Shared decision-making in neurosurgery: a scoping review

Corell, Alba; Guo, Annie; Vecchio, Tomás Gómez; Ozanne, Anneli; Jakola, Asgeir Store

doi:10.1007/s00701-021-04867-3

Cited by 23 publications

(11 citation statements)

References 61 publications

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“…11 However, studies have shown that SDM implementation is poor in the neurosurgical setting and in the care of cancers without curative potential, including GBM. 12,13 For patients with GBM, the process of SDM can be further complicated by the cognitive decline associated with the progressive disease. 14 To better understand SDM in GBM and the perspective of the patient, a roundtable discussion was held on May 23, 2019, in New York, NY, with a panel of interested patient advocacy leaders from organizations focused on brain tumors.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Shared Decision-Making for Patients with Glioblastoma

Musella¹,

DeVitto²,

Anthony³

et al. 2021

PPA

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Navigating care for patients with cancer can be overwhelming considering the multiple specialists they encounter and the numerous decisions they must make. For patients with glioblastoma (GBM), management is further complicated by a poor prognosis, feelings of isolation, urgency to treat, and cognitive decline associated with this rare and progressive disease. For these reasons, it is imperative that shared decision-making (SDM) be integrated into standard practice to ensure that the risks and benefits of all treatments are discussed and weighed with the patient’s expectations and goals in mind. In this manuscript, the importance of SDM in GBM and the potential benefits to the practice and patient are discussed from the unique perspective of advocacy leaders. Their insights from interactions with patients and caregivers provide a template for empowering patients, improving patient-physician communication and understanding, and reducing patient and caregiver anxieties. Ultimately, increased SDM may lead to a better quality of life and improved treatment outcomes.

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Section: Introductionmentioning

confidence: 99%

The Importance of Shared Decision-Making for Patients with Glioblastoma

Musella¹,

DeVitto²,

Anthony³

et al. 2021

PPA

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“…For instance, communication de cits and cognitive impairments related to the neurologic sequelae of primary brain tumors may impair end-of-life discussions and, therefore, lead to unmet patient preferences [13][14][15]. In recent years, there have been signi cant efforts to increase shared decision making in the eld of neurooncology in order to improve patient quality of life [16][17][18]. In this shared decision making among patients with primary brain tumors, the early intervention of palliative care and enrollment in hospice care may be re ected in our data showing increased home deaths over time.…”

Section: Discussionmentioning

confidence: 99%

Disparities in Place of Death for Patients with Primary Brain Tumors and Brain Metastases in the United States

Shenker

McLaughlin

Chino

et al. 2022

Preprint

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Purpose Patients with primary or metastatic brain tumors often require intensive end-of-life care, for which place of death may serve as a quality metric. Death at home or hospice are considered a more ‘ideal’ location. Comprehensive information on place of death of people with brain tumors is lacking. Methods Using CDC Wonder Database data, those who died in the U.S. from a solid cancer from 2003 to 2016 were included and place of death for those with primary brain, brain metastases, and solid non-brain tumors were compared. Multivariate logistic regression tested for disparities in place of death. Results By 2016, 51.1% of patients with primary brain tumors and 45.2% with brain metastases died at home. 15.9% of patients with primary brain tumors and 23.6% with brain metastases died in the hospital. Black patients were least likely to die at home or hospice. For patients with primary brain tumors, being married (OR = 2.25 (95%CI 2.16–2.34), p < 0.01) and having an advanced degree (OR = 1.204 (95%CI 1.15–1.26), p < 0.01) increased odds of home/hospice death; older age (OR = 0.50 (95%CI 0.46–0.54), p < 0.01) decreased odds for home/hospice death. For patients with brain metastases, being married (OR = 2.19 (95%CI 2.11–2.26), p < 0.01) increased odds of home/hospice death and male sex (OR = 0.87 (095%CI .85–0.89), p < 0.01) and older age (OR = 0.59 (95%CI 0.47–0.75), p < 0.01) decreased odds of home/hospice death. Conclusion Although the trends are improving, disparities exist in place of death in the brain tumor population. Focused interventions are indicated to increase the utilization of hospice in those with metastatic cancer, under-represented minority groups, and the elderly population.

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“…Recently non-invasive classification methods have shown promising results in prediction of glioma-subtypes based upon pre-operative imaging [ 10 – 13 ]. Non-invasive methods are opening up to discuss tailored therapies that would assist the surgeons and patients in the shared decision making process [ 14 ]. However, many challenges remain before bringing these tools into clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

Ali

Lidemar

et al. 2022

BMC biomed eng

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Background For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) is desirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help the classification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated data with ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consuming process with high demand on medical personnel. As an alternative automatic segmentation is often used. However, it does not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRI acquisition parameters across imaging centers, as segmentation is an ill-defined problem. Analogous to visual object tracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas in MR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding box areas (e.g. ellipse shaped boxes) for classification without a significant drop in performance. Method In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments were conducted on two datasets (US and TCGA) consisting of multi-modality MRI scans where the US dataset contained patients with diffuse low-grade gliomas (dLGG) exclusively. Results Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and 79.50% for IDH mutation/wild-type on TCGA dataset. Comparisons with that of using annotated GT tumor data for training showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype). Conclusion Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for training a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With more data that can be made available, this may be a reasonable trade-off where decline in performance may be counteracted with more data.

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Shared decision-making in neurosurgery: a scoping review

Cited by 23 publications

References 61 publications

The Importance of Shared Decision-Making for Patients with Glioblastoma

The Importance of Shared Decision-Making for Patients with Glioblastoma

Disparities in Place of Death for Patients with Primary Brain Tumors and Brain Metastases in the United States

Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

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