Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial

被引:7
作者
van Egdom, Laurentine S. E. [1 ]
Pusic, Andrea [2 ]
Verhoef, Cornelis [1 ]
Hazelzet, Jan A. [3 ]
Koppert, Linetta B. [1 ]
机构
[1] Erasmus MC Canc Inst, Dept Surg Oncol, Dr Molenwaterpl 40, NL-3000 CA Rotterdam, Netherlands
[2] Brigham & Womens Hosp, Dept Plast & Reconstruct Surg, Patient Reported Outcomes Value & Experience PROV, 75 Francis St, Boston, MA 02115 USA
[3] Erasmus MC, Univ Med Ctr, Dept Publ Hlth, Rotterdam, Netherlands
关键词
breast cancer surgery; machine learning; patient-reported outcomes; CONSERVING THERAPY; HEALTH OUTCOMES; FOLLOW-UP; MASTECTOMY; SURVIVAL;
D O I
10.1111/tbj.13804
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long-term physical, sexual, and psychosocial outcomes is very important in treatment decision-making. Patient-reported outcomes (PROs) can help facilitate this shared decision-making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs.
引用
收藏
页码:1213 / 1215
页数:3
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