Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?

BACKGROUND: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. METHODS: The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. RESULTS: The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. CONCLUSIONS: The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous.

Comprehending the disruptive influence of workplace gaslighting behaviours and mobbing on nurses' career entrenchment: A multi-centre inquiry.

AIM: To explore the disruptive influence of workplace gaslighting behaviours and mobbing on nurses' career entrenchment across multiple healthcare centres. DESIGN: A multi-centre cross-sectional. METHODS: Data were collected from 483 nurses from various healthcare settings in Egypt, spanning from January 2024 to February 2024. The Gaslighting at Work Questionnaire, Luxembourg Workplace Mobbing Scale and Career Entrenchment Scale were employed for data collection. RESULTS: The study revealed moderate levels of gaslighting, mobbing and nurses' career entrenchment. Also, there is a negative correlation between nurses' career entrenchment and both gaslighting and mobbing, while gaslighting and mobbing exhibit a positive correlation. The study also highlighted regional disparities in the prevalence of these phenomena, with the highest incidences noted in urban healthcare settings. CONCLUSION: The findings underscore the critical impact of workplace gaslighting and mobbing on nurses' career entrenchment. REPORTING METHOD: The relevant reporting method has been adhered to, that is, STROBE. IMPLICATION FOR THE PROFESSION: The future of the nursing profession requires building productive nurses who can cope with negative workplace experiences. This could be achieved by cultivating a workplace culture that has zero tolerance for these experiences. Offering counselling services or employee assistance programmes to help nurses cope with the emotional toll of these negative experiences is a promising strategy. IMPACT: This study is the first to examine serious workplace practices like gaslighting and mobbing in a nursing context, emphasizing their effect on nursing-sensitive indicators like career entrenchment. It is one of the important initiatives geared towards upgrading the competitiveness and magnetism of healthcare organizations in the era of green human resources management. Results provide valuable insights for nurse leaders to control nursing turnover and shortage crises in different endeavours. PATIENT OR PUBLIC CONTRIBUTION: In our study, nurses from diverse geographical regions and varied specialties actively participate, offering a rich tapestry of experiences and perspectives.

Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke.

BACKGROUND: A study was undertaken to assess the effectiveness of open-source large language models (LLMs) in extracting clinical data from unstructured mechanical thrombectomy reports in patients with ischemic stroke caused by a vessel occlusion. METHODS: We deployed local open-source LLMs to extract data points from free-text procedural reports in patients who underwent mechanical thrombectomy between September 2020 and June 2023 in our institution. The external dataset was obtained from a second university hospital and comprised consecutive cases treated between September 2023 and March 2024. Ground truth labeling was facilitated by a human-in-the-loop (HITL) approach, with time metrics recorded for both automated and manual data extractions. We tested three models-Mixtral, Qwen, and BioMistral-assessing their performance on precision, recall, and F1 score across 15 clinical categories such as National Institute of Health Stroke Scale (NIHSS) scores, occluded vessels, and medication details. RESULTS: The study included 1000 consecutive reports from our primary institution and 50 reports from a secondary institution. Mixtral showed the highest precision, achieving 0.99 for first series time extraction and 0.69 for occluded vessel identification within the internal dataset. In the external dataset, precision ranged from 1.00 for NIHSS scores to 0.70 for occluded vessels. Qwen showed moderate precision with a high of 0.85 for NIHSS scores and a low of 0.28 for occluded vessels. BioMistral had the broadest range of precision, from 0.81 for first series times to 0.14 for medication details. The HITL approach yielded an average time savings of 65.6% per case, with variations from 45.95% to 79.56%. CONCLUSION: This study highlights the potential of using LLMs for automated clinical data extraction from medical reports. Incorporating HITL annotations enhances precision and also ensures the reliability of the extracted data. This methodology presents a scalable privacy-preserving option that can significantly support clinical documentation and research endeavors.

AI-Assisted Diagnostics in Dentistry: An Eye-Tracking Study on User Behavior.

Background: Artificial Intelligence (AI) has increasingly been integrated into dental practices, notably in radiographic imaging like Orthopantomograms (OPGs), transforming diagnostic protocols. Eye tracking technology offers a method to understand how dentists' visual attention may differ between conventional and AI-assisted diagnostics, but its integration into daily clinical practice is challenged by the cost and complexity of traditional systems. Material and Methods: Thirty experienced practitioners and dental students participated to evaluate the effectiveness of two low-budget eye-tracking systems, including the Peye Tracker (Eye Tracking Systems LTD, Southsea, UK) and Webgazer.js (Brown University, Providence, Rhode Island) in a clinical setting to assess their utility in capturing dentists' visual engagement with OPGs. The hardware and software setup, environmental conditions, and the process for eye-tracking data collection and analysis are illustrated. Results: The study found significant differences in eye-tracking accuracy between the two systems, with Webgazer.js showing higher accuracy compared to Peye Tracker (p<0.001). Additionally, the influence of visual aids (glasses vs. contact lenses) on the performance of eye-tracking systems revealed significant differences for both Peye Tracker (p<0.05) and Webgazer.js (p<0.05). Conclusions: Low-budget eye-tracking devices present challenges in achieving the desired accuracy for analyzing dentists' visual attention in clinical practice, highlighting the need for continued innovation and improvement in this technology. Key words:Artificial intelligence, Eye-tracking device, low-budget, dentistry.

Comparison of T1-weighted landmark placement and ROI transfer onto diffusion-weighted EPI sequences for targeted tractography tasks in the optic nerve.

Diffusion-based tractography in the optic nerve requires sampling strategies assisted by anatomical landmark information (regions of interest [ROIs]). We aimed to investigate the feasibility of expert-placed, high-resolution T1-weighted ROI-data transfer onto lower spatial resolution diffusion-weighted images. Slab volumes from 20 volunteers were acquired and preprocessed including distortion bias correction and artifact reduction. Constrained spherical deconvolution was used to generate a directional diffusion information grid (fibre orientation distribution-model [FOD]). Three neuroradiologists marked landmarks on both diffusion imaging variants and structural datasets. Structural ROI information (volumetric interpolated breath-hold sequence [VIBE]) was respectively registered (linear with 6/12 degrees of freedom [DOF]) onto single-shot EPI (ss-EPI) and readout-segmented EPI (rs-EPI) volumes, respectively. All eight ROI/FOD-combinations were compared in a targeted tractography task of the optic nerve pathway. Inter-rater reliability for placed ROIs among experts was highest in VIBE images (lower confidence interval 0.84 to 0.97, mean 0.91) and lower in both ss-EPI (0.61 to 0.95, mean 0.79) and rs-EPI (0.59 to 0.86, mean 0.70). Tractography success rate based on streamline selection performance was highest in VIBE-drawn ROIs registered (6-DOF) onto rs-EPI FOD (70.0% over 5%-threshold, capped to failed ratio 39/16) followed by both 12-DOF-registered (67.5%; 41/16) and nonregistered VIBE (67.5%; 40/23). On ss-EPI FOD, VIBE-ROI-datasets obtained fewer streamlines overall with each at 55.0% above 5%-threshold and with lower capped to failed ratio (6-DOF: 35/36; 12-DOF: 34/34, nonregistered 33/36). The combination of VIBE-placed ROIs (highest inter-rater reliability) with 6-DOF registration onto rs-EPI targets (best streamline selection performance) is most suitable for white matter template generation required in group studies.

Analogous electronic states in graphene and planer metallic quantum dots.

Graphene nanostructures offer wide range of applications due to their distinguished and tunable electronic properties. Recently, atomic and molecular graphene were modeled following simple free-electron scattering by periodic muffin tin potential leading to remarkable agreement with density functional theory. Here we extend the analogy of the π -electronic structures and quantum effects between atomic graphene quantum dots (QDs) and homogeneous planer metallic counterparts of similar size and shape. Specifically, we show that at high binding energies, below the M ¯ -point gap, graphene QDs enclose confined states and standing wave quasiparticle interference patterns analogous to those reported on coinage metal surfaces for nanoscale confining structures such as vacancy islands and quantum corrals. These confined and quantum corral-like states in graphene QDs can be resolved in tomography experiments using angle-resolved photoemission spectroscopy. Likewise, the shape of near-Fermi frontier orbitals in graphene quantum dots can be reproduced from electron confinement within homogeneous metal QDs of identical size and shape. Furthermore, confined states analogous to those found in metallic quantum stadiums can be realized in coupled QDs of graphene for reduced separation. The present study offer a simple fundamental understanding of graphene electronic structures and also open the way towards efficient modeling of novel graphene-based nanostructures.

Is high-intensity focused ultrasound a magical solution to endometriosis? A systematic review.

Background: Endometriosis presents a significant challenge in gynecological endocrinology, affecting approximately 1 in 10 women of reproductive age. Abdominal wall endometriosis (AWE) and rectosigmoid deep infiltrating endometriosis (DIE) pose unique clinical complexities. High-intensity focused ultrasound (HIFU) has emerged as a novel alternative for treating these conditions, offering a noninvasive option with potential therapeutic benefits. Methods: A systematic review was conducted following PRISMA guidelines to investigate the safety and efficacy of HIFU therapy for AWE and rectosigmoid DIE. The literature search encompassed databases from inception to January 20, 2024. Eligible studies included observational studies, case reports, and clinical trials evaluating HIFU treatment for endometriosis. Data extraction and risk of bias assessment were performed following established protocols. Results: Fourteen studies were included, comprising 330 patients with AWE and 28 patients with rectosigmoid DIE. HIFU treatment demonstrated significant efficacy, with many patients experiencing complete remission, and clinical effectiveness. Reductions in lesion volume posttreatment were consistent across studies. However, safety concerns were noted, including pain at the treatment site, hematuria, and skin burns. Adverse effects underscored the importance of careful patient selection and monitoring during HIFU therapy. Conclusion: HIFU therapy shows promise as a noninvasive approach for managing AWE and rectosigmoid DIE. While efficacy outcomes are encouraging, safety considerations warrant attention. Further research, particularly randomized controlled trials with larger sample sizes, is needed to validate findings and optimize treatment protocols.

The Impact of Wrist Fractures on Long-Term Basketball Performance.

Background: Wrist joint fractures may present challenging obstacles for the rehabilitating athlete upon return to play. Although current literature has examined the effects of specific injuries to the upper extremity for basketball athletes, little is known about long-term performance outcomes following wrist joint fractures. Methods: Review of all National Basketball Association players who sustained wrist joint fractures was conducted. Player characteristics, preinjury and postinjury performance, and overall efficiency were analyzed. Results: A total of 31 players were deemed appropriate for inclusion, with an average age of 26.2 ± 4.3 years and a mean of 5.2 ± 3.6 years played before injury and 4.1 ± 3.1 years played upon return. Operative management was pursued in 48.4% of players. Players demonstrated a significant decrease in win shares before injury (mean: 24.3 ± 31.5) compared to after return from injury (mean: 9.6 ± 19.6) (p < 0.032). Multivariate regression demonstrated that increased points per game before injury (standardized ß: 0.71; 95% confidence interval [CI]: 1.2-8.5, p < 0.011) and increased win shares before injury (ß: 1.0; 95% CI: 1.4-9.5, p < 0.001) were both independently predictive with increased win shares after return to play. Player position, age, management type, and all other statistics were not significantly associated with any other findings upon return. Conclusion: Overall efficiency following return to play in the setting of basketball-associated wrist joint fractures appears to be decreased in athletes. However, preinjury performance as captured through win share efficiency and points per game appears to be predictive of increased productivity after successful return.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

OBJECTIVES: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

Stroke Caused by Vasculitis Induced by Periodontitis-Associated Oral Bacteria after Wisdom Teeth Extraction.

Invasive dental procedures, such as wisdom teeth removal, have been identified as potential triggers for vascular events due to the entry of oral bacteria into the bloodstream, leading to acute vascular inflammation and endothelial dysfunction. This study presents the case of a 27-year-old healthy male who developed ischemic stroke resulting from bacteremia after undergoing wisdom teeth extraction. Initially, the patient experienced fever and malaise, which were followed by right-sided hemiplegia. Diagnostic imaging, including a CT scan, identified a subacute infarction in the posterior crus of the left internal capsule, and MRI findings indicated inflammatory changes in the masticatory muscles. Further investigations involving biopsies of the masticatory muscles, along with blood and cerebrospinal fluid samples, confirmed bacterial meningitis with associated vasculitis. Notably, oral bacteria linked to periodontitis, including Porphyromonas gingivalis, Fusobacterium nucleatum, Tannerella forsythia, and Parvimonas micra, were found in the biopsies and microbiological analyses. To the best of our knowledge, this is the first reported case showing that bacteremia following dental procedures can lead to such severe neurological outcomes. This case underscores the importance of recognizing bacteremia-induced vasculitis in patients presenting with neurological symptoms post-dental procedures, emphasizing the broader implications of oral infections in such pathologies.

Isolation of two new cucurbitane-type triterpenoids from Kedrostis gijef.

Two new cucurbitane-type triterpenoids (2,3), together with two known compounds (1,4), were isolated from the aerial parts of Kedrostis gijef. The structure of all compounds was elucidated based on NMR, HRESIMS analyses, and by comparison with the literature. Additionally, the cytotoxic activity against HeLa, Caco-2, and SH-SY5Y cell lines was determined using MTT colorimetric assay.

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.This retrospective single-center study included ncCT data of 150 consecutive patients (30 for each of the five scanners) who had undergone routine imaging after minor head trauma. The images were reconstructed using filtered back projection (FBP) and a vendor-agnostic DLD method. Using a 4-point Likert scale, three readers performed a subjective evaluation assessing the following quality criteria: overall diagnostic image quality, image noise, gray matter-white matter differentiation (GM-WM), artifacts, sharpness, and diagnostic confidence. Objective analysis included evaluation of noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and an artifact index for the posterior fossa.In subjective image quality assessment, DLD showed constantly superior results compared to FBP in all categories and for all scanners (p<0.05) across all readers. The objective image quality analysis showed significant improvement in noise, SNR, and CNR as well as for the artifact index using DLD for all scanners (p<0.001).The vendor-agnostic deep learning denoising algorithm provided significantly superior results in the subjective as well as in the objective analysis of ncCT images of patients with minor head trauma concerning all parameters compared to the FBP reconstruction. This effect has been observed in all five included scanners. · Significant improvement of image quality for 5 scanners due to the vendor-agnostic DLD. · Subjects were patients with routine imaging after minor head trauma. · Reduction of artifacts in the posterior fossa due to the DLD. · Access to improved image quality even for older scanners from different vendors. · Kapper C, Müller L, Kronfeld A et al. Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2290-4781.

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper.

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow.

PURPOSE: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance. MATERIALS AND METHODS: The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard. RESULTS: AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation. CONCLUSION: The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Concurrent inflammation-related brain reorganization in multiple sclerosis and depression.

BACKGROUND: Neuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. METHODS: We included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FINDINGS: MS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). CONCLUSIONS: Similar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.

Oral PD-L1 inhibitor GS-4224 selectively engages PD-L1 high cells and elicits pharmacodynamic responses in patients with advanced solid tumors.

BACKGROUND: Checkpoint inhibitors targeting the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) pathway are effective therapies in a range of immunogenic cancer types. Blocking this pathway with an oral therapy could benefit patients through greater convenience, particularly in combination regimens, and allow flexible management of immune-mediated toxicities. METHODS: PD-L1 binding activity was assessed in engineered dimerization and primary cell target occupancy assays. Preclinical antitumor activity was evaluated in ex vivo and in vivo human PD-L1-expressing tumor models. Human safety, tolerability, pharmacokinetics, and biomarker activity were evaluated in an open-label, multicenter, sequential dose-escalation study in patients with advanced solid tumors. Biomarkers evaluated included target occupancy, flow cytometric immunophenotyping, plasma cytokine measurements, and T-cell receptor sequencing. RESULTS: GS-4224 binding caused dimerization of PD-L1, blocking its interaction with PD-1 and leading to reversal of T-cell inhibition and increased tumor killing in vitro and in vivo. The potency of GS-4224 was dependent on the density of cell surface PD-L1, with binding being most potent on PD-L1-high cells. In a phase 1 dose-escalation study in patients with advanced solid tumors, treatment was well tolerated at doses of 400-1,500 mg once daily. Administration of GS-4224 was associated with a dose-dependent increase in plasma GS-4224 exposure and reduction in free PD-L1 on peripheral blood T cells, an increase in Ki67 among the PD-1-positive T-cell subsets, and elevated plasma cytokines and chemokines. CONCLUSIONS: GS-4224 is a novel, orally bioavailable small molecule inhibitor of PD-L1. GS-4224 showed evidence of expected on-target biomarker activity, including engagement of PD-L1 and induction of immune-related pharmacodynamic responses consistent with PD-L1 blockade. TRIAL REGISTRATION NUMBER: NCT04049617.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.

Population Pharmacokinetics of Sacituzumab Govitecan in Patients with Metastatic Triple-Negative Breast Cancer and Other Solid Tumors.

BACKGROUND AND OBJECTIVE: Sacituzumab govitecan (SG) is an antibody-drug conjugate composed of an antibody with affinity for Trop-2 coupled to SN-38 via hydrolyzable linker. SG is approved for patients with metastatic triple-negative breast cancer (mTNBC) who have received two or more prior chemotherapies (at least one in a metastatic setting) and for patients with pretreated hormone receptor positive (HR+)/human epidermal growth factor receptor 2 negative (HER2-) metastatic breast cancer. METHODS: In these analyses, the pharmacokinetics of SG, free SN-38, and total antibody (tAB) were characterized using data from 529 patients with mTNBC or other solid tumors across two large clinical trials (NCT01631552; ASCENT, NCT02574455). Three population pharmacokinetic models were constructed using non-linear mixed-effects modeling; clinically relevant covariates were evaluated to assess their impact on exposure. Models for SG and tAB were developed independently whereas free SN-38 was sequentially generated via a first-order release process from SG. RESULTS: Pharmacokinetics of the three analytes were each described by a two-compartment model with estimated body weight-based scaling exponents for clearance and volume. Typical parameter estimates for clearance and steady-state volume of distribution were 0.133 L/h and 3.68 L for SG and 0.0164 L/h and 4.26 L for tAB, respectively. Mild-to-moderate renal impairment, mild hepatic impairment, age, sex, baseline albumin level, tumor type, UGT1A1 genotype, or Trop-2 expression did not have a clinically relevant impact on exposure for any of the three analytes. CONCLUSIONS: These analyses support the approved SG dosing regimen of 10 mg/kg as intravenous infusion on days 1 and 8 of 21-day cycles and did not identify a need for dose adjustment based on evaluated covariates or disease characteristics.

Pharmacokinetics and Safety of Firsocostat, an Acetyl-Coenzyme A Carboxylase Inhibitor, in Participants with Mild, Moderate, and Severe Hepatic Impairment.

Firsocostat is an oral, liver-targeted inhibitor of acetyl-coenzyme A carboxylase in development for the treatment of metabolic dysfunction-associated steatohepatitis. Hepatic organic anion transporting polypeptides play a significant role in the disposition of firsocostat with minimal contributions from uridine diphospho-glucuronosyltransferase and cytochrome P450 3A enzymes. This phase 1 study evaluated the pharmacokinetics and safety of firsocostat in participants with mild, moderate, or severe hepatic impairment. Participants with stable mild, moderate, or severe hepatic impairment (Child-Pugh A, B, or C, respectively [n = 10 per cohort]) and healthy matched controls with normal hepatic function (n = 10 per cohort) received a single oral dose of firsocostat (20 mg for mild and moderate hepatic impairment; 5 mg for severe hepatic impairment) with intensive pharmacokinetic sampling over 96 h. Safety was monitored throughout the study. Firsocostat plasma exposure (AUCinf) was 83%, 8.7-fold, and 30-fold higher in participants with mild, moderate, and severe hepatic impairment, respectively, relative to matched controls. Firsocostat was generally well tolerated, and all reported adverse events were mild in nature. Dose adjustment is not necessary for the administration of firsocostat in patients with mild hepatic impairment. However, based on the observed increases in firsocostat exposure, dose adjustment should be considered for patients with moderate or severe hepatic impairment, and additional safety and efficacy data from future clinical trials will further inform dose adjustment.

Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing.

OBJECTIVES: To investigate the clinical feasibility and image quality of accelerated brain diffusion-weighted imaging (DWI) with deep learning image reconstruction and super resolution. METHODS: 85 consecutive patients with clinically indicated MRI at a 3 T scanner were prospectively included. Conventional diffusion-weighted data (c-DWI) with four averages were obtained. Reconstructions of one and two averages, as well as deep learning diffusion-weighted imaging (DL-DWI), were accomplished. Three experienced readers evaluated the acquired data using a 5-point Likert scale regarding overall image quality, overall contrast, diagnostic confidence, occurrence of artefacts and evaluation of the central region, basal ganglia, brainstem, and cerebellum. To assess interrater agreement, Fleiss' kappa (Ï°) was determined. Signal intensity (SI) levels for basal ganglia and the central region were estimated via automated segmentation, and SI values of detected pathologies were measured. RESULTS: Intracranial pathologies were identified in 35 patients. DL-DWI was significantly superior for all defined parameters, independently from applied averages (p-value <0.001). Optimum image quality was achieved with DL-DWI by utilizing a single average (p-value <0.001), demonstrating very good (80.9%) to excellent image quality (14.5%) in nearly all cases, compared to 12.5% with very good and 0% with excellent image quality for c-MRI (p-value <0.001). Comparable results could be shown for diagnostic confidence. Inter-rater Fleiss' Kappa demonstrated moderate to substantial agreement for virtually all defined parameters, with good accordance, particularly for the assessment of pathologies (p = 0.74). Regarding SI values, no significant difference was found. CONCLUSION: Ultra-fast diffusion-weighted imaging with super resolution is feasible, resulting in highly accelerated brain imaging while increasing diagnostic image quality.