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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024:10.1038/s41568-024-00705-7. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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2
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Zhou L, Ji Q, Peng H, Chen F, Zheng Y, Jiao Z, Gong J, Li W. Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning. Eur Radiol 2024; 34:3644-3655. [PMID: 37994966 DOI: 10.1007/s00330-023-10316-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. METHODS Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. RESULTS Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. CONCLUSIONS A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. CLINICAL RELEVANCE STATEMENT A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. KEY POINTS • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhizaoju Road, Huangpu District, Shanghai, 20011, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Yi Zheng
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | | | - Jian Gong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical Unversity, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
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3
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Hughes DJ, Josephides E, O'Shea R, Manickavasagar T, Horst C, Hunter S, Tanière P, Nonaka D, Van Hemelrijck M, Spicer J, Goh V, Bille A, Karapanagiotou E, Cook GJR. Predicting programmed death-ligand 1 (PD-L1) expression with fluorine-18 fluorodeoxyglucose ([ 18F]FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters in resectable non-small cell lung cancer. Eur Radiol 2024:10.1007/s00330-024-10651-5. [PMID: 38388716 DOI: 10.1007/s00330-024-10651-5] [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: 10/19/2023] [Revised: 12/24/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). PD-L1 and glucose transporter 1 expression are closely associated, and studies demonstrate correlation of PD-L1 with glucose metabolism. AIM The aim of this study was to investigate the association of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters with PD-L1 expression in primary lung tumour and lymph node metastases in resected NSCLC. METHODS We conducted a retrospective analysis of 210 patients with node-positive resectable stage IIB-IIIB NSCLC. PD-L1 tumour proportion score (TPS) was determined using the DAKO 22C3 immunohistochemical assay. Semi-automated techniques were used to analyse pre-operative [18F]FDG-PET/CT images to determine primary and nodal metabolic parameter scores (including max, mean, peak and peak adjusted for lean body mass standardised uptake values (SUV), metabolic tumour volume (MTV), total lesional glycolysis (TLG) and SUV heterogeneity index (HISUV)). RESULTS Patients were predominantly male (57%), median age 70 years with non-squamous NSCLC (68%). A majority had negative primary tumour PD-L1 (TPS < 1%; 53%). Mean SUVmax, SUVmean, SUVpeak and SULpeak values were significantly higher (p < 0.05) in those with TPS ≥ 1% in primary tumour (n = 210) or lymph nodes (n = 91). However, ROC analysis demonstrated only moderate separability at the 1% PD-L1 TPS threshold (AUCs 0.58-0.73). There was no association of MTV, TLG and HISUV with PD-L1 TPS. CONCLUSION This study demonstrated the association of SUV-based [18F]FDG-PET/CT metabolic parameters with PD-L1 expression in primary tumour or lymph node metastasis in resectable NSCLC, but with poor sensitivity and specificity for predicting PD-L1 positivity ≥ 1%. CLINICAL RELEVANCE STATEMENT Whilst SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography metabolic parameters may not predict programmed death-ligand 1 positivity ≥ 1% in the primary tumour and lymph nodes of resectable non-small cell lung cancer independently, there is a clear association which warrants further investigation in prospective studies. TRIAL REGISTRATION Non-applicable KEY POINTS: • Programmed death-ligand 1 immunohistochemistry has a predictive role in non-small cell lung cancer immunotherapy; however, it is both heterogenous and dynamic. • SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters were significantly higher in primary tumour or lymph node metastases with positive programmed death-ligand 1 expression. • These SUV-based parameters could potentially play an additive role along with other multi-modal biomarkers in selecting patients within a predictive nomogram.
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Affiliation(s)
- Daniel Johnathan Hughes
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- King's College London & Guy's and St Thomas' PET Centre, London, UK
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Eleni Josephides
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Robert O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Thubeena Manickavasagar
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Carolyn Horst
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sarah Hunter
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Philippe Tanière
- Department of Histopathology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Daisuke Nonaka
- Department of Histopathology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - James Spicer
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrea Bille
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Eleni Karapanagiotou
- Cancer Centre at Guy's, Guy's and St Thomas' NHS Foundation Trust, London, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Gary J R Cook
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK.
- King's College London & Guy's and St Thomas' PET Centre, London, UK.
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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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5
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McGale JP, Howell HJ, Beddok A, Tordjman M, Sun R, Chen D, Wu AM, Assi T, Ammari S, Dercle L. Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy. Pharmaceuticals (Basel) 2024; 17:210. [PMID: 38399425 PMCID: PMC10892847 DOI: 10.3390/ph17020210] [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: 12/25/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.
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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 10032, USA (H.J.H.)
| | - Harrison J. Howell
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA (H.J.H.)
| | - Arnaud Beddok
- Department of Radiation Oncology, Institut Godinot, 51100 Reims, France
| | - Mickael Tordjman
- Department of Radiology, Hôtel Dieu Hospital, APHP, 75014 Paris, France
| | - Roger Sun
- Department of Radiation Oncology, Gustave Roussy, 94800 Villejuif, France
| | - Delphine Chen
- Department of Molecular Imaging and Therapy, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Anna M. Wu
- Department of Immunology and Theranostics, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA;
| | - Tarek Assi
- International Department, Gustave Roussy Cancer Campus, 94805 Villejuif, France
| | - Samy Ammari
- Department of Medical Imaging, BIOMAPS, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France
- ELSAN Department of Radiology, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Laurent Dercle
- Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA (H.J.H.)
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Wang Y, Li Y, Jiang H, Zuo C, Xu W. Elevated splenic 18F-fluorodeoxyglucose positron emission tomography/computed tomography activity is associated with 5-year risk of recurrence in non-metastatic invasive ductal carcinoma of the breast. Br J Radiol 2024; 97:237-248. [PMID: 38263821 PMCID: PMC11027281 DOI: 10.1093/bjr/tqad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE To construct prediction models including baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters of tumoural lesions and non-tumour lymphoid tissue for recurrence-free survival within 5 years (5y-RFS) after imaging examination in patients with invasive ductal carcinomas (IDCs) of the breast. METHODS The study included 101 consecutive female patients. Univariable and multivariable Cox regression were used to identify clinicopathological and metabolic parameters associated with risk of recurrence. Four prediction models based on the results of multivariable analysis were constructed and visualized as nomograms. Performance of each nomogram was evaluated using the concordance index (C-index), integrated discrimination improvement, decision curve analysis (DCA), and calibration curve. RESULTS N3 status, total metabolic tumour volume, the maximum standardized uptake value of spleen, and spleen-to-liver ratio were significant predictors of 5y-RFS. The nomogram including all significant predictors demonstrated superior predictive performance for 5y-RFS, with a C-index of 0.907 (95% CI, 0.833-0.981), greatest net benefit on DCA, good accuracy on calibration curves, and excellent risk stratification on Kaplan-Meier curves. CONCLUSIONS The model that included metabolic parameters of the spleen had the best performance for predicting 5y-RFS in patients with IDCs of the breast. This model may guide personalized treatment decisions and inform patients and clinicians about prognosis. ADVANCES IN KNOWLEDGE This research identifies 18F-FDG PET/CT metabolic parameters of non-tumour lymphoid tissue as predictors of recurrence in breast cancer.
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Affiliation(s)
- Yiting Wang
- Department of Nuclear Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, PR China
| | - Yuchao Li
- Department of Nuclear Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, PR China
| | - Hongyuan Jiang
- Department of Nuclear Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, PR China
| | - Changjing Zuo
- Department of Nuclear Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, PR China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, PR China
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Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz LH, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics (Basel) 2023; 13:3483. [PMID: 37998619 PMCID: PMC10670510 DOI: 10.3390/diagnostics13223483] [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: 09/20/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023] Open
Abstract
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
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Affiliation(s)
- Hayley Higgins
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Abanoub Nakhla
- Department of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The Netherlands;
| | - Andrew Lotfalla
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - David Khalil
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Parth Doshi
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Vandan Thakkar
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Dorsa Shirini
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| | - Maria Bebawy
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Samy Ammari
- Département d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
- ELSAN Département de Radiologie, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Lawrence H. Schwartz
- Department of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Michael Postow
- Melanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Laurent Dercle
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
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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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9
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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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10
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Erasmus JJ, Vlahos I. Editorial Commentary: Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC. J Thorac Oncol 2023; 18:556-558. [PMID: 37087116 DOI: 10.1016/j.jtho.2023.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 04/24/2023]
Affiliation(s)
- Jeremy J Erasmus
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Ioannis Vlahos
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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11
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Malet J, Ancel J, Moubtakir A, Papathanassiou D, Deslée G, Dewolf M. Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC. Life (Basel) 2023; 13:life13041051. [PMID: 37109580 PMCID: PMC10142835 DOI: 10.3390/life13041051] [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: 03/09/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Anti-PD-1/PD-L1 therapy indications are broadened in non-small cell lung cancer (NSCLC) although immune checkpoint inhibitors (ICI) do not provide benefits for the entire population. Texture features based on positron emission tomography/computed tomography (PET/CT), especially entropy (based on a gray-level co-occurrence matrix (GLCM)), could be interesting as predictors in NSCLC. The aim of our retrospective study was to evaluate the association between GLCM-entropy and response to anti-PD-1/PD-L1 monotherapy at the first evaluation in stage III or IV NSCLC, comparing patients with progressive disease (PD) and non-progressive disease (non-PD). In total, 47 patients were included. Response Evaluation Criteria in Solid Tumors (RECIST 1.1) were used to evaluate the response to ICI treatment (nivolumab, pembrolizumab, or atezolizumab). At the first evaluation, 25 patients were PD and 22 were non-PD. GLCM-entropy was not predictive of response at the first evaluation. Furthermore, GLCM-entropy was not associated with progression-free survival (PFS) (p = 0.393) or overall survival (OS) (p = 0.220). Finally, GLCM-entropy measured in PET/CT performed before ICI initiation in stage III or IV NSCLC was not predictive of response at the first evaluation. However, this study demonstrates the feasibility of using texture parameters in routine clinical practice. The interest of measuring PET/CT texture parameters in NSCLC remains to be evaluated in larger prospective studies.
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Affiliation(s)
- Julie Malet
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
| | - Julien Ancel
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
- INSERM UMRS 1250, University of Reims Champagne-Ardenne, 51100 Reims, France
| | - Abdenasser Moubtakir
- Department of Nuclear Medicine, Institut Godinot, 1, Rue du Général Koenig, 51100 Reims, France
| | - Dimitri Papathanassiou
- Department of Nuclear Medicine, Institut Godinot, 1, Rue du Général Koenig, 51100 Reims, France
- UFR de Médecine, Reims-Champagne Ardenne University, 1, Rue Cognacq-Jay, CEDEX 51095 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l'Information et de la Communication), EA 3804, University of Reims Champagne-Ardenne, Moulin de la Housse, BP 1039, CEDEX 51687 Reims, France
| | - Gaëtan Deslée
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
- INSERM UMRS 1250, University of Reims Champagne-Ardenne, 51100 Reims, France
| | - Maxime Dewolf
- Department of Respiratory Diseases, Reims University Hospital, 45, Rue Cognacq-Jay, 51100 Reims, France
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12
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Ventura D, Schindler P, Masthoff M, Görlich D, Dittmann M, Heindel W, Schäfers M, Lenz G, Wardelmann E, Mohr M, Kies P, Bleckmann A, Roll W, Evers G. Radiomics of Tumor Heterogeneity in 18F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15082297. [PMID: 37190228 DOI: 10.3390/cancers15082297] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
We aimed to evaluate the predictive and prognostic value of baseline 18F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" (n = 33) and "non-responder" (n = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for "PET-Skewness" and 0.75 predicting overall progression for "PET-Median". In terms of progression-free survival analysis, patients with a lower value of PET-Skewness (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; p < 0.001) and higher value of PET-Median (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; p < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.
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Affiliation(s)
- David Ventura
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Philipp Schindler
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Max Masthoff
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Muenster, 48149 Muenster, Germany
| | - Matthias Dittmann
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Walter Heindel
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Schäfers
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Lenz
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Eva Wardelmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Gerhard-Domagk-Institute of Pathology, University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Mohr
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Peter Kies
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Annalen Bleckmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Evers
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
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13
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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14
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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15
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Dercle L, Sun S, Seban RD, Mekki A, Sun R, Tselikas L, Hans S, Bernard-Tessier A, Mihoubi Bouvier F, Aide N, Vercellino L, Rivas A, Girard A, Mokrane FZ, Manson G, Houot R, Lopci E, Yeh R, Ammari S, Schwartz LH. Emerging and Evolving Concepts in Cancer Immunotherapy Imaging. Radiology 2023; 306:32-46. [PMID: 36472538 DOI: 10.1148/radiol.210518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.
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Affiliation(s)
- Laurent Dercle
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Shawn Sun
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Romain-David Seban
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Ahmed Mekki
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Roger Sun
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Lambros Tselikas
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Sophie Hans
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Alice Bernard-Tessier
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Fadila Mihoubi Bouvier
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Nicolas Aide
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Laetitia Vercellino
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Alexia Rivas
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Antoine Girard
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Fatima-Zohra Mokrane
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Guillaume Manson
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Roch Houot
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Egesta Lopci
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Randy Yeh
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Samy Ammari
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
| | - Lawrence H Schwartz
- From the Department of Radiology, New York Presbyterian Hospital-Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.)
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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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Chen D, Liu X, Hu C, Hao R, Wang O, Xiao Y. Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis. Future Oncol 2022:1-14. [PMID: 36475996 DOI: 10.2217/fon-2022-0333] [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: 03/31/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022] Open
Abstract
Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.
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Affiliation(s)
- Danxiang Chen
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Xia Liu
- Department of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Chunlei Hu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Rutian Hao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Ouchen Wang
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Yanling Xiao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
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Silva SB, Wanderley CWS, Gomes Marin JF, de Macedo MP, do Nascimento ECT, Antonacio FF, Figueiredo CS, Trinconi Cunha M, Cunha FQ, de Castro Junior G. Tumor glycolytic profiling through 18F-FDG PET/CT predicts immune checkpoint inhibitor efficacy in advanced NSCLC. Ther Adv Med Oncol 2022; 14:17588359221138386. [PMID: 36506107 PMCID: PMC9730014 DOI: 10.1177/17588359221138386] [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: 06/29/2022] [Accepted: 10/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background A significant proportion of patients with non-small-cell lung cancer (NSCLC) do not respond to immune checkpoint inhibitors (ICIs). Since metabolic reprogramming with increased glycolysis is a hallmark of cancer and is involved in immune evasion, we used 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) to evaluate the baseline glycolytic parameters of patients with advanced NSCLC submitted to ICIs, and assessed their predictive value. Methods 18F-FDG PET/CT results in the 3 months before ICIs treatment were included. Maximum standardized uptake values, whole metabolic tumor volume (wMTV), and whole-body total lesion glycolysis (wTLG) were evaluated. Cutoff values for high or low glycolytic categories were determined using receiver-operating characteristic curves. Progression-free survival (PFS) and overall survival (OS) were evaluated. Patients with a complete response and a matching group with resistance to ICIs underwent immunohistochemistry analysis. An unsupervised k-means clustering model integrating programmed cell death ligand 1 (PD-L1) expression, glycolytic parameters, and ICIs therapy was performed. Results In all, 98 patients were included. Lower baseline 18F-FDG PET/CT parameters were associated with responses to ICIs. Patients with low wMTV or wTLG had improved PFS and OS. High wTLG, strong tumor expression of glucose transporter-1, and lack of responses were significantly associated. Patients with low glycolytic parameters benefited from ICIs, regardless of chemotherapy. Conversely, those with high parameters benefited from the addition of chemotherapy. Patients with higher wTLG and lower PD-L1 were associated with progression and worse survival to ICIs monotherapy. Conclusions Glycolytic metabolic profiles established through baseline 18F-FDG PET/CT are useful biomarkers for evaluating ICI therapy in advanced NSCLC.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Fernando Q. Cunha
- Center for Research in Inflammatory Diseases (CRID) and Department of Pharmacology, Ribeirao Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil
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Tankyevych O, Trousset F, Latappy C, Berraho M, Dutilh J, Tasu JP, Lamour C, Cheze Le Rest C. Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy. Cancers (Basel) 2022; 14:cancers14235931. [PMID: 36497415 PMCID: PMC9739232 DOI: 10.3390/cancers14235931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/07/2022] [Accepted: 11/23/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6−8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan−Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.
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Affiliation(s)
- Olena Tankyevych
- Nuclear Medicine Department, Poitiers University Hospital, 86000 Poitiers, France
- LaTIM, INSERM, UMR 1101, 29200 Brest, France
| | - Flora Trousset
- Nuclear Medicine Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Claire Latappy
- Nuclear Medicine Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Moran Berraho
- Nuclear Medicine Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Julien Dutilh
- Oncology Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Jean Pierre Tasu
- LaTIM, INSERM, UMR 1101, 29200 Brest, France
- Medical School, University of Poitiers, 86000 Poitiers, France
- Radiology Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Corinne Lamour
- Oncology Department, Poitiers University Hospital, 86000 Poitiers, France
| | - Catherine Cheze Le Rest
- Nuclear Medicine Department, Poitiers University Hospital, 86000 Poitiers, France
- LaTIM, INSERM, UMR 1101, 29200 Brest, France
- Medical School, University of Poitiers, 86000 Poitiers, France
- Correspondence:
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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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21
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Paul AG, Miller S, Heilbrun LK, Smith DW. MRI- and PET-Based Assessment of Radiological and Clinical Factors Associated With Cervical Cancer Response to External Beam Radiation Therapy. Cureus 2022; 14:e30645. [DOI: 10.7759/cureus.30645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/23/2022] [Indexed: 11/06/2022] Open
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22
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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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23
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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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24
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Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
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Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
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25
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Crombé A, Lafon M, Nougaret S, Kind M, Cousin S. Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. Eur J Radiol 2022; 155:110472. [PMID: 35985090 DOI: 10.1016/j.ejrad.2022.110472] [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: 01/10/2022] [Revised: 07/09/2022] [Accepted: 08/09/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. METHODS This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. RESULTS 101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. CONCLUSION Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France; Department of Imaging, Pellegrin University Hospital, F-33300 Bordeaux, France; University of Bordeaux, UMR CNRS 5251, INRIA Project team Models in Oncology (Monc), F-33400 Talence, France.
| | - Mathilde Lafon
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Stéphanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, F-34295 Montpellier, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
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26
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Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:jitc-2022-004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France .,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,INSERM U1030, Gustave Roussy, Villejuif, France
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27
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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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28
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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29
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When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation. Comput Biol Med 2022; 145:105499. [DOI: 10.1016/j.compbiomed.2022.105499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/26/2022] [Accepted: 04/03/2022] [Indexed: 02/07/2023]
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30
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Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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The “digital biopsy” in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT. Eur J Nucl Med Mol Imaging 2022; 49:3401-3411. [DOI: 10.1007/s00259-022-05783-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 03/25/2022] [Indexed: 01/06/2023]
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The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria. J Clin Med 2022; 11:jcm11061740. [PMID: 35330068 PMCID: PMC8948743 DOI: 10.3390/jcm11061740] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022] Open
Abstract
Simple Summary The introduction of immune checkpoint inhibitors has represented a milestone in cancer treatment. Despite PD-L1 expression being the standard biomarker used before the start of therapy, there is still a strict need to identify complementary non-invasive biomarkers in order to better select patients. In this context, radiomics is an emerging approach for examining medical images and clinical data by capturing multiple features hidden from human eye and is potentially able to predict response assessment and survival in the course of immunotherapy. We reviewed the available studies investigating the role of radiomics in cancer patients, focusing on non-small cell lung cancer treated with immune checkpoint inhibitors. Although preliminary research shows encouraging results, different issues need to be solved before radiomics can enter into clinical practice. Abstract Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an “ideal” biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.
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Liao X, Liu M, Wang R, Zhang J. Potentials of Non-Invasive 18F-FDG PET/CT in Immunotherapy Prediction for Non–Small Cell Lung Cancer. Front Genet 2022; 12:810011. [PMID: 35186013 PMCID: PMC8855498 DOI: 10.3389/fgene.2021.810011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/31/2021] [Indexed: 12/26/2022] Open
Abstract
The immune checkpoint inhibitors (ICIs), by targeting cytotoxic-T-lymphocyte-associated protein 4, programmed cell death 1 (PD-1), or PD-ligand 1, have dramatically changed the natural history of several cancers, including non–small cell lung cancer (NSCLC). There are unusual response manifestations (such as pseudo-progression, hyper-progression, and immune-related adverse events) observed in patients with ICIs because of the unique mechanisms of these agents. These specific situations challenge response and prognostic assessment to ICIs challenging. This review demonstrates how 18F-FDG PET/CT can help identify these unusual response patterns in a non-invasive and effective way. Then, a series of semi-quantitative parameters derived from 18F-FDG PET/CT are introduced. These indexes have been recognized as the non-invasive biomarkers to predicting the efficacy of ICIs and survival of NSCLC patients according to the latest clinical studies. Moreover, the current situation regarding the functional criteria based on 18F-FDG PET/CT for immunotherapeutic response assessment is presented and analyzed. Although the criteria based on 18F-FDG PET/CT proposed some resolutions to overcome limitations of morphologic criteria in the assessment of tumor response to ICIs, further researches should be performed to validate and improve these assessing systems. Then, the last part in this review displays the present status and a perspective of novel specific PET probes targeting key molecules relevant to immunotherapy in prediction and response assessment.
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Hashimoto K, Kaira K, Yamaguchi O, Shiono A, Mouri A, Miura Y, Kobayashi K, Imai H, Matsusaka Y, Kuji I, Kagamu H. Visual Assessment of 18F-FDG Uptake on PET to Predict Survival Benefit to PD-1 Blockade in Non-Small Cell Lung Cancer. Clin Nucl Med 2022; 47:108-116. [PMID: 35006104 DOI: 10.1097/rlu.0000000000004009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Programmed death 1 (PD-1) blockade is a standard treatment for patients with metastatic non-small cell lung cancer (NSCLC). Approximately 20% patients receiving PD-1 blockade monotherapy can survive for more than 5 years. However, there are limited data on the optimal biomarkers for predicting long-term outcomes. Therefore, this study aimed to evaluate the prognostic significance of 18F-FDG uptake in patients with NSCLC responding to PD-1 blockade. PATIENTS AND METHODS Thirty-eight patients with advanced NSCLC who underwent 18F-FDG PET after confirmation of clinical response to PD-1 blockade monotherapy were retrospectively included in this study. Visual assessment using a 5-point scale score according to 18F-FDG uptake was performed, and the 18F-FDG uptake cutoff score for prolonged response to PD-1 blockade was defined as 3 (low score: 1, 2, or 3 and high score: 4 or 5). RESULTS A significantly greater number of patients with low scores had a performance status of 0 or 1 than patients with high scores. Among the 38 patients, 20 (53%) had a low score and 18 (47%) had a high score. Progression-free survival and overall survival were significantly longer in patients with low scores than in patients with high scores. Low 18F-FDG uptake was an independent prognostic factor for predicting favorable progression-free survival and overall survival, as confirmed by multivariate analysis. CONCLUSIONS Tumors with lower 18F-FDG uptake on PET than normal hepatic lesions exhibit the possibility of prolonged response to PD-1 blockade. Visual assessment on PET is easy for every clinician and is understandable to confirm aggressive tumor activity.
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Affiliation(s)
| | | | | | | | | | - Yu Miura
- From the Department of Respiratory Medicine
| | | | - Hisao Imai
- From the Department of Respiratory Medicine
| | - Yohji Matsusaka
- Department of Nuclear Medicine, Comprehensive Cancer Center, International Medical Center, Saitama Medical University, Saitama, Japan
| | - Ichiei Kuji
- Department of Nuclear Medicine, Comprehensive Cancer Center, International Medical Center, Saitama Medical University, Saitama, Japan
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Gong J, Bao X, Wang T, Liu J, Peng W, Shi J, Wu F, Gu Y. A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer. Oncoimmunology 2022; 11:2028962. [PMID: 35096486 PMCID: PMC8794258 DOI: 10.1080/2162402x.2022.2028962] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.
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Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao Bao
- Department of Radiology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiyu Liu
- Department of Radiology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Fengying Wu
- Department of Oncology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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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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Dual time point imaging of staging PSMA PET/CT quantification; spread and radiomic analyses. Ann Nucl Med 2022; 36:310-318. [DOI: 10.1007/s12149-021-01705-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/29/2021] [Indexed: 11/01/2022]
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Prognostic Value of 18F-FDG PET/CT Volume-Based Metabolic Parameters in Patients with Node-Negative Stage II Esophageal Squamous Cell Carcinoma. Metabolites 2021; 12:metabo12010007. [PMID: 35050129 PMCID: PMC8781087 DOI: 10.3390/metabo12010007] [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: 11/30/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 12/02/2022] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is a major cancer prevalent in Asian males. Pretreatment tumor burden can be prognostic for ESCC. We studied the prognostic value of metabolic parameters of 2-deoxy-2-[18F] fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) and the serum squamous cell carcinoma antigen (SCC-Ag) level in node-negative stage II ESCC patients. Eighteen males underwent staging evaluation were included. The volume-based metabolic parameters derived from 18F-FDG PET/CT, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were obtained using the PET Volume Computer Assisted Reading application. The Spearman correlation coefficients were calculated to assess the relationship between metabolic parameters and pretreatment serum SCC-Ag levels. Based on the 5-year follow-up, patients were sub-divided into the demised and the stable groups. Potential prognostic value was assessed by independent t-test and the Mann–Whitney U test. The association of overall survival was assessed using univariate and multivariate Cox regression analyses. The demised group showed significant higher values in serum SCC-Ag, as well as in MTV and TLG, but not SUVmax and SUVmean. The SUVmax, MTV, TLG, and serum SCC-Ag showed significant association with overall survival. Our findings suggest potential usage of pretreatment volume-based metabolic parameters of 18F-FDG PET/CT and serum SCC-Ag as prognostic factors for node-negative stage II ESCC patients.
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First-Line Pembrolizumab Mono- or Combination Therapy of Non-Small Cell Lung Cancer: Baseline Metabolic Biomarkers Predict Outcomes. Cancers (Basel) 2021; 13:cancers13236096. [PMID: 34885206 PMCID: PMC8656760 DOI: 10.3390/cancers13236096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Positron-emission tomography/computed tomography (PET/CT) is used for staging of non-small cell lung cancer (NSCLC) and can help to estimate prognosis in patients treated with immune checkpoint inhibitor (ICI) therapy. Most available data in that field were derived from cohorts treated in higher therapy lines using ICI monotherapy with different drugs. Currently, however, most advanced NSCLC patients receive first-line ICI treatment, often in combination with cytotoxic chemotherapy. We evaluated prognostic PET/CT biomarkers in 85 patients receiving first-line ICI, 70 (82%) of them as a chemotherapy–ICI combination. We found that patients with a higher metabolically active tumor volume (MTV) had a significantly poorer survival and lower radiological response rate. In patients with high MTV, a concomitantly low bone marrow to liver ratio indicated a better prognosis. Our results demonstrate that PET/CT-derived biomarkers can aid therapeutic decision-making in ICI-treated NSCLC. Abstract Quantitative biomarkers derived from positron-emission tomography/computed tomography (PET/CT) have been suggested as prognostic variables in immune-checkpoint inhibitor (ICI) treated non-small cell lung cancer (NSCLC). As such, data for first-line ICI therapy and especially for chemotherapy–ICI combinations are still scarce, we retrospectively evaluated baseline 18F-FDG-PET/CT of 85 consecutive patients receiving first-line pembrolizumab with chemotherapy (n = 70) or as monotherapy (n = 15). Maximum and mean standardized uptake value, total metabolic tumor volume (MTV), total lesion glycolysis, bone marrow-/and spleen to liver ratio (BLR/SLR) were calculated. Kaplan–Meier analyses and Cox regression models were used to assess progression-free/overall survival (PFS/OS) and their determinant variables. Median follow-up was 12 months (M; 95% confidence interval 10–14). Multivariate selection for PFS/OS revealed MTV as most relevant PET/CT biomarker (p < 0.001). Median PFS/OS were significantly longer in patients with MTV ≤ 70 mL vs. >70 mL (PFS: 10 M (4–16) vs. 4 M (3–5), p = 0.001; OS: not reached vs. 10 M (5–15), p = 0.004). Disease control rate was 81% vs. 53% for MTV ≤/> 70 mL (p = 0.007). BLR ≤ 1.06 vs. >1.06 was associated with better outcomes (PFS: 8 M (4–13) vs. 4 M (3–6), p = 0.034; OS: 19 M (12-/) vs. 6 M (4–12), p = 0.005). In patients with MTV > 70 mL, concomitant BLR ≤ 1.06 indicated a better prognosis. Higher MTV is associated with inferior PFS/OS in first-line ICI-treated NSCLC, with BLR allowing additional risk stratification.
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Chen Q, Zhang L, Mo X, You J, Chen L, Fang J, Wang F, Jin Z, Zhang B, Zhang S. Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 2021; 49:345-360. [PMID: 34402924 DOI: 10.1007/s00259-021-05509-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/27/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC. METHODS We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed. RESULTS Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001). CONCLUSIONS Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.
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Affiliation(s)
- Qiuying Chen
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Jingjing You
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China
| | - Luyan Chen
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China
| | - Jin Fang
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
- Graduate College, Jinan University, Guangzhou, Guangdong, People's Republic of China.
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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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Guo H, Xu K, Duan G, Wen L, He Y. Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine. Ann Nucl Med 2021; 36:1-14. [PMID: 34727331 DOI: 10.1007/s12149-021-01683-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/30/2021] [Indexed: 12/19/2022]
Abstract
With a 5-year overall survival of approximately 20%, lung cancer has always been the number one cancer-specific killer all over the world. As a fusion of positron emission computed tomography (PET) and computed tomography (CT), PET/CT has revolutionized cancer imaging over the past 20 years. In this review, we focused on the optimization of the function of 18F-flurodeoxyglucose (FDG)-PET/CT in diagnosis, prognostic prediction and therapy management of lung cancers by computer programs. FDG-PET/CT has demonstrated a surprising role in development of therapeutic biomarkers, prediction of therapeutic responses and long-term survival, which could be conducive to solving existing dilemmas. Meanwhile, novel tracers and optimized procedures are also developed to control the quality and improve the effect of PET/CT. With the continuous development of some new imaging agents and their clinical applications, application value of PET/CT has broad prospects in this area.
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Affiliation(s)
- Haoyue Guo
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China
| | - Kandi Xu
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China
| | - Guangxin Duan
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Ling Wen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
- School of Medicine, Tongji University, No. 1239 Siping Road, Shanghai, 200092, China.
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Immune Checkpoint Inhibitors in Advanced NSCLC: [ 18F]FDG PET/CT as a Troubleshooter in Treatment Response. Diagnostics (Basel) 2021; 11:diagnostics11091681. [PMID: 34574022 PMCID: PMC8471751 DOI: 10.3390/diagnostics11091681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/02/2021] [Accepted: 09/11/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: The aim of this study was to investigate whether [18F]FDG PET/CT-derived semi-quantitative parameters can predict immunotherapy treatment response in non-small cell lung cancer (NSCLC) patients. Secondly, immune-related adverse events (irAEs) and lymphoid cell-rich organs activation were evaluated. Materials and Methods: Twenty-eight patients who underwent [18F]FDG PET/CT scans before and at first restaging therapy with immuno-checkpoint inhibitors (ICIs) were retrospectively analyzed. PET-based semi-quantitative parameters extracted from both scans were respectively: SUVmax and SUVpeak of the target lesion, whole-body metabolic tumor volume (MTVWB), and whole-body total lesion glycolysis (TLGWB), as well as their interval changes (ΔSUVmaxTL, ΔSUVpeakTL, ΔMTVWB, ΔTLGWB). These PET-derived parameters were correlated to controlled disease (CD) assessed by RECIST 1.1. IrAEs, if present, were also described and correlated with clinical benefit (CB). SUVmax of the spleen and bone marrow at restaging scans were also correlated to CB. Results: The CD was achieved in 54% of patients. Out of 28 eligible patients, 13 (46%) experienced progressive disease (PD), 7 showed SD, 7 had PR, and only in one patient CR was achieved. ΔSUVmaxTL (p = 0.002) and ΔSUVpeakTL (p < 0.001) as well as ΔMTVWB (p < 0.001) and ΔTLGWB (p < 0.005) were significantly associated with PD vs. non-PD. IrAEs and lymphoid cell-rich organs activation did not correlate with CB. Conclusions: [18F]FDG PET/CT by using interval changes of PET-derived semi-quantitative parameters could represent a reliable tool in immunotherapy treatment response evaluation in NSCLC patients.
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Liberini V, Mariniello A, Righi L, Capozza M, Delcuratolo MD, Terreno E, Farsad M, Volante M, Novello S, Deandreis D. NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images. Cancers (Basel) 2021; 13:4543. [PMID: 34572771 PMCID: PMC8464855 DOI: 10.3390/cancers13184543] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 12/24/2022] Open
Abstract
Lung cancer remains the leading cause of cancer-related death, and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the tumor immune microenvironment (TME). While the response in molecular targeted therapies relies on the presence of specific genetic alterations in tumor cells, accurate ICI biomarkers of response are lacking, and clinical outcome likely depends on multiple factors that are both host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in vitro/ex vivo analysis (ranging from conventional pathology to molecular biology) and in vivo analysis, where molecular imaging is showing an exponential growth and use due to technological advancements and to the new bioinformatics approaches applied to image analyses that allow the recovery of specific features in specific tumor subclones.
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Affiliation(s)
- Virginia Liberini
- Department of Medical Science, Division of Nuclear Medicine, University of Turin, 10126 Turin, Italy;
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy
| | - Annapaola Mariniello
- Thoracic Oncology Unit, Department of Oncology, S. Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy; (A.M.); (M.D.D.); (S.N.)
| | - Luisella Righi
- Pathology Unit, Department of Oncology, S. Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy; (L.R.); (M.V.)
| | - Martina Capozza
- Molecular & Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126 Torino, Italy; (M.C.); (E.T.)
| | - Marco Donatello Delcuratolo
- Thoracic Oncology Unit, Department of Oncology, S. Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy; (A.M.); (M.D.D.); (S.N.)
| | - Enzo Terreno
- Molecular & Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126 Torino, Italy; (M.C.); (E.T.)
| | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100 Bolzano, Italy;
| | - Marco Volante
- Pathology Unit, Department of Oncology, S. Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy; (L.R.); (M.V.)
| | - Silvia Novello
- Thoracic Oncology Unit, Department of Oncology, S. Luigi Gonzaga Hospital, University of Turin, 10043 Orbassano, Italy; (A.M.); (M.D.D.); (S.N.)
| | - Désirée Deandreis
- Department of Medical Science, Division of Nuclear Medicine, University of Turin, 10126 Turin, Italy;
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Response Prediction and Evaluation Using PET in Patients with Solid Tumors Treated with Immunotherapy. Cancers (Basel) 2021; 13:cancers13123083. [PMID: 34205572 PMCID: PMC8234914 DOI: 10.3390/cancers13123083] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary In cancer treatment, immunotherapy is increasingly becoming important as a component of first-line treatment and has improved the prognosis of patients since its introduction. A large group of patients, however, do not respond to immunotherapy, and predicting a treatment response remains challenging. Furthermore, evaluating a response using conventional computed tomography (CT) scans is not straightforward due to the different mechanism of action of immunotherapy compared to chemotherapy. This review provides an overview of positron emission tomography (PET) in predicting and evaluating treatment response to immunotherapy. Abstract In multiple malignancies, checkpoint inhibitor therapy has an established role in the first-line treatment setting. However, only a subset of patients benefit from checkpoint inhibition, and as a result, the field of biomarker research is active. Molecular imaging with the use of positron emission tomography (PET) is one of the biomarkers that is being studied. PET tracers such as conventional 18F-FDG but also PD-(L)1 directed tracers are being evaluated for their predictive power. Furthermore, the use of artificial intelligence is under evaluation for the purpose of response prediction. Response evaluation during checkpoint inhibitor therapy can be challenging due to the different response patterns that can be observed compared to traditional chemotherapy. The additional information provided by PET can potentially be of value to evaluate a response early after the start of treatment and provide the clinician with important information about the efficacy of immunotherapy. Furthermore, the use of PET to stratify between patients with a complete response and those with a residual disease can potentially guide clinicians to identify patients for which immunotherapy can be discontinued and patients for whom the treatment needs to be escalated. This review provides an overview of the use of positron emission tomography (PET) to predict and evaluate treatment response to immunotherapy.
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A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study. Diagnostics (Basel) 2021; 11:diagnostics11060979. [PMID: 34071518 PMCID: PMC8229740 DOI: 10.3390/diagnostics11060979] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 01/06/2023] Open
Abstract
Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice.
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Wang X, Wu K, Li X, Jin J, Yu Y, Sun H. Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma. Front Oncol 2021; 11:595430. [PMID: 34055595 PMCID: PMC8152935 DOI: 10.3389/fonc.2021.595430] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 04/12/2021] [Indexed: 01/13/2023] Open
Abstract
Purpose We aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features. Materials and Methods We conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data. Results There was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001). Conclusions In patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Ke Wu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Xiaoran Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Junjie Jin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Yang Yu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Dall'Olio FG, Calabrò D, Conci N, Argalia G, Marchese PV, Fabbri F, Fragomeno B, Ricci D, Fanti S, Ambrosini V, Ardizzoni A. Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography as a promising biomarker in patients with advanced non-small cell lung cancer treated with first-line pembrolizumab. Eur J Cancer 2021; 150:99-107. [PMID: 33892411 DOI: 10.1016/j.ejca.2021.03.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 03/01/2021] [Accepted: 03/08/2021] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Immune checkpoint inhibitors (ICIs) have become the standard of care in the management of advanced non-small cell lung cancer (NSCLC). Nevertheless, only a small proportion of patients benefit from ICIs. The aim of the present study is to assess whether 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography ([18F]FDG-PET/CT)-derived parameters may be used as biomarkers in patients with advanced NSCLC receiving first-line pembrolizumab. MATERIALS AND METHODS This is a monocentric retrospective cohort study including patients with advanced NSCLC (stage IV) and Programmed death-ligand 1 (PD-L1) expression ≥50% treated with pembrolizumab. A control group of patients treated with epidermal growth factor receptor (EGFR) inhibitors for EGFR-mutated NSCLC was also enrolled. Only patients with a positive [18F]18F-FDG PET/CT result within 60 days from treatment initiation were included.Total metabolic tumour volume (tMTV) was calculated for each lesion using a dedicated software (PET VCAR; GE Healthcare), which semiautomatically delineates the tumour's contours with a maximum standardised uptake value (SUVmax) threshold of 42% within the lesion. tMTV was obtained summing each lesion's MTV. Potential prognostic parameters for overall survival (OS) were analysed (tMTV, SUVmax, bone/liver metastasis, neutrophil:lymphocyte ratio ≥4, Eastern Cooperative Oncology Group performance status ≥2, lactate dehydrogenase above the upper limit of normal). RESULTS Overall, 34 patients treated with first line-pembrolizumab and 40 patients treated with EGFR tyrosine kinase inhibitors were included. In the pembrolizumab group, the median follow-up was 20.3, while the median OS was 4.7 months (95% confidence interval [CI] = 0.3-9.1) for patients with tMTV ≥75 cm3 vs not reached (NR) for patients with tMTV <75 cm3 (95% CI = NR-NR; hazard ratio [HR] = 5.37; 95% CI = 1.72-16.77; p = 0.004). No difference was found in the control group (HR = 1.43; 95% CI = 0.61-3.34; p = 0.411). CONCLUSION Our data suggest that tMTV ≥75cm3 can be used as a prognostic biomarker of poor outcomes in patients with PD-L1-high advanced NSCLC treated with first-line pembrolizumab. This information could be useful for the selection of patients who may require the addition of chemotherapy to pembrolizumab.
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Affiliation(s)
- Filippo G Dall'Olio
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy.
| | - Diletta Calabrò
- IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy; Nuclear Medicine, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
| | - Nicole Conci
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy
| | - Giulia Argalia
- IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy; Nuclear Medicine, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
| | | | - Francesca Fabbri
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy
| | - Benedetta Fragomeno
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy
| | - Dalia Ricci
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy
| | - Stefano Fanti
- IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy; Nuclear Medicine, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
| | - Valentina Ambrosini
- IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy; Nuclear Medicine, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
| | - Andrea Ardizzoni
- Medical Oncology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Italy
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