Comparative study of fixation of intertrochanteric fracture of the femur by proximal femur nail versus dynamic hip screw

Background: Intertrochanteric fractures are common in old age group, but uncommon in younger age group. The goal of treatment of intertrochanteric fractures is restoration to pre-injury status at the earliest. The purpose of this study is to compare the functional outcome of the two fixation devices proximal femur nail (PFN) and dynamic hip screw available for intertrochanteric fractures in terms of the eventual functional outcome of the patient.Methods: Prospective study of 30 cases of Intertrochanteric fractures admitted and operated in KIMS hospital from November 2017 to May 2019. Follow-up of these patients was done at 6 weeks, 12 weeks and 24 weeks with functional evaluation was done using Harris hip score at the 24th week.Results: The results at the end of 24 weeks follow-up were calculated by the Harris hip score were better with the PFN. 66.7% of the patients operated with PFN gave excellent results as compared to 60% of patients operated with dynamic hip screw (DHS).Conclusions: We conclude that the use of PFN for the fixation of trochanteric fractures against the proven DHS offered better results along with a few advantages. PFN required smaller incision, shorter duration of surgery, less blood loss and faster recovery and better functional outcome at the end of 24 weeks. But still PFN is technically more demanding than the DHS and was found to have longer fluoroscopy exposure. 

Dose Guidance for Radiotherapy-Oriented Deep Learning Segmentation

Deep learning-based image segmentation for radiotherapy is intended to speed up the planning process and yield consistent results. However, most of these segmentation methods solely rely on distribution and geometry-associated training objectives without considering tumor control and the sparing of healthy tissues. To incorporate dosimetric effects into segmentation models, we propose a new training loss function that extends current state-of-the-art segmentation model training via a dose-based guidance method. We hypothesized that adding such a dose-guidance mechanism improves the robustness of the segmentation with respect to the dose (i.e., resolves distant outliers and focuses on locations of high dose/dose gradient). We demonstrate the effectiveness of the proposed method on Gross Tumor Volume segmentation for glioblastoma treatment. The obtained dosimetry-based results show reduced dose errors relative to the ground truth dose map using the proposed dosimetry-segmentation guidance, outperforming state-of-the-art distribution and geometry-based segmentation losses