The evolving clinical management of cerebral metastases

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes

OBJECTIVE

To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification.

MATERIALS AND METHODS

A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model.

RESULTS

The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively.

CONCLUSION

The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

Diagnostic Accuracy of Arterial Spin Labeling in Comparison With Dynamic Susceptibility Contrast-Enhanced Perfusion for Brain Tumor Surveillance at 3T MRI

Purpose

We aimed to compare arterial spin labeling (ASL) with dynamic susceptibility contrast (DSC) enhanced perfusion MRI for the surveillance of primary and metastatic brain tumors at 3T, both in terms of lesion perfusion metrics and diagnostic accuracy.

Methods

In this retrospective study, we included 115 patients, who underwent both ASL and DSC perfusion in the same 3T MRI scanning session between 1 January and 31 December 2019. ASL-derived cerebral blood flow (CBF) maps and DSC-derived relative cerebral blood volume (rCBV) maps, both uncorrected and corrected for leakage, were created with commercially available software. Lesions were identified as T2-/T2-FLAIR hyperintensity with or without contrast enhancement. Measurements were done by placing a region of interest in the visually determined area of highest perfusion, copying to the contralateral normal appearing white matter (NAWM), and then propagating to the other perfusion maps. Pearson’s correlation coefficients were calculated between the CBF and rCBV ratios of tumor versus NAWM. Accuracy for diagnosing tumor progression was calculated as the area under the receiver operating characteristics (ROC) curve (AUC) for the ASL-CBF and leakage corrected DSC-rCBV ratios.

Results

We identified 178 lesions, 119 with and 59 without contrast enhancement. Correlation coefficients between ASL-derived CBF versus DSC-derived rCBV ratios were 0.60–0.67 without and 0.72–0.78 with leakage correction in all lesions (n = 178); these were 0.65–0.80 in enhancing glioma (n = 80), 0.58–0.73 in non-enhancing glioma, and 0.14–0.40 in enhancing metastasis (n = 31). No significant correlation was found in enhancing (n = 8) or non-enhancing (n = 7) lymphomas. The areas under the ROC curves (AUCs) for all patients were similar for ASL and DSC (0.73–0.78), and were higher for enhancing glioma (AUC = 0.78–0.80) than for non-enhancing glioma (AUC = 0.56–0.62). In brain metastasis, the AUC was lower for ASL-derived CBF (AUC = 0.72) than for DSC-derived rCBV ratios (AUC = 0.87–0.93).

Conclusion

We found that ASL and DSC have more or less the same diagnostic accuracy. Our findings suggest that ASL can be used as an alternative to DSC to measure perfusion in enhancing and non-enhancing gliomas and brain metastasis at 3T. For lymphoma, this should be further investigated in a larger population.

Nausea and vomiting in end-of-life care: managing this debilitating symptom in the community

Nausea and vomiting (N&V) are common, debilitating and distressing symptoms for patients with advanced cancer, precipitating admission to hospital for intravenous antiemetic and re-hydration (Glare et al, 2011). The causes of N&V in end-of-life care (EOLC) are multifaceted, with appropriate therapy guided by thorough assessment (Walsh et al, 2017; Watson et al, 2019). Cyclizine and levomepromazine can, depending on aetiology, be cited as effective antiemetic agents for patients with advanced cancer (Ingleton and Larkin, 2015; Watson et al, 2019). Conversely, careful consideration of the use of dexamethasone for the management of N&V in EOLC should be taken, due to known side effects (Ferrel and Paice, 2019). This case study will use a systematic approach to critically appraise the management of N&V, experienced by a community patient receiving EOLC from the district nurses.

WBRT for brain metastases from non-small cell lung cancer: for whom and when?-Contemporary point of view

The incidence of brain metastases (BM) is estimated between 20% and 40% of patients with solid cancer. The most common cause of this failure is lung cancer, and in locally advanced non-small cell lung cancer (NSCLC) BM represent a common site of relapse in 30-55% cases. The basic criteria of therapeutic decision-making are based on the significant prognostic factors which are components of prognostic scores. The standard approach to treatment of BM from NSCLC include whole brain radiotherapy (WBRT) which is used as adjuvant modality after local therapy (surgery or stereotactic radiosurgery) or as primary treatment and it remains the primary modality of treatment for patients with multiple metastases. WBRT is also used in combination with systemic therapy. The aim of presented review of literature is trying to answer which patients with BM from NSCLC should receive WBRT and when it could be omitted. There were presented the aspects of application of WBRT in relation to (I) choice between WBRT or the best supportive care and (II) employment of WBRT in combination with local treatment modalities [surgical resection or stereotactic radio-surgery (SRS)] and/or with systemic therapy. According to data from literature we concluded that the most important factor that needs to be considered when assessing the suitability of a patient for WBRT is the patient's prognosis based on the Lung-molGPA score. WBRT should be applied in treatment of multiple BM from lung cancer in patients with favourable prognosis and in in patients with presence of EML4-ALK translocation before therapy with crizotinib. Whereas WBRT could be omitted in patients with poor prognosis and after primary SRS.

KISS1 in breast cancer progression and autophagy

Tumor suppressors are cellular proteins typically expressed in normal (non-cancer) cells that not only regulate such cellular functions as proliferation, migration and adhesion, but can also be secreted into extracellular space and serve as biomarkers for pathological conditions or tumor progression. KISS1, a precursor for several shorter peptides, known as metastin (Kisspeptin-54), Kisspeptin-14, Kisspeptin-13 and Kisspeptin-10, is one of those metastasis suppressor proteins, whose expression is commonly downregulated in the metastatic tumors of various origins. The commonly accepted role of KISS1 in metastatic tumor progression mechanism is the ability of this protein to suppress colonization of disseminated cancer cells in distant organs critical for the formation of the secondary tumor foci. Besides, recent evidence suggests involvement of KISS1 in the mechanisms of tumor angiogenesis, autophagy and apoptosis regulation, suggesting a possible role in both restricting and promoting cancer cell invasion. Here, we discuss the role of KISS1 in regulating metastases, the link between KISS1 expression and the autophagy-related biology of cancer cells and the perspectives of using KISS1 as a potential diagnostic marker for cancer progression as well as a new anti-cancer therapeutics.

Angiosarcoma-related cerebral metastases: a systematic review of the literature

Angiosarcoma-related cerebral metastases have only been recorded in a few case reports and case series and have not been systematically reviewed to date. Our objective was therefore to perform a systematic literature review on cases of angiosarcomas metastasizing to the brain to inform current practice. All three major libraries-PubMed/MEDLINE, Embase, and Cochrane-were systematically searched, until January 2019. Articles in English reporting angiosarcoma-related cerebral metastases via hematogenous route were included. Our search yielded 45 articles (38 case reports, 5 retrospective studies, 1 case series and 1 letter to the editor), totaling 48 patients (mean age 47.9 years). The main primary site was the heart. The mean time of diagnosis of cerebral metastases following primary tumor identification was 4.9 months. In 15 cases, the brain was the only metastatic site. In cases of multiple extracerebral metastases, the most common sites were the lung and bone. Acute intracerebral supratentorial hemorrhage was the most common presenting radiological feature. Treatment strategies were almost equally divided between the surgical (with or without adjuvant treatment) and the medical arm. Mean overall survival was 7.2 months while progression-free survival was 1.5 months. To our knowledge, this is the first systematic literature review on angiosarcoma-related cerebral metastases. This pathology proves to be an extremely rare clinical entity and carries a poor prognosis, and no consensus has been reached regarding treatment.

Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type

Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.

Pseudoprogression of Melanoma Brain Metastases

PURPOSE OF REVIEW

Immune checkpoint inhibitors are increasingly being used to treat melanoma brain metastases. One potential complication of immune checkpoint inhibitors is a phenomenon called pseudoprogression, in which a tumor transiently increases in size due to lymphocyte infiltration. This article reviews the characteristics of pseudoprogression and their clinical implications.

RECENT FINDINGS

Pseudoprogression can be challenging to differentiate from true progression noted clinically or radiographically, thereby complicating management decisions and potentially confusing patients and their families. The transient tumor enlargement can also cause symptoms that mimic true tumor progression. Because the use of immunotherapy on melanoma brain metastases is a relatively new treatment paradigm, there is limited evidence to guide clinical decision-making and prognostication related to pseudoprogression.

Very Large Metastases to the Brain: Retrospective Study on Outcomes of Surgical Management

BACKGROUND

The incidence of brain metastases is rising. To our knowledge, no published study focuses exclusively on brain metastases larger than 4 cm. We present our surgical outcomes for patients with brain metastases larger than 4 cm.

METHODS

This is a retrospective chart review of inpatient data at our institution from January 2006 to September 2015. Primary end points included overall survival, progression-free survival, and local recurrence rate.

RESULTS

Sixty-one patients had a total of 67 brain metastases larger than 4 cm: 52 were supratentorial and 15 were infratentorial. Forty-three patients underwent surgical resection. Average duration of disease freedom after resection was 4.79 months (range, 0-30 months). Excluding patients with residual on immediate postoperative magnetic resonance imaging, the average rate of local recurrence was 7 months (range, 1-14 months). Overall survival after surgery excluding patients who chose palliation in the immediate postoperative period averaged 8.76 months (range, 1-37 months). Thirty-five of 43 patients (81.4%) had stable or improved neurologic examinations postoperatively. Six patients (13.95%) developed surgical complications. There were 3 major complications (6.98%): 2 pseudomeningoceles required intervention and 1 postoperative hematoma required external ventricular drain placement. There were 3 minor complications (6.98%): 1 self-limited pseudomeningocele, 1 subgaleal fluid collection, and 1 postoperative seizure.

CONCLUSIONS

Surgery resulted in stable or improved neurologic examination in 81.4% of cases. On statistical analysis, significantly increased overall survival was noted in patients undergoing surgical resection, and those with higher Karnofsky Performance Scale and lower number of brain metastases at presentation. There is a need for further studies to evaluate management of brain metastases larger than 4 cm.