M S Lakshmi’s research while affiliated with Coventry University and other places

This paper is focused on statistical, artificial neural networks and fuzzy logic based feature evaluation indices for determining the most/least clinically significant image cytometric prognostic factors for assessment of nodal involvement in breast cancer patients. Seven different prognostic factors, {tumour type, tumour grade, DNA ploidy, S-phase faction, G₀G₁/G₂M ratio, minimum (start) and maximum (end) nuclear pleomorphism indices}, are assessed by means of a multilayer feedforward backpropagation neural networks based feature evaluation index as an artificial neural network approach, a fuzzy logic-based feature evaluation index derived from the fuzzy k-nearest neighbour classifier as a fuzzy logic method, and a logistic regression-based statistical analysis. The results suggest that the artificial neural network and fuzzy based indices may be more reliable than their statistical counterpart. Overall results obtained for all the three methods highlight the fact that only one method's outcome may not be adequate to reliably determine the most/least clinically important factors for assessment of nodal involvement in breast cancer patients. Our results appear to suggest that S-phase fraction and tumour type may be the most and least clinically significant markers, respectively, and should be closely investigated for the assessment of breast cancer nodal involvement

This study aims to identify the most and least significant prognostic factors for breast cancer survival analysis by means of feature evaluation indices derived from multilayer feedforward backpropagation neural networks (MLFFBPNN), fuzzy k-nearest neighbour classifier (FK-NN) and a logistic regression-based backward stepwise method (ER). The data used for the survival analysis were collected from 100 women who had been clinically diagnosed with breast disease in the form of carcinoma or benign conditions. The data set consists of seven different histological and cytological prognostic factors and two corresponding outputs to be predicted (whether the patient is alive or dead within 5 years of diagnosis). The MLFFBPNN, FK-NN and LR based indices identified different subsets of the factors as the most significant sets. We therefore suggest that it could be dangerous to rely on one method's outcome for assessment of such factors. It should also be noted that "S-phase fraction" (SPF) is the common cytological factor identified by all three methods while none of the three methods identified another cytological factor, namely "minimum (start) nuclear pleomorphism index" (NPI_min). We, therefore, conclude that "S-phase fraction" and "minimum (start) nuclear pleomorphism index" appear to be the most and least important prognostic factors, respectively, for survival analysis in breast cancer patients, and should be investigated thoroughly in future clinical studies in oncology.

Presents a new hybrid system which integrates a neural network and fuzzy rule-based system learning methods. The data used in this study were collected from 100 women who were clinically diagnosed with breast cancer in the form of carcinoma or benign conditions. The data set contains seven different histological and cytological factors, and two nodal outputs (positive and negative nodal status) to be predicted for nodal involvement assessment in breast cancer patients. The hybrid system yielded the highest predictive accuracy of 73%, compared with statistical, neural networks and fuzzy logic methods. The overall results are encouraging and reveal the efficiency of the hybrid system.

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.

Aims to predict a breast cancer patient's prognosis and to determine the most important prognostic factors by means of logistic regression (LR) as a conventional statistical method, multilayer backpropagation neural network (MLBPNN) as a neural network method, fuzzy K-nearest neighbour algorithm (FK-NN) as a fuzzy logic method, a fuzzy measurement based on the FK-NN and the leave-one-out error method. The data used for breast cancer prognostic prediction were collected from 100 women who were clinically diagnosed with breast disease in the form of carcinoma or benign conditions. The data set consists of 7 image cytometric prognostic factors and 2 corresponding outputs to be predicted: whether the patient is alive or dead within 5 years of diagnosis. The LR stratified a 5-factor subset with a prognostic predictive accuracy of 82%, while the highest predictive accuracy of the MLBPNN was 87% obtained from two subsets. In this study, the FK-NN yielded the highest predictive accuracy of 88% achieved by eight different subsets, of which the subset with the highest fuzzy measurement was {tumour histology, DNA ploidy, SPF, G₀G₁/G₂M ratio}. Although the three methods resulted in different models, the results suggest that tumour histology, DNA ploidy and SPF (S-phase fraction), which are included in all three methods, may be the most significant factors for achieving accurate and reliable breast cancer prognostic prediction.

The paper aims to assess breast cancer prognostic markers and to determine an optimum subset that can yield high prediction accuracy for an individual breast cancer patient's prognosis by means of a fuzzy measurement derived from the fuzzy k-nearest neighbour algorithm (FK-NN). The analyses are carried out for both nodal involvement and five-year survival. The data set used for the analysis of breast cancer prognosis consists of seven input markers (histology type, grade, DNA ploidy, S-Phase Fraction (SPF), G₀G₁/G₂M ratio, minimum and maximum nuclear pleomorphism indices (NPI)) and two corresponding outputs to be predicted (negative or positive nodal status in the case of nodal involvement assessment, and whether the patient is alive or dead within 5 years of diagnosis for survival analysis). The highest predictive accuracy is 78% with the fuzzy measurement of 0.7254 for nodal involvement assessment, and 88% with the fuzzy measurement of 0.8183 for survival analysis. The best results are obtained from the subset (Histology type, Grade, DNA. Ploidy, SPF (%), G₀G₁/G₂M Ratio) for survival prediction and the subset (Grade, SPF, minimum NPI) for nodal involvement analysis

Chromosomal abnormalities are commonly associated with cancer, and their importance in the pathogenesis of the disease has been well recognized. Also recognized in recent years is the possibility that, together with chromosomal abnormalities, DNA ploidy of breast cancer aspirate cells, measured by image cytometric techniques, may correlate with prognosis of the disease. Here, we have examined the use of an artificial neural network to predict: 1) subclinical metastatic disease in the regional lymph nodes and 2) histological assessment, through the analysis of data obtained by image cytometric techniques of fine needle aspirates of breast tumors. The cellular features considered were: 1) DNA ploidy measured in terms of nuclear DNA content as well as by cell cycle distribution; 2) size of the S-phase fraction; and 3) nuclear pleomorphism. A further objective of the study was to analyze individual markers in terms of impact significance on predicting outcome in both cases. DNA ploidy, indicated by cell cycle distribution, was found markedly to influence the prediction of nodal spread of breast cancer, and nuclear pleomorphism to a lesser degree. Furthermore, a comparison between histological assessment and artificial neural network prediction shows a closer correlation between the neural approach and the development of further metastases as indicated in subsequent follow-up, than does histological assessment.