Literature DB >> 17889446

Tumor volume combined with number of positive lymph node stations is a more important prognostic factor than TNM stage for survival of non-small-cell lung cancer patients treated with (chemo)radiotherapy.

Cary Dehing-Oberije1, Dirk De Ruysscher, Hiska van der Weide, Monique Hochstenbag, Gerben Bootsma, Wiel Geraedts, Cordula Pitz, Jean Simons, Jaap Teule, Ali Rahmy, Paul Thimister, Harald Steck, Philippe Lambin.   

Abstract

PURPOSE: The current tumor, node, metastasis system needs refinement to improve its ability to predict survival of patients with non-small-cell lung cancer (NSCLC) treated with (chemo)radiation. In this study, we investigated the prognostic value of tumor volume and N status, assessed by using fluorodeoxyglucose-positron emission tomography (PET). PATIENTS AND METHODS: Clinical data from 270 consecutive patients with inoperable NSCLC Stages I-IIIB treated radically with (chemo)radiation were collected retrospectively. Diagnostic imaging was performed using either integrated PET-computed tomography or computed tomography and PET separately. The Kaplan-Meier method, as well as Cox regression, was used to analyze data.
RESULTS: Univariate survival analysis showed that number of positive lymph node stations (PLNSs), as well as N stage on PET, was associated significantly with survival. The final multivariate Cox model consisted of number of PLNSs, gross tumor volume (i.e., volume of the primary tumor plus lymph nodes), sex, World Health Organization performance status, and equivalent radiation dose corrected for time; N stage was no longer significant.
CONCLUSIONS: Number of PLNSs, assessed by means of fluorodeoxyglucose-PET, was a significant factor for survival of patients with inoperable NSCLC treated with (chemo)radiation. Risk stratification for this group of patients should be based on gross tumor volume, number of PLNSs, sex, World Health Organization performance status, and equivalent radiation dose corrected for time.

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Year:  2007        PMID: 17889446     DOI: 10.1016/j.ijrobp.2007.07.2323

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


  38 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.  Revisiting the relationship between tumour volume and diameter in advanced NSCLC patients: An exercise to maximize the utility of each measure to assess response to therapy.

Authors:  M Nishino; D M Jackman; P J DiPiro; H Hatabu; P A Jänne; B E Johnson
Journal:  Clin Radiol       Date:  2014-05-22       Impact factor: 2.350

Review 3.  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

4.  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 5.  Radiation oncology in the era of precision medicine.

Authors:  Michael Baumann; Mechthild Krause; Jens Overgaard; Jürgen Debus; Søren M Bentzen; Juliane Daartz; Christian Richter; Daniel Zips; Thomas Bortfeld
Journal:  Nat Rev Cancer       Date:  2016-03-18       Impact factor: 60.716

6.  Metabolic tumor volume is an independent prognostic factor in patients treated definitively for non-small-cell lung cancer.

Authors:  Percy Lee; Jose G Bazan; Philip W Lavori; Dilani K Weerasuriya; Andrew Quon; Quynh-Thu Le; Heather A Wakelee; Edward E Graves; Billy W Loo
Journal:  Clin Lung Cancer       Date:  2011-06-24       Impact factor: 4.785

Review 7.  Prognostic value of metabolic tumor burden in lung cancer.

Authors:  Piotr Obara; Yonglin Pu
Journal:  Chin J Cancer Res       Date:  2013-12       Impact factor: 5.087

Review 8.  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

9.  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

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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