Publish in OALib Journal
APC: Only $99
Background: Head and neck cancers (HNCs) constitute 5% of all
cancers globally and are the most common cancers in India. Chemotherapy and
radiotherapy have not been proved to be effective in advanced cases and the
prognosis remains dismal. This underscores the need for newer treatment options
in these cases. Nimotuzumab, an anti-epidermal growth factor receptor
(anti-EGFR) monoclonal antibody, was safer when combined with chemo- or
radio-therapy. Aim: To evaluate the safety and efficacy of concurrently administered
nimotuzumab with chemo-radiotherapy in patients with advanced inoperable
squamous cell carcinomas of head and neck (LASCCHN). Methods:？This was an open-label, single arm study evaluating 57 patients with
histologically confirmed inoperable LASCCHN (stages III and IV) and eastern
co-operative oncology group (ECOG) performance status < 2. Informed consent
was obtained from all patients. The patients were administered IV cisplatin 30
mg/m2？and IV nimotuzumab 200 mg weekly for 6 weeks, along with
radiotherapy of 6600 cGy over 33 fractions. Patients were evaluated over
response evaluation criteria in solid tumors (RECIST) criteria 24 weeks after
the last cycle of chemotherapy. Results: Mean age of patient was 50 years old
(29 - 79 years old). The most common site of cancer was oral cavity (56.1%).
Forty six patients (80.7%) completed 6 cycles of therapy. Objective response
rate (ORR) was 80.7%, with 34 patients (59.6%) achieving complete response
(CR), and 12 (21%) achieving partial response (PR). Stable disease (SD) was
noted in 8 (14%) patients and progressive disease in 3 (5.2%) patients.
Conclusion: Addition of nimotuzumab is a safe and efficacious option in
patients with inoperable LASCCHN. Our observations confirm the available Phase
II data. The long term survival benefits based on this encouraging response
rate need to be further evaluated in this subset of cancer patients.
In this paper, a multi label variant of CLUBAS  algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.