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Lung Cancer

  • Hale Basak Caglar
  • Francesc Casas
  • Luhua Wang
  • Nenad Filipovic
  • Branislav Jeremic
Chapter
Part of the Medical Radiology book series (MEDRAD)

Abstract

In addition to investigation of various treatment approaches to optimize outcome, identification of both prognostic and predictive factors should be attempted due to its key role in the treatment decision-making process, as the current therapeutic approach utilizes a risk stratification paradigm. Of a series of patient-, tumour- and treatment-related factors, the most consistent ones independently influencing outcomes in both non-small cell (NSCLC) and small cell lung cancer (SCLC) were performance status, weight loss, stage and concurrent radiotherapy and chemotherapy as well as prophylactic cranial irradiation in small cell lung cancer. While age and gender are probably not important prognosticators, further investigations are needed to clarify the role of tumour location and histology. Due to widespread practice of using induction (neoadjuvant) therapy before surgery in locally advanced NSCLC, predictors of survival were investigated. Mediastinal nodal clearance seems to be the strongest predictor of improved survival, while right pneumonectomy seems to be the major predictor of postoperative morbidity and mortality. Finally, a number of patient-, comorbidity- and dosimetry-related factors or biomarkers as predictors of treatment-related toxicity were investigated. Although no firm evidence exists that any of these clearly predicts toxicity in NSCLC, evidence exists in SCLC. Two commonly used dosimetric factors are mean lung dose and V20. More studies investigating the important aspects of optimization of the use of radiotherapy or radiochemotherapy in lung cancer patients are urgently needed.

Keywords

Overall Survival Small Cell Lung Cancer Advanced NSCLC Neuron Specific Enolase Radiation Therapy Oncology Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hale Basak Caglar
    • 1
  • Francesc Casas
    • 2
  • Luhua Wang
    • 3
  • Nenad Filipovic
    • 4
  • Branislav Jeremic
    • 5
  1. 1.Department of Radiation OncologyAcibadem UniversityIstanbulTurkey
  2. 2.Department of Radiation OncologyUniversity ClinicBarcelonaSpain
  3. 3.Department of Radiation OncologyChinese Medical Academy of SciencesBeijingChina
  4. 4.BioIRC Centre for BioengineeringKragujevacSerbia
  5. 5.Division of Radiation and Clinical OncologyStellenbosch University and Tygerberg HospitalCape TownSouth Africa

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