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Midroni J, Salunkhe R, Liu Z, Chow R, Boldt G, Palma D, Hoover D, Vinogradskiy Y, Raman S. Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00481-4. [PMID: 38631538 DOI: 10.1016/j.ijrobp.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes.
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Affiliation(s)
- Julie Midroni
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Rohan Salunkhe
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zhihui Liu
- Biostatistics, Princess Margaret Cancer Center, Toronto, Canada
| | - Ronald Chow
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - David Palma
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada; Ontario Institute for Cancer Research, Toronto, Canada
| | - Douglas Hoover
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, United States of America; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, United States of America
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Brown KH, Ghita-Pettigrew M, Kerr BN, Mohamed-Smith L, Walls GM, McGarry CK, Butterworth KT. Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics. Radiother Oncol 2024; 192:110106. [PMID: 38253201 DOI: 10.1016/j.radonc.2024.110106] [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: 09/25/2023] [Revised: 01/10/2024] [Accepted: 01/17/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND PURPOSE Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation. MATERIALS AND METHODS Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model. RESULTS Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96). CONCLUSIONS This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.
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Affiliation(s)
- Kathryn H Brown
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK.
| | - Mihaela Ghita-Pettigrew
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Brianna N Kerr
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Letitia Mohamed-Smith
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK
| | - Gerard M Walls
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, UK; Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK
| | - Conor K McGarry
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Northern Ireland, UK
| | - Karl T Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland, 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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Akkad N, Thomas TS, Luo S, Knoche E, Sanfilippo KM, Keller JW. A real-world study of pneumonitis in non-small cell lung cancer patients receiving durvalumab following concurrent chemoradiation. J Thorac Dis 2023; 15:6427-6435. [PMID: 38249904 PMCID: PMC10797388 DOI: 10.21037/jtd-22-1604] [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: 01/31/2023] [Accepted: 10/11/2023] [Indexed: 01/23/2024]
Abstract
Background Locally advanced non-small cell lung cancer (LA-NSCLC) treated with the programmed death-ligand 1 inhibitor durvalumab has been associated with significant rates of pneumonitis, which has led to higher rates of discontinuation of therapy in real-world populations. Thus far there has been no consensus in the literature on the impact of pneumonitis on survival. Methods This is a retrospective cohort study of veterans receiving durvalumab between 12/5/2017 and 4/15/2020. Participants were identified using VINCI data services. Patients were followed through 9/14/2021. Development of clinical pneumonitis was assessed through review of documentation and graded using CTCAE 4.0 criteria. Univariate logistic regression analysis evaluated for associations between body mass index (BMI), age, race, co-morbidity index, chemotherapy regimen, chronic obstructive pulmonary disease (COPD) severity, and development of clinical pneumonitis. Progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan-Meier methods. Cox proportional hazards models were utilized to evaluate the association between risk of death at 1 and 2 years and candidate predictor variables. Results A total of 284 patients were included in this study. Sixty-one patients developed clinically significant pneumonitis, 7 patients developed grade 5 pneumonitis (death from pneumonitis). The median OS in patients that developed pneumonitis was 27.8 vs. 36.9 months in patients that did not develop pneumonitis (P=0.22). BMI was found to be a clinical predictor of pneumonitis (P=0.04). COPD severity, race, age at durvalumab start date, chemotherapy regimen, and Romano comorbidity index were not significant predictors of pneumonitis. Cox proportional hazards analysis failed to demonstrate an association between the development of pneumonitis and risk of death in this population. Conclusions The incidence of clinically significant pneumonitis is higher than noted in the PACIFIC trial in this cohort, however this high rate of pneumonitis does not have an impact on OS or PFS. Obesity was found to be a significant predictor of pneumonitis in this patient population.
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Affiliation(s)
- Neha Akkad
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
| | - Theodore S. Thomas
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
- St. Louis Veterans Health Administration Medical Center Research Service, St. Louis, MO, USA
| | - Suhong Luo
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
| | - Eric Knoche
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
- St. Louis Veterans Health Administration Medical Center Research Service, St. Louis, MO, USA
| | - Kristen M. Sanfilippo
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
- St. Louis Veterans Health Administration Medical Center Research Service, St. Louis, MO, USA
| | - Jesse W. Keller
- Washington University School of Medicine/Barnes Jewish Hospital, St. Louis, MO, USA
- St. Louis Veterans Health Administration Medical Center Research Service, St. Louis, MO, USA
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Gates EDH, Hippe DS, Vesselle HJ, Zeng J, Bowen SR. Independent association of metabolic tumor response on FDG-PET with pulmonary toxicity following risk-adaptive chemoradiation for unresectable non-small cell lung cancer: Inherent radiosensitivity or immune response? Radiother Oncol 2023; 185:109720. [PMID: 37244360 PMCID: PMC10525017 DOI: 10.1016/j.radonc.2023.109720] [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: 03/20/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND In the context of a phase II trial of risk-adaptive chemoradiation, we evaluated whether tumor metabolic response could serve as a correlate of treatment sensitivity and toxicity. METHODS Forty-five patients with AJCCv7 stage IIB-IIIB NSCLC enrolled on the FLARE-RT phase II trial (NCT02773238). [18F]fluorodeoxyglucose (FDG) PET-CT images were acquired prior to treatment and after 24 Gy during week 3. Patients with unfavorable on-treatment tumor response received concomitant boosts to 74 Gy total over 30 fractions rather than standard 60 Gy. Metabolic tumor volume and mean standardized uptake value (SUVmean) were calculated semi-automatically. Risk factors of pulmonary toxicity included concurrent chemotherapy regimen, adjuvant anti-PDL1 immunotherapy, and lung dosimetry. Incidence of CTCAE v4 grade 2+ pneumonitis was analyzed using the Fine-Gray method with competing risks of metastasis or death. Peripheral germline DNA microarray sequencing measured predefined candidate genes from distinct pathways: 96 DNA repair, 53 immunology, 38 oncology, 27 lung biology. RESULTS Twenty-four patients received proton therapy, 23 received ICI, 26 received carboplatin-paclitaxel, and 17 pneumonitis events were observed. Pneumonitis risk was significantly higher for patients with COPD (HR 3.78 [1.48, 9.60], p = 0.005), those treated with immunotherapy (HR 2.82 [1.03, 7.71], p = 0.043) but not with carboplatin-paclitaxel (HR 1.98 [0.71, 5.54], p = 0.19). Pneumonitis rates were similar among selected patients receiving 74 Gy radiation vs 60 Gy (p = 0.33), proton therapy vs photon (p = 0.60), or with higher lung dosimetric V20 (p = 0.30). Patients in the upper quartile decrease in SUVmean (>39.7%) were at greater risk for pneumonitis (HR 4.00 [1.54, 10.44], p = 0.005) and remained significant in multivariable analysis (HR 3.34 [1.23, 9.10], p = 0.018). Germline DNA gene alterations in immunology pathways were most frequently associated with pneumonitis. CONCLUSION Tumor metabolic response as measured by mean SUV is associated with increased pneumonitis risk in a clinical trial cohort of NSCLC patients independent of treatment factors. This may be partially attributed to patient-specific differences in immunogenicity.
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Affiliation(s)
- Evan D H Gates
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States; Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States.
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Hu Q, Wang S, Ma L, Sun Z, Liu Z, Deng S, Zhou J. Radiological assessment of immunotherapy effects and immune checkpoint-related pneumonitis for lung cancer. J Cell Mol Med 2023; 28:e17895. [PMID: 37525480 PMCID: PMC10902575 DOI: 10.1111/jcmm.17895] [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: 05/11/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/02/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.
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Affiliation(s)
- Qiongjie Hu
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Shaofang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Li Ma
- Department of Orthopedics, Songzi HospitalRenmin Hospital of Wuhan UniversityWuhanChina
| | - Ziyan Sun
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Zilin Liu
- Department of OrthopedicsRenmin Hospital of Wuhan UniversityWuhanChina
| | - Shuang Deng
- Department of OrthopedicsRenmin Hospital of Wuhan UniversityWuhanChina
| | - Jianlin Zhou
- Department of OrthopedicsRenmin Hospital of Wuhan UniversityWuhanChina
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Granata V, Fusco R, Setola SV, Simonetti I, Picone C, Simeone E, Festino L, Vanella V, Vitale MG, Montanino A, Morabito A, Izzo F, Ascierto PA, Petrillo A. Immunotherapy Assessment: A New Paradigm for Radiologists. Diagnostics (Basel) 2023; 13:diagnostics13020302. [PMID: 36673112 PMCID: PMC9857844 DOI: 10.3390/diagnostics13020302] [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: 11/17/2022] [Revised: 12/31/2022] [Accepted: 01/08/2023] [Indexed: 01/14/2023] Open
Abstract
Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Ester Simeone
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Lucia Festino
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Vito Vanella
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Maria Grazia Vitale
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Alessandro Morabito
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Paolo Antonio Ascierto
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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