Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer.
Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer.
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DOI：
10.3390/cancers14010253
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发表时间：
2022-01-05
期刊：
影响因子：
5.2
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Gomez Marti JL;Brufsky A;Wells A;Jiang X
Breast cancer is the most frequently diagnosed cancer and second leading cause of cancer-related death among women worldwide. After initial tumor resection, breast cancer may recur locally and/or in distant organs within several months to years or even decades. Multiple methods exist to prognosticate disease progression in the early months and years after diagnosis. However, further efforts are needed to identify risk factors that relate to recurrence beyond the initial 5-year window. In this study, we applied machine learning to retrieve single and interactive clinical and pathological risk factors of 5-, 10- and 15-year metastases. Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms can identify not only risk factors but also interactions among these risks, which consequently may increase the risk of developing metastatic breast cancer. We proposed to apply a previously developed machine-learning method to discern risk factors of 5-, 10- and 15-year metastases. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive Risk Factor Learner (MBIL) to the electronic health record (EHR)-based Lynn Sage Database (LSDB) from the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastases from the LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and reliance on interactivity between risk factors. Results: We found that, with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long-term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.
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影响因子：
168.9
作者：
Davies, Christina;Pan, Hongchao;Godwin, Jon;Gray, Richard;Arriagada, Rodrigo;Raina, Vinod;Abraham, Mirta;Medeiros Alencar, Victor Hugo;Badran, Atef;Bonfill, Xavier;Bradbury, Joan;Clarke, Michael;Collins, Rory;Davis, Susan R.;Delmestri, Antonella;Forbes, John F.;Haddad, Peiman;Hou, Ming-Feng;Inbar, Moshe;Khaled, Hussein;Kielanowska, Joanna;Kwan, Wing-Hong;Mathew, Beela S.;Mittra, Indraneel;Mueller, Bettina;Nicolucci, Antonio;Peralta, Octavio;Pernas, Fany;Petruzelka, Lubos;Pienkowski, Tadeusz;Radhika, Ramachandran;Rajan, Balakrishnan;Rubach, Maryna T.;Tort, Sera;Urrutia, Gerard;Valentini, Miriam;Wang, Yaochen;Peto, Richard
通讯作者：
Peto, Richard
DOI：
10.1158/1078-0432.ccr-10-1533
发表时间：
2010-12-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
O'Brien KM;Cole SR;Tse CK;Perou CM;Carey LA;Foulkes WD;Dressler LG;Geradts J;Millikan RC
通讯作者：
Millikan RC
影响因子：
64.8
作者：
Sammut SJ;Crispin-Ortuzar M;Chin SF;Provenzano E;Bardwell HA;Ma W;Cope W;Dariush A;Dawson SJ;Abraham JE;Dunn J;Hiller L;Thomas J;Cameron DA;Bartlett JMS;Hayward L;Pharoah PD;Markowetz F;Rueda OM;Earl HM;Caldas C
通讯作者：
Caldas C
影响因子：
45.3
作者：
Kwan, Marilyn L.;Kushi, Lawrence H.;Caan, Bette J.
通讯作者：
Caan, Bette J.
影响因子：
3.8
作者：
Sopik, Victoria;Sun, Ping;Narod, Steven A.
通讯作者：
Narod, Steven A.