TY - JOUR
T1 - Prognostic factors for metachronous contralateral breast cancer
T2 - A comparison of the linear Cox regression model and its artificial neural network extension
AU - Mariani, L.
AU - Coradini, D.
AU - Biganzoli, E.
AU - Boracchi, P.
AU - Marubini, E.
AU - Pilotti, S.
AU - Salvadori, B.
AU - Silvestrini, R.
AU - Veronesi, U.
AU - Zucali, R.
AU - Rilke, F.
PY - 1997
Y1 - 1997
N2 - The purpose of the present study was to assess prognostic factors for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (≤ 45 years) with tumors of lobular histology or located at inner/central mammary quadrants were at greater risk of developing CBC. ER and PgR were also shown to have a prognostic role. Their effect, however, was not simple in relation to the presence of interactions between ER and age, and between PgR and histology. In fact, ER appeared to play a protective role in young patients, whereas the opposite was true in older women. Higher levels of PgR implied a greater hazard of CBC occurrence in infiltrating duct carcinoma or tumors with an associated extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.
AB - The purpose of the present study was to assess prognostic factors for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (≤ 45 years) with tumors of lobular histology or located at inner/central mammary quadrants were at greater risk of developing CBC. ER and PgR were also shown to have a prognostic role. Their effect, however, was not simple in relation to the presence of interactions between ER and age, and between PgR and histology. In fact, ER appeared to play a protective role in young patients, whereas the opposite was true in older women. Higher levels of PgR implied a greater hazard of CBC occurrence in infiltrating duct carcinoma or tumors with an associated extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.
KW - Breast neoplasms
KW - Models
KW - Multiple primary
KW - Neural networks (computer)
KW - Prognosis
KW - Statistical analysis
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U2 - 10.1023/A:1005765403093
DO - 10.1023/A:1005765403093
M3 - Article
C2 - 9232275
AN - SCOPUS:8244219678
SN - 0167-6806
VL - 44
SP - 167
EP - 178
JO - Breast Cancer Research and Treatment
JF - Breast Cancer Research and Treatment
IS - 2
ER -