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. 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. 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. 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.”

Purpose of Review: To review state of art and relevant advances in the molecular genetics and management of ependymomas of children and adults.Recent Findings: Ependymomas may occur either in the brain or in the spinal cord. Compared with intracranial ependymomas, spinal ependymomas are less frequent and exhibit a better prognosis. The new WHO classification of CNS tumors of 2021 has subdivided ependymomas into different histomolecular subgroups with different outcome. The majority of studies have shown a major impact of extent of resection; thus, a complete resection must be performed, whenever possible, at first surgery or at reoperation. Conformal radiotherapy is recommended for grade 3 or incompletely resected grade II tumors. Proton therapy is increasingly employed especially in children to reduce the risk of neurocognitive and endocrine sequelae. Craniospinal irradiation is reserved for metastatic disease. Chemotherapy is not useful as primary treatment and is commonly employed as salvage treatment for patients failing surgery and radiotherapy.Summary: Standard treatments are still the mainstay of treatment: the discovery of new druggable pathways will hopefully increase the therapeutic armamentarium in the near future.

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. 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. 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. 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.

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. 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. 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. 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.

Objective: Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. Methods: The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). Results: In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. Conclusions: The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma’s MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.