title: Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment18F-FDG PET/CT Imaging.
abstract: Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUVmaxin18F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and18F-FDG PET/CT-derived textural features.Methods:From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment18F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both18F-FDG PET and CT. The current most accurate prediction model with SUVmaxas a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5).Results:Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the18F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUVmaxmodel. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively.Conclusion:The predictive values of the constructed models were superior to the standard method (SUVmax). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery.
authors.affiliation: Department of Surgery (Division of Surgical Oncology), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands.
authors.forename: Roelof J
authors.initials: RJ
authors.lastname: Beukinga
authors.affiliation: Department of Surgery (Division of Surgical Oncology), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Jan B
authors.initials: JB
authors.lastname: Hulshoff
authors.affiliation: Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Lisanne V
authors.initials: LV
authors.lastname: van Dijk
authors.affiliation: Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Christina T
authors.initials: CT
authors.lastname: Muijs
authors.affiliation: Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Johannes G M
authors.initials: JGM
authors.lastname: Burgerhof
authors.affiliation: Department of Pathology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and.
authors.forename: Gursah
authors.initials: G
authors.lastname: Kats-Ugurlu
authors.affiliation: Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands.
authors.forename: Riemer H J A
authors.initials: RHJA
authors.lastname: Slart
authors.affiliation: MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
authors.forename: Cornelis H
authors.initials: CH
authors.lastname: Slump
authors.affiliation: Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
authors.forename: Véronique E M
authors.initials: VEM
authors.lastname: Mul
authors.affiliation: Department of Surgery (Division of Surgical Oncology), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands j.t.m.plukker@umcg.nl.
authors.forename: John Th M
authors.initials: JTM
authors.lastname: Plukker
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D059186:Chemoradiotherapy, Adjuvant / Q000379:methods*; D003198:Computer Simulation; D004938:Esophageal Neoplasms / Q000000981:diagnostic imaging* / Q000628:therapy*; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D020360:Neoadjuvant Therapy / Q000379:methods; D010043:Outcome and Process Assessment, Health Care / Q000379:methods; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods*; D011379:Prognosis; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: 18F-FDG PET/CT; esophageal cancer; response prediction; textural analysis