Publications by authors named "Kristy K Brock"

Background: Hematoxylin and eosin (H&E) staining is widely considered to be the gold-standard diagnostic tool for histopathology evaluation. However, the fatty nature of some tissue types, such as breast tissue, presents challenges with cryo-sectioning, often resulting in artifacts that can make histopathologic interpretation and correlation with other imaging modalities virtually impossible. We present an optimized on-block H&E staining technique that improves contrast for identifying collagenous stroma during cryo-fluorescence tomography (CFT) sectioning.

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Purpose/objectives: Tracking patient dose in radiation oncology is challenging due to disparate electronic systems from various vendors. Treatment planning systems (TPS), radiation oncology information systems (ROIS), and electronic health records (EHR) lack uniformity, complicating dose tracking and reporting. To address this, we examined practices in multiple radiation oncology settings and proposed guidelines for current systems.

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  • The study addresses the challenges faced in liver surgeries due to the complex anatomy of liver blood vessels and the limitations of traditional 2D ultrasound imaging, which is often affected by noise and artifacts.
  • Researchers developed an AI-based "2D-weighted U-Net model" to improve intraoperative ultrasonography by enhancing the real-time detection and segmentation of key liver blood vessels.
  • The deep learning model demonstrated high accuracy in identifying various vessels, achieving Dice scores between 0.84 and 0.96, with plans to extend its use for more comprehensive liver vascular mapping in future surgeries.
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Background And Purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC).

Materials And Methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities.

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Multiple tools are available for commissioning and quality assurance of deformable image registration (DIR), each with their own advantages and disadvantages in the context of radiotherapy. The selection of appropriate tools should depend on the DIR application with its corresponding available input, desired output, and time requirement. Discussions were hosted by the ESTRO Physics Workshop 2021 on Commissioning and Quality Assurance for DIR in Radiotherapy.

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  • The study focuses on using radiomic features from contrast-enhanced CT scans to distinguish between osteoradionecrosis (ORN) and normal mandibular bone in head and neck cancer patients treated with radiotherapy.
  • Data from 150 patients was analyzed, with feature extraction performed using PyRadiomics and a Random Forest classifier used to identify key features, resulting in an accuracy of 88%.
  • The findings highlight specific radiomic features that can differentiate ORN from healthy tissue, paving the way for future research on early detection and intervention strategies.
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  • Image segmentation of the liver is crucial for liver cancer treatment planning, but manual methods are impractical due to scale, leading to a shift towards deep learning models for automation.
  • This study focuses on developing a generalizable deep learning model that segments the liver in T1-weighted MR images using three architectures: nnUNet, PocketNet, and Swin UNETR, with data from six different institutions totaling 819 images.
  • The results show that nnUNet and PocketNet achieved high similarity scores in liver segmentation, suggesting they can effectively perform segmentation on a diverse dataset, benefiting both intra- and inter-institutional applications.
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  • Several new software methods have been developed to assess the minimum ablative margin during thermal ablation of colorectal liver metastases, aiming to enhance patient outcomes in a multi-institutional context.
  • This retrospective study analyzed 400 cases of thermal ablation over 13 years, focusing on minimum ablative margins and their correlation with local disease progression rates.
  • Results showed that a minimum ablative margin of 5 mm or more significantly reduces the risk of local tumor progression, confirming the importance of this margin across various institutions.
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Systemic immunotherapies have led to tremendous progress across the cancer landscape. However, several challenges exist, potentially limiting their efficacy in the treatment of solid tumors. Direct intratumoral injection can increase the therapeutic index of immunotherapies while overcoming many of the barriers associated with systemic administration, including limited bioavailability to tumors and potential systemic safety concerns.

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  • * By utilizing contrast-enhanced CT images from three patients, the researchers simulated temperature distribution during MWA, aiming to predict effective ablation zones for better treatment planning.
  • * Results showed a strong correlation between predicted and actual ablation zones, with Dice scores ranging from 0.73 to 0.86, demonstrating that these 3D models can enhance accuracy in MWA strategies and treatment outcomes.
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  • The radiation therapy field is rapidly developing AI models, but there is a lack of adoption in clinical practice due to unclear guidelines on their development and validation.
  • A Delphi process was used to create a comprehensive guideline, involving discussions among authors to identify key topics like decision making, image analysis, and ethics related to AI in radiation therapy.
  • The resulting guideline includes 19 highly recommended statements aimed at improving the development and reporting of AI tools, ultimately facilitating their integration into clinical workflows.
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Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text].

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  • CT hepatic arteriography (CTHA) is very effective at detecting colorectal liver metastases (CLMs) but struggles with specificity for small, incidental lesions due to pseudolesions and ambiguous imaging signatures.
  • A study involving 22 patients highlighted the identification of incidental ring-hyperenhancing liver micronodules (RHLMs) during CTHA, revealing that 41.7% of CTHA images contained these nodules, with many subsequently confirmed as CLMs.
  • The research suggests that RHLMs detected in CTHA may serve as an early indicator for small CLMs, which could help in improving the accuracy of liver ablation procedures.
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  • The study aimed to evaluate how effectively two different image registration methods—deformable (DIR) and rigid (RIR)—can quantify minimal ablative margins (MAM) in patients undergoing thermal ablation for colorectal liver metastasis (CLM).
  • Out of 72 patients analyzed, DIR showed better registration accuracy (0.96-0.98) compared to RIR (0.67-0.98), along with a higher predictive capability for local tumor outcomes, evidenced by a higher AUC (0.89 vs. 0.72).
  • The results suggest that DIR is a superior method for quantifying MAM during intraprocedural CT imaging, thus improving the prediction of local tumor outcomes after thermal
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  • The study focuses on the importance of using personalized medicine approaches, particularly survival prediction models, rather than relying solely on generalized evidence from clinical trials, highlighting the relevance of patient-specific outcomes.
  • Bayesian parametric survival models were developed and assessed against traditional models like Cox Proportional Hazards and Random Survival Forest, demonstrating their effectiveness with less complexity in parameter tuning and lower risk of overfitting.
  • The research indicates that Bayesian models not only perform comparably to existing models but also offer the advantage of refining predictions through Bayes rule without the need for full retraining, thus enhancing their practicality in medical settings.
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Objectives: The aim of this study was to investigate the prognostic value of 3-dimensional minimal ablative margin (MAM) quantified by intraprocedural versus initial follow-up computed tomography (CT) in predicting local tumor progression (LTP) after colorectal liver metastasis (CLM) thermal ablation.

Materials And Methods: This single-institution, patient-clustered, tumor-based retrospective study included patients undergoing microwave and radiofrequency ablation between 2016 and 2021. Patients without intraprocedural and initial follow-up contrast-enhanced CT, residual tumors, or with follow-up less than 1 year without LTP were excluded.

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External beam radiation therapy (EBRT) of liver cancers can cause local liver atrophy as a result of tissue damage or hypertrophy as a result of liver regeneration. Predicting those volumetric changes would enable new strategies for liver function preservation during treatment planning. However, understanding of the spatial dose/volume relationship is still limited.

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Purpose: To determine the feasibility of quantitative apparent diffusion coefficient (ADC) acquisition during magnetic resonance imaging-guided brachytherapy (MRgBT) using reduced field-of-view (rFOV) diffusion-weighted imaging (DWI).

Methods And Materials: T2-weighted (T2w) MR and full-FOV single-shot echo planar (ssEPI) DWI were acquired in 7 patients with cervical or vaginal malignancy at baseline and prior to brachytherapy, while rFOV-DWI was acquired during MRgBT following brachytherapy applicator placement. The gross target volume (GTV) was contoured on the T2w images and registered to the ADC map.

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  • This study evaluates a standardized method for thermal ablation of colorectal liver metastases (CRLM), focusing on technical effectiveness and local tumor progression-free survival (LTPFS).
  • The trial will include up to 50 patients, assessing various factors like minimal ablative margins, adverse events, and anesthesia time over a follow-up period of up to 2 years.
  • The STEREOLAB trial aims to implement a precise workflow using advanced imaging and guidance techniques to improve ablation outcomes for CRLM patients.
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  • This study focuses on improving the delivery of cancer treatment by evaluating autosegmentation methods that outline key organs at risk (OARs) in head and neck cancer patients using low-resolution MRIs from a specific machine known as the MR-linac.
  • Researchers investigated 20 autosegmentation approaches, including both population-based methods and deep learning techniques, comparing their effectiveness in accurately identifying OARs against established ground truth contours.
  • Results showed varying performance across methods, with additional dosimetric analysis performed on the best and worst methods, highlighting the importance of accurate dose reconstruction for effective patient treatment.
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Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead.

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Purpose: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships.

Methods And Materials: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs.

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Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources.

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MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose.

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