Literature DB >> 29533721

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

Roelof J Beukinga1, Jan Binne Hulshoff1, Véronique E M Mul1, Walter Noordzij1, Gursah Kats-Ugurlu1, Riemer H J A Slart1, John T M Plukker1.   

Abstract

Purpose To assess the value of baseline and restaging fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29533721     DOI: 10.1148/radiol.2018172229

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  36 in total

1.  AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

Authors:  Isabella Castiglioni; Francesca Gallivanone; Paolo Soda; Michele Avanzo; Joseph Stancanello; Marco Aiello; Matteo Interlenghi; Marco Salvatore
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-11       Impact factor: 9.236

2.  Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement.

Authors:  Ji Eun Park; Donghyun Kim; Ho Sung Kim; Seo Young Park; Jung Youn Kim; Se Jin Cho; Jae Ho Shin; Jeong Hoon Kim
Journal:  Eur Radiol       Date:  2019-07-26       Impact factor: 5.315

3.  PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy.

Authors:  Lidija Antunovic; Rita De Sanctis; Luca Cozzi; Margarita Kirienko; Andrea Sagona; Rosalba Torrisi; Corrado Tinterri; Armando Santoro; Arturo Chiti; Renata Zelic; Martina Sollini
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-03-26       Impact factor: 9.236

4.  Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy.

Authors:  Wei Mu; Ilke Tunali; Jhanelle E Gray; Jin Qi; Matthew B Schabath; Robert J Gillies
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-05       Impact factor: 9.236

5.  Preoperative Prediction of Pathologic Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Cancer Using 18F-FDG PET/CT and DW-MRI: A Prospective Multicenter Study.

Authors:  Alicia S Borggreve; Lucas Goense; Peter S N van Rossum; Sophie E Heethuis; Richard van Hillegersberg; Jan J W Lagendijk; Marnix G E H Lam; Astrid L H M W van Lier; Stella Mook; Jelle P Ruurda; Marco van Vulpen; Francine E M Voncken; Berthe M P Aleman; Annemarieke Bartels-Rutten; Jingfei Ma; Penny Fang; Benjamin C Musall; Steven H Lin; Gert J Meijer
Journal:  Int J Radiat Oncol Biol Phys       Date:  2020-01-25       Impact factor: 7.038

6.  Impact of pathological complete response following neoadjuvant chemoradiotherapy in esophageal cancer.

Authors:  Tamer Soror; Gerlinda Kho; Kuai-Le Zhao; Mahmoud Ismail; Harun Badakhshi
Journal:  J Thorac Dis       Date:  2018-07       Impact factor: 2.895

7.  Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

Authors:  Chen-Yi Xie; Yi-Huai Hu; Joshua Wing-Kei Ho; Lu-Jun Han; Hong Yang; Jing Wen; Ka-On Lam; Ian Yu-Hong Wong; Simon Ying-Kit Law; Keith Wan-Hang Chiu; Jian-Hua Fu; Varut Vardhanabhuti
Journal:  Cancers (Basel)       Date:  2021-04-29       Impact factor: 6.639

Review 8.  Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

Authors:  Nina J Wesdorp; Tessa Hellingman; Elise P Jansma; Jan-Hein T M van Waesberghe; Ronald Boellaard; Cornelis J A Punt; Joost Huiskens; Geert Kazemier
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-12-16       Impact factor: 9.236

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

Authors:  Wei Mu; Ilke Tunali; Jin Qi; Matthew B Schabath; Robert James Gillies
Journal:  Radiol Artif Intell       Date:  2020-01-29

Review 10.  Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature.

Authors:  Chen-Yi Xie; Chun-Lap Pang; Benjamin Chan; Emily Yuen-Yuen Wong; Qi Dou; Varut Vardhanabhuti
Journal:  Cancers (Basel)       Date:  2021-05-19       Impact factor: 6.639

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