Evelyn E C de Jong1, Wouter van Elmpt2, Stefania Rizzo3, Anna Colarieti4, Gianluca Spitaleri5, Ralph T H Leijenaar6, Arthur Jochems7, Lizza E L Hendriks8, Esther G C Troost9, Bart Reymen10, Anne-Marie C Dingemans11, Philippe Lambin12. 1. The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands. Electronic address: e.dejong@maastrichtuniversity.nl. 2. Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl. 3. Department of Radiology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. Electronic address: stefania.rizzo@ieo.it. 4. Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy. Electronic address: anna.colarieti@gmail.com. 5. Department of Thoracic Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. Electronic address: gianluca.spitaleri@ieo.it. 6. The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands. Electronic address: ralph.leijenaar@maastrichtuniversity.nl. 7. The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands. Electronic address: a.jochems@maastrichtuniversity.nl. 8. Department of Pulmonary Diseases, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands. Electronic address: lizza.hendriks@mumc.nl. 9. Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Händelallee 26/Bldg. 130, 01309 Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. Electronic address: esther.troost@uniklinikum-dresden.de. 10. Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands. Electronic address: bart.reymen@maastro.nl. 11. Department of Pulmonary Diseases, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands. Electronic address: a.dingemans@mumc.nl. 12. The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands. Electronic address: philippe.lambin@maastrichtuniversity.nl.
Abstract
OBJECTIVES: Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy. MATERIALS AND METHODS: Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS). RESULTS: In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624). CONCLUSION: The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.
RCT Entities:
OBJECTIVES: Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinomapatients undergoing chemotherapy. MATERIALS AND METHODS: Two datasets of chemo-naive stage IV adenocarcinomapatients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS). RESULTS: In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624). CONCLUSION: The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.
Authors: Janna E van Timmeren; Wouter van Elmpt; Ralph T H Leijenaar; Bart Reymen; René Monshouwer; Johan Bussink; Leen Paelinck; Evelien Bogaert; Carlos De Wagter; Elamin Elhaseen; Yolande Lievens; Olfred Hansen; Carsten Brink; Philippe Lambin Journal: Radiother Oncol Date: 2019-04-11 Impact factor: 6.280
Authors: Beatriz Pontes; Francisco Núñez; Cristina Rubio; Alberto Moreno; Isabel Nepomuceno; Jesús Moreno; Jon Cacicedo; Juan Manuel Praena-Fernandez; German Antonio Escobar Rodriguez; Carlos Parra; Blas David Delgado León; Eleonor Rivin Del Campo; Felipe Couñago; Jose Riquelme; Jose Luis Lopez Guerra Journal: Rep Pract Oncol Radiother Date: 2021-12-30
Authors: Athanasios K Anagnostopoulos; Anastasios Gaitanis; Ioannis Gkiozos; Emmanouil I Athanasiadis; Sofia N Chatziioannou; Konstantinos N Syrigos; Dimitris Thanos; Achilles N Chatziioannou; Nikolaos Papanikolaou Journal: Cancers (Basel) Date: 2022-03-25 Impact factor: 6.639