Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

Journal Article · Sat Oct 01 00:00:00 EDT 2011 · International Journal of Radiation Oncology, Biology and Physics

DOI:https://doi.org/10.1016/j.ijrobp.2011.03.012· OSTI ID:21587719

Sabbe, Nick ^[1]; Oberije, Cary ^[2]; Vandecasteele, Katrien ^[3]; Thas, Olivier ^[1]; De Ruysscher, Dirk; Lambin, Phillipe ^[2]; Van Meerbeeck, Jan ^[4]; De Neve, Wilfried ^[3]; Thierens, Hubert ^[5]

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1. Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Ghent (Belgium)
2. Department of Radiation Oncology (MAASTRO Clinic), Research Institute of Growth and Development, Maastricht University Medical Center, Maastricht (Netherlands)
3. Department of Radiation Oncology, Ghent University Hospital, Ghent (Belgium)
4. Department of Respiratory Medicine, Ghent University Hospital, Ghent (Belgium)
5. Department of Basic Medical Sciences, Ghent University, Ghent (Belgium)

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

• OSTI ID: 21587719
• Journal Information: International Journal of Radiation Oncology, Biology and Physics, Journal Name: International Journal of Radiation Oncology, Biology and Physics Journal Issue: 2 Vol. 81; ISSN IOBPD3; ISSN 0360-3016
• Country of Publication: United States
• Language: English

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62 RADIOLOGY AND NUCLEAR MEDICINE
ALKALOIDS
AMINES
AUTONOMIC NERVOUS SYSTEM AGENTS
AZINES
AZOLES
BODY
CHEMOTHERAPY
COMBINED THERAPY
DIGESTIVE SYSTEM
DISEASES
DOSES
DRUGS
ESOPHAGUS
GENES
HETEROCYCLIC COMPOUNDS
LUNGS
LYMPH NODES
LYMPHATIC SYSTEM
MEDICINE
NEOPLASMS
NICOTINE
NUCLEAR MEDICINE
ORGANIC COMPOUNDS
ORGANIC NITROGEN COMPOUNDS
ORGANS
PARASYMPATHOLYTICS
PARASYMPATHOMIMETICS
PYRIDINES
PYRROLES
PYRROLIDINES
RADIATION DOSES
RADIOLOGY
RADIOTHERAPY
RESPIRATORY SYSTEM
SURGERY
THERAPY