Literature DB >> 19095367

Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy.

Cary Dehing-Oberije1, Shipeng Yu, Dirk De Ruysscher, Sabine Meersschout, Karen Van Beek, Yolande Lievens, Jan Van Meerbeeck, Wilfried De Neve, Bharat Rao, Hiska van der Weide, Philippe Lambin.   

Abstract

PURPOSE: Radiotherapy, combined with chemotherapy, is the treatment of choice for a large group of non-small-cell lung cancer (NSCLC) patients. Recent developments in the treatment of these patients have led to improved survival. However, the clinical TNM stage is highly inaccurate for the prediction of survival, and alternatives are lacking. The objective of this study was to develop and validate a prediction model for survival of NSCLC patients, treated with chemoradiotherapy. PATIENTS AND METHODS: The clinical data from 377 consecutive inoperable NSCLC patients, Stage I-IIIB, treated radically with chemoradiotherapy were collected. A prognostic model for 2-year survival was developed, using 2-norm support vector machines. The performance of the model was expressed as the area under the curve of the receiver operating characteristic and assessed using leave-one-out cross-validation, as well as two external data sets.
RESULTS: The final multivariate model consisted of gender, World Health Organization performance status, forced expiratory volume in 1 s, number of positive lymph node stations, and gross tumor volume. The area under the curve, assessed by leave-one-out cross-validation, was 0.74, and application of the model to the external data sets yielded an area under the curve of 0.75 and 0.76. A high- and low-risk group could be clearly identified using a risk score based on the model.
CONCLUSION: The multivariate model performed very well and was able to accurately predict the 2-year survival of NSCLC patients treated with chemoradiotherapy. The model could support clinicians in the treatment decision-making process.

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Year:  2008        PMID: 19095367     DOI: 10.1016/j.ijrobp.2008.08.052

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  28 in total

1.  A Prognostic Model to Predict Mortality among Non-Small-Cell Lung Cancer Patients in the U.S. Military Health System.

Authors:  Jie Lin; Corey A Carter; Katherine A McGlynn; Shelia H Zahm; Joel A Nations; William F Anderson; Craig D Shriver; Kangmin Zhu
Journal:  J Thorac Oncol       Date:  2015-12       Impact factor: 15.609

2.  Refining Prognosis in Lung Cancer: A Report on the Quality and Relevance of Clinical Prognostic Tools.

Authors:  Alyson L Mahar; Carolyn Compton; Lisa M McShane; Susan Halabi; Hisao Asamura; Ramon Rami-Porta; Patti A Groome
Journal:  J Thorac Oncol       Date:  2015-11       Impact factor: 15.609

3.  Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.

Authors:  David V Fried; Osama Mawlawi; Lifei Zhang; Xenia Fave; Shouhao Zhou; Geoffrey Ibbott; Zhongxing Liao; Laurence E Court
Journal:  Radiology       Date:  2015-07-15       Impact factor: 11.105

Review 4.  Predicting outcomes in radiation oncology--multifactorial decision support systems.

Authors:  Philippe Lambin; Ruud G P M van Stiphout; Maud H W Starmans; Emmanuel Rios-Velazquez; Georgi Nalbantov; Hugo J W L Aerts; Erik Roelofs; Wouter van Elmpt; Paul C Boutros; Pierluigi Granone; Vincenzo Valentini; Adrian C Begg; Dirk De Ruysscher; Andre Dekker
Journal:  Nat Rev Clin Oncol       Date:  2012-11-20       Impact factor: 66.675

5.  A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making.

Authors:  Cary Oberije; Georgi Nalbantov; Andre Dekker; Liesbeth Boersma; Jacques Borger; Bart Reymen; Angela van Baardwijk; Rinus Wanders; Dirk De Ruysscher; Ewout Steyerberg; Anne-Marie Dingemans; Philippe Lambin
Journal:  Radiother Oncol       Date:  2014-05-17       Impact factor: 6.280

Review 6.  Predictive and prognostic value of tumor volume and its changes during radical radiotherapy of stage III non-small cell lung cancer : A systematic review.

Authors:  Lukas Käsmann; Maximilian Niyazi; Oliver Blanck; Christian Baues; René Baumann; Sophie Dobiasch; Chukwuka Eze; Daniel Fleischmann; Tobias Gauer; Frank A Giordano; Yvonne Goy; Jan Hausmann; Christoph Henkenberens; David Kaul; Lisa Klook; David Krug; Matthias Mäurer; Cédric M Panje; Johannes Rosenbrock; Lisa Sautter; Daniela Schmitt; Christoph Süß; Alexander H Thieme; Maike Trommer-Nestler; Sonia Ziegler; Nadja Ebert; Daniel Medenwald; Christian Ostheimer
Journal:  Strahlenther Onkol       Date:  2017-10-13       Impact factor: 3.621

7.  Multistate Statistical Modeling: A Tool to Build a Lung Cancer Microsimulation Model That Includes Parameter Uncertainty and Patient Heterogeneity.

Authors:  Mathilda L Bongers; Dirk de Ruysscher; Cary Oberije; Philippe Lambin; Carin A Uyl-de Groot; V M H Coupé
Journal:  Med Decis Making       Date:  2015-03-02       Impact factor: 2.583

8.  Combining COPD with clinical, pathological and demographic information refines prognosis and treatment response prediction of non-small cell lung cancer.

Authors:  Joseph Putila; Nancy Lan Guo
Journal:  PLoS One       Date:  2014-06-26       Impact factor: 3.240

9.  Impact of early tumor reduction on outcome differs by histological subtype in stage III non-small-cell lung cancer treated with definitive radiotherapy.

Authors:  Hiromitsu Kanzaki; Masaaki Kataoka; Atsushi Nishikawa; Kotaro Uwatsu; Kei Nagasaki; Noriko Nishijima; Takashi Ochi; Teruhito Mochizuki
Journal:  Int J Clin Oncol       Date:  2016-04-28       Impact factor: 3.402

10.  Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial.

Authors:  Erik Roelofs; Lucas Persoon; Sebastiaan Nijsten; Wolfgang Wiessler; André Dekker; Philippe Lambin
Journal:  Radiother Oncol       Date:  2013-02-05       Impact factor: 6.280

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