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Treatment of head and neck cancer has evolved with improved understanding of the biology of the disease. Although surgery remains the primary modality for the management of these cancers, the introduction of radiation therapy and most recently, of chemotherapy has prolonged survival. We report our experience with combination photodynamic therapy (PDT) and chemotherapy in the treatment of head and neck squamous cell carcinoma (HNSCC). One hundred and two patients with stage III and IV HNSCC were treated with 1) PDT with Photofrin and argon laser radiation (ALR); 2) ALR alone; 3) chemotherapy alone; or 4) ALR and chemotherapy. One hundred and two patients were treated. The overall median survival for the entire group was 24 months with a 3 year survival of 51%, however, the number of patients at early stage (group I) was much greater than in stages III and IV. Sixty-one patients (60%) with T1 and T2 stage disease were treated with ALR alone, and 39 (38%) received PDT. The 3 year survival was 84% in the PDT group vs. 30% with ALR alone. Twenty-four patients (23%) with T3 and T4 disease were treated with chemotherapy alone and 21 (20%) with ALR and chemotherapy. The 3 year survival was only 19% for the chemotherapy group. Twenty-three patients (23%) with advanced disease received both PDT and chemotherapy. The median survival was 38 months and the 3 year survival was 35%. The combination of ALR and PDT has a significant therapeutic effect and when combined with chemotherapy may be used as an effective therapeutic modality in the treatment of
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How to get the Python Dataframe first row
I have a list of dict, and I want to convert to a pandas data frame, but the first row of the data frame is missing. I have read Python Pandas: Selecting rows and columns, but did not work.
As the data frame has no first row, I want to know how to get the first row.
import pandas as pd
import numpy as np
s = [[{‘_id’ : ‘1’, ‘_type’ : ‘apple’, ‘value’ : ‘3’},
{‘_id’ : ‘1’, ‘_type’ : ‘banana’, ‘value’ : ‘2’},
{‘_id’ : ‘1’, ‘_type’ : ‘pear’, ‘value’ : ‘3’}],
[{‘_id’ : ‘1’, ‘_type’ : ‘banana’, ‘value’ : ‘2’},
{‘_id’ : ‘2’, ‘_type’ : ‘apple’, ‘value’ : ‘3’},
{‘_id’ : ‘3’, ‘_type’ : ‘pear’, ‘value’ : ‘2’}]]
#df = pd.DataFrame()
df= pd.DataFrame(s)
#this is my trial
df.head()
output:
#result output:
_id _type value
0 1 apple 3
1 banana 2
2 1 pear 3
A:
Your Data
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