Auto planning

BACKGROUND AND PURPOSE: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. MATERIALS AND METHODS: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures...

BACKGROUND AND PURPOSE: Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART. MATERIALS AND METHODS: A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean ) to OARs...

The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy, or surgical planning. Despite a good ability to extract large organs, the capacity of U-Net inspired architectures to automatically delineate smaller structures remains a major issue, especially given the increase in receptive field size as we go deeper into the network...

PURPOSE: To evaluate the clinical applicability of a commercial artificial intelligence (AI)-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone-beam CT (iCBCT) acquisitions for intact prostate and prostate bed treatments. METHODS AND MATERIALS: DLAS models were trained using 116 iCBCT datasets with manually delineated organs-at-risk (OARs - bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines...

BACKGROUND: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning...

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs...

Sensitive zone of global climate change has been formed in China, and it has become a hot topic how can agriculture ensure food security and the supply of important agricultural products while achieving the "Dual Carbon" goal in the country. Based on such background, this paper uses the IPCC carbon emission calculation method, environmental input-output model and economic-water-carbon coefficient method to measure agricultural net carbon emissions, adopts bivariate spatial auto-correlation analysis and SYS-GMM to explore separately the relationship between agricultural net carbon emissions and effective supply of agricultural products, as well as the carbon reduction effect, growth effect and reasonable range of green technology innovation...

PURPOSE: Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS: 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively...

The unprecedented and growing number of cancer survivors requires comprehensive quality care that includes cancer surveillance, symptom management, and health promotion to reduce morbidity and mortality and improve quality of life. However, coordinated and sustainable survivorship care has been challenged by barriers at multiple levels. We outline the survivorship programs at Northwestern Medicine and the Robert H. Lurie Comprehensive Cancer Center that have evolved over two decades. Our current survivorship clinics comprise STAR (Survivors Taking Action and Responsibility) for adult survivors of childhood cancers; Adult Specialty Survivorship for survivors of breast, colorectal and testicular cancers, lymphomas, and leukemias; and Gynecologic Oncology Survivorship...

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency...

As autonomous driving technology continues to advance and gradually become a reality, ensuring the safety of autonomous driving in complex traffic scenarios has become a key focus and challenge in current research. Model-free deep reinforcement learning (Deep Reinforcement Learning) methods have been widely used for addressing motion planning problems in complex traffic scenarios, as they can implicitly learn interactions between vehicles. However, current planning methods based on deep reinforcement learning exhibit limited robustness and generalization performance...

PURPOSE: Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-observer variability. There has been little research investigating gross failure rates and failure modes of such systems. METHOD: 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI-generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid)...

Caregiving is recognised as a source of stress with potential for negative health impacts as well as positive outcomes and development of resilience. For young carers, children, and adolescents providing care for close family members, adaptation through resilience is crucial, yet work using a resilience approach is limited. This study explored protective factors and pathways to resilience in a sample of young carers, through application of the socioecological model in caring relationships. An in-depth qualitative approach was used, with in-person interviews facilitated by auto-driven photo elicitation...

PURPOSE: Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric computed tomography (CT) data and cross-scanner compatibility remain unclear. This study aimed to evaluate the performance of AI-based auto-segmentation models trained on adult CT data when applied to pediatric data sets and explore the improvement in performance gained by including pediatric training data...

Objective. We report on paraspinal motion and the clinical implementation of our proprietary software that leverages Varian's intrafraction motion review (IMR) capability for quantitative tracking of the spine during paraspinal SBRT. The work is based on our prior development and analysis on phantoms. Approach. To address complexities in patient anatomy, digitally reconstructed radiographs (DRR's) that highlight only the spine or hardware were constructed as tracking reference. Moreover, a high-pass filter and first-pass coarse search were implemented to enhance registration accuracy and stability...

BACKGROUND: Late preterm births account for a large portion of preterm births, yet the optimal method of nutrition and enteral feeding in this population remains unclear and often involves intravenous (IV) fluids. PURPOSE: To develop and implement a late preterm feeding protocol in order to decrease the necessity of IV access, decrease the use of starter parenteral nutrition (PN), and reduce the pain endured by an infant in the neonatal intensive care unit. METHODS: The Plan-Do-Study-Act quality improvement model was utilized as a framework for the implementation of this quality improvement project...

BACKGROUND: Patient registries are an important tool for networking medical caregivers and research, especially in the field of rare diseases. Individuals afflicted by multi-organ autoimmune diseases typically suffer from inflammation of multiple organs. PROJECT: GAIN (German genetic multi-organ Auto-Immunity Network) is the German network for research and therapy optimisation for individuals with congenital multi-organ autoimmune diseases. As a sub-project of the network, the registry systematically collects data from this patient group and makes it available for research purposes...

Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation
(MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013).
Approach: In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012)...

CONTEXT: Multiple sclerosis (MS) is an inflammatory, chronic, demyelinating, and neurodegenerative disorder of central nervous system, determined by an auto-immune dysfunction. Severe disability generally occurs in patients with progressive forms of MS that typically develop either after an earlier relapsing phase or less commonly from disease onset. Despite advances in research to slow the progression of MS, this condition remains a life-limiting disease with symptoms impacting negatively the lives of patients and caregivers...

BACKGROUND: Training senior radiation therapists as "adapters" to manage influencers and target editing is critical in daily online adaptive radiotherapy (oART) for cervical cancer. The purpose of this study was to evaluate the accuracy and dosimetric outcomes of automatic contouring and identify the key areas for modification. METHODS: A total of 125 oART fractions from five postoperative cervical cancer patients and 140 oART fractions from five uterine cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective study...