Squamous Cell Carcinoma Submitted to Cemiplimab Immunotherapy: A Single-Center Retrospective Study. Cancer Biother Radiopharm 2024; 39:46-54. [PMID: 37883658 DOI: 10.1089/cbr.2023.0110] [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] [Indexed: 10/28/2023] Open
Abstract
Background: Baseline 2-deoxy-2[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET)-derived parameters and 12-week metabolic response were investigated as prognostic factors in advanced cutaneous squamous cell carcinoma (cSCC) submitted to cemiplimab immunotherapy. Materials and Methods: Clinical records of 25 cSCC patients receiving cemiplimab, submitted to [18F]FDG positron emission tomography/computed tomography (PET/CT) at baseline and after ∼12 weeks, were retrospectively reviewed. The Kaplan-Meier (KM) method was applied to analyze differences in event-free survival (EFS), and Cox regression analysis was employed to identify the prognostic factors. Results: At the 12-week PET/CT evaluation, 16 patients (64%) were classified as responders (complete or partial response) and 9 (36%) as nonresponders ("unconfirmed progressive metabolic disease") according to immune PET Response Criteria in Solid Tumors (iPERCIST). By KM analysis, baseline metabolic tumor volume (MTV) and total lesion glycolysis (TLG) significantly correlated with the EFS (p < 0.05). Furthermore, the KM analysis showed that the lack of metabolic response at 12 weeks was associated with meaningfully shorter EFS (7.2 ± 1 months in nonresponders vs. 20.3 ± 2.3 months in responders). In Cox multivariate analysis, metabolic response at 12 weeks remained the only predictor of the EFS (p < 0.05). Conclusions: Baseline tumor load (i.e., MTV and TLG) and metabolic response at 12 weeks may have a prognostic impact in cSCC patients treated with cemiplimab.
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Affiliation(s)
- Luca Filippi
- Nuclear Medicine Unit, Department of Oncohaematology, Fondazione PTV Policlinico Tor Vergata University Hospital, Rome, Italy
| | - Ilaria Proietti
- Dermatology Unit "Daniele Innocenzi," "A. Fiorini" Hospital, Terracina, Italy
| | - Vincenzo Petrozza
- Department of Medico-Surgical Sciences and Biotechnologies, Pathology Unit, ICOT Hospital, University of Rome "La Sapienza," Rome, Italy
| | - Concetta Potenza
- Dermatology Unit "Daniele Innocenzi," "A. Fiorini" Hospital, Terracina, Italy
| | - Oreste Bagni
- Nuclear Medicine Unit, Santa Maria Goretti Hospital, Latina, Italy
| | - Orazio Schillaci
- Department of Biomedicine and Prevention, University Tor Vergata, Rome, Italy
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Gu F, Wu Q. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives. Eur J Nucl Med Mol Imaging 2023; 50:3538-3557. [PMID: 37460750 PMCID: PMC10547641 DOI: 10.1007/s00259-023-06299-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.
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Affiliation(s)
- Fengyun Gu
- School of Mathematics and Physics, North China Electric Power University, 102206, Beijing, China.
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland.
| | - Qi Wu
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland
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Liu X, Zou Q, Sun Y, Liu H, Cailiang G. Role of multiple dual-phase 18F-FDG PET/CT metabolic parameters in differentiating adenocarcinomas from squamous cell carcinomas of the lung. Heliyon 2023; 9:e20180. [PMID: 37767476 PMCID: PMC10520777 DOI: 10.1016/j.heliyon.2023.e20180] [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: 04/12/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Purpose To evaluate the ability of multiple dual-phase 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters to distinguish the histological subtypes of non-small cell lung cancer (NSCLC). Methods Data from 127 patients with non-small cell lung cancer who underwent preoperative dual-phase 18F-FDG PET/CT scanning at the PET-CT center of our hospital from December 2020 to October 2021 were collected, and the metabolic parameters of their primary lesions were measured and analyzed retrospectively. Intraclass correlation coefficients (ICC) were calculated for consistency between readers. Metabolic parameters in the early (SUVpeak, SUVmean, SUVmin, SUVmax, MTV, and TLG) and delayed phases (dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, dpMTV, and dpTLG) were calculated. We drew receiver operating characteristic (ROC) curves to compare the differences in different metabolic parameters between the adenocarcinoma (AC) and squamous cell carcinoma (SCC) groups and evaluated the ability of different metabolic parameters to distinguish AC from SCC. Results Inter-reader agreement, as assessed by the intraclass correlation coefficient (ICC), was good (ICC = 0.71, 95% CI:0.60-0.79). The mean MTV, SUVmax, TLG, SUVpeak, SUVmean, dpSUVmax, dpTLG, dpSUVpeak, dpSUVmean, and dpSUVmin of the tumors were significantly higher in SCC lesions than in AC lesions (P = 0.049, < 0.001, 0.016, < 0.001, 0.001, < 0.001, 0.018, < 0.001, 0.001, and 0.001, respectively). The diagnostic efficacy of the metabolic parameters in 18F-FDG PET/CT for differentiating adenocarcinoma from squamous cell carcinoma ranged from high to low as follows: SUVpeak (AUC = 0.727), SUVmax (AUC = 0.708), dpSUVmax (AUC = 0.699), dpSUVpeak (AUC = 0.698), TLG (AUC = 0.695), and dpTLG (AUC = 0.692), SUVmean (AUC = 0.690), dpSUVmean (AUC = 0.687), dpSUVmin (AUC = 0.680), SUVmin (AUC = 0.676), and MTV (AUC = 0.657). Conclusions Squamous cell carcinoma of the lung had higher mean MTV, SUVmax, TLG, SUVpeak, SUVmean, SUVmin, dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, and dpTLG than AC, which can be helpful tools in differentiating between the two. The metabolic parameters of the delayed phase (2 h after injection) 18F-FDG PET/CT did not improve the diagnostic efficacy in distinguishing lung AC from SCC. Conventional dual-phase 18F-FDG PET/CT is not recommended.
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Affiliation(s)
| | | | - Yu Sun
- Department of Nuclear Medicine, Chongqing University Three Gorges Hospital, Wanzhou, 404100, Chongqing, China
| | - Huiting Liu
- Department of Nuclear Medicine, Chongqing University Three Gorges Hospital, Wanzhou, 404100, Chongqing, China
| | - Gao Cailiang
- Department of Nuclear Medicine, Chongqing University Three Gorges Hospital, Wanzhou, 404100, Chongqing, China
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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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Evangelista L, Fiz F, Laudicella R, Bianconi F, Castello A, Guglielmo P, Liberini V, Manco L, Frantellizzi V, Giordano A, Urso L, Panareo S, Palumbo B, Filippi L. PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature. Cancers (Basel) 2023; 15:3258. [PMID: 37370869 DOI: 10.3390/cancers15123258] [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: 05/09/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. MATERIALS AND METHODS A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. RESULTS Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy. CONCLUSIONS Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.
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Affiliation(s)
- Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
| | - Francesco Fiz
- Nuclear Medicine Department, E.O. "Ospedali Galliera", 16128 Genoa, Italy
- Nuclear Medicine Department and Clinical Molecular Imaging, University Hospital, 72076 Tübingen, Germany
| | - Riccardo Laudicella
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90100 Palermo, Italy
| | - Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti, 06125 Perugia, Italy
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Virginia Liberini
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy
| | - Luigi Manco
- Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy
| | - Viviana Frantellizzi
- Department of Radiological Sciences, Oncology and Anatomo-Pathology, Sapienza University of Rome, 00161 Rome, Italy
| | - Alessia Giordano
- Nuclear Medicine Unit, IRCCS CROB, Referral Cancer Center of Basilicata, 85028 Rionero in Vulture, Italy
| | - Luca Urso
- Department of Nuclear Medicine PET/CT Centre, S. Maria della Misericordia Hospital, 45100 Rovigo, Italy
| | - Stefano Panareo
- Nuclear Medicine Unit, Oncology and Haematology Department, University Hospital of Modena, 41124 Modena, Italy
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, 06125 Perugia, Italy
| | - Luca Filippi
- Nuclear Medicine Section, Santa Maria Goretti Hospital, 04100 Latina, Italy
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Yoshida A, Yamanoi K, Okunomiya A, Sagae Y, Sunada M, Taki M, Ukita M, Kurata Y, Himoto Y, Kido A, Horie A, Yamaguchi K, Hamanishi J, Mandai M. A case of paraovarian tumor of borderline malignancy with decrease of apparent diffusion coefficient value and marked 18F-fluorodeoxyglucose accumulation. Int Cancer Conf J 2023; 12:126-130. [PMID: 36896204 PMCID: PMC9989115 DOI: 10.1007/s13691-022-00590-7] [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: 11/20/2022] [Accepted: 12/25/2022] [Indexed: 01/01/2023] Open
Abstract
Para-ovarian cysts are occasionally encountered in clinical practice; however, malignant tumors derived from them are rare. Due to its rarity, the characteristic imaging findings of para-ovarian tumors with borderline malignancy (PTBM) are largely unknown. Herein, we report a case of PTBM, along with imaging findings. A 37-year-old woman came to our department with a suspected malignant adnexal tumor. Pelvic contrast-enhanced magnetic resonance imaging (MRI) revealed a solid part within the cystic tumor with a decrease in the apparent diffusion coefficient (ADC) value (1.16 × 10-3 mm2/s). We also performed Positron Emission Tomography-MRI and showed a strong accumulation of 18F-fluorodeoxyglucose (FDG) in the solid part (SUVmax = 14.8). In addition, the tumor appeared to develop independently of the ovary. Because tumor was derived from para-ovarian cyst, we suspected PTBM preoperatively and planned fertility sparing treatment. Pathological examination revealed a serous borderline tumor and PTBM was confirmed. PTBM can have unique imaging characteristics, including a low ADC value and high FDG accumulation. When a tumor appears to develop from para-ovarian cysts, borderline malignancy can be suspected, even if imaging findings suggest malignant potential.
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Affiliation(s)
- Akimi Yoshida
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Koji Yamanoi
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Asuka Okunomiya
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Yusuke Sagae
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Masumi Sunada
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Mana Taki
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Masayo Ukita
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Radiology and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto City, Japan
| | - Yuki Himoto
- Department of Diagnostic Radiology and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto City, Japan
| | - Aki Kido
- Department of Diagnostic Radiology and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto City, Japan
| | - Akihito Horie
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Ken Yamaguchi
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Junzo Hamanishi
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto City, Kyoto 606-8507 Japan
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Tamaki N, Hirata K, Kotani T, Nakai Y, Matsushima S, Yamada K. Four-dimensional quantitative analysis using FDG-PET in clinical oncology. Jpn J Radiol 2023:10.1007/s11604-023-01411-4. [PMID: 36947283 PMCID: PMC10366296 DOI: 10.1007/s11604-023-01411-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/02/2023] [Indexed: 03/23/2023]
Abstract
Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) has been commonly used in many oncological areas. High-resolution PET permits a three-dimensional analysis of FDG distributions on various lesions in vivo, which can be applied for tissue characterization, risk analysis, and treatment monitoring after chemoradiotherapy and immunotherapy. Metabolic changes can be assessed using the tumor absolute FDG uptake as standardized uptake value (SUV) and metabolic tumor volume (MTV). In addition, tumor heterogeneity assessment can potentially estimate tumor aggressiveness and resistance to chemoradiotherapy. Attempts have been made to quantify intratumoral heterogeneity using radiomics. Recent reports have indicated the clinical feasibility of a dynamic FDG PET-computed tomography (CT) in pilot cohort studies of oncological cases. Dynamic imaging permits the assessment of temporal changes in FDG uptake after administration, which is particularly useful for differentiating pathological from physiological uptakes with high diagnostic accuracy. In addition, several new parameters have been introduced for the in vivo quantitative analysis of FDG metabolic processes. Thus, a four-dimensional FDG PET-CT is available for precise tissue characterization of various lesions. This review introduces various new techniques for the quantitative analysis of FDG distribution and glucose metabolism using a four-dimensional FDG analysis with PET-CT. This elegant study reveals the important role of tissue characterization and treatment strategies in oncology.
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Affiliation(s)
- Nagara Tamaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Kenji Hirata
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Tomoya Kotani
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshitomo Nakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shigenori Matsushima
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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9
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Clinical applications of long axial field-of-view PET/CT scanners in oncology. Clin Transl Imaging 2023. [DOI: 10.1007/s40336-023-00547-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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10
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Filippi L, Dimitrakopoulou-Strauss A, Evangelista L, Schillaci O. Long axial field-of-view PET/CT devices: are we ready for the technological revolution? Expert Rev Med Devices 2022; 19:739-743. [DOI: 10.1080/17434440.2022.2141111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Luca Filippi
- Department of Nuclear Medicine, Santa Maria Goretti Hospital, Via Canova 3, 04100 Latina, Italy
| | | | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine (DIMED), University of Padua, Via Giustiniani, 35128, Padua, Italy
| | - Orazio Schillaci
- Department of Biomedicine and Prevention, University Tor Vergata, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
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11
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Xiao C, Tian H, Zheng Y, Yang Z, Li S, Fan T, Xu J, Bai G, Liu J, Deng Z, Li C, He J. Glycolysis in tumor microenvironment as a target to improve cancer immunotherapy. Front Cell Dev Biol 2022; 10:1013885. [PMID: 36200045 PMCID: PMC9527271 DOI: 10.3389/fcell.2022.1013885] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/07/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer cells and immune cells all undergo remarkably metabolic reprogramming during the oncogenesis and tumor immunogenic killing processes. The increased dependency on glycolysis is the most typical trait, profoundly involved in the tumor immune microenvironment and cancer immunity regulation. However, how to best utilize glycolytic targets to boost anti-tumor immunity and improve immunotherapies are not fully illustrated. In this review, we describe the glycolytic remodeling of various immune cells within the tumor microenvironment (TME) and the deleterious effects of limited nutrients and acidification derived from enhanced tumor glycolysis on immunological anti-tumor capacity. Moreover, we elucidate the underlying regulatory mechanisms of glycolytic reprogramming, including the crosstalk between metabolic pathways and immune checkpoint signaling. Importantly, we summarize the potential glycolysis-related targets that are expected to improve immunotherapy benefits. Our understanding of metabolic effects on anti-tumor immunity will be instrumental for future therapeutic regimen development.
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Affiliation(s)
- Chu Xiao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - He Tian
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yujia Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Zhenlin Yang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Shuofeng Li
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Tao Fan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiachen Xu
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Guangyu Bai
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jingjing Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Ziqin Deng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Chunxiang Li
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- *Correspondence: Chunxiang Li, ; Jie He,
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- *Correspondence: Chunxiang Li, ; Jie He,
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