ASTRA: Atomic Surface Transformations for Radiotherapy Quality Assurance.

Treatment for glioblastoma, an aggressive brain tumour usually relies on radiotherapy. This involves planning how to achieve the desired radiation dose distribution, which is known as treatment planning. Treatment planning is impacted by human errors, inter-expert variability in segmenting (or outlining) the tumor target and organs-at-risk, and differences in segmentation protocols. Erroneous segmentations translate to erroneous dose distributions, and hence sub-optimal clinical outcomes. Reviewing segmentations is time-intensive, significantly reduces the efficiency of radiation oncology teams, and hence restricts timely radiotherapy interventions to limit tumor growth. Moreover, to date, radiation oncologists review and correct segmentations without information on how potential corrections might affect radiation dose distributions, leading to an ineffective and suboptimal segmentation correction workflow. In this paper, we introduce an automated deep-learning based method: atomic surface transformations for radiotherapy quality assurance (ASTRA), that predicts the potential impact of local segmentation variations on radiotherapy dose predictions, thereby serving as an effective dose-aware sensitivity map of segmentation variations. On a dataset of 100 glioblastoma patients, we show how the proposed approach enables assessment and visualization of areas of organs-at-risk being most susceptible to dose changes, providing clinicians with a dose-informed mechanism to review and correct segmentations for radiation therapy planning. These initial results suggest strong potential for employing such methods within a broader automated quality assurance system in the radiotherapy planning workflow. Code to reproduce this is available at https://github.com/amithjkamath/astraClinical Relevance: ASTRA shows promise in indicating what regions of the OARs are more likely to impact the distribution of radiation dose.

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring.

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process.

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.

BACKGROUND AND OBJECTIVE: Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. METHODS: The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. RESULTS: The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. CONCLUSIONS: The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.

Evaluation of the Functional and Radiological Outcomes of Antibiotic-Coated Intramedullary Interlocking Nailing for Gustilo and Anderson Grades 2 and 3 Open Tibial Shaft Fractures.

BACKGROUND: Tibial shaft fractures are the most common fractures among long bones. At present, implants coated with broad-spectrum antibiotics have been developed, and antimicrobial eluting implants are widely used in clinical practice. MATERIALS AND METHODS: This prospective study was conducted among 40 patients with tibial shaft fractures who visited the Department of Orthopedics in RL Jalappa Hospital, Tamaka, Kolar, Karnataka, from February 2021 to September 2021. As it is a large trauma center near the national highway, all 40 cases, including the referral cases, were operated within two months of the initiation of the study, with the last case operated in March 2021. The inclusion criteria were: patients aged more than 18 years, diaphyseal tibial fractures definitively treated by antibiotic coated intramedullary nailing, and Gustilo and Anderson grades 2, 3A, and 3B open tibial shaft fractures. All patients with grades 2, 3A, and 3B open fractures of the tibial shaft were treated with antibiotic-coated nails and followed up at one, three, and six months post-surgery. RESULTS: The mean age of patients was 35.6 years, and the mean union time of fractures was 4.2 months. Road traffic accidents (RTA) are the most common etiology for tibial bone fractures. In this study, grade 3A open fractures had the highest number of cases (N = 26). No patients in the present study developed superficial or deep infections post-operatively. All patients were assessed with Johner-Wruhs criteria at each follow-up, and they showed improvement in knee and ankle joint mobility, pain, and deformity. Most patients achieved good functional results after postoperative follow-up, followed by those with excellent results. According to the radiographic union scale in tibial shaft fractures criteria, 23 patients showed good radiological results after postoperative follow-up, followed by 15 patients with excellent and 2 patients with fair results. CONCLUSION: Most of the patients showed good to excellent functional and radiological results according to Johner-Wruhs and Radiographic Union Scale for Tibial fractures (RUST) criteria. The use of antibiotic-coated nails to treat compound tibial fractures was associated with a decreased risk of deep wound infections and good fracture healing.

Risk factors, lipid profile, and histopathological study of oral cancers in Kolar district: a case-control study.

AIMS: To estimate serum lipid profile in oral squamous cell carcinoma and correlate the risk factors and lipid profile with oral squamous cell carcinoma. MATERIALS AND METHODS: Lipid profile was done in agriculturists/laborers in the age group of 30-70 years; 56 subjects (cases = 28, control = 28) were included. Study was carried out for a duration of four months; statistical analyses applied were mean, standard deviation, and independent 't' test. P < 0.05 was considered statistically significant. RESULTS: Eleven cases had buccal mucosa cancer, nine had tongue carcinoma, and eight had gingivobuccal sulcus carcinoma. Lipid profile such as total cholesterol, triglycerides, LDL cholesterol, non-high-density lipoprotein (non-HDL) cholesterol, and very-low-density lipoprotein (VLDL) were marginally and slightly elevated in cases compared to controls. HDL was grossly decreased in cases compared to controls. CONCLUSIONS: There was a significant association between HDL and squamous cell carcinoma; maximum number of SCC had a history of smoking in the range of 10-19 years, irrespective of other lipid parameters, constrained to the fact that lipids are genetically determined, have geographical variation, and are highly skewed.