This system proves to provide good descriptions of the deformities, is user friendly, facilitates the planning of the corrective surgical procedure, and enhances the communicative lingo between surgeons and members of cleft multidisciplinary care teams. It is broadly applicable in outreach missions with limited resources and cleft referral centers with considerable load.

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This study showed no difference between Mebo and Biafine in the incidence and severity of breast skin dermatitis during radiation therapy. However, the use of Mebo ointment was associated with decreased severe pruritus and pain which could positively affect patient comfort and quality of life.

Radiotherapy-related fibrosis remains one of the most challenging treatment related side effects encountered by patients with head and neck cancer. Several established and ongoing novel therapies have been studied with paucity of data in how to best treat these patients. This review aims to provide researchers and health care providers with a comprehensive review on the presentation, etiology, and therapeutic options for this serious condition.

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.