Survival of patients with head and neck cancer. Impact of physical status and comorbidities.

BACKGROUND: Prognostic factors (e.g., gender, tumor stage, and hypoxia) have an impact on survival in patients with head and neck cancer. Thus, the impact of physical status and comorbidities on treatment decision and survival were evaluated. PATIENTS AND METHODS: A total of 169 primary, inoperable patients with squamous cell cancer of the head and neck were retrospectively investigated. Patients were treated with hyperfractionated accelerated radio(chemo)therapy (HARcT) or hypofractionated radio(chemo)therapy (HypoRcT). Depending on the individual patient's situation (Karnofsky Performance Index, KPI), treatment for patients with a KPI of 80-100% was generally radiochemotherapy and for patients with a KPI ≤ 70% treatment was radiotherapy alone. In addition, all comorbidities were evaluated. Uni- and multivariate proportional hazards model were used, and overall survival (OS) was estimated by the Kaplan-Meier method. RESULTS: Treatment consisted of HARcT for 76 patients (45%), HART for 28 patients (17%), HypoRcT for 14 patients(8%), and HypoRT for 51 patients (30%). Of the patients, 107 patients (63%) presented with a KPI of 80-100%. OS (20%) was significantly better for patients with a KPI of 80-100%, while the OS for patients with a KPI ≤ 70% was 8% (p < 0.001). Good KPI, total irradiation dose (> 70 Gy), and chemotherapy were significant prognostic factors for better OS. CONCLUSION: Our retrospective analysis shows that performance status with dependency on comorbidities was an independent risk factor for OS.

Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval.

OBJECTIVES: We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS: We propose firstly a method for enriching multilingual thesauri which extracts new terms from parallel corpora, and secondly, a new approach for bilingual lexicon extraction from comparable corpora, which uses a bilingual thesaurus as a pivot. We illustrate their use in multi-language information retrieval (English/German) in the medical domains. RESULTS: Our experiments show that these automatically extracted bilingual lexicons are accurate enough (85% precision for term extraction) for semi-automatically enriching mono- or bi-lingual thesauri such as the universal medical language system, and that their use in cross-language information retrieval significantly improves the retrieval performance (from 22 to 40% average precision) and clearly outperforms existing bilingual lexicon resources (both general lexicons and specialized ones). CONCLUSION: We show in this paper first that bilingual lexicon extraction from parallel corpora in the medical domain could lead to accurate, specialized lexicons, which can be used to help enrich existing thesauri and second that bilingual lexicons extracted from comparable corpora outperform general bilingual resources for cross-language information retrieval.