Advancements in predicting outcomes in patients with glioma: a surgical perspective

被引:13
作者
Jakola, Asgeir Store [1 ,2 ,3 ]
Sagberg, Lisa Millgard [4 ,5 ]
Gulati, Sasha [3 ,4 ]
Solheim, Ole [3 ,4 ]
机构
[1] Sahlgrens Acad, Inst Physiol & Neurosci, Dept Clin Neurosci, Bla Straket 7, S-41345 Gothenburg, Sweden
[2] Sahlgrens Univ Hosp, Dept Neurosurg, Gothenburg, Sweden
[3] NTNU, Dept Neuromed & Movement Sci, Trondheim, Norway
[4] St Olavs Hosp, Dept Neurosurg, Trondheim, Norway
[5] NTNU, Dept Publ Hlth & Nursing, Trondheim, Norway
基金
瑞典研究理事会;
关键词
Glioma; brain neoplasm; neurosurgery; prediction; personalised medicine; LOW-GRADE GLIOMAS; QUALITY-OF-LIFE; RECURSIVE PARTITIONING ANALYSIS; SHARED DECISION-MAKING; PROGNOSTIC-FACTORS; MALIGNANT GLIOMA; II GLIOMAS; TUMOR LATERALITY; TEXTURE ANALYSIS; IDH STATUS;
D O I
10.1080/14737140.2020.1735367
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon. Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons. Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.
引用
收藏
页码:167 / 177
页数:11
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