Mustafa Cem UZAL, İmran ÖMÜRLÜ KURT, Mevlüt TÜRE, Zehra Füsun TOKATLI, Ruşen ALAS ÇOŞAR

Developing and comparing two diff erent prognostic indexes for predicting disease-free survival of nonmetastatic breast cancer patients

Amaç: Cox regresyon analizi ve recursive partitioning analizine dayanan sağkalım ağacı (ST) ile non-metastatik meme kanserli hastaların alt gruplara ayrılmasında farklı prognostik indeksler (Pİ) belirlemek ve bu metodların tahmin güçlerini Kaplan-Meier analizi ile karşılaştırmaktır. Yöntem ve gereç: Veriler, her bir prognostik faktör için 410 hastadan elde edildi. Cox regresyon analizi, ortak değişkenlere göre yaşamsal dağılımı inceleyen bir yöntemdir. ST yöntemi ise recursive partitioning algoritmasına dayanan ağaca yapılı bir sağkalım analizidir. Çalışmada, train ve test setleri için Harrell’ın uyum indeksine göre hata oranları incelendi. Ayrıca train seti için yaşam eğrileri Kaplan-Meier yöntemi ile tahmin edildi. Hastalıksız sağkalım, hastalığın ilk tanısından (ilk tedavinin başlangıcından) ilk nüksüne kadar geçen zaman olarak hesaplandı. Bulgular: 48 aylık ortanca takip sonrası 100 (% 24,4) hastada hastalıksız sağkalım açısından en az bir olay görüldü. Cox regresyon analizinde HER2/neu ve aksiller nodal durumuna dayanan basit bir Pİ geliştirildi. ST metodunda üç değişken belirlendi ve bunlar HER2/neu, aksiller nodal durum ve östrojen reseptör durumu idi. Nüksü belirleyen en önemli faktör aksiller nodal durum idi. Sonuç: ST ve Cox regresyon analizi ile elde edilen Pİ’ler, hastalıksız sağkalımın tahmin edilmesinde benzer performans gösterdi. Modellerin hata oranlarının, train ve test setlerinde birbirilerine yakın olduğu belirlendi. Ayrıca HER2/neu ve aksiller nodal durumun, meme kanserli hastalarda hastalıksız sağkalım süresinin tahmini için en önemli faktörler olduğu belirlendi.

Nonmetastatik meme kanserli hastalarda hastalıksız sağkalımın belirlenmesinde iki farklı prognostik indeksin geliştirilmesi ve kıyaslanması

Aim: To determine 2 diff erent prognostic indexes (PI) for the diff erentiation of subgroups of nonmetastatic breast cancer patients with the Cox regression analysis and survival tree (ST) methods and the additional usage of the Kaplan- Meier estimates to investigate the predictive power of these methods. Materials and methods: Prognostic factors data were collected for 410 patients. Th e Cox regression analysis examines the relationship of the survival distribution and covariates. Th e ST method is a tree-structured survival analysis based on a recursive partitioning algorithm. In this study, Harrell’s concordance indexes of models for training and test sets were computed. Furthermore, survival curves were estimated by the Kaplan-Meier method. Disease-free survival (DFS) was calculated from the time of initial diagnosis (initiation of the fi rst treatment) to the fi rst recurrence of disease. Results: Aft er a median follow-up of 48 months, 100 (24.4%) patients have had at least 1 of the DFS events. In Cox regression analysis, we proposed the simple PI, which is a sum of axillary nodal and HER2/neu status. In the ST method, we identifi ed 3 variables: HER2/neu, axillary nodal, and estrogen receptor status. Th e axillary nodal status was the most important determining factor for recurrence. Conclusion: We found that the PI of the ST and Cox regression methods had similar performance levels in predicting DFS, and the error rates of the models were close to each other in the training and test sets. Furthermore, we determined that the axillary nodal status and HER2/neu were the most important determining factors for prediction of DFS in breast cancer patients.

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