The Expanding Role of Radiosurgery for Brain Metastases

O’Beirn, Mark; Benghiat, Helen; Meade, Sara; Heyes, G.; Sawlani, Vijay; Kong, Anthony; Hartley, A.; Sanghera, Paul

doi:10.3390/medicines5030090

Cited by 38 publications

(41 citation statements)

References 108 publications

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“…Stereotactic radiosurgery (SRS) is considered standard of care in patients with one or multiple brain metastases (BM) of limited size and number [1]. The high incidence of BM [2] and the necessity to manually contour each lesion for definition of the planning target volumes of radiosurgery results in high workload for the attending physicians.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

et al. 2020

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Introduction: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

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Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

et al. 2020

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“…Without treatment, median survival rates are exceedingly low [10]. Stereotactic radiosurgery (SRS), which involves the administration of targeted high dose radiation to affected areas, has emerged as an effective way to manage systemic disease and improve local control [11]. Prior to the widespread use of SRS, whole brain radiotherapy (WBRT) and surgery were the mainstays of treatment.…”

Section: Discussionmentioning

confidence: 99%

Endometrial adenocarcinoma presenting as a suprasellar mass: lessons to be learned

Granina¹,

Fehniger²,

Kondziolka³

et al. 2020

ecancer

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A 66-year-old woman with a history of stage IA mixed endometrioid and serous endometrial cancer presented to our centre with 2 weeks of worsening headaches nearly 4 years after her initial surgery. At admission, she manifested bitemporal hemianopsia, difficulty walking and clinical and laboratory findings of panhypopituitarism, including diabetes insipidus. Magnetic resonance imaging of the brain revealed a 2.7 cm sellar/suprasellar mass compressing the optic chiasm and infiltrating the pituitary stalk. Computerised tomography documented mediastinal, lung, adrenal and liver involvement, including a 2.5 cm palpable left supraclavicular node that on excisional biopsy demonstrated metastatic endometrial adenocarcinoma. Due to the advanced stage of her cancer as well as the presence of multiple metastases, including lung and hepatic metastases causing postobstructive pneumonia and coagulopathy, the sellar/suprasellar mass was treated with fractionated radiosurgery rather than surgical excision.

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“…Stereotactic radiosurgery (SRS) has become increasingly important in the management of brain metastases [38]. Following SRS, one-third of brain metastases increase in size, suggesting treatment failure [39].…”

Section: Metastasis-treatment Responsementioning

confidence: 99%

Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions

et al. 2020

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MRI has a vital role in the assessment of intracranial lesions. Conventional MRI has limited specificity and multiparametric MRI using diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance spectroscopy allows more accurate assessment of the tissue microenvironment. The purpose of this educational pictorial review is to demonstrate the role of multiparametric MRI for diagnosis, treatment planning and for assessing treatment response, as well as providing a practical approach for performing and interpreting multiparametric MRI in the clinical setting. A variety of cases are presented to demonstrate how multiparametric MRI can help differentiate neoplastic from non-neoplastic lesions compared to conventional MRI alone.

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The Expanding Role of Radiosurgery for Brain Metastases

Cited by 38 publications

References 108 publications

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Endometrial adenocarcinoma presenting as a suprasellar mass: lessons to be learned

Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions

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