Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning..

Survey of Laboratory Stewardship Governance at US Academic Medical Centers.

Background: Efforts to appropriately utilize laboratory tests have been underway for several decades. However, limited information is available regarding the status of laboratory stewardship at academic medical centers. Prior to initiating a laboratory stewardship committee, a study was initiated to gain insights from peer institutions.

Evaluating Outcomes for Women with Metastatic Breast Cancer: Palliative Care Consultations, Hospital Charges, and Length of Stay.

: Women with late-stage metastatic breast cancer are at an increased risk of pain and distress from symptoms and often struggle with associated emotional and financial burden of their disease. Palliative care is known to alleviate symptom burden in patients with end-stage, terminal diseases but is often underutilized in both inpatient and outpatient settings. The current study aims to investigate the prevalence of palliative care consultation on inpatients with metastatic breast cancer and examine the association between palliative care consultation and length of hospital stay and total hospital charges.

AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.

Glioblastoma is an aggressive brain cancer with a poor prognosis. The O6-methylguanine-DNA methyltransferase (MGMT) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to develop an artificial intelligence (AI) framework for predicting MGMT methylation.

Substance use disorder and severe maternal morbidity: is there a differential impact?

Background: Substance use disorder (SUD) is a disease characterized by behavior patterns of substance use leading to dysfunction in cognition, mood, and quality of life. The prevalence of perinatal SUD in the United States continues to rise and has adverse effects on the maternal-infant dyad. Mirroring the rise in SUD is an increasing prevalence of severe maternal morbidity (SMM).

Ultra-Hypofractionated Prostate Radiotherapy With Online Adaptive Technique: A Case Report.

Ultra-hypofractionated radiotherapy (UHF RT) is revolutionizing the treatment approach for low- and intermediate-risk prostate cancer patients. This study reports the planning process of UHF RT utilizing the cone beam computed tomography (CBCT)-based online adaptive radiotherapy (OART) treatment with the Ethos system, focusing on a comparative analysis between OART and image-guided radiotherapy (IGRT) plans. We also assessed the pre-planning capabilities of the Ethos system against the CyberKnife (CK) (Accuray, Sunnyvale, CA) system.

Investigating the impact of rapidplan on ethos automated planning.

Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP).

Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet.

. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN).

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach.

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently.

Managing Inconsistent Bladder Volumes in the Prostate Cancer Patient Using Daily Online Adaptive RT: A Case Report.

Purpose: We report the use of online adaptive radiotherapy (OART) aiming to improve dosimetric parameters in the prostate cancer patient who had lower urinary tract symptoms that caused him not to adhere to the standard bladder filling protocol.

Methods And Materials: The reference treatment plan for adaptive radiotherapy plan was generated for the pelvis and the solitary bony lesion using the Ethos treatment planning system. For each treatment session, high-quality iterative reconstructed cone beam CT (CBCT) images were acquired, and the system automatically generated an optimal adaptive plan after verification of contours.

Perinatal substance use disorder: Examining the impact on adverse pregnancy outcomes.

Objective: Substance use disorder is a growing concern in the USA, especially among pregnant women. This study was undertaken to assess the impact of substance use disorder on adverse pregnancy outcomes using a nationwide sample of inpatient pregnancy hospitalizations in the USA, and to elucidate the influence on each type of adverse pregnancy outcome.

Study Design: A cross-sectional analysis of inpatient pregnancy hospitalizations in the USA from the Healthcare Cost and Utilization Project National Inpatient Sample from 2016 to 2020 was conducted.

GATE Monte Carlo approach to heterogeneity dose distribution in small fields used in radiation therapy.

The Accurate dosage prediction in Radiation Therapy is challenging, prompting a need for precision beyond conventional clinical Treatment Planning Systems (TPS). Monte Carlo-based methods are sought for their superior accuracy. The aim of this study is to compare dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, focusing on smaller fields.

Racial and Ethnic Disparities Among Pregnancies with Substance Use Disorder: Impact on Perinatal Outcomes.

To examine racial/ethnic disparities in severe maternal morbidity (SMM) and adverse pregnancy outcomes (APOs) among pregnant patients with substance use disorder (SUD) compared to individuals without SUD. We conducted a cross-sectional analysis of inpatient hospitalizations of pregnant people from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from 2016 to 2019. ICD-10 codes were used to identify the frequency of SMM and/or APO between those with and without SUD by race/ethnicity.

Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy.

Background And Purpose: Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy.

Materials And Methods: A novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation.

Overall survival in advanced hepatocellular carcinoma treated with concomitant systemic therapy and stereotactic body radiation therapy or systemic therapy alone.

Introduction: First-line systemic therapy (ST) options for advanced hepatocellular carcinoma (HCC) include tyrosine kinase inhibitors and immunotherapy (IO). Evolving data suggest prolonged overall survival (OS) when ST is combined with stereotactic body radiation therapy (SBRT), although evidence is significantly limited in HCC populations. We hypothesized that advanced HCC patients in the National Cancer Database (NCDB) would have improved OS when receiving ST+SBRT vs ST alone.

Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom.

Background: In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aimed to develop and evaluate various deep learning-based approaches for automatic quality assurance of Magnetic Resonance (MR) images using the American College of Radiology (ACR) standards.

Methods: The study involved the development, optimization, and testing of custom convolutional neural network (CNN) models.

Coq10 for preventing cardiotoxicity in breast cancer patients treated with trastuzumab.

Trastuzumab is a successful treatment option for HER2-positive breast cancer, but a decline in left ventricular ejection fraction (LVEF) and an increase in inflammatory and cardiac enzyme biomarkers can lead to cessation and termination of therapy. This study aimed to investigate the ability of Coenzyme Q10 (Coq10) to avoid these adverse effects. The study included 100 female patients with HER2+ (HER2+3 or amplified gene) breast cancer.

Breast cancer outcomes based on method of detection in community-based breast cancer registry.

Purpose: The impact of opportunistic screening mammography in the United States is difficult to quantify, partially due to lack of inclusion regarding method of detection (MOD) in national registries. This study sought to determine the feasibility of MOD collection in a multicenter community registry and to compare outcomes and characteristics of breast cancer based on MOD.

Methods: We conducted a retrospective study of breast cancer patients from a multicenter tumor registry in Missouri from January 2004 - December 2018.

Characterization of combined endoscopies and aerodigestive care: An analysis of utilization and financial feasibility.

Background: Aerodigestive care is one model of multi-disciplinary care, which is a valuable tool for both providers and patients. Aerodigestive care models are associated with improved outcomes, reduced anesthesia exposure, reduction in hospital admissions, and fewer days of missed work or school. This is the first study to explore national usage and cost trends in combined endoscopy utilization to identify gaps in care and the potential for financial resource optimization.

Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients.

To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditional generative adversarial network (cGAN) for adaptive radiotherapy planning (ART).First fraction CBCT and planning CT (pCT) were collected from 93 Head and Neck patients who underwent external beam radiotherapy. The dataset was divided into training, validation, and test sets of 58, 10 and 25 patients respectively.

Survival in Metastatic Renal Cell Carcinoma Treated With Immunotherapy and Stereotactic Radiation Therapy or Immunotherapy Alone: A National Cancer Database Analysis.

Purpose: Immunotherapy (IO) has significantly improved outcomes in metastatic renal cell carcinoma (mRCC). Preclinical evidence suggests that responses to IO may be potentiated via immunomodulatory effects of stereotactic radiation therapy (SRT). We hypothesized that clinical outcomes from the National Cancer Database (NCDB) would demonstrate improved overall survival (OS) in patients with mRCC receiving IO + SRT versus IO alone.

Virtual pretreatment patient-specific quality assurance of volumetric modulated arc therapy using deep learning.

Background: Automatic patient-specific quality assurance (PSQA) is recently explored using artificial intelligence approaches, and several studies reported the development of machine learning models for predicting the gamma pass rate (GPR) index only.

Purpose: To develop a novel deep learning approach using a generative adversarial network (GAN) to predict the synthetic measured fluence.

Methods And Materials: A novel training method called "dual training," which involves the training of the encoder and decoder separately, was proposed and evaluated for cycle GAN (cycle-GAN) and conditional GAN (c-GAN).

Association of Anxiety and Gastrointestinal Comorbidities in Repeat Hospital Admissions in Pediatric Cyclic Vomiting Syndrome.

Introduction: Cyclic vomiting syndrome (CVS) is a functional gastrointestinal disorder with recurrent episodes of intense nausea and vomiting and thus may require frequent hospitalizations. There is paucity of data exploring the association of psychiatric and gastrointestinal comorbidities in repeat hospitalizations among pediatric patients with CVS.

Methods: We analyzed the Pediatric Health Information System database and included all patients up to 18 years of age with a diagnosis of CVS between 2016 and 2020.

Investigation of radiomics and deep convolutional neural networks approaches for glioma grading.

To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader validation sets.Seven public datasets were considered: (1) low-grade glioma or high-grade glioma (369 patients, BraTS'20) (2) well-differentiated liposarcoma or lipoma (115, LIPO); (3) desmoid-type fibromatosis or extremity soft-tissue sarcomas (203, Desmoid); (4) primary solid liver tumors, either malignant or benign (186, LIVER); (5) gastrointestinal stromal tumors (GISTs) or intra-abdominal gastrointestinal tumors radiologically resembling GISTs (246, GIST); (6) colorectal liver metastases (77, CRLM); and (7) lung metastases of metastatic melanoma (103, Melanoma). Radiomic analysis was performed on 464 (2016) radiomic features for the BraTS'20 (others) datasets respectively.

The role of radiotherapy in newly diagnosed primary CNS lymphoma: A descriptive review and a pragmatic approach to clinical practice.

Earlier, prior to the development of effective systemic therapy, monotherapy with whole-brain radiotherapy (WBRT) was widely used to treat primary central nervous system lymphoma (PCNSL). Recently, chemotherapy, especially with high dose methotrexate (HDMTX), has largely replaced WBRT as upfront treatment, and the most accepted standard of care is induction with a combination drug therapy followed by consolidation therapy with either autologous stem-cell transplantation (ASCT) or radiation. Whilst WBRT is an effective component of treatment, it is occasionally associated with risk of permanent, irreversible neurotoxicity when doses of more than 30 Gy are used.