Determinants of complete remission induction and maintenance in chemotherapy with or without irradiation of XXXX .

@entity1 @entity632 @entity66 @entity417 @entity786 @entity11770 @entity2879 @entity1891 @entity880 @entity1499 @entity151 @entity2753
The possible influence of pretreatment @entity1 characteristics upon the probabilities of complete remission (CR) induction and maintenance was investigated in a series of 815 nonresected @entity1 with @entity151 . All @entity1 underwent pretreatment staging which enabled allocation of 391 @entity1 to trials for limited stage disease and 424 @entity1 to trials for @entity66 . Three controlled trials for @entity66 stage were conducted between 1973 and 1981. All therapeutic regimens consisted of combinations of between three and six agents ( @entity11770 , @entity786 , @entity1499 , @entity1891 , @entity632 , @entity2879 ) with or without irradiation. Thirty-five % of the limited stage @entity1 and 18% of the extensive stage @entity1 were alive and had achieved a complete remission 16 weeks after initiation of the treatment, i.e., after four cycles of chemotherapy. Relationships between pretreatment characteristics and the probability to pass this benchmark were examined by logistic regression analysis. The probability of CR was negatively related to increased serum @entity880 dehydrogenase and male sex in both disease stages. Pretreatment @entity417 (less than 12 g/liter) and poor performance status were associated with a reduced CR rate in limited and extensive stage disease, respectively. Factors related to the maintenance of complete remission were subsequently examined in the 211 complete responders by use of Cox's regression analysis. Complete responders with @entity66 prior to treatment had greater cumulative risk of relapse than those with limited disease (P less than 0.01). @entity2753 had a significant negative influence on the remission duration in limited disease while age greater than 60 years and bone marrow metastases had significantly negative influence in @entity66 . Using the models it was possible to identify subgroups of @entity1 with CR rates ranging from 5 to 55% and to stratify complete responders according to estimated risks of subsequent relapse.
