The exposure to alcohol in utero has been known to damage the developing foetus. Foetal alcohol spectrum disorders is an umbrella term that highlights a range of adverse effects linked to alcohol ...exposure in utero. Multiple studies have shown specific brain abnormalities, including a reduction in brain size, specifically in the deep nuclei and cerebellum, and parietal and temporal lobe white matter changes. While studies ascertained that other prenatal risk factors, such as maternal use of illicit drugs or lack of pre-natal care, and post-natal risk factors, such as physical or sexual abuse and low socioeconomic status, may be involved in the pathology of variances in foetal neurological abnormalities, prenatal alcohol exposure remained the strongest factor for effects on brain structure and function. Particularly, the number of days of alcohol consumption per week and drinking during all three trimesters of the pregnancy indicating the strongest relationship with brain abnormalities. Further studies are needed to explain pre-natal risk factors in isolation as well as in combination for neurodevelopmental outcomes. The diverse phenotypic presentations described indicate that the diagnostic criteria of foetal alcohol spectrum disorder must be refined to better represent the range of neurologic anomalies.
This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of ...automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.
There have been many applications and influences of Artificial intelligence (AI) in many sectors and its professionals, that of radiotherapy and the medical physicist is no different. AI and ...technological advances have necessitated changing roles of medical physicists due to the development of modernized technology with image-guided accessories for the radiotherapy treatment of cancer patients. Given the changing role of medical physicists in ensuring patient safety and optimal care, AI can reshape radiotherapy practice now and in some years to come. Medical physicists’ roles in radiotherapy practice have evolved to meet technology for the management of better patient care in the age of modern radiotherapy. This short review provides an insight into the influence of AI on the changing role of medical physicists in each specific chain of the workflow in radiotherapy in which they are involved.
Objective:
The objectives of this study are: (1) to develop a convolutional neural network model that yields pseudo high-energy CT (CT
pseudo_high
) from simple image processed low-energy CT (CT
low
...) images, and (2) to create a pseudo iodine map (IM
pseudo
) and pseudo virtual non-contrast (VNC
pseudo
) images for thoracic and abdominal regions.
Methods:
Eighty patients who underwent dual-energy CT (DECT) examinations were enrolled. The data obtained from 55, 5, and 20 patients were used for training, validation, and testing, respectively. The ResUnet model was used for image generation model and was trained using CT
low
and high-energy CT (CT
high
) images. The proposed model performance was evaluated by calculating the CT values, image noise, mean absolute errors (MAEs), and histogram intersections (HIs).
Results:
The mean difference in the CT values between CT
pseudo_high
and CT
high
images were less than 6 Hounsfield unit (HU) for all evaluating patients. The image noise of CT
pseudo_high
was significantly lower than that of CT
high
. The mean MAEs was less than 15 HU, and HIs were almost 1.000 for all the patients. The evaluation metrics of IM and VNC exhibited the same tendency as that of the comparison between CT
pseudo_high
and CT
high
images.
Conclusions:
Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images.
Advances in knowledges:
We constructed the CNN-based model which can generate pseudo DECT image and DECT-derived material-specific image using only simple image-processed CT
low
images for the thoracic and abdominal regions.
Intensity-modulated radiotherapy (IMRT) is a well-established radiotherapy technique for delivering radiation to cancer with high conformity while sparing the surrounding normal tissue. Two main ...purposes of this study are: (1) to investigate dose calculation accuracy of helical IMRT (HIMRT) and volumetric-modulated arc therapy (VMAT) on surface region and (2) to evaluate the dosimetric efficacy of HIMRT and VMAT for scalp-sparing in whole brain radiotherapy (WBRT).
First, using a radiochromic film and water-equivalent phantom with three types of boluses (1, 3, 5 mm), calculation/measurement dose agreement at the surface region in the VMAT and HIMRT plans were examined. Then, HIMRT, 6MV-VMAT and 10MV-VMAT with scalp-sparing, and two conventional three-dimensional conformal radiotherapy plans (6MV-3DCRT and 10MV-3DCRT; as reference data) were created for 30 patients with brain metastasis (30 Gy/10 fractions). The mean dose to the scalp and the scalp volume receiving 24 and 30 Gy were compared.
The percentage dose differences between the calculation and measurement were within 7%, except for the HIMRT plan at a depth of 1 mm. The averaged mean scalp doses Gy, V24Gy %, and V30Gy % (1SD) for 6MV-3DCRT, 10MV-3DCRT, HIMRT, 6MV-VMAT, and 10MV-VMAT were 26.6 (1.1), 86.4 (7.3), 13.2 (4.2), 25.4 (1.0), 77.8 (7.5), 13.2 (4.2), 23.2 (1.5), 42.8 (19.2), 0.2 (0.5), 23.6 (1.6), 47.5 (17.9), 1.2 (1.8), and 22.7 (1.7), 36.4 (17.6), 0.7 (1.1), respectively.
Regarding the dose parameters, HIMRT achieved a lower scalp dose compared with 6MV-VMAT. However, the highest ability to reduce the mean scalp dose was showed in 10MV-VMAT.
Scalp-sparing WBRT using HIMRT or VMAT may prevent radiation-induced alopecia in patients with BM.
Objectives:
To identify issues of principle and practice giving rise to misunderstandings in reviewing evidence, to illustrate these by reference to the Nordic Cochrane Review (NCR) and its ...interpretation of two trials of mammographic screening, and to draw lessons for future reviewing of published results.
Methods:
A narrative review of the publications of the Nordic Cochrane Review of mammographic screening (NCR), the Swedish Two-County Trial (S2C) and the Canadian National Breast Screening Study 1 and 2 (CNBSS-1 and CNBSS-2).
Results:
The NCR concluded that the S2C was unreliable, despite the review’s complaints being shown to be mistaken, by direct reference to the original primary publications of the S2C. Repeated concerns were expressed by others about potential subversion of randomisation in CNBSS-1 and CNBSS-2; however, the NCR continued to rely heavily on the results of these trials. Since 2022, however, eyewitness evidence of such subversion has been in the public domain.
Conclusions:
An over-reliance on nominal satisfaction of checklists of criteria in systematic reviewing can lead to erroneous conclusions. This occurred in the case of the NCR, which concluded that mammographic screening was ineffective or minimally effective. Broader and more even-handed reviews of the evidence show that screening confers a substantial reduction in breast cancer mortality.
Advances in knowledge:
Those carrying out systematic reviews should be aware of the dangers of over-reliance on checklists and guidelines. Readers of systematic reviews should be aware that a systematic review is just another study, with the capability that all studies have of coming to incorrect conclusions. When a review seems to overturn the current position, it is essential to revisit the publications of the primary research.
Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, ...because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners’ unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.
Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language ...processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.
Objective:
The objective of this review was to examine the impact of previous mammogram availability on radiologists’ performance from screening populations and experimental studies.
Materials and ...Methods:
A search of the literature was conducted using five databases: MEDLINE, PubMed, Web of Science, ScienceDirect, and CINAHL as well as Google and reference lists of articles. Keywords were combined with “AND” or “OR” or “WITH” and included “prior mammograms, diagnostic performance, initial images, diagnostic efficacy, subsequent images, previous imaging, and radiologist’s performance”. Studies that assessed the impact of previous mammogram availability on radiologists’ performance were reviewed. The Standard for Reporting Diagnostic Accuracy guidelines was used to critically appraise individual sources of evidence.
Results:
A total of 15 articles were reviewed. The sample of mammogram cases used across these studies varied from 36 to 1,208,051. Prior mammograms did not affect sensitivity with priors: 62–86% (mean = 73.3%); without priors: 69.4–87.4% (mean = 75.8%) and cancer detection rate, but increased specificity with priors: 72–96% (mean = 87.5%); without priors: 63–87% (mean = 80.5%) and reduced false-positive rates with priors: 3.7 to 36% (mean = 19.9%); without priors 13.3–49% (mean = 31.4%), recall rates with priors: 3.8–57% (mean = 26.6%); without priors: 4.9%–67.5% (mean = 37.9%), and abnormal interpretation rate decreased by 4% with priors. Evidence for the associations between the availability of prior mammograms and positive-predictive value, area under the curve (AUC) from the receiver operating characteristic curve (ROC) and localisation ROC AUC, and positive-predictive value of recall is limited and unclear.
Conclusion:
Availability of prior mammograms reduces recall rates, false-positive rates, abnormal interpretation rates, and increases specificity without affecting sensitivity and cancer detection rate.
Objective
To evaluate patient characteristics, risk factors, disease course, and management of cervical vertebral osteomyelitis in patients who had radiation for head and neck cancers.
Methods
A ...retrospective cohort study (case series) of patients diagnosed with post-radiation osteomyelitis of the cervical spine between 2012 and 2021. Data were collected from the patient’s medical files.
Results
Seven patients (71% male) with post-radiation cervical osteomyelitis were reviewed. The median patient age was 64 years. The mean interval between diagnosis of osteomyelitis and the first and last radiotherapy course was 8.3 and 4.0 years, respectively. A medical or surgical event preceded the diagnosis in four patients (57%) by a mean of 46.25 days. Common imaging findings were free air within the cervical structures and fluid collection. Four patients recovered from osteomyelitis during the follow-up within an average of 65 days.
Conclusion:
Post-radiation osteomyelitis is characterized by a subtle presentation, challenging diagnosis, prolonged treatment, and poor outcome. Clinicians should maintain a high index of suspicion for the long-term after radiotherapy. Multidisciplinary evaluation and management are warranted.
Advances in knowledge:
The study describes post-radiotherapy osteomyelitis of the cervical spine, a rare and devastating complication. Literature data regarding this complication are sparse.
