UNASSIGNED: Abdominal radiographs remain useful in newborns. Given the high radiation sensitivity of this population, it is necessary to optimize acquisition techniques to minimize radiation exposure.
UNASSIGNED: Evaluate the effects of three additional filtrations on radiation dose and image quality in abdominal X-rays of newborns using an anthropomorphic phantom.
UNASSIGNED: Abdominal radiographs of an anthropomorphic newborn phantom were performed using acquisition parameters ranging from 55 to 70 kV and from 0.4 to 2.5 mAs, without and with three different additional filtrations: 0.1 mm copper (Cu) + 1 mm aluminum (Al), 0.2 mm copper + 1 mm aluminum, and 2 mm aluminum. For each X-ray the dose area product (DAP) was measured, the signal-to-noise ratio (SNR) was calculated, and image quality (IQ) was evaluated by two blinded radiologists using the absolute visual grading analysis (VGA) method.
UNASSIGNED: Adding an additional filtration resulted in a significant reduction in DAP, with a decrease of 42% using 2 mm Al filtration, 65% with 0.1 mm Cu + 1 mm Al filtration, and 78% with 0.2 mm Cu + 1 mm Al filtration (p < 0.01). The addition of 2 mm aluminum filtration does not significantly decrease the SNR (p = 0.31), CNR (p = 0.52) or the IQ (p = 0.12 and 0.401 for reader 1 and 2, respectively). However, adding copper-containing filtration leads to a significant decrease in, SNR, CNR and IQ.
UNASSIGNED: Adding a 2 mm Al additional filtration for abdominal radiographs in newborns can significantly reduce the radiation dose without causing a significant decrease in image quality.

OBJECTIVE: Clinical radiographic imaging is seated upon the principle of differential keV photon transmission through an object. At clinical x-ray energies the scattering of photons causes signal noise and is utilized solely for transmission measurements. However, scatter - particularly Compton scatter, is characterizable. In this work we hypothesized that modern radiation sources and detectors paired with deep learning techniques can use scattered photon information constructively to resolve superimposed attenuators in planar x-ray imaging.
METHODS: We simulated a monoenergetic x-ray imaging system consisting of a pencil beam x-ray source directed at an imaging target positioned in front of a high spatial- and energy-resolution detector array. The setup maximizes information capture of transmitted photons by measuring off-axis scatter location and energy. The signal was analyzed by a convolutional neural network, and a description of scattering material along the axis of the beam was derived. The system was virtually designed/tested using Monte Carlo processing of simple phantoms consisting of 10 pseudo-randomly stacked air/bone/water materials, and the network was trained by solving a classification problem.
RESULTS: From our simulations we were able to resolve traversed material depth information to a high degree, within our simple imaging task. The average accuracy of the material identification along the beam was 0.91±0.01, with slightly higher accuracy towards the entrance/exit peripheral surfaces of the object. The average sensitivity and specificity was 0.91 and 0.95, respectively.
CONCLUSIONS: Our work provides proof of principle that deep learning techniques can be used to analyze scattered photon patterns which can constructively contribute to the information content in radiography, here used to infer depth information in a traditional 2D planar setup. This principle, and our results, demonstrate that the information in Compton scattered photons may provide a basis for further development. The work was limited by simple testing scenarios and without yet integrating complexities or optimizations. The ability to scale performance to the clinic remains unexplored and requires further study.

Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDIvol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.

Background: Brentuximab Vedotin (BV) has revolutionized the treatment landscape for Hodgkin\'s lymphoma, yet its effects on pre-existing autoimmune disorders remain elusive. Methods: Here, we present four cases of patients with concurrent autoimmune conditions-Crohn\'s disease, vitiligo, type I diabetes, and minimal change disease-undergoing BV therapy for Hodgkin\'s lymphoma. The patients were treated with A-AVD instead of ABVD due to advanced-stage disease with high IPI scores. Results: Our findings reveal the surprising and complex interplay between BV exposure and autoimmune manifestations, highlighting the need for multidisciplinary collaboration in patient management. Notably, the exacerbation of autoimmune symptoms was observed in the first three cases where T-cell-mediated autoimmunity predominated. Additionally, BV exposure precipitated autoimmune thrombocytopenia in the vitiligo patient, underscoring the profound disruptions in immune regulation. Conversely, in the minimal change disease case, a disease characterized by a blend of B- and T-cell-mediated immunity, the outcome was favorable. Conclusions: This paper underscores the critical importance of vigilance toward autoimmune flare-ups induced by BV in patients with concurrent autoimmune conditions, offering insights for tailored patient care.

UNASSIGNED: The KRAS G12C inhibitor sotorasib was approved for treating advanced NSCLC in the second line or later on the basis of the CodeBreaK100 trial. Nevertheless, data on the real-world efficacy and safety of sotorasib, and to its optimal dose, remain limited.
UNASSIGNED: Patients treated with sotorasib for NSCLC through the Veterans Health Administration were retrospectively identified from the Corporate Data Warehouse. Survival, response, and toxicity data were obtained from chart review.
UNASSIGNED: Among the 128 patients treated with sotorasib through the Veterans Health Administration, objective response rate was 34%, progression-free survival (PFS) six months, and overall survival 12 months. Similar PFS was observed among the 16 patients who received frontline sotorasib without any prior systemic therapy for NSCLC. Toxicity leading to sotorasib interruption or dose reduction occurred in 37% of patients, whereas sotorasib discontinuation for toxicity occurred in 25%. Notably, sotorasib dose reduction was associated with substantially improved PFS and OS.
UNASSIGNED: In this real-world study, the observed efficacy of sotorasib was similar to the results of CodeBreaK100. Patients who received frontline sotorasib had similar PFS to our overall cohort, suggesting that first-line sotorasib monotherapy may benefit patients who are not eligible for chemotherapy. Toxicities leading to sotorasib interruption, dose reduction, or discontinuation were common. Sotorasib dose reduction was associated with improved survival, suggesting that sotorasib dose reduction may not compromise efficacy.

OBJECTIVE: Colorectal cancer (CRC) is the third-leading cause of death in the world. Although the prognosis has improved due to improvement of chemotherapy, metastatic CRC is still a recalcitrant disease, with a 5-year survival of only 13%. Irinotecan (IRN) is used as first-line chemotherapy for patients with unresectable CRC. However, there are severe side effects, such as neutropenia and diarrhea, which are dose-limiting. We have previously shown that methionine restriction (MR), effected by recombinant methioninase (rMETase), lowered the effective dose of IRN of colon-cancer cells in vitro. The aim of the present study was to evaluate the efficacy of the combination of low-dose IRN and MR on colon-cancer in nude mice.
METHODS: HCT-116 colon-cancer cells were cultured and subcutaneously injected into the flank of nude mice. After the tumor size reached approximately 100 mm3, 18 mice were randomized into three groups; Group 1: untreated control on a normal diet; Group 2: high-dose IRN on a normal diet (2 mg/kg, i.p.); Group 3: low-dose IRN (1 mg/kg i.p.) on MR effected by a methionine-depleted diet.
RESULTS: There was no significant difference between the control mice and the mice treated with high-dose IRN, without MR. However, low-dose IRN combined with MR was significantly more effective than the control and arrested colon-cancer growth (p=0.03). Body weight loss was reversible in the mice treated by low-dose IRN combined with MR.
CONCLUSIONS: The combination of low-dose IRN and MR acted synergistically in arresting HCT-116 colon-cancer grown in nude mice. The present study indicates the MR has the potential to reduce the effective dose of IRN in the clinic.

Our aim was to define a lower limit of reduced injected activity in delayed [18F]FDG total-body (TB) PET/CT in pediatric oncology patients. Methods: In this single-center prospective study, children were scanned for 20 min with TB PET/CT, 120 min after intravenous administration of a 4.07 ± 0.49 MBq/kg dose of [18F]FDG. Five randomly subsampled low-count reconstructions were generated using ¼, ⅛, [Formula: see text], and [Formula: see text] of the counts in the full-dose list-mode reference standard acquisition (20 min), to simulate dose reduction. For the 2 lowest-count reconstructions, smoothing was applied. Background uptake was measured with volumes of interest placed on the ascending aorta, right liver lobe, and third lumbar vertebra body (L3). Tumor lesions were segmented using a 40% isocontour volume-of-interest approach. Signal-to-noise ratio, tumor-to-background ratio, and contrast-to-noise ratio were calculated. Three physicians identified malignant lesions independently and assessed the image quality using a 5-point Likert scale. Results: In total, 113 malignant lesions were identified in 18 patients, who met the inclusion criteria. Of these lesions, 87.6% were quantifiable. Liver SUVmean did not change significantly, whereas a lower signal-to-noise ratio was observed in all low-count reconstructions compared with the reference standard (P < 0.0001) because of higher noise rates. Tumor uptake (SUVmax), tumor-to-background ratio, and total lesion count were significantly lower in the reconstructions with [Formula: see text] and [Formula: see text] of the counts of the reference standard (P < 0.001). Contrast-to-noise ratio and clinical image quality were significantly lower in all low-count reconstructions than with the reference standard. Conclusion: Dose reduction for delayed [18F]FDG TB PET/CT imaging in children is possible without loss of image quality or lesion conspicuity. However, our results indicate that to maintain comparable tumor uptake and lesion conspicuity, PET centers should not reduce the injected [18F]FDG activity below 0.5 MBq/kg when using TB PET/CT in pediatric imaging at 120 min after injection.

Background: Cognitive impairment is a core symptom of schizophrenia and is associated with functional outcomes. Improving cognitive function is an important treatment goal. Studies have reported beneficial cognitive effects of the second-generation antipsychotic (SGA) ziprasidone. Reducing the dose of first-generation antipsychotics (FGA) might also improve cognitive function. This study compared the cognitive effects in long-stay patients who were randomized to groups who underwent FGA dose reduction or switched to ziprasidone. Methods: High-dose FGA was reduced to an equivalent of 5 mg of haloperidol in 10 patients (FGA-DR-condition), and 13 patients switched to ziprasidone 80 mg b.i.d. (ZIPRA condition). Five domains of cognitive function were assessed before dose reduction or switching (T0) and after 1 year (T1). This study was approved by the ethics committee of the Open Ankh (CCMO number 338) and registered at the Netherlands Trial Register (code 5864). Results: Non-significant deterioration was seen in all cognitive domains studied in the FGA-DR condition, whereas there was a non-significant improvement in all cognitive domains in the ZIPRA condition. The most robust difference between conditions, in favor of ziprasidone, was in executive function. Conclusions: In patients with severe chronic schizophrenia, ziprasidone had a non-significant and very modest beneficial effect on cognitive function compared with FGA dose reduction. Larger trials are needed to further investigate this effect.

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.

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.