Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches.

摘要

Accurate and reliable decision making in breast cancer prognosis can help in the planning of suitable surgery and therapy and, generally, optimise patient management through the different stages of the disease. In recent years, several prognostic factors have been used as indicators of disease progression in breast cancer. In this paper we investigate a fuzzy method, namely fuzzy k-nearest neighbour technique for breast cancer prognosis, and for determining the significance of prognostic markers and subsets of the markers, which include histology type, tumour grade, DNA ploidy, S-phase fraction, G0G1/G2M ratio, and minimum (start) and maximum (end) nuclear pleomorphism indices. We also compare the method with (a) logistic regression as a statistical method, and (b) multilayer feed forward backpropagation neural networks as an artificial neural network tool, the latter two techniques having been widely used for cancer prognosis. Nodal involvement and survival analyses in breast cancer are carried out for 100 women who were clinically diagnosed with breast disease in the form of carcinoma and benign conditions, and seven prognostic markers collected for each patient. For nodal involvement analysis, node positive and negative patients are predicted whereas survival analysis is carried out for two categories: whether a patient is alive or dead within 5 years of diagnosis. The results obtained show that the fuzzy method yields the highest predictive accuracy of 88% for both nodal involvement and survival analyses obtained from the subsets of [tumour grade, S-phase fraction, minimum (start) nuclear pleomorphism index] and [tumour histology type, DNA ploidy, S-phase fraction, G0G1/G2M ratio], respectively. We believe that this technique has produced more reliable prognostic factor models than those obtained using either the statistical or artificial neural networks-based methods.