Literature DB >> 33937811

Radiomics of 18F Fluorodeoxyglucose PET/CT Images Predicts Severe Immune-related Adverse Events in Patients with NSCLC.

Wei Mu1, Ilke Tunali1, Jin Qi1, Matthew B Schabath1, Robert James Gillies1.   

Abstract

PURPOSE: To investigate the performance of pretreatment fluorine 18 (18F) fluorodeoxyglucose PET/CT radiomics in predicting severe immune-related adverse events (irSAEs) among patients with advanced non-small cell lung cancer (NSCLC) treated with immunotherapy, which is important in optimizing treatment plans and alleviating future complications with early interventions.
MATERIALS AND METHODS: The retrospective arm of this study included 146 patients with histologically confirmed stage IIIB-IV NSCLC who were treated with immune checkpoint blockade between June 2011 and December 2017 and who were split into training (n = 97) and test (n = 49) cohorts. A prospective validation arm enrolled 48 patients before initiation of immunotherapy between January 2018 and June 2019 as an independent test cohort. Radiomics features extracted from baseline (preimmunotherapy treatment) PET, CT, and PET/CT fusion images were used to generate a radiomics score (RS) to quantify patient risk for developing irSAEs by an improved least absolute shrinkage and selection operator method. Weighted multivariable logistic regression analysis was then used to develop a nomogram model to predict irSAEs, which was assessed by its calibration, discrimination, and clinical usefulness.
RESULTS: The radiomics nomogram, incorporating the RS, type of immune checkpoint blockade, and dosing schedule, was able to predict patients with and without irSAEs with area under the receiver operating characteristic curve of 0.92 (95% confidence interval [CI]: 0.86, 0.98), 0.92 (95% CI: 0.86, 0.99), and 0.88 (95% CI: 0.78, 0.97) in the training, test, and prospective validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram model had the highest overall net benefit.
CONCLUSION: A high RS is a significant risk factor for development of irSAEs, demonstrating the value of PET/CT images in predicting irSAEs. By the identification, at baseline, of patients with NSCLC most likely to have irSAEs, treatment plans can be optimized before initiation of immunotherapy.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Yousefi. 2020 by the Radiological Society of North America, Inc.

Entities:  

Year:  2020        PMID: 33937811      PMCID: PMC8074998          DOI: 10.1148/ryai.2019190063

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN: 2638-6100


  30 in total

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2.  Cancer statistics, 2018.

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Journal:  N Engl J Med       Date:  2018-04-16       Impact factor: 91.245

4.  Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images.

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5.  Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer.

Authors:  Martin Reck; Delvys Rodríguez-Abreu; Andrew G Robinson; Rina Hui; Tibor Csőszi; Andrea Fülöp; Maya Gottfried; Nir Peled; Ali Tafreshi; Sinead Cuffe; Mary O'Brien; Suman Rao; Katsuyuki Hotta; Melanie A Leiby; Gregory M Lubiniecki; Yue Shentu; Reshma Rangwala; Julie R Brahmer
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Authors:  Alan E Renton; Hannah A Pliner; Carlo Provenzano; Amelia Evoli; Roberta Ricciardi; Michael A Nalls; Giuseppe Marangi; Yevgeniya Abramzon; Sampath Arepalli; Sean Chong; Dena G Hernandez; Janel O Johnson; Emanuela Bartoccioni; Flavia Scuderi; Michelangelo Maestri; J Raphael Gibbs; Edoardo Errichiello; Adriano Chiò; Gabriella Restagno; Mario Sabatelli; Mark Macek; Sonja W Scholz; Andrea Corse; Vinay Chaudhry; Michael Benatar; Richard J Barohn; April McVey; Mamatha Pasnoor; Mazen M Dimachkie; Julie Rowin; John Kissel; Miriam Freimer; Henry J Kaminski; Donald B Sanders; Bernadette Lipscomb; Janice M Massey; Manisha Chopra; James F Howard; Wilma J Koopman; Michael W Nicolle; Robert M Pascuzzi; Alan Pestronk; Charlie Wulf; Julaine Florence; Derrick Blackmore; Aimee Soloway; Zaeem Siddiqi; Srikanth Muppidi; Gil Wolfe; David Richman; Michelle M Mezei; Theresa Jiwa; Joel Oger; Daniel B Drachman; Bryan J Traynor
Journal:  JAMA Neurol       Date:  2015-04       Impact factor: 18.302

Review 7.  Immune-related adverse events with immune checkpoint blockade: a comprehensive review.

Authors:  J M Michot; C Bigenwald; S Champiat; M Collins; F Carbonnel; S Postel-Vinay; A Berdelou; A Varga; R Bahleda; A Hollebecque; C Massard; A Fuerea; V Ribrag; A Gazzah; J P Armand; N Amellal; E Angevin; N Noel; C Boutros; C Mateus; C Robert; J C Soria; A Marabelle; O Lambotte
Journal:  Eur J Cancer       Date:  2016-01-05       Impact factor: 9.162

8.  Acquired resistance to fractionated radiotherapy can be overcome by concurrent PD-L1 blockade.

Authors:  Simon J Dovedi; Amy L Adlard; Grazyna Lipowska-Bhalla; Conor McKenna; Sherrie Jones; Eleanor J Cheadle; Ian J Stratford; Edmund Poon; Michelle Morrow; Ross Stewart; Hazel Jones; Robert W Wilkinson; Jamie Honeychurch; Tim M Illidge
Journal:  Cancer Res       Date:  2014-10-01       Impact factor: 12.701

Review 9.  Current Diagnosis and Management of Immune Related Adverse Events (irAEs) Induced by Immune Checkpoint Inhibitor Therapy.

Authors:  Vivek Kumar; Neha Chaudhary; Mohit Garg; Charalampos S Floudas; Parita Soni; Abhinav B Chandra
Journal:  Front Pharmacol       Date:  2017-02-08       Impact factor: 5.810

10.  Serum levels of soluble CD163 and CXCL5 may be predictive markers for immune-related adverse events in patients with advanced melanoma treated with nivolumab: a pilot study.

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Journal:  Oncotarget       Date:  2018-02-15
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  10 in total

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Review 2.  Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine.

Authors:  Haoyue Guo; Kandi Xu; Guangxin Duan; Ling Wen; Yayi He
Journal:  Ann Nucl Med       Date:  2021-11-02       Impact factor: 2.668

Review 3.  Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

Authors:  Cyra Y Kang; Samantha E Duarte; Hye Sung Kim; Eugene Kim; Jonghanne Park; Alice Daeun Lee; Yeseul Kim; Leeseul Kim; Sukjoo Cho; Yoojin Oh; Gahyun Gim; Inae Park; Dongyup Lee; Mohamed Abazeed; Yury S Velichko; Young Kwang Chae
Journal:  Oncologist       Date:  2022-06-08       Impact factor: 5.837

4.  A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer.

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Journal:  Oncoimmunology       Date:  2022-01-25       Impact factor: 8.110

5.  Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images.

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Journal:  Front Physiol       Date:  2022-07-25       Impact factor: 4.755

Review 6.  Role of radiomics in predicting immunotherapy response.

Authors:  Gargi Kothari
Journal:  J Med Imaging Radiat Oncol       Date:  2022-05-17       Impact factor: 1.667

7.  Predictive value of baseline metabolic tumor volume for non-small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis.

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Journal:  Front Oncol       Date:  2022-09-06       Impact factor: 5.738

Review 8.  Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

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10.  New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes.

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Journal:  Thorac Cancer       Date:  2022-01-20       Impact factor: 3.500

  10 in total

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