Resolving spatial response heterogeneity in glioblastoma.

PURPOSE: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.

BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.

Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.

BACKGROUND: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.

Quantitative Assessment of Tumor Contact with Neurogenic Zones and Its Effects on Survival: Insights beyond Traditional Predictors.

So far, the cellular origin of glioblastoma (GBM) needs to be determined, with prevalent theories suggesting emergence from transformed endogenous stem cells. Adult neurogenesis primarily occurs in two brain regions: the subventricular zone (SVZ) and the subgranular zone (SGZ) of the hippocampal dentate gyrus. Whether the proximity of GBM to these neurogenic niches affects patient outcome remains uncertain. Previous studies often rely on subjective assessments, limiting the reliability of those results. In this study, we assessed the impact of GBM's relationship with the cortex, SVZ and SGZ on clinical variables using fully automated segmentation methods. In 177 glioblastoma patients, we calculated optimal cutpoints of minimal distances to the SVZ and SGZ to distinguish poor from favorable survival. The impact of tumor contact with neurogenic zones on clinical parameters, such as overall survival, multifocality, MGMT promotor methylation, Ki-67 and KPS score was also examined by multivariable regression analysis, chi-square test and Mann-Whitney-U. The analysis confirmed shorter survival in tumors contacting the SVZ with an optimal cutpoint of 14 mm distance to the SVZ, separating poor from more favorable survival. In contrast, tumor contact with the SGZ did not negatively affect survival. We did not find significant correlations with multifocality or MGMT promotor methylation in tumors contacting the SVZ, as previous studies discussed. These findings suggest that the spatial relationship between GBM and neurogenic niches needs to be assessed differently. Objective measurements disprove prior assumptions, warranting further research on this topic.

Single brain metastases - prognostic factors and impact of residual tumor burden on overall survival.

Background: Brain metastases (BM) are a common and challenging issue, with their incidence on the rise due to advancements in systemic therapies and increased patient survival. Most patients present with single BM, some of them without any further extracranial metastasis (i.e., solitary BM). The significance of postoperative intracranial tumor volume in the treatment of singular and solitary BM is still debated. Objective: This study aimed to determine the impact of resection and postoperative tumor burden on overall survival (OS) in patients with single BM. Methods: Patients with surgically treated single BM between 04/2007-01/2020 were retrospectively included. Residual tumor burden (RTB) was determined by manual segmentation of early postoperative brain MRI (72 h). Survival analyses were performed using Kaplan-Meier estimates for univariate analysis and Cox regression proportional hazards model for multivariate analysis, using preoperative Karnofsky performance status scale (KPSS), age, sex, RTB, incomplete resection and singular/solitary BM as covariates. Results: 340 patients were included, median age 64 years (54-71). 119 patients (35%) had solitary BM, 221 (65%) singular BM. Complete resection (RTB=0) was achieved in 73%, median preoperative tumor burden was 11.2 cm3 (5-25), and RTB 0 cm3 (0-0.2). Median OS of patients with singular BM was 13 months (4-33) vs 20 months (5-92) for solitary BM; p=0.062. Multivariate analysis revealed singular BM as independent risk factor for poorer OS: HR 1.840 (1.202-2.817), p=0.005. Complete vs. incomplete resection showed no significant OS difference (13 vs. 13 months, p=0.737). When focusing on solitary BM, complete resection led to a longer OS than incomplete resection (21 vs. 8 months), without statistical significance(p=0.250). Achieving RTB=0 resulted in higher OS for patients with solitary BM compared to singular BM (21 vs. 12 months, p=0.027). Patients who received postoperative radiotherapy (RT) had significantly longer OS compared to those without it (14 vs. 4 months, p<0.001), with favorable OS in those receiving stereotactic radiosurgery (SRS) (15 months (3-42), p<0.001) or hypofractionated stereotactic radiotherapy (HSRT). Conclusion: When complete intracranial tumor resection RTB=0 is achieved, patients with solitary BM have a favorable outcome compared to singular BM. Singular BM was confirmed as independent risk factor. There is a strong presumption that complete resection leads to an improved oncological prognosis. Patients with solitary BM tend to benefit with a favorable outcome following complete resection. Hence, surgical resection should be considered as a treatment option for patients presenting with either no or minimal extracranial disease. Furthermore, the highly favorable impact of postoperative RT on OS was demonstrated and confirmed, especially with SRS or HSRT.

Adjuvant re-irradiation vs. no early re-irradiation of resected recurrent glioblastoma: pooled comparative cohort analysis from two tertiary centers.

BACKGROUND: The optimal management strategy for recurrent glioblastoma (rGBM) remains uncertain, and the impact of re-irradiation (Re-RT) on overall survival (OS) is still a matter of debate. This study included patients who achieved gross total resection (GTR) after a second surgery after recurrence, following the GlioCave criteria. METHODS: Inclusion criteria include being 18 years or older, having histologically confirmed locally recurrent IDHwt or IDH unknown GBM, achieving MRI-proven GTR after the second surgery, having a Karnofsky performance status of at least 60% after the second surgery, having a minimum interval of 6 months between the first radiotherapy and the second surgery, and a maximum of 8 weeks from second surgery to the start of Re-RT. RESULTS: A total of 44 patients have met the inclusion criteria. The median OS after the second surgery was 14 months. All patients underwent standard treatment after initial diagnosis, including maximum safe resection, adjuvant radiochemotherapy and adjuvant chemotherapy. Re-RT did not significantly impact OS. However, MGMT promoter methylation status and a longer interval (> 12 months) between treatments were associated with better OS. Multivariate analysis revealed the MGMT status as the only significant predictor of OS. CONCLUSION: Factors such as MGMT promoter methylation status and treatment interval play crucial roles in determining patient outcomes after second surgery. Personalized treatment strategies should consider these factors to optimize the management of rGBM. Prospective research is needed to define the value of re-RT after second surgery and to inform decision making in this situation.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

Background: The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods: One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results: The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions: Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation.

OBJECTIVES: Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction. MATERIALS AND METHODS: Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS: The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose. CONCLUSIONS: Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study.

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

Amino acid PET vs. RANO MRI for prediction of overall survival in patients with recurrent high grade glioma under bevacizumab therapy.

PURPOSE: To summarize evidence on the comparative value of amino acid (AA) PET and conventional MRI for prediction of overall survival (OS) in patients with recurrent high grade glioma (rHGG) under bevacizumab therapy. METHODS: Medical databases were screened for studies with individual data on OS, follow-up MRI, and PET findings in the same patient. MRI images were assessed according to the RANO criteria. A receiver operating characteristic curve analysis was used to predict OS at 9 months. RESULTS: Five studies with a total of 72 patients were included. Median OS was significantly lower in the PET-positive than in the PET-negative group. PET findings predicted OS with a pooled sensitivity and specificity of 76% and 71%, respectively. Corresponding values for MRI were 32% and 82%. Area under the curve and sensitivity were significantly higher for PET than for MRI. CONCLUSION: For monitoring of patients with rHGG under bevacizumab therapy, AA-PET should be preferred over RANO MRI.

Radiotherapy concepts for spinal metastases-results from an online survey among radiation oncologists of the German Society for Radiation Oncology.

PURPOSE: Spinal metastases (SM) are a common radiotherapy (RT) indication. There is limited level I data to drive decision making regarding dose regimen (DR) and target volume definition (TVD). We aim to depict the patterns of care for RT of SM among German Society for Radiation Oncology (DEGRO) members. METHODS: An online survey on conventional RT and Stereotactic Body Radiation Therapy (SBRT) for SM, distributed via e­mail to all DEGRO members, was completed by 80 radiation oncologists between February 24 and April 29, 2022. Participation was voluntary and anonymous. RESULTS: A variety of DR was frequently used for conventional RT (primary: n = 15, adjuvant: n = 14). 30 Gy/10 fractions was reported most frequently. TVD in adjuvant RT was heterogenous, with a trend towards larger volumes. SBRT was offered in 65% (primary) and 21% (adjuvant) of participants' institutions. A variety of DR was reported (primary: n = 40, adjuvant: n = 27), most commonly 27 Gy/3 fractions and 30 Gy/5 fractions. 59% followed International Consensus Guidelines (ICG) for TVD. CONCLUSION: We provide a representative depiction of RT practice for SM among DEGRO members. DR and TVD are heterogeneous. SBRT is not comprehensively practiced, especially in the adjuvant setting. Further research is needed to provide a solid data basis for detailed recommendations.

Efficacy and toxicity of bimodal radiotherapy in WHO grade 2 meningiomas following subtotal resection with carbon ion boost: Prospective phase 2 MARCIE trial.

BACKGROUND: Novel radiotherapeutic modalities using carbon ions provide an increased relative biological effectiveness (RBE) compared to photons, delivering a higher biological dose while reducing radiation exposure for adjacent organs. This prospective phase 2 trial investigated bimodal radiotherapy using photons with carbon-ion (C12)-boost in patients with WHO grade 2 meningiomas following subtotal resection (Simpson grade 4 or 5). METHODS: A total of 33 patients were enrolled from July 2012 until July 2020. The study treatment comprised a C12-boost (18 Gy [RBE] in 6 fractions) applied to the macroscopic tumor in combination with photon radiotherapy (50 Gy in 25 fractions). The primary endpoint was the 3-year progression-free survival (PFS), and the secondary endpoints included overall survival, safety and treatment toxicities. RESULTS: With a median follow-up of 42 months, the 3-year estimates of PFS, local PFS and overall survival were 80.3%, 86.7%, and 89.8%, respectively. Radiation-induced contrast enhancement (RICE) was encountered in 45%, particularly in patients with periventricularly located meningiomas. Patients exhibiting RICE were mostly either asymptomatic (40%) or presented immediate neurological and radiological improvement (47%) after the administration of corticosteroids or bevacizumab in case of radiation necrosis (3/33). Treatment-associated complications occurred in 1 patient with radiation necrosis who died due to postoperative complications after resection of radiation necrosis. The study was prematurely terminated after recruiting 33 of the planned 40 patients. CONCLUSIONS: Our study demonstrates a bimodal approach utilizing photons with C12-boost may achieve a superior local PFS to conventional photon RT, but must be balanced against the potential risks of toxicities.

The importance of planning CT-based imaging features for machine learning-based prediction of pain response.

Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, semantic and clinical features were collected for all patients. Random forest (RFC) and support vector machine (SVM) classifiers were compared using repeated nested cross-validation. The best radiomics classifier was trained on CTV with an area under the receiver-operator curve (AUROC) of 0.62 ± 0.01 (RFC; 95% confidence interval). The semantic model achieved a comparable AUROC of 0.63 ± 0.01 (RFC), significantly below the clinical model (SVM, AUROC: 0.80 ± 0.01); and slightly lower than the spinal instability neoplastic score (SINS; LR, AUROC: 0.65 ± 0.01). A combined model did not improve performance (AUROC: 0,74 ± 0,01). We could demonstrate that radiomics and semantic analyses of planning CTs allowed for limited prediction of therapy response to palliative RT. ML predictions based on established clinical parameters achieved the best results.

Cytoreduction of Residual Tumor Burden Is Decisive for Prolonged Survival in Patients with Recurrent Brain Metastases-Retrospective Analysis of 219 Patients.

BACKGROUND: Despite advances in treatment for brain metastases (BMs), the prognosis for recurrent BMs remains poor and requires further research to advance clinical management and improve patient outcomes. In particular, data addressing the impact of tumor volume and surgical resection with regard to survival remain scarce. METHODS: Adult patients with recurrent BMs between December 2007 and December 2022 were analyzed. A distinction was made between operated and non-operated patients, and the residual tumor burden (RTB) was determined by using (postoperative) MRI. Survival analysis was performed and RTB cutoff values were calculated using maximally selected log-rank statistics. In addition, further analyses on systemic tumor progression and (postoperative) tumor therapy were conducted. RESULTS: In total, 219 patients were included in the analysis. Median age was 60 years (IQR 52-69). Median preoperative tumor burden was 2.4 cm3 (IQR 0.8-8.3), and postoperative tumor burden was 0.5 cm3 (IQR 0.0-2.9). A total of 95 patients (43.4%) underwent surgery, and complete cytoreduction was achieved in 55 (25.1%) patients. Median overall survival was 6 months (IQR 2-10). Cutoff RTB in all patients was 0.12 cm3, showing a significant difference (p = 0.00029) in overall survival (OS). Multivariate analysis showed preoperative KPSS (HR 0.983, 95% CI, 0.967-0.997, p = 0.015), postoperative tumor burden (HR 1.03, 95% CI 1.008-1.053, p = 0.007), and complete vs. incomplete resection (HR 0.629, 95% CI 0.420-0.941, p = 0.024) as significant. Longer survival was significantly associated with surgery for recurrent BMs (p = 0.00097), and additional analysis demonstrated the significant effect of complete resection on survival (p = 0.0027). In the subgroup of patients with systemic progression, a cutoff RTB of 0.97 cm3 (p = 0.00068) was found; patients who had received surgery also showed prolonged OS (p = 0.036). Single systemic therapy (p = 0.048) and the combination of radiotherapy and systemic therapy had a significant influence on survival (p = 0.036). CONCLUSIONS: RTB is a strong prognostic factor for survival in patients with recurrent BMs. Operated patients with recurrent BMs showed longer survival independent of systemic progression. Maximal cytoreduction should be targeted to achieve better long-term outcomes.

Identifying core MRI sequences for reliable automatic brain metastasis segmentation.

BACKGROUND: Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation. METHODS: We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE + T2-FLAIR, T1-CE + T1 + T2-FLAIR, T1-CE + T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 100 patients from five centers. RESULTS: The model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89. CONCLUSIONS: A T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions.

Comorbidity in limited disease small-cell lung cancer: Age-adjusted Charlson comorbidity index and its association with overall survival following chemoradiotherapy.

Background: Combined, platinum-based thoracic chemoradiotherapy (TCR) is the current state-of-the-art treatment for patients with limited disease (LD) small-cell lung cancer (SCLC). There is only limited data available regarding the effect of comorbidities on survival following TRC. The purpose of this study is to assess the age-adjusted Charlson comorbidity index (ACCI) as a predictor of overall survival in LD-SCLC patients undergoing TCR. Patients and methods: We retrospectively analyzed 367 SCLC patients diagnosed with LD-SCLC who received TCR between 2003 and 2017. We evaluated the ACCI (n = 348) as a predictor of overall survival (OS). In this cohort, 322 patients (88%) received platinum-based TCR (either cisplatin or carboplatin), and 37 (10%) patients received vincristine based TCR. Median radiation dose was 60 Gy (range 24-66 Gy). Additionally, 83% of patients (n = 303) received prophylactic cranial irradiation (PCI, 30 Gy in 2 Gy fractions). Kaplan-Meier survival analysis was performed for OS. For comparison of survival curves, Log-rank (Mantel-Cox) test was used. Univariate and multivariate Cox proportional-hazards ratios (HRs) were used to assess the influence of cofactors on OS. Results: Patients with an ACCI > 6 had a significantly shorter OS compared with patients with an ACCI ≤ 6 (median 11 vs. 20 months; p = 0.005). Univariate analysis for OS revealed a statistically significant effect for ACCI > 6 (HR 1.7; 95% CI 1.2-2.4; p = 0.003), PCI (HR 0.5; 95% CI 0.3-0.7; p < 0.001), and Karnofsky performance status ≤ 70% (KPS) (HR 1.4; 95% CI 1.1-1.90; p = 0.015). In multivariate analysis, OS was significantly associated with PCI (HR 0.6; 95% CI 0.4-0.9; p = 0.022) and ACCI > 6 (HR 1.5; 95% CI 1.0-2.1; p = 0.049). Conclusion: Comorbidity is significantly associated with survival in patients with LD-SCLC undergoing TCR. The ACCI may be a valuable tool to identify patients with a shorter survival and thus might be used for risk stratification and oncological decision making.

Hypofractionated stereotactic radiotherapy (HFSRT) versus single fraction stereotactic radiosurgery (SRS) to the resection cavity of brain metastases after surgical resection (SATURNUS): study protocol for a randomized phase III trial.

BACKGROUND: The brain is a common site for cancer metastases. In case of large and/or symptomatic brain metastases, neurosurgical resection is performed. Adjuvant radiotherapy is a standard procedure to minimize the risk of local recurrence and is increasingly performed as local stereotactic radiotherapy to the resection cavity. Both hypofractionated stereotactic radiotherapy (HFSRT) and single fraction stereotactic radiosurgery (SRS) can be applied in this case. Although adjuvant stereotactic radiotherapy to the resection cavity is widely used in clinical routine and recommended in international guidelines, the optimal fractionation scheme still remains unclear. The SATURNUS trial prospectively compares adjuvant HFSRT with SRS and seeks to detect the superiority of HFSRT over SRS in terms of local tumor control. METHODS: In this single center two-armed randomized phase III trial, adjuvant radiotherapy to the resection cavity of brain metastases with HFSRT (6 - 7 × 5 Gy prescribed to the surrounding isodose) is compared to SRS (1 × 12-20 Gy prescribed to the surrounding isodose). Patients are randomized 1:1 into the two different treatment arms. The primary endpoint of the trial is local control at the resected site at 12 months. The trial is based on the hypothesis that HFSRT is superior to SRS in terms of local tumor control. DISCUSSION: Although adjuvant stereotactic radiotherapy after resection of brain metastases is considered standard of care treatment, there is a need for further prospective research to determine the optimal fractionation scheme. To the best of our knowledge, the SATURNUS study is the only randomized phase III study comparing different regimes of postoperative stereotactic radiotherapy to the resection cavity adequately powered to detect the superiority of HFSRT regarding local control. TRIAL REGISTRATION: The study was retrospectively registered with ClinicalTrials.gov, number NCT05160818, on December 16, 2021. The trial registry record is available on  https://clinicaltrials.gov/study/NCT05160818 . The presented protocol refers to version V1.3 from March 21, 2021.

Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas.

BACKGROUND: The fifth version of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) in 2021 brought substantial changes. Driven by the enhanced implementation of molecular characterization, some diagnoses were adapted while others were newly introduced. How these changes are reflected in imaging features remains scarcely investigated. MATERIALS AND METHODS: We retrospectively analyzed 226 treatment-naive primary brain tumor patients from our institution who received extensive molecular characterization by epigenome-wide methylation microarray and were diagnosed according to the 2021 WHO brain tumor classification. From multimodal preoperative 3T MRI scans, we extracted imaging metrics via a fully automated, AI-based image segmentation and processing pipeline. Subsequently, we examined differences in imaging features between the three main glioma entities (glioblastoma, astrocytoma, and oligodendroglioma) and particularly investigated new entities such as astrocytoma, WHO grade 4. RESULTS: Our results confirm prior studies that found significantly higher median CBV (p = 0.00003, ANOVA) and lower median ADC in contrast-enhancing areas of glioblastomas, compared to astrocytomas and oligodendrogliomas (p = 0.41333, ANOVA). Interestingly, molecularly defined glioblastoma, which usually does not contain contrast-enhancing areas, also shows significantly higher CBV values in the non-enhancing tumor than common glioblastoma and astrocytoma grade 4 (p = 0.01309, ANOVA). CONCLUSIONS: This work provides extensive insights into the imaging features of gliomas in light of the new 2021 WHO CNS tumor classification. Advanced imaging shows promise in visualizing tumor biology and improving the diagnosis of brain tumor patients.

Comparison of first-line radiosurgery for small-cell and non-small cell lung cancer brain metastases (CROSS-FIRE).

INTRODUCTION: Historical reservations regarding stereotactic radiosurgery (SRS) for small-cell lung cancer (SCLC) brain metastases include concerns for short-interval and diffuse central nervous system (CNS) progression, poor prognoses, and increased neurological mortality specific to SCLC histology. We compared SRS outcomes for SCLC and non-small cell lung cancer (NSCLC) where SRS is well established. METHODS: Multicenter first-line SRS outcomes for SCLC and NSCLC from 2000 to 2022 were retrospectively collected (n = 892 SCLC, n = 4785 NSCLC). Data from the prospective Japanese Leksell Gamma Knife Society (JLGK0901) clinical trial of first-line SRS were analyzed as a comparison cohort (n = 98 SCLC, n = 814 NSCLC). Overall survival (OS) and CNS progression were analyzed using Cox proportional hazard and Fine-Gray models, respectively, with multivariable adjustment for cofactors including age, sex, performance status, year, extracranial disease status, and brain metastasis number and volume. Mutation-stratified analyses were performed in propensity score-matched retrospective cohorts of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) positive NSCLC, mutation-negative NSCLC, and SCLC. RESULTS: OS was superior for patients with NSCLC compared to SCLC in the retrospective dataset (median OS = 10.5 vs 8.6 months; P < .001) and in the JLGK0901 dataset. Hazard estimates for first CNS progression favoring NSCLC were similar in both datasets but reached statistical significance in the retrospective dataset only (multivariable hazard ratio = 0.82, 95% confidence interval = 0.73 to 0.92, P = .001). In the propensity score-matched cohorts, there were continued OS advantages for NSCLC patients (median OS = 23.7 [EGFR and ALK positive NSCLC] vs 13.6 [mutation-negative NSCLC] vs 10.4 months [SCLC], pairwise P values < 0.001), but no statistically significant differences in CNS progression were observed in the matched cohorts. Neurological mortality and number of lesions at CNS progression were similar for NSCLC and SCLC patients. Leptomeningeal progression was increased in patients with NSCLC compared to SCLC in the retrospective dataset only (multivariable hazard ratio = 1.61, 95% confidence interval = 1.14 to 2.26, P = .007). CONCLUSIONS: After SRS, SCLC histology was associated with shorter OS compared to NSCLC. CNS progression occurred earlier in SCLC patients overall but was similar in patients matched on baseline factors. SCLC was not associated with increased neurological mortality, number of lesions at CNS progression, or leptomeningeal progression compared to NSCLC. These findings may better inform clinical expectations and individualized decision making regarding SRS for SCLC patients.

Brain metastases in the elderly - Impact of residual tumor volume on overall survival.

Background: Due to demographic changes and an increased incidence of cancer with age, the number of patients with brain metastases (BMs) constantly increases, especially among the elderly. Novel systemic therapies, such as immunotherapy, have led to improved survival in recent years, but intracranial tumor progression may occur independently of a systemically effective therapy. Despite the growing number of geriatric patients, they are often overlooked in clinical trials, and there is no consensus on the impact of BM resection on survival. Objectives: The aim of this study was to analyze the impact of resection and residual tumor volume on clinical outcome and overall survival (OS) in elderly patients suffering from BM. Methods: Patients ≥ 75 years who had surgery for BM between April 2007 and January 2020 were retrospectively included. Residual tumor burden (RTB) was determined by segmentation of early postoperative brain MRI (72 h). Contrast-enhancing tumor subvolumes were segmented manually. "Postoperative tumor volume" refers to the targeted BMs. Impact of preoperative Karnofsky performance status scale (KPSS), age, sex and RTB on OS was analyzed. Survival analyses were performed using Kaplan-Meier estimates for the univariate analysis and the Cox regression proportional hazards model for the multivariate analysis. Results: One hundred and one patients were included. Median age at surgery was 78 years (IQR 76-81). Sixty-two patients (61%) had a single BM; 16 patients (16%) had two BMs; 13 patients (13%) had three BMs; and 10 patients (10%) had more than three BMs. Median preoperative tumor burden was 10.3 cm3 (IQR 5-25 cm3), and postoperative tumor burden was 0 cm3 (IQR 0-1.1 cm3). Complete cytoreduction (RTB = 0) was achieved in 52 patients (52%). Complete resection of the targeted metastases was achieved in 78 patients (78%). Median OS was 7 months (IQR 2-11). In univariate analysis, high preoperative KPSS (HR 0.986, 95% CI 0.973-0.998, p = 0.026) and small postoperative tumor burden (HR 1.025, 95% CI 1.002-1.047, p = 0.029) were significantly associated with prolonged OS. Patients with RTB = 0 survived significantly longer than those with residual tumor did (12 [IQR 5-19] vs. 5 [IQR 3-7] months, p = 0.007). Furthermore, prolongation of survival was significantly associated with surgery in patients with favorable KPSS, with an adjusted HR of 0.986 (p = 0.026). However, there were no significances regarding age. Conclusions: RTB is a strong predictor for prolonged OS, regardless of age or cancer type. Postoperative MRI should confirm the extent of resection, as intraoperative estimates do not warrant a complete resection. It is crucial to aim for maximal cytoreduction to achieve the best long-term outcomes for these patients, despite the fact the patients are advanced in age.