Background: Treatment of elderly glioblastoma patients (EGP) is a challenge in neuro-oncology. The comprehensive geriatric assessment (CGA) is currently used to assess geriatric oncological patients with other types of tumors. We performed a large retrospective study to analyze its predictive role in EGP. Methods: Patients aged ≥65 years with histologically confirmed diagnosis of glioblastoma were enrolled. CGA included the following tests: the Cumulative Illness Rating Scale-Comorbidity and Severity Index, Activities of Daily Living, Instrumental Activities of Daily Living, the Mini Mental State Examination, and the Geriatric Depression Scale. Based on CGA results, each patient was categorized as fit, vulnerable, or frail. Results: We enrolled 113 patients. According to the CGA scores, 35% of patients were categorized as “fit”, 30% as “vulnerable”, and 35% as “frail” patients. Median overall survival was 16.5, 12.1, and 10.3 months in fit, vulnerable, and frail patients (p = 0.1), respectively. On multivariate analysis, the CGA score resulted an independent predictor of survival; indeed, vulnerable and frail patients had a hazard ratio of 1.5 and 2.2, respectively, compared to fit patients (p = 0.04). No association between CGA and progression-free survival (PFS) was demonstrated. Conclusions: The CGA score proved to be a significant predictor of mortality in EGP, and it could be a useful treatment decision tool.

Objective: To assess whether RAF and MEK inhibitors (RAFi/MEKi) can provide long-term clinical benefit in adult patients with BRAF V600-mutant glial and glioneuronal tumors (GGNT), we analyzed tumor response and long-term outcome in a retrospective cohort. Methods: We performed a retrospective search in the institutional databases of six neuroncology departments for adult patients with recurrent or disseminated BRAF V600-mutant GGNT treated with RAFi/MEKi. Results: Twenty-eight adults with recurrent or disseminated BRAF V600-mutant gangliogliomas (n=9), pleomorphic xanthoastrocytomas (n=9), and diffuse gliomas (n=10) were included in the study. At the time treatment with RAFi/MEKi was started, all tumors displayed radiological features of high-grade neoplasms. Thirteen patients received RAFi as single agents [vemurafenib (n=11), dabrafenib (n=2)], and 15 combinations of RAFi/MEKi [vemurafenib+cobimetinib (n=5), dabrafenib+trametinib (n=10)]. Eleven patients achieved a partial or complete response (11/28, 39%), with a median reduction of -78% in their tumor burden. Responders experienced a median increase of 10 points in their Karnofsky Performance Status (KPS) and a median progression-free survival of 18 months, which was longer than achieved with first-line treatment (i.e., 7 months, p=0.047). Responders had better KPS (p=0.018), tended to be younger (p=0.061) and to be treated earlier (p=0.099) compared to non-responders. Five patients were rechallenged with RAFi/MEKi at progression, with novel tumor responses in two. On univariate and multivariate analyses, response to RAFi/MEKi was an independent predictor of overall survival. Conclusions: Our study highlights the clinical benefits of RAFi/MEKi in adult patients with BRAF-mutant GGNT, encourages the systematic screening and rechallenge in responders.

Background. O⁶-methylguanine (O⁶-MeG)-DNA methyltransferase (MGMT) methylation status is a predictive factor for alkylating treatment efficacy in glioblastoma patients, but its prognostic role is still unclear. We performed a large, multicenter study to evaluate the association between MGMT methylation value and survival. Methods. We evaluated glioblastoma patients with an assessment of MGMT methylation status by pyrosequencing from nine Italian centers. The inclusion criteria were histological diagnosis of IDH wild-type glioblastoma, Eastern Cooperative Oncology Group Performance Status (ECOG-PS) ≤2, and radio-chemotherapy treatment with temozolomide. The relationship between OS and MGMT was investigated with a time-dependent Receiver Operating Characteristics (ROC) curve and Cox regression models. Results. In total, 591 newly diagnosed glioblastoma patients were analyzed. The median OS was 16.2 months. The ROC analysis suggested a cut-off of 15% for MGMT methylation. The 2-year Overall Survival (OS) was 18.3% and 51.8% for MGMT methylation <15% and ≥15% (p < 0.0001). In the multivariable analysis, MGMT methylation <15% was associated with impaired survival (p < 0.00001). However, we also found a non-linear association between MGMT methylation and OS (p = 0.002): median OS was 14.8 months for MGMT in 0–4%, 18.9 months for MGMT in 4–40%, and 29.9 months for MGMT in 40–100%. Conclusions. Our findings suggested a non-linear relationship between OS and MGMT promoter methylation, which implies a varying magnitude of prognostic effect across values of MGMT promoter methylation by pyrosequencing in newly diagnosed IDH wild-type glioblastoma patients treated with chemoradiotherapy.

(1) Background: We investigated the role of [11C]-methionine PET in a cohort of newly diagnosed glioblastoma multiforme (GBM) patients to evaluate whether it could modify the extent of surgical resection and improve radiation therapy volume delineation. (2) Methods: Newly diagnosed GBM patients, ages 18–70, with a Karnofsky performance scale (KPS) ≥ 70 with available MRI and [11C]-methionine PET were included. Patients were treated with different amounts of surgical resection followed by radio-chemotherapy. The role of [11C]-methionine PET in surgical and RT planning was analyzed. A threshold of SUVmax was searched. (3) Results: From August 2013 to April 2016, 93 patients were treated and included in this analysis. Residual tumor volume was detected in 63 cases on MRI and in 78 on [11C]-methionine PET, including 15 receiving gross total resection. The location of uptake was mainly observed in FLAIR abnormalities. [11C]-methionine uptake changed RT volume in 11% of patients. The presence of [11C]-methionine uptake in patients receiving GTR proved to influence survival (p = 0.029). The threshold of the SUVmax conditioning outcome was five. (4) Conclusions: [11C]-methionine PET allowed to detect areas at higher risk of recurrence located in FLAIR abnormalities in patients affected by GBM. A challenging issue is represented by integrating morphological and functional imaging to better define the extent of surgical resection to perform.

Background: Differentiating radionecrosis from neoplastic progression after stereotactic radiosurgery (SRS) for brain metastases is a diagnostic challenge. Previous studies have often been limited by datasets lacking histologically confirmed diagnoses. This study aimed to develop automated models for distinguishing radionecrosis from disease progression on brain MRI, utilizing cases with definitive histopathological confirmation. Methods: This multi-center retrospective study included patients who underwent surgical resection for suspected brain metastasis progression after SRS. Presurgical FLAIR and postcontrast T1 (T1w-ce) were segmented using a convolutional neural network (CNN) and compared with manual segmentation by means of Dice score. Radiomics features were extracted from each lesion, and a Random Forest model was trained on 70% of the internal dataset and evaluated on the remaining 30% and the complete external dataset. A 3DResNet-CNN was trained on the same split dataset. Validation was performed on the external dataset. Postsurgical histology was available for all cases. Results: One hundred and twenty four brain metastases were included (104 from center 1 and 20 from center 2). Sole radionecrosis was histologically detected in 34 cases (27.4%). In the internal dataset, univariate and multivariate analysis identified 131 significantly different radiomics features, including GLDM_DNUN and GLDM_SDE within the enhancing area on the T1w-ce. On the external test dataset, the Random Forest model and the 3DResNet-CNN yielded accurate results in terms of accuracy (80.0%, 85.0%), AUROC (0.830, 0.893) and sensitivity (92.8%, 100%) in radionecrosis prediction, respectively. Conclusion: Artificial intelligence could be employed to differentiate between radionecrosis and brain metastasis progression upon SRS, potentially reducing unnecessary surgical interventions.

Background: Total marrow (lymphoid) irradiation (TMI/TMLI) is a radiotherapy treatment used to selectively target the bone marrow and lymph nodes in conditioning regimens for allogeneic hematopoietic stem cell transplantation. A complex field geometry is needed to cover the large planning target volume (PTV) of TMI/TMLI with volumetric modulated arc therapy (VMAT). Five isocenters and ten overlapping fields are needed for the upper body, while, for patients with large anatomical conformation, two specific isocenters are placed on the arms. The creation of a field geometry is clinically challenging and is performed by a medical physicist (MP) specialized in TMI/TMLI.Purpose: To develop convolutional neural networks (CNNs) for automatically generating the field geometry of TMI/TMLI.Methods: The dataset comprised 117 patients treated with TMI/TMLI between 2011 and 2023 at our Institute. The CNN input image consisted of three channels, obtained by projecting along the sagittal plane: (1) average CT pixel intensity within the PTV; (2) PTV mask; (3) brain, lungs, liver, bowel, and bladder masks. This “averaged” frontal view combined the information analyzed by the MP when setting the field geometry in the treatment planning system (TPS). Two CNNs were trained to predict the isocenters coordinates and jaws apertures for patients with (CNN‐1) and without (CNN‐2) isocenters on the arms. Local optimization methods were used to refine the models output based on the anatomy of the patient. Model evaluation was performed on a test set of 15 patients in two ways: (1) by computing the root mean squared error (RMSE) between the CNN output and ground truth; (2) with a qualitative assessment of manual and generated field geometries—scale: 1 = not adequate, 4 = adequate—carried out in blind mode by three MPs with different expertise in TMI/TMLI. The Wilcoxon signed‐rank test was used to evaluate the independence of the given scores between manual and generated configurations (p < 0.05 significant).Results: The average and standard deviation values of RMSE for CNN‐1 and CNN‐2 before/after local optimization were 15 ± 2/13 ± 3 mm and 16 ± 2/18 ± 4 mm, respectively. The CNNs were integrated into a planning automation software for TMI/TMLI such that the MPs could analyze in detail the proposed field geometries directly in the TPS. The selection of the CNN model to create the field geometry was based on the PTV width to approximate the decision process of an experienced MP and provide a single option of field configuration. We found no significant differences between the manual and generated field geometries for any MP, with median values of 4 versus 4 (p = 0.92), 3 versus 3 (p = 0.78), 4 versus 3 (p = 0.48), respectively. Starting from October 2023, the generated field geometry has been introduced in our clinical practice for prospective patients.Conclusions: The generated field geometries were clinically acceptable and adequate, even for an MP with high level of expertise in TMI/TMLI. Incorporating the knowledge of the MPs into the development cycle was crucial for optimizing the models, especially in this scenario with limited data.