Literature DB >> 9232275

Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear Cox regression model and its artificial neural network extension.

L Mariani1, D Coradini, E Biganzoli, P Boracchi, E Marubini, S Pilotti, B Salvadori, R Silvestrini, U Veronesi, R Zucali, F Rilke.   

Abstract

The purpose of the present study was to assess prognostic factor 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 (< or = 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.

Entities:  

Mesh:

Substances:

Year:  1997        PMID: 9232275     DOI: 10.1023/a:1005765403093

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  13 in total

1.  Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer.

Authors:  Bin Yang; Chengxing Liu; Ren Wu; Jing Zhong; Ang Li; Lu Ma; Jian Zhong; Saisai Yin; Changsheng Zhou; Yingqian Ge; Xinwei Tao; Longjiang Zhang; Guangming Lu
Journal:  Front Oncol       Date:  2022-06-23       Impact factor: 5.738

2.  Estrogen receptor status in relation to risk of contralateral breast cancer-a population-based cohort study.

Authors:  Maria E C Sandberg; Per Hall; Mikael Hartman; Anna L V Johansson; Sandra Eloranta; Alexander Ploner; Kamila Czene
Journal:  PLoS One       Date:  2012-10-08       Impact factor: 3.240

3.  Prognostic factors of second primary contralateral breast cancer in early-stage breast cancer.

Authors:  Zheng Li; Fabrice Sergent; Michel Bolla; Yunfeng Zhou; Isabelle Gabelle-Flandin
Journal:  Oncol Lett       Date:  2014-10-17       Impact factor: 2.967

Review 4.  Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and Challenges.

Authors:  Zhou Lulin; Ethel Yiranbon; Henry Asante Antwi
Journal:  Biomed Res Int       Date:  2016-08-24       Impact factor: 3.411

5.  CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging.

Authors:  Yucheng Zhang; Edrise M Lobo-Mueller; Paul Karanicolas; Steven Gallinger; Masoom A Haider; Farzad Khalvati
Journal:  BMC Med Imaging       Date:  2020-02-03       Impact factor: 1.930

6.  Prediction and clinical utility of a contralateral breast cancer risk model.

Authors:  Daniele Giardiello; Ewout W Steyerberg; Michael Hauptmann; Muriel A Adank; Delal Akdeniz; Carl Blomqvist; Stig E Bojesen; Manjeet K Bolla; Mariël Brinkhuis; Jenny Chang-Claude; Kamila Czene; Peter Devilee; Alison M Dunning; Douglas F Easton; Diana M Eccles; Peter A Fasching; Jonine Figueroa; Henrik Flyger; Montserrat García-Closas; Lothar Haeberle; Christopher A Haiman; Per Hall; Ute Hamann; John L Hopper; Agnes Jager; Anna Jakubowska; Audrey Jung; Renske Keeman; Iris Kramer; Diether Lambrechts; Loic Le Marchand; Annika Lindblom; Jan Lubiński; Mehdi Manoochehri; Luigi Mariani; Heli Nevanlinna; Hester S A Oldenburg; Saskia Pelders; Paul D P Pharoah; Mitul Shah; Sabine Siesling; Vincent T H B M Smit; Melissa C Southey; William J Tapper; Rob A E M Tollenaar; Alexandra J van den Broek; Carolien H M van Deurzen; Flora E van Leeuwen; Chantal van Ongeval; Laura J Van't Veer; Qin Wang; Camilla Wendt; Pieter J Westenend; Maartje J Hooning; Marjanka K Schmidt
Journal:  Breast Cancer Res       Date:  2019-12-17       Impact factor: 6.466

7.  Applications of machine learning in cancer prediction and prognosis.

Authors:  Joseph A Cruz; David S Wishart
Journal:  Cancer Inform       Date:  2007-02-11

8.  Relation of risk of contralateral breast cancer to the interval since the first primary tumour.

Authors:  C Rubino; R Arriagada; S Delaloge; M G Lê
Journal:  Br J Cancer       Date:  2009-11-17       Impact factor: 7.640

9.  A gradient boosting algorithm for survival analysis via direct optimization of concordance index.

Authors:  Yifei Chen; Zhenyu Jia; Dan Mercola; Xiaohui Xie
Journal:  Comput Math Methods Med       Date:  2013-11-20       Impact factor: 2.238

10.  DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

Authors:  Jared L Katzman; Uri Shaham; Alexander Cloninger; Jonathan Bates; Tingting Jiang; Yuval Kluger
Journal:  BMC Med Res Methodol       Date:  2018-02-26       Impact factor: 4.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.