Literature DB >> 31218571

Combining the radiomic features and traditional parameters of 18F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery.

Yu-Hung Chen1, Kun-Han Lue1, Sung-Chao Chu2, Bee-Song Chang3, Ling-Yi Wang4, Dai-Wei Liu5, Shu-Hsin Liu1,6, Yin-Kai Chao7, Sheng-Chieh Chan8.   

Abstract

OBJECTIVES: To investigate the role of the traditional and radiomic parameters of 18F-FDG PET for predicting the outcomes of patients with esophageal squamous cell carcinoma (SqCC).
METHODS: Forty-four patients with primary esophageal SqCC who underwent neoadjuvant chemoradiotherapy (CCRT) followed by esophagectomy (tri-modality treatment) were retrospectively analyzed. All patients underwent 18F-FDG PET/CT before and after neoadjuvant CCRT. The radiomic features were calculated using the pre-treatment PET scan. Pre-treatment radiomic features and changes in the PET-derived traditional parameters after neoadjuvant CCRT were analyzed according to the pathological response to esophagectomy, disease-free survival (DFS), and overall survival (OS). We further developed a scoring system based on the independent survival prognosticators and compared our model to the traditional TNM staging system and surgical pathology.
RESULTS: A pre-treatment primary tumor histogram entropy ≥ 3.69 predicts an unfavorable response to neoadjuvant CCRT (OR = 19.25, p = 0.009). An SUVmax reduction ratio ≤ 0.76, a pre-treatment primary tumor code similarity ≤ 0.0235, and incomplete pathological remission were independently associated with poor OS (p = 0.019, 0.033, and 0.038, respectively) and DFS (p = 0.049, 0.021, and 0.009, respectively). The three survival prognosticators were used to construct a scoring system (score 0-1, 2, and 3). Patients with a score of 2 or 3 had a significantly worse survival outcome than those with a score of 0-1 (HRs for OS: 3.58 for score 2, and 15.19 for score 3, p < 0.001; HRs for DFS: 1.39 for score 2 and 6.04 for score 3, p = 0.001).This survival prediction model was superior to the traditional TNM staging system (p < 0.001 versus p = 0.061 for OS, and p = 0.001 versus p = 0.027 for DFS) and the model based on surgical pathology (p < 0.001 versus p = 0.049 for OS, and p = 0.001 versus p = 0.022 for DFS).
CONCLUSIONS: The 18F-FDG PET-derived radiomic parameter is useful for predicting the surgical pathological response in patients with esophageal SqCC treated with the tri-modality method. Using a combination of traditional and radiomic PET parameters with clinical profiles enables better stratification of patients into subgroups with various survival rates.

Entities:  

Keywords:  18F-FDG PET; Esophageal cancer; Prognosis; Radiomics; Squamous cell carcinoma; Treatment response

Year:  2019        PMID: 31218571     DOI: 10.1007/s12149-019-01380-7

Source DB:  PubMed          Journal:  Ann Nucl Med        ISSN: 0914-7187            Impact factor:   2.668


  11 in total

Review 1.  (Neo)adjuvant Chemoradiotherapy is Beneficial to the Long-term Survival of Locally Advanced Esophageal Squamous Cell Carcinoma: A Network Meta-analysis.

Authors:  Zixian Jin; Dong Chen; Meng Chen; Chunguo Wang; Bo Zhang; Jian Zhang; Chengchu Zhu; Jianfei Shen
Journal:  World J Surg       Date:  2021-09-05       Impact factor: 3.352

Review 2.  Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.

Authors:  David Morland; Elizabeth Katherine Anna Triumbari; Luca Boldrini; Roberto Gatta; Daniele Pizzuto; Salvatore Annunziata
Journal:  Diagnostics (Basel)       Date:  2022-05-27

3.  Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.

Authors:  Sara Dastmalchian; Ozden Kilinc; Louisa Onyewadume; Charit Tippareddy; Debra McGivney; Dan Ma; Mark Griswold; Jeffrey Sunshine; Vikas Gulani; Jill S Barnholtz-Sloan; Andrew E Sloan; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-09-26       Impact factor: 9.236

4.  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 5.  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

6.  18F-FDG PET/CT Metrics Are Correlated to the Pathological Response in Esophageal Cancer Patients Treated With Induction Chemotherapy Followed by Neoadjuvant Chemo-Radiotherapy.

Authors:  Nicola Simoni; Gabriella Rossi; Giulio Benetti; Michele Zuffante; Renato Micera; Michele Pavarana; Stefania Guariglia; Emanuele Zivelonghi; Valentina Mengardo; Jacopo Weindelmayer; Simone Giacopuzzi; Giovanni de Manzoni; Carlo Cavedon; Renzo Mazzarotto
Journal:  Front Oncol       Date:  2020-11-27       Impact factor: 6.244

Review 7.  Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.

Authors:  Zhenwei Shi; Zhen Zhang; Zaiyi Liu; Lujun Zhao; Zhaoxiang Ye; Andre Dekker; Leonard Wee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-12-23       Impact factor: 10.057

8.  Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

Authors:  Berardino De Bari; Loriane Lefevre; Julie Henriques; Roberto Gatta; Antoine Falcoz; Pierre Mathieu; Christophe Borg; Nicola Dinapoli; Hatem Boulahdour; Luca Boldrini; Vincenzo Valentini; Dewi Vernerey
Journal:  Cancers (Basel)       Date:  2022-08-22       Impact factor: 6.575

Review 9.  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

Review 10.  Application of radiomics and machine learning in head and neck cancers.

Authors:  Zhouying Peng; Yumin Wang; Yaxuan Wang; Sijie Jiang; Ruohao Fan; Hua Zhang; Weihong Jiang
Journal:  Int J Biol Sci       Date:  2021-01-01       Impact factor: 6.580

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