Abstract This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks ...involved in the radiotherapy workflow. The radiotherapy framework is presented on two different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy (ART). The second level is referred to as biology-driven workflow, explored in research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A twofold role for AI is defined according to these two different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers which were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or to multiomics, when complemented by clinical and biological parameters (i.e., biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI’s growing role in personalized radiotherapy.
Abstract Recent advancements in computed tomography (CT) technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware ...enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual x-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilising PCD-CT in comparison to conventional standard energy integrating detector CT (EID-CT).
Abstract Objective To efficiently use medical resources and offer optimal personalized treatment for individuals with Omicron infection, it's vital to predict the disease's outcome early on. This ...research developed three machine learning models to foresee the results for Omicron-infected patients. Methods Data from 253 Omicron-infected patients, including their CT scans, clinical details, and relevant laboratory values, were studied. The patients were categorized into two groups based on their disease progression: favorable prognosis and unfavorable prognosis. Patients manifesting respiratory failure, acute liver or kidney impairment, or fatalities were placed in the “poor” group. Those lacking such symptoms were allocated to the “good” group. The participants were randomly split into training set (202) and validation set (51) with an 8:2 ratio. Radiomics features were produced using image processing, focused segmentation, feature extraction, and selection, leading to the establishment of a radiomics model. A univariate logistic regression method identified potential clinical factors contributing to a clinical model's development. Eventually, the fused feature set, integrating radiomics features and clinical indicators, was used for the combined model. The model's prediction performance was assessed using the area under the receiver operating characteristic curve (AUC). The model's clinical usefulness was evaluated by generating calibration and decision curves. Results Compared to other classification models, the combined model showcased the best classification performance. It achieved an AUC of 0.848 and accuracy of 0.763 in the training set, and 0.797 and 0.750 in the validation set, respectively. Conclusion This study employed machine learning model to accurately predict the prognosis of Omicron-infected patients. ADVANCES IN KNOWLEDGE： (1) Topic innovation: At present, there is a lack of research on the use of CT images to construct machine learning models to predict the prognosis of patients with Omicjon infection. This study intends to establish clinical, radiomics and combined models to provide more possibilities for the identification of the two. (2) Platform innovation: The feature extraction and screening and the establishment of omics model in this study will be completed in the intelligent scientific research platform, which can reduce the error caused by human error, simplify the operation steps and save the time of data processing time.
Abstract Objectives This article is an evaluation of the current trial processes within a national proton beam therapy (PBT) clinical trial service in the United Kingdom. The work within the article ...identifies priority challenges associated with the implementation of PBT trials with a view to improving patient trial processes. Methods The nominal group technique (NGT) was used. Five Clinical Trials Radiographers were asked the target question “what are the major challenges when implementing PBT clinical trials and facilitating PBT trial-related activities?” Participants individually and silently listed their challenges to the target question. Following this, group discussion clarified and refined responses. Participants then individually selected five challenges that they deemed most pertinent to the target question, giving a weighted score (out of 10). Individual scores were combined to provide a ranked, weighted order of challenges. Further group discussion identified improvement strategies to the highest scored challenges. Results After combining lists generated by participants, 59 challenges were identified. Group discussion eliminated 27 responses. Eighteen were merged, resulting in 14 challenges. The two challenges that ranked highest were: (i) lack of initial understanding of the responsibilities of teams and who the relevant stakeholders were, and (ii) that a national PBT service requires the provision of shared care across multi-disciplinary teams and sites. Improvement areas include the development of shared protocols, clarifying stakeholder responsibilities and improving communication between centres to streamline PBT trial processes. Conclusions This work has identified priority areas requiring development to improve the conduct of a national PBT clinical trials programme. Advances in knowledge This is the first publication to evaluate current clinical trial processes for the United Kingdom’s PBT service.
The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on ...radiographs of the appendicular skeleton.
The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated.
Patient-wise sensitivity increased from 72% to 80% (
< .05) and patient-wise specificity increased from 81% to 85% (
< .05) in exams aided by the AI tool compared to the unaided exams. The increase in sensitivity resulted in a relative reduction of missed fractures of 29%. The average rate of false positives per patient decreased from 0.16 to 0.14, corresponding to a relative reduction of 21%. There was no significant difference in average reading time spent per exam. The largest gain in fracture detection performance, with AI support, across all readers, was on nonobvious fractures with a significant increase in sensitivity of 11 percentage points (pp) (60%-71%).
The diagnostic performance for detection of traumatic fractures on radiographs of the appendicular skeleton improved among nonspecialist readers tested AI fracture detection support tool showed an overall reader improvement in sensitivity and specificity when supported by an AI tool. Improvement was seen in both sensitivity and specificity without negatively affecting the interpretation time.
The division and analysis of obvious and nonobvious fractures are novel in AI reader comparison studies like this.
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded ...in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
The CT arthrogram is an underrated diagnostic study of the joint. Although MRI is considered superior to CT in joint imaging due to its higher resolution, CT arthrograms provide unique insights into ...the knee joint, with simultaneous dynamic assessment and an option for management in some conditions. In this pictorial essay, I will discuss the standard techniques and various pathologies affecting the knee joint and their CT arthrography appearance.
We validated an auto-contouring algorithm for heart substructures in lung cancer patients, aiming to establish its accuracy and reliability for radiotherapy (RT) planning. We focus on contouring an ...amalgamated set of subregions in the base of the heart considered to be a new organ at risk, the cardiac avoidance area (CAA), to enable maximum dose limit implementation in lung RT planning.
The study validates a deep-learning model specifically adapted for auto-contouring the CAA (which includes the right atrium, aortic valve root, and proximal segments of the left and right coronary arteries). Geometric, dosimetric, quantitative, and qualitative validation measures are reported. Comparison with manual contours, including assessment of interobserver variability, and robustness testing over 198 cases are also conducted.
Geometric validation shows that auto-contouring performance lies within the expected range of manual observer variability despite being slightly poorer than the average of manual observers (mean surface distance for CAA of 1.6 vs 1.2 mm, dice similarity coefficient of 0.86 vs 0.88). Dosimetric validation demonstrates consistency between plans optimized using auto-contours and manual contours. Robustness testing confirms acceptable contours in all cases, with 80% rated as "Good" and the remaining 20% as "Useful."
The auto-contouring algorithm for heart substructures in lung cancer patients demonstrates acceptable and comparable performance to human observers.
Accurate and reliable auto-contouring results for the CAA facilitate the implementation of a maximum dose limit to this region in lung RT planning, which has now been introduced in the routine setting at our institution.
This article seeks to determine the prevalence of a complete circle of Willis (CoW) and its common morphological variations in a south Trinidad population, while also investigating the influence of ...gender, age, and ethnicity on CoW morphology.
A prospective, descriptive, cross-sectional study was done on the magnetic resonance images for consecutive patients who had a brain MRI/magnetic resonance angiography at a tertiary health institution in south Trinidad between October 2019 and September 2020. Patients with significant cerebrovascular disease and/or a history of prior neurosurgical intervention were excluded.
A complete CoW was seen in 24.3%, with more complete circles observed in younger participants (≤45 years) and Afro-Trinidadians. No gender predilection for a complete CoW was demonstrated. The most common variations in the anterior and posterior parts of the circle were a hypoplastic anterior communicating artery (8.6%,
= 13) and bilateral aplastic posterior communicating arteries (18.4%,
= 28), respectively.
Significant variations exist in the CoW of a south Trinidad population with a frequency of complete in 24.3%, and more complete circles in younger patients and Afro-Trinidadians. Gender did not influence CoW morphology.
Structural abnormalities in the CoW may be linked to future incidence of cerebrovascular diseases and should therefore be communicated to the referring physician in the written radiology report. Knowledge of variant anatomy and its frequency for a particular populations is also required by neurosurgeons and neuro-interventional radiologists to help with preprocedural planning and to minimize complications.
