Benchmarking palliative care practices in neurooncology: a german perspective.

PURPOSE: To benchmark palliative care practices in neurooncology centers across Germany, evaluating the variability in palliative care integration, timing, and involvement in tumor board discussions. This study aims to identify gaps in care and contribute to the discourse on optimal palliative care strategies. METHODS: A survey targeting both German Cancer Society-certified and non-certified university neurooncology centers was conducted to explore palliative care frameworks and practices for neurooncological patients. The survey included questions on palliative care department availability, involvement in tumor boards, timing of palliative care integration, and use of standardized screening tools for assessing palliative burden and psycho-oncological distress. RESULTS: Of 57 centers contacted, 46 responded (81% response rate). Results indicate a dedicated palliative care department in 76.1% of centers, with palliative specialists participating in tumor board discussions at 34.8% of centers. Variability was noted in the initiation of palliative care, with early integration at the diagnosis stage in only 30.4% of centers. The survey highlighted a significant lack of standardized spiritual care assessments and minimal use of advanced care planning. Discrepancies were observed in the documentation and treatment of palliative care symptoms and social complaints, underscoring the need for comprehensive care approaches. CONCLUSION: The study highlights a diverse landscape of palliative care provision within German neurooncology centers, underscoring the need for more standardized practices and early integration of palliative care. It suggests the necessity for standardized protocols and guidelines to enhance palliative care's quality and uniformity, ultimately improving patient-centered care in neurooncology.

Evolutionary trajectories of small cell lung cancer under therapy.

The evolutionary processes that underlie the marked sensitivity of small cell lung cancer (SCLC) to chemotherapy and rapid relapse are unknown1-3. Here we determined tumour phylogenies at diagnosis and throughout chemotherapy and immunotherapy by multiregion sequencing of 160 tumours from 65 patients. Treatment-naive SCLC exhibited clonal homogeneity at distinct tumour sites, whereas first-line platinum-based chemotherapy led to a burst in genomic intratumour heterogeneity and spatial clonal diversity. We observed branched evolution and a shift to ancestral clones underlying tumour relapse. Effective radio- or immunotherapy induced a re-expansion of founder clones with acquired genomic damage from first-line chemotherapy. Whereas TP53 and RB1 alterations were exclusively part of the common ancestor, MYC family amplifications were frequently not constituents of the founder clone. At relapse, emerging subclonal mutations affected key genes associated with SCLC biology, and tumours harbouring clonal CREBBP/EP300 alterations underwent genome duplications. Gene-damaging TP53 alterations and co-alterations of TP53 missense mutations with TP73, CREBBP/EP300 or FMN2 were significantly associated with shorter disease relapse following chemotherapy. In summary, we uncover key processes of the genomic evolution of SCLC under therapy, identify the common ancestor as the source of clonal diversity at relapse and show central genomic patterns associated with sensitivity and resistance to chemotherapy.

DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification.

The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.

Surgical management and outcome of newly diagnosed glioblastoma without contrast enhancement (low-grade appearance): a report of the RANO resect group.

BACKGROUND: Resection of the contrast-enhancing (CE) tumor represents the standard of care in newly diagnosed glioblastoma. However, some tumors ultimately diagnosed as glioblastoma lack contrast enhancement and have a 'low-grade appearance' on imaging (non-CE glioblastoma). We aimed to (a) volumetrically define the value of non-CE tumor resection in the absence of contrast enhancement, and to (b) delineate outcome differences between glioblastoma patients with and without contrast enhancement. METHODS: The RANO resect group retrospectively compiled a global, eight-center cohort of patients with newly diagnosed glioblastoma per WHO 2021 classification. The associations between postoperative tumor volumes and outcome were analyzed. Propensity score-matched analyses were constructed to compare glioblastomas with and without contrast enhancement. RESULTS: Among 1323 newly diagnosed IDH-wildtype glioblastomas, we identified 98 patients (7.4%) without contrast enhancement. In such patients, smaller postoperative tumor volumes were associated with more favorable outcome. There was an exponential increase in risk for death with larger residual non-CE tumor. Accordingly, extensive resection was associated with improved survival compared to lesion biopsy. These findings were retained on a multivariable analysis adjusting for demographic and clinical markers. Compared to CE glioblastoma, patients with non-CE glioblastoma had a more favorable clinical profile and superior outcome as confirmed in propensity score analyses by matching the patients with non-CE glioblastoma to patients with CE glioblastoma using a large set of clinical variables. CONCLUSIONS: The absence of contrast enhancement characterizes a less aggressive clinical phenotype of IDH-wildtype glioblastomas. Maximal resection of non-CE tumors has prognostic implications and translates into favorable outcome.

Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology.

Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.

Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer.

BACKGROUND: The expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC. PATIENTS AND METHODS: Fifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses. RESULTS: An intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2). CONCLUSION: The developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy.

Prognostic evaluation of re-resection for recurrent glioblastoma using the novel RANO classification for extent of resection: A report of the RANO resect group.

BACKGROUND: The value of re-resection in recurrent glioblastoma remains controversial as a randomized trial that specifies intentional incomplete resection cannot be justified ethically. Here, we aimed to (1) explore the prognostic role of extent of re-resection using the previously proposed Response Assessment in Neuro-Oncology (RANO) classification (based upon residual contrast-enhancing (CE) and non-CE tumor), and to (2) define factors consolidating the surgical effects on outcome. METHODS: The RANO resect group retrospectively compiled an 8-center cohort of patients with first recurrence from previously resected glioblastomas. The associations of re-resection and other clinical factors with outcome were analyzed. Propensity score-matched analyses were constructed to minimize confounding effects when comparing the different RANO classes. RESULTS: We studied 681 patients with first recurrence of Isocitrate Dehydrogenase (IDH) wild-type glioblastomas, including 310 patients who underwent re-resection. Re-resection was associated with prolonged survival even when stratifying for molecular and clinical confounders on multivariate analysis; ≤1 cm3 residual CE tumor was associated with longer survival than non-surgical management. Accordingly, "maximal resection" (class 2) had superior survival compared to "submaximal resection" (class 3). Administration of (radio-)chemotherapy in the absence of postoperative deficits augmented the survival associations of smaller residual CE tumors. Conversely, "supramaximal resection" of non-CE tumor (class 1) was not associated with prolonged survival but was frequently accompanied by postoperative deficits. The prognostic role of residual CE tumor was confirmed in propensity score analyses. CONCLUSIONS: The RANO resect classification serves to stratify patients with re-resection of glioblastoma. Complete resection according to RANO resect classes 1 and 2 is prognostic.

Prognostic validation of a new classification system for extent of resection in glioblastoma: A report of the RANO resect group.

BACKGROUND: Terminology to describe extent of resection in glioblastoma is inconsistent across clinical trials. A surgical classification system was previously proposed based upon residual contrast-enhancing (CE) tumor. We aimed to (1) explore the prognostic utility of the classification system and (2) define how much removed non-CE tumor translates into a survival benefit. METHODS: The international RANO resect group retrospectively searched previously compiled databases from 7 neuro-oncological centers in the USA and Europe for patients with newly diagnosed glioblastoma per WHO 2021 classification. Clinical and volumetric information from pre- and postoperative MRI were collected. RESULTS: We collected 1,008 patients with newly diagnosed IDHwt glioblastoma. 744 IDHwt glioblastomas were treated with radiochemotherapy per EORTC-26981/22981 (TMZ/RT→TMZ) following surgery. Among these homogenously treated patients, lower absolute residual tumor volumes (in cm3) were favorably associated with outcome: patients with "maximal CE resection" (class 2) had superior outcome compared to patients with "submaximal CE resection" (class 3) or "biopsy" (class 4). Extensive resection of non-CE tumor (≤5 cm3 residual non-CE tumor) was associated with better survival among patients with complete CE resection, thus defining class 1 ("supramaximal CE resection"). The prognostic value of the resection classes was retained on multivariate analysis when adjusting for molecular and clinical markers. CONCLUSIONS: The proposed "RANO categories for extent of resection in glioblastoma" are highly prognostic and may serve for stratification within clinical trials. Removal of non-CE tumor beyond the CE tumor borders may translate into additional survival benefit, providing a rationale to explicitly denominate such "supramaximal CE resection."

Recurrent brain metastases: the role of resection of in a comprehensive multidisciplinary treatment setting.

BACKGROUND: Treatment decision for recurrent symptomatic brain metastases (BM) is challenging with scarce data regarding surgical resection. We therefore evaluated the efficacy of surgery for pretreated, recurrent BM in a comprehensive multidisciplinary treatment setting. METHODS: In a retrospective single center study, patients were analyzed, who underwent surgical resection of recurrent BM between 2007 and 2019. Intracranial event-free survival (EFS) and overall survival (OS) were evaluated by Kaplan-Maier and Cox regression analysis. RESULTS: We included 107 patients with different primary tumor entities and individual previous treatment for BM. Primary tumors comprised non-small cell lung cancer (NSCLC) (37.4%), breast cancer (19.6%), melanoma (13.1%), gastro-intestinal cancer (10.3%) and other, rare entities (19.6%). The number of previous treatments of BM ranged from one to four; the adjuvant treatment modalities comprised: none, focal or whole brain radiotherapy, brachytherapy and radiosurgery. The median pre-operative Karnofsky Performance Score (KPS) was 70% (range 40-100) and improved to 80% (range 0-100) after surgery. The complication rate was 26.2% and two patients died during the perioperative period. Sixty-seven (62.6%) patients received postoperative local radio-oncologic and/or systemic therapy. Median postoperative EFS and OS were 7.1 (95%CI 5.8-8.2) and 11.1 (95%CI 8.4-13.6) months, respectively. The clinical status (postoperative KPS ≥ 70 (HR 0.27 95%CI 0.16-0.46; p < 0.001) remained the only independent factor for survival in multivariate analysis. CONCLUSIONS: Surgical resection of recurrent BM may improve the clinical status and thus OS but is associated with a high complication rate; therefore a very careful patient selection is crucial.

Resection of symptomatic non-small cell lung cancer brain metastasis in the setting of multiple brain metastases.

OBJECTIVE: Current guidelines primarily suggest resection of brain metastases (BMs) in patients with limited lesions. With a growing number of highly effective local and systemic treatment options, this view may be challenged. The purpose of this study was to evaluate the role of metastasectomy, disregarding BM count, in a comprehensive treatment setting. METHODS: In this monocentric retrospective analysis, the authors included patients who underwent resection for at least 1 BM and collected demographic, clinical, and tumor-associated parameters. Prognostic factors for local control and overall survival (OS) were analyzed with the log-rank test and Cox proportional hazards analysis. RESULTS: The authors analyzed 216 patients. One hundred twenty-nine (59.7%) patients were diagnosed with a single/solitary BM, whereas 64 (29.6%) patients had 2-3 BMs and the remaining 23 (10.6%) had more than 3 BMs. With resection of symptomatic BMs, a significant improvement in Karnofsky Performance Scale (KPS) was achieved (p < 0.001), thereby enabling adjuvant radiotherapy for 199 (92.1%) patients and systemic treatment for 119 (55.1%) patients. During follow-up, 83 (38.4%) patients experienced local recurrence. BM count did not significantly influence local control rates. By the time of analysis, 120 (55.6%) patients had died; the leading cause of death was systemic tumor progression. The mean (range) OS after surgery was 12.7 (0-88) months. In univariate analysis, the BM count did not influence OS (p = 0.844), but age < 65 years (p = 0.007), preoperative and postoperative KPS ≥ 70 (p = 0.002 and p = 0.005, respectively), systemic metastases other than BM (p = 0.004), adjuvant radiation therapy (p < 0.001), and adjuvant systemic treatment (p < 0.001) were prognostic factors. In regression analysis, the presence of extracranial metastases (HR 2.30, 95% CI 1.53-3.48, p < 0.001), adjuvant radiation therapy (HR 0.97, 95% CI 0.23-0.86, p = 0.016), and adjuvant systemic treatment (HR 0.37, 95% CI 0.25-0.55, p < 0.001) remained as independent factors for survival. CONCLUSIONS: Surgery for symptomatic BM from non-small cell lung cancer may be indicated even for patients with multiple lesions in order to alleviate their neurological symptoms and to consequently facilitate further treatment.

Tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and treatment.

The discovery of the oncometabolite 2-hydroxyglutarate in isocitrate dehydrogenase 1-mutated (IDH1-mutated) tumor entities affirmed the role of metabolism in cancer. However, large databases with tissue metabolites that are modulated by IDH1 mutation remain an area of development. Here, we present an unprecedented and valuable resource for tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and tumor treatments in 101 tissue samples from 73 diffuse glioma patients (24 astrocytoma, 17 oligodendroglioma, 32 glioblastoma), investigated by NMR-based metabolomics and supported by RNA-Seq. We discovered comparison-specific metabolites and pathways modulated by IDH1 (IDH1 mutation status cohort) and tumor entity. The Longitudinal investigation cohort provides metabolic profiles of untreated and corresponding treated glioma samples at first progression. Most interestingly, univariate and multivariate cox regressions and Kaplan-Meier analyses revealed that tissue metabolites correlate with progression-free and overall survival. Thus, this study introduces potentially novel candidate prognostic and surrogate metabolite biomarkers for future prospective clinical studies, aiming at further refining patient stratification in diffuse glioma. Furthermore, our data will facilitate the generation of so-far-unanticipated hypotheses for experimental studies to advance our molecular understanding of glioma biology.

Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases.

BACKGROUND: The BRAF V600E mutation is present in approximately 50% of patients with melanoma brain metastases and an important prerequisite for response to targeted therapies, particularly BRAF inhibitors. As heterogeneity in terms of BRAF mutation status may occur in melanoma patients, a wild-type extracranial primary tumor does not necessarily rule out a targetable mutation in brain metastases using BRAF inhibitors. We evaluated the potential of MRI radiomics for a noninvasive prediction of the intracranial BRAF mutation status. METHODS: Fifty-nine patients with melanoma brain metastases from two university brain tumor centers (group 1, 45 patients; group 2, 14 patients) underwent tumor resection with subsequent genetic analysis of the intracranial BRAF mutation status. Preoperative contrast-enhanced MRI was manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. Finally, the best performing radiomics model was applied to the test data. Diagnostic performances were evaluated using receiver operating characteristic (ROC) analyses. RESULTS: Twenty-two of 45 patients (49%) in group 1, and 8 of 14 patients (57%) in group 2 had an intracranial BRAF V600E mutation. A linear support vector machine classifier using a six-parameter radiomics signature yielded an area under the ROC curve of 0.92 (sensitivity, 83%; specificity, 88%) in the test data. CONCLUSIONS: The developed radiomics classifier allows a noninvasive prediction of the intracranial BRAF V600E mutation status in patients with melanoma brain metastases with high diagnostic performance.

The evolving role of neurosurgery for central nervous system metastases in the era of personalized cancer therapy.

Recent therapeutic advances involving the use of systemic targeted treatments and immunotherapeutic agents in patients with advanced cancers have translated into improved survival rates. Despite the emergence of such promising pharmacological therapies and extended survival, the frequency of metastases in the central nervous system has steadily increased. Effective medical and surgical therapies are available for many patients with brain metastases and need to be incorporated into multi-disciplinary care protocols. The role of neurosurgeons is evolving within these multi-disciplinary care teams. Surgical resection of brain metastases can provide immediate relief from neurological symptoms due to large lesions and provides the histopathological diagnosis in cases of no known primary malignancy. In situations where immunotherapy is part of the oncological treatment plan, surgery may be proposed for expeditious relief of edema to remove the need for steroids. In patients with multiple brain metastases and mixed response to therapeutics or radiosurgery, tumour resampling allows tissue analysis for druggable targets or to distinguish radiation effects from progression. Ventriculo-peritoneal shunting may improve quality of life in patients with hydrocephalus associated with leptomeningeal tumour dissemination and may allow for time to administer more therapy thus prolonging overall survival. Addressing the limited efficacy of many oncological drugs for brain metastases due to insufficient blood-brain barrier penetrance, clinical trial protocols in which surgical specimens are analysed after pre-surgical administration of therapeutics offer pharmacodynamic insights. Comprehensive neurosurgical assessment remains an integral element of multi-disciplinary oncological care of patients with brain metastases and is integral to tumour biology research and therapeutic advancement.

Clinical Characteristics and Magnetic Resonance Imaging-Based Prediction of the KLF4K409Q Mutation in Meningioma.

BACKGROUND: Meningioma is the most common primary brain tumor in adults. In recent years, several non-neurofibromin 2 mutations, i.e., AKT1, SMO, TRAF7, and KLF4 mutations, specific for meningioma have been identified. This study aims to analyze the clinical impact and imaging characteristics of the KLF4K409Q mutation in meningioma. METHODS: Clinical, neuropathologic, and imaging data of 170 patients who underwent meningioma resection between 2013 and 2018 were retrospectively collected and tumors were analyzed for the presence of the KLF4K409Q mutation. We collected imaging characteristics, performed volumetric analysis of tumor size and peritumoral edema (PTBE), and calculated the edema index (EI, i.e., ratio of PTBE to tumor volume). Receiver operating characteristic curve analysis was performed to identify cut-off EI values to predict the mutational status of KLF4. RESULTS: Eighteen (10.6%) of the meningiomas carried the KLF4K409Q mutation; these were significantly associated with a secretory subtype (P < 0.001) and sphenoid wing location (P = 0.029). Smaller tumor size (P = 0.007), an increased PTBE (P = 0.012), and an increased EI (P = 0.001) proved to be significantly associated with the KLF4K409Q mutation. In receiver operating characteristic curve analysis, EI predicted the KLF4K409Q mutation with an area under the curve of 0.728 (P = 0.0016). CONCLUSIONS: The KLF4K409Q mutation is associated with a distinct small tumor subtype, prone to substantial PTBE. EI is a reliable parameter to predict the KLF4K409Q mutation in meningioma, thus providing a tool for improvement of pre- and perioperative medical management.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

The Debatable Benefit of Gross-Total Resection of Brain Metastases in a Comprehensive Treatment Setting.

Background and Purpose: The value of gross-total surgical resection remains debatable in patients with brain metastases (BMs) as most patients succumb to systemic disease progression. In this study, we evaluated the impact of the extent of resection of singular/solitary BM on in-brain recurrence (iBR), focusing on local recurrence (LR) and overall survival (OS) in an interdisciplinary adjuvant treatment setting. Patients and Methods: In this monocentric retrospective analysis, we included patients receiving surgery of one BM and subsequent adjuvant treatment. A radiologist and a neurosurgeon determined in consensus the extent of resection based on magnetic resonance imaging. The OS was calculated using Kaplan-Meier estimates; prognostic factors for LR and OS were analysed by Log rank test and Cox proportional hazards. Results: We analyzed 197 patients. Gross-total resection was achieved in 123 (62.4%) patients. All patients were treated with adjuvant radiotherapy, and 130 (66.0%) received systemic treatment. Ninety-six (48.7%) patients showed iBR with an LR rate of 23.4%. LR was not significantly influenced by the extent of resection (p = 0.139) or any other parameter. The median OS after surgery was 18 (95%CI 12.5-23.5) months. In univariate analysis, the extent of resection did not influence OS (p = 0.6759), as opposed to adjuvant systemic treatment (p < 0.0001) and controlled systemic disease (p = 0.039). Systemic treatment and controlled disease status remained independent factors for OS (p < 0.0001 and p = 0.009, respectively). Conclusions: In this study, the extent of resection of BMs neither influenced the LR nor the OS of patients receiving interdisciplinary adjuvant treatment.

Cyberknife® hypofractionated stereotactic radiosurgery (CK-hSRS) as salvage treatment for brain metastases.

PURPOSE: The introduction of hypofractionated stereotactic radiosurgery (hSRS) extended the treatment modalities beyond the well-established single-fraction stereotactic radiosurgery and fractionated radiotherapy. Here, we report the efficacy and side effects of hSRS using Cyberknife® (CK-hSRS) for the treatment of patients with critical brain metastases (BM) and a very poor prognosis. We discuss our experience in light of current literature. METHODS: All patients who underwent CK-hSRS over 3 years were retrospectively included. We applied a surface dose of 27 Gy in 3 fractions. Rates of local control (LC), systemic progression-free survival (PFS), and overall survival (OS) were estimated using Kaplan-Meier method. Treatment-related complications were rated using the Common Terminology Criteria for Adverse Events (CTCAE). RESULTS: We analyzed 34 patients with 75 BM. 53% of the patients had a large tumor, tumor location was eloquent in 32%, and deep seated in 15%. 36% of tumors were recurrent after previous irradiation. The median Karnofsky Performance Status was 65%. The actuarial rates of LC at 3, 6, and 12 months were 98%, 98%, and 78.6%, respectively. Three, 6, and 12 months PFS was 38%, 32%, and 15%, and OS was 65%, 47%, and 28%, respectively. Median OS was significantly associated with higher KPS, which was the only significant factor for survival. Complications CTCAE grade 1-3 were observed in 12%. CONCLUSION: Our radiation schedule showed a reasonable treatment effectiveness and tolerance. Representing an optimal salvage treatment for critical BM in patients with a very poor prognosis and clinical performance state, CK-hSRS may close the gap between surgery, stereotactic radiosurgery, conventional radiotherapy, and palliative care.

Stereotactic radiosurgery for treating meningiomas eligible for complete resection.

BACKGROUND: For meningiomas, complete resection is recommended as first-line treatment while stereotactic radiosurgery (SRS) is established for meningiomas of smaller size considered inoperable. If the patient´s medical condition or preference excludes surgery, SRS remains a treatment option. We evaluated the efficacy and safety of SRS in a cohort comprising these cases. METHODS: In this retrospective single-centre analysis we included patients receiving single fraction SRS either by modified LINAC or robotic guidance by Cyberknife for potentially resectable intracranial meningiomas. Treatment-related adverse events as well as local and regional control rates were determined from follow-up imaging and estimated by the Kaplan-Meier method. RESULTS: We analyzed 188 patients with 218 meningiomas. The median radiological, and clinical follow-up periods were 51.4 (6.2-289.6) and 55.8 (6.2-300.9) months. The median tumor volume was 4.2 ml (0.1-22), and the mean marginal radiation dose was 13.0 ± 3.1 Gy, with reference to the 80.0 ± 11.2% isodose level. Local recurrence was observed in one case (0.5%) after 239 months. The estimated 2-, 5-, 10- and 15-year regional recurrence rates were 1.5%, 3.0%, 6.6% and 6.6%, respectively. Early adverse events (≤ 6 months after SRS) occurred in 11.2% (CTCEA grade 1-2) and resolved during follow-up in 7.4% of patients, while late adverse events were documented in 14.4% (grade 1-2; one case grade 3). Adverse effects (early and late) were associated with the presence of symptoms or neurological deficits prior to SRS (p < 0.03) and correlated with the treatment volume (p < 0.02). CONCLUSION: In this analysis SRS appears to be an effective treatment for patients with meningiomas eligible for complete resection and provides reliable long-term local tumor control with low rates of mild morbidity.