Dose reduction (DR) of first-generation biologics for plaque psoriasis (TNF-alpha inhibitors (i) and interleukin (IL)-12/23i) has been described in a previous scoping review. The literature on the DR of the newest generation of biologics (IL-17/23i) was scarce. The current review provides a literature update on the previous scoping review on the DR of all biologics, including the newest generation, with a focus on the uptake and implementation of DR in practice. The current literature search on DR revealed 14 new articles in addition to those in the previous review. Four of the newly found articles tested DR strategies, mostly focusing on first-generation biologics; only guselkumab (IL-23i) was included in one study. The other 10 studies showed data on regaining response after failure of DR, safety, cost-effectiveness, and uptake and implementation, as well as information about IL-17/23i. The eligibility criteria to start DR included both absolute and relative Psoriasis Area and Severity Index (PASI) scores (PASI ≤3/≤5/PASI 75-100) and/or Dermatology Life Quality Index (DLQI) ≤3/≤5, or BSA ≤1/≤2, or Physician Global Assessment (PGA) ≤1/0-2 during a period ranging from 12 weeks to ≥1 year. Most studies used PASI ≤5 and/or DLQI ≤5 or PGA ≤1 for ≥6 months. DR strategies were mostly performed by stepwise interval prolongation in two steps (to 67% of the standard dose, followed by 50%). Some studies of IL-17/23i reduced the dose to ±25%. The tested DR strategies on stepwise or fixed DR on TNF-αi and IL-12/23i (three studies), as well as one \"on-demand\" dosing study on IL-23i guselkumab, were successful. In the case of relapse of DR on TNF-αi and IL-12/23i, clinical effectiveness was regained by retreatment with the standard dose. All studies showed substantial cost savings with the biologic DR of TNF-αi and IL-12/23i. The identified barriers against the implementation of DR were mainly a lack of guidelines and scientific evidence on effectiveness and safety, and a lack of time and (technical) support. The identified facilitators were mainly clear guidelines, feasible protocols, adequate education of patients and physicians, and cost reduction. In conclusion, DR seems promising, but a research gap still exists in randomized, prospective studies testing DR strategies, especially of IL-17/23i, hampering the completion of guidelines on DR. Taking into account the identified barriers and facilitators most likely results in a more successful implementation of biologic DR in practice.

The benefits of TDM-guided TNFi therapy in patients with rheumatic disease was still controversial. This systematic review and meta-analysis was conducted to explore if the TDM-guided TNFi therapy is superior to empirical-guided therapy.
We systematically searched PubMed, Web of Science, Cochrane Library, and EMBASE databases for articles published between database inception and October 05, 2023. Studies reporting endpoints in TDM-guided TNFi therapy and empirical therapy were included. Results would be presented in risk ratio (RR) and mean difference, with 95 % confidence interval (CI) reported. This study is registered with PROSPERO (CRD42022353956).
A total of 14 studies (eight RCTs and six cohort studies) involving 2427 patients were included in this meta-analysis. In the scenario of response prediction, compared with empirical-guided therapy, TDM-guided TNFi therapy had association with higher treat-to-target rates (RR 1.30, 95 % CI 1.02-1.65, P=0.03, I2=79 %), more specifically, higher low disease activity rates (RR 2.11, 95 % CI 1.22-3.66, P=0.007, I2=61 %), but no difference in clinical remission rates (RR 0.98,95 % CI 0.87-1.11, P=0.75, I2=0 %). In the scenario of dose reduction prediction, lower relapse rates (RR 0.73, 95 % CI 0.65-0.82, P <0.00001, I2=0 %) were observed compared with empirical-guided dose reduction strategy, but no difference (RR 1.24, 95 % CI 0.85-1.80, P=0.27, I2=57 %) between TDM-guided dose reduction and standard-dosing therapy. No significant difference was observed in change of disease activity score, mean disease activity score, radiographic progression, and safety. And TDM-guided therapy was associated with reduced cost per patient per year calculated as the total accumulated sum of therapy cost.
TDM-guided TNFi therapy was associated with increased rates of low disease activity and decreased risks of relapse, and may save cost compared with empirical-guided therapy in patients with rheumatic disease. But this does not mean that the use of TDM-guided TNFi therapy can be advocated, because there is no difference in clinical remission rates and many other outcomes. More researches, especially randomized clinical trials are needed to verify this conclusion in the future.

BACKGROUND: Biologic therapy is an effective treatment for inflammatory bowel disease [IBD]. However due to cost and safety concerns, dose de-escalation strategies after achieving remission have been suggested.
OBJECTIVE: To critically review available data on dose de-escalation of biologics [or other advanced therapies] in IBD. We will focus on studies evaluating de-escalation to standard dosing in patients initially optimised, and also on studies assessing de-escalation from standard dosing.
METHODS: A systematic bibliographic search was performed.
RESULTS: The mean frequency of de-escalation after previous dose intensification [12 studies, 1,474 patients] was 34%. The corresponding frequency of de-escalation from standard dosing [five studies, 3,842 patients] was 4.2%. The relapse rate of IBD following anti-tumour necrosis factor [TNF] de-escalation to standard dosing in patients initially dose-escalated [10 studies, 301 patients] was 30%. The corresponding relapse rate following anti-TNF de-escalation from standard dosing [nine studies, 494 patients] was 38%. The risk of relapse was lower for patients in clinical, biologic, and endoscopic/radiological remission at the time of de-escalation. A role of anti-TNF therapeutic drug monitoring in the decision to dose de-escalate has been demonstrated. In patients relapsing after de-escalation, re-escalation is generally effective. De-escalation is not consistently associated with a better safety profile. The cost-effectiveness of the de-escalation strategy remains uncertain. Finally, there is not enough evidence to recommend dose de-escalation of biologics different from anti-TNFs or small molecules.
CONCLUSIONS: Any consideration for de-escalation of biologic therapy in IBD must be tailored, taking into account the risks and consequences of a flare and patients\' preferences.

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT.
We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.
Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.
Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

In 1971, the first computed tomography (CT) scan was performed on a patient\'s brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and stroke, intracranial hemorrhage and trauma, while CT angiography (CTA) has become the standard for first-line cerebrovascular evaluation; however, resulting improvements in patient management and clinical outcomes come at the cost of radiation exposure, increasing the risk for secondary morbidity. Therefore, radiation dose optimization should always be part of technical advancements in CT imaging but how can the dose be optimized? What dose reduction can be achieved without compromising diagnostic value, and what is the potential of the upcoming technologies artificial intelligence and photon counting CT? In this article, we look for answers to these questions by reviewing dose reduction techniques with respect to the major clinical indications of NCCT and CTA of the head, including a brief perspective on what to expect from current and future developments in CT technology with respect to radiation dose optimization.

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, new indications continued to emerge, such as assessment of vertebral fractures or degenerative spine disease, preoperative and postoperative evaluation, or CT-guided interventions at the spine; however, improvements in patient management and clinical outcomes come along with higher radiation exposure, which increases the risk for secondary malignancies. Therefore, technical developments in CT acquisition and reconstruction must always include efforts to reduce the radiation dose. But how exactly can the dose be reduced? What amount of dose reduction can be achieved without compromising the clinical value of spinal CT examinations and what can be expected from the rising stars in CT technology: artificial intelligence and photon counting CT? In this article, we try to answer these questions by systematically reviewing dose reduction techniques with respect to the major clinical indications of spinal CT. Furthermore, we take a concise look on the dose reduction potential of future developments in CT hardware and software.

Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question \"What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?\" Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36-70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

BACKGROUND: The long-term use of anti-TNF-α agents can lead to adverse effects, such as infections and immune-mediated cutaneous reactions. Whether de-escalation by dose reduction or interval lengthening reduces these adverse effects is uncertain. This systematic review aims to compare the incidence of infections and skin manifestations after anti-TNF-α dose de-escalation with standard dosing.
METHODS: MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched from inception to 14 January 2022. Randomized controlled trials (RCTs) and observational studies comparing anti-TNF-α de-escalation strategies with standard dosing among patients with inflammatory conditions, that report on infections, skin manifestations, or both, were included. The risk of bias was assessed with the revised Cochrane risk-of bias tool (RCTs) or the Newcastle-Ottawa scale (non-RCTs).
RESULTS: Fourteen RCTs and six observational studies (or 2706 patients) were included. Eight RCTs had low risk of bias or some concerns. Four non-RCTs were of good methodological quality. The studies described patients with axial spondyloarthritis (8 studies, 780 patients), rheumatoid arthritis (7 studies, 1458 patients), psoriasis (3 studies, 332 patients), or inflammatory bowel disease (2 studies, 136 patients). De-escalation strategies included interval lengthening (12 studies, 1317 patients), dose reduction (6 studies, 1130 patients), or both (2 studies, 259 patients). Overall, the occurrence of infections and skin manifestations did not differ between standard treatment and de-escalation. The disappearance of infections or skin manifestations after de-escalation was only reported in two studies. The majority of studies focused on etanercept and adalimumab. Heterogeneity in reporting of infections and skin manifestations precluded meta-analysis.
CONCLUSIONS: We found that anti-TNF-α de-escalation does not reduce infections or skin reactions. A de-escalation strategy should not be recommended for the sole purpose of reducing drug-related adverse effects. The meticulous documentation of adverse effects is recommended to further address this question.
BACKGROUND: PROSPERO CRD42021252977.

Background and Aim: As one of the second-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors, afatinib brings survival benefits to patients with common and rare EGFR mutations. This study aimed to compare the effectiveness and safety of 30 and 40 mg of afatinib in patients with non-small cell lung cancer (NSCLC) using qualitative and quantitative analysis methods so as to provide reference for clinical medication. Methods: The PubMed, Embase, ClinicalTrials.gov, Cochrane Library, China National Knowledge Infrastructure, and WanFang databases were thoroughly searched from inception to February 26, 2021. Two researchers independently screened the literature, extracted data, and evaluated the quality. RevMan and Stata 15.0 were used for meta-analysis. Results: Twelve cohort studies including 1290 patients for final analysis were selected; of which, 1129 patients were analyzed to measure the effectiveness outcomes and 470 patients were analyzed for safety outcomes. In patients with non-brain metastasis, the progression-free survival of the first- or second-line treatment with reduced-dose afatinib was equivalent to the conventional dose. In terms of safety, the reduced dose could significantly lower the incidence of severe diarrhea and severe rash, but not the total incidence of diarrhea, rash, and all levels of paronychia. Conclusions: The incidence of common serious adverse reactions was significantly lower with 30 mg of afatinib than with 40 mg of afatinib in patients with NSCLC. The effectiveness appeared to be similar to that in patients with non-brain metastasis. This study provides a reference for clinical dose reduction of afatinib. Systematic Review Registration: [PROSPERO], identifier [CRD42021238043].