Supramaximal resection: retrospective study on IDH-wildtype Glioblastomas based on the new RANO-Resect classification.

BACKGROUND: The prognostic value of the extent of resection in the management of Glioblastoma is a long-debated topic, recently widened by the 2022 RANO-Resect Classification, which advocates for the resection of the non-enhancing disease surrounding the main core of tumors (supramaximal resection, SUPR) to achieve additional survival benefits. We conducted a retrospective analysis to corroborate the role of SUPR by the RANO-Resect Classification in a single center, homogenous cohort of patients. METHODS: Records of patients operated for WHO-2021 Glioblastomas at our institution between 2007 and 2018 were retrospectively reviewed; volumetric data of resected lesions were computed and classified by RANO-Resect criteria. Survival and correlation analyses were conducted excluding patients below near-total resection. RESULTS: 117 patients met the inclusion criteria, encompassing 45 near-total resections (NTR), 31 complete resections (CR), and 41 SUPR. Median progression-free and overall survival were 11 and 15 months for NTR, 13 and 17 months or CR, 20 and 24 months for SUPR, respectively (p < 0.001), with inverse correlation observed between survival and FLAIR residual volume (r -0.28). SUPR was not significantly associated with larger preoperative volumes or higher rates of postoperative deficits, although it was less associated with preoperative neurological deficits (OR 3.37, p = 0.003). The impact of SUPR on OS varied between MGMT unmethylated (HR 0.606, p = 0.044) and methylated (HR 0.273, p = 0.002) patient groups. CONCLUSIONS: Results of the present study support the validity of supramaximal resection by the new RANO-Resect classification, also highlighting a possible surgical difference between tumors with methylated and unmethylated MGMT promoter.

Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.

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.

Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.

PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models. MATERIALS AND METHODS: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR. RESULTS: The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process. CONCLUSION: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.