RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation.

Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate. This research aims to address this gap by introducing a novel 'Region-Focused Selection Plus (RFS+)' strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation.

Neurocutaneous Melanosis with Leptomeningeal Melanoma Involving Supratentorium and Infratentorium.

Neurocutaneous melanoma is a rare congenital syndrome associated with congenital melanocytic nevi with meningeal melanosis or melanoma. The disease is aggressive and has a high propensity for leptomeningeal metastases. We present the case history of a man with neurocutaneous melanoma managed with radical excision followed by hypofractionated adjuvant radiotherapy. One year, eight months later, he had a recurrence of the condition with leptomeningeal spread and was managed with re-excision of the recurrent lesion. Although our patient was disease-free for 20 months after the initial surgery, he survived only approximately five months after the second surgery, which reflects the associated poor prognosis of the disease.

Hypofractionated Palliative Radiotherapy in Locally Advanced Inoperable Head and Neck Cancer: CMC Vellore Experience.

BACKGROUND: A novel, short duration, palliative radiotherapy schedule for inoperable head and neck cancer was evaluated in terms of palliation of cancer-related symptoms and acute toxicities. MATERIALS AND METHODS: Thirty-six patients with inoperable head and neck cancer were included in the study (2010-2012). All patients received 40 Gy in 10 fractions (equivalent dose: 49.8 Gy in conventional fractionation) with 2 fractions per week. Treatment-related toxicity was assessed using Radiation Therapy Oncology Group criteria. Functional Assessment of Cancer Therapy (Head and Neck, FACT H and N) quality of life (QOL) tool was administered before starting and at the completion of radiotherapy. Mean value before and after treatment was compared (paired t-test, P = 0.05, two-tailed for significance). RESULTS: Thirty-three patients (male: 29, female: 4, mean age: 57.8 ± 9.7 years) were included in the analysis (three patients discontinued treatment due to socioeconomic reasons). All patients had advanced inoperable head and neck cancers (27% IVA, 61% IVB, 9% IVC, TNM stage and 3% recurrent disease). Distressing pain at primary site (42%), dysphagia (18%), neck swelling (30%), and hoarseness (10%) were common presentations. Incidence of grade III mucositis and dermatitis and pain was 18%, 3%, and 24%, respectively. Planned radiotherapy without any interruptions was completed by 73% patients. QOL assessment showed improvement in social well-being (17.4 vs. 20.01, P = 0.03), but no significant change was observed in head and neck specific score (25.1 vs. 25.0, P = NS) after treatment. Reduction of pain was observed in 88% patients and 60% patients had improvement of performance status. Median overall survival of the cohort was 7 months. CONCLUSIONS: The study shows that this short duration palliative radiotherapy schedule is a clinically viable option for advanced inoperable head and neck cancer to achieve significant palliation of the main presenting symptoms like pain, dysphagia, and throat pain.