Citations

Abstract Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI‐driven tools are being developed to integrate diverse data sources, including multi‐omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end‐of‐life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up‐to‐date evidence, and captures critical patient‐reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.

Aims Schistosomiasis remains endemic in various parts of the world, and insights into pathogen immunobiology are mainly based on experimental models, while studies on human tissues are limited. Methods We explored the role of immune checkpoint pathway by evaluating the immunohistochemical expression of programmed death-ligand 1 (PD-L1) in a retrospective cohort of patients with bilharzial cystitis. Inflammation severity by conventional histology and staining intensity by immunohistochemistry were assigned three-tier scores (0/1+/2+), and a cut-off for staining percentage was set at 5%. Results 38 biopsies from 31 patients were considered adequate for evaluation, and positive staining was detected in 80.6% of patients (34 biopsies). High expressors (22.6%) showed strong positive membranous staining (score 2+) with high staining density (more than 5% of inflammatory cells). Low expressors (58.1%) showed mild/moderate staining (score 1+) predominantly in less than 5% of the cells (91.6%) or expressed restricted cytoplasmic staining (6/31). All high expressors showed severe inflammation (score 2+) (p<0.001), and viable ova were only observed in these cases. Calcified ova were associated with mild/moderate inflammation or absent/minimal inflammation, correlating with low expressors or non-expressors (19.4%), respectively. Conclusion Schistosomal granuloma exhibits upregulated PD-L1 expression proportional to inflammation severity and pathogen viability, highlighting a critical immune checkpoint engagement in disease pathology.

Background Dementia with Lewy bodies (DLB) is a common degenerative dementia, but no studies investigate bereaved caregiver experiences. Objective To investigate the experiences of caregivers three months after the death of persons with DLB using a mixed-methods approach. Methods Dyads of individuals with moderate-advanced DLB and their primary informal caregivers were followed prospectively every 6 months until the person with DLB died. Caregivers completed a study visit with questionnaires and a semi-structured interview ∼3 months later. Spearman correlation coefficients and Wilcoxon rank-sum tests evaluated the relationships of post-death measures with pre-death patient and caregiver variables. Thematic analysis was used to analyze the interviews. Results Seventy-three caregivers completed visits (mean 3.5 months post-death). Most of the caregivers were women (82.2%) and spouses (76.7%) or adult children (17.8%). Over 40% had scores indicating risk for clinical depression. Post-death caregiver experiences (depression, quality of life, grief, resilience) correlated with pre-death caregiver experiences. Post-death experiences did not associate with patient characteristics, disease-related symptoms, or healthcare services used in the last 6 months of life. Trajectories for caregiver measures from pre- to post-death visits varied widely. Interview themes included grief and sadness, anger, guilt and regret, relief, appreciation/gratitude, and adjusting to a new normal. Conclusions The finding that pre-death caregiving experiences have the strongest association with post-death experiences emphasizes the critical importance of accessible and evidence-based caregiver support before and after the death of a person with DLB. Research is needed to develop interventions for current and bereaved caregivers of individuals with DLB. Trial registration information NCT04829656 (submitted 2021-03-22).

ABSTRACT Objective Despite the availability of approved treatments, a substantial proportion of patients with bipolar disorder experience treatment‐resistant bipolar depression (TRBD), characterized by persistent depressive symptoms unresponsive to standard therapies. However, a universally accepted definition of TRBD is lacking. This consensus document, developed by the International Society for Bipolar Disorders (ISBD) Task Force on TRBD, aims to provide a standardized definition of TRBD to facilitate clinical trials, research, and treatment strategies. Methods The Task Force employed a literature review, clinical trials analysis, and expert consensus meetings to define TRBD. Results TRBD was defined as the failure to achieve a significant and sustained clinical response after at least two approved and adequately dosed pharmacological treatments, administered for a sufficient duration with treatment adherence. For bipolar I (BD‐I) depression, approved treatments included quetiapine (300–600 mg/day for ≥ 8 weeks), lurasidone (20–120 mg/day for ≥ 6 weeks), the combination of olanzapine (6–12 mg/day) and fluoxetine (25–75 mg/day for ≥ 8 weeks), cariprazine (1.5–3 mg/day for ≥ 6 weeks), and lumateperone (42 mg/day for ≥ 6 weeks). For bipolar II (BD‐II) depression, approved treatments included quetiapine (300–600 mg/day for ≥ 8 weeks) and lumateperone (42 mg/day for ≥ 6 weeks). Conclusion This consensus definition aims to provide clarity for clinical trials, improve consistency in research, and guide treatment approaches and inform regulatory pathways. It represents a foundational step in addressing the unmet needs in TRBD and promoting the development of innovative therapeutic strategies. Future efforts will focus on adapting the definition to better align with real‐world clinical challenges and optimize patient care.

Abstract Background Flow‐related ghost artifact from a 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence results from unsaturated magnetization of incoming arterial flow. This is especially common on MR scanners equipped with smaller radiofrequency (RF) transmit coils. A high‐performance compact 3T (C3T) scanner features a smaller RF‐transmit coil (inner diameter 37 cm, length 40 cm), leading to a rapid fall‐off of the B1 field below the neck. This configuration results in more intense flow‐related ghost artifacts, especially in younger patients. Purpose The C3T scanner's smaller RF‐transmit coil provides RF field coverage over the brain region, causing bright in‐flow arterial signals and flow‐related artifacts in the conventional 3D‐MPRAGE scans. The purpose of this study is to suppress these artifacts by adding a RF saturation band (RFSB) pulses to the 3D‐MPRAGE sequence. Methods The RFSB was added to the 3D‐MPRAGE sequence as a preparation pulse option. To test the effectiveness, 37 subjects were scanned on the C3T under an IRB‐approved protocol using 3D‐MPRAGE with and without RFSB. Ten of those subjects underwent repeated scans with and without RFSB to evaluate test‐retest reliability. A consensus evaluation by two neuroradiologists was performed on all data to compare signal to noise ratio, image contrast, presence of artifacts, and diagnostic confidence. Quantitative analysis included calculating test‐retest differences by image subtraction and evaluating the variance in flow‐artifact‐induced image intensity between the scans with and without RFSB. Additionally, one subject was scanned on a whole‐body 3T scanner using a transmit/receive (T/R) head coil to demonstrate the method's applicability across different MRI platforms as a proof of concept. Results The Wilcoxon signed‐rank test of the neuroradiologist evaluations showed a significant reduction in artifacts and an improvement in diagnostic confidence in the posterior fossa region with and without RFSB ( p < 0.0001). Test–retest analysis showed that adding RFSB significantly reduced image intensity variability in the cerebellum, even among subjects without visible flow artifacts. The normalized difference decreased from 8.71% to 6.41% ( p = 0.0059), suggesting improved image reliability in regions prone to flow‐related artifacts. Additionally, similar findings were observed in scans on a whole‐body 3T with a T/R head coil, demonstrating the broader applicability of this method. Conclusion Incorporating RFSB into 3D‐MPRAGE scans effectively reduces flow‐related ghost artifact on the C3T scanner, improving image quality and diagnostic confidence. These findings suggest that the proposed method could be widely implemented across MRI systems utilizing a smaller RF‐transmit coil.

Abstract Background Dose‐driven continuous scanning (DDCS) is a type of beam delivery in Hitachi pencil beam scanning (PBS) particle therapy system. Particle beam is irradiated continuously between spots to achieve a higher effective dose rate and a shorter beam delivery time. The beam is only turned off at break spots, the end of a treatment layer and during spill change. The break spot is a unique feature of the DDCS introduced by the treatment planning system (TPS) to interrupt continuous scanning. DDCS separates the dose delivery of a spot into a move dose and stop dose components. The move dose is controlled by the scanning speed and beam current, while the stop dose delivers the remaining monitor units (MUs). Hence, rigorous quality assurance (QA) of the scanning speed and functionality of break spot are required to ensure safe and successful DDCS delivery. Purpose We aim to report for the first time the QA methodology and result for DDCS, focusing on the scanning speed and break spot validation. Methods A rectangular spiral irradiation pattern with different degree of break spots were irradiated on a 2D strip ionization chamber (CROSSmini) detector using 50 time resolution to validate the break spots. Four methods of measuring scanning speeds were proposed. Using a spiral irradiation pattern, methods one and two applied linear regression to the scanning time measured by the CROSSmini for different inter‐spot distances, with measurements taken at sampling rates of 5 and 20 kHz, respectively. Methods three and four measured the scanning time of single inter‐spot distances in the X and Y directions. Method three measured the scanning time with the CROSSmini while method four measured the rise and fall time of the scanning magnet (SCM) current. All measurements were performed with 70.2, 150.2 and 228.7 MeV proton beam energies and three different beam currents of 8, 14 and 20 MU/s. Lastly, the scanning speed tolerance limit was calculated analytically under the requirement of avoiding a beam abort when the stop dose was less than zero. Results The data recorded by the CROSSmini under the spiral irradiation patterns with break spots was reconstructed into a 2D dose, and the break spots’ operations were visually validated for all the energies and beam currents. There were no statistically significant difference in the measured X and Y scanning speeds across the four methods. However, the last method involving the SCM current yielded the largest type A uncertainty of up to 25% () due to the noisy signal. The first method with the linear regression and 5 kHz acquisition rate was preferred due to a smaller type A uncertainty of up to 2.0% and a lower false negative rate in detecting the rising and falling edges during scanning. The calculated tolerance limit based on our current DDCS setting was found to be 0.50 times of the expected scanning speed. Conclusion We have introduced QA protocols for DDCS, designed to ensure complete and safe beam delivery without interruptions caused by move doses exceeding planned spot doses.

ABSTRACT Objectives Sarcomas are rare, mesenchymal tumors that require multimodality treatment provided by multidisciplinary teams. This study seeks to better understand sarcoma patient narratives and quality of life in relation to the social determinants of health (SDoH). Methods Eligible patients who were diagnosed with sarcoma, treated by our institution's orthopedic oncology team, and identified with at least one social risk factor were contacted about participating in a semi‐structured interview. SDoH information collected included (1) Economic Stability, (2) Access to Health Care, (3) Neighborhood & Physical Environment, (4) Social & Community Context, and (5) Education Access and Quality. Interviews were then recorded, de‐identified, transcribed, and coded by reviewers. The validated Framework Method for qualitative research was used to summarize themes. Descriptive statistics were used to summarize the percentage of participants that identified with each SDoH concern. Results 100% of patients ( N = 17) discussed how quality and access to healthcare affected their experience. 88.2% of participants expressed the importance of transportation. 29.4% of participants discussed how they thought their educational background affected their cancer care. Other common themes centered around social support, employment, financial security, and insurance. Conclusion Our research elicited ways in which SDoH affected the care experience of patients with sarcoma. The identified domains offer clinicians valuable information into non‐clinical factors, such as employment concerns or transportation issues, that may affect care for patients with sarcoma. Interdisciplinary care providers (social workers, psychologists, financial navigators) may also help promote open dialogues with patients about their social circumstances and actively facilitate connections to institutional resources or community support systems.