Purpose: To determine whether the difference between MRI-based and ultrasound (US)-based volume measurements are associated with MRI/US-targeted fusion-guided biopsy outcomes.
Materials And Methods: This retrospective, single-center study involved 4177 consecutive patients biopsied between 2010 and 2023 using both MRI/US-targeted fusion and systematic biopsy. Biopsies were indicated because of elevated PSA levels or abnormal multiparametric MRI results.
Purpose: The aim of this study was to assess the feasibility of quantifying morphologic changes in tumors during immunotherapy, as a reflection of response or survival.
Methods And Materials: A retrospective single-center analysis was performed in patients with unresectable liver cancer previously enrolled in clinical trials combining immunotherapy (tremelimumab ± durvalumab) and locoregional treatment (either ablation or transarterial chemoembolization). Conventional response (RECIST 1.
Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking", particularly in cases where the data/models may contradict the patient.
View Article and Find Full Text PDFPurpose: Targeting accuracy determines outcomes for percutaneous needle interventions. Augmented reality (AR) in IR may improve procedural guidance and facilitate access to complex locations. This study aimed to evaluate percutaneous needle placement accuracy using a goggle-based AR system compared to an ultrasound (US)-based fusion navigation system.
View Article and Find Full Text PDFBackground Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results.
View Article and Find Full Text PDFPurpose: To develop and evaluate a smartphone augmented reality (AR) system for a large 50-mm liver tumor ablation with treatment planning for composite overlapping ablation zones.
Materials And Methods: A smartphone AR application was developed to display tumor, probe, projected probe paths, ablated zones, and real-time percentage of the ablated target tumor volume. Fiducial markers were attached to phantoms and an ablation probe hub for tracking.
Background: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.
Purpose: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.
Study Type: Retrospective.
Objective: To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach.
Methods: Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study.
Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.
View Article and Find Full Text PDFPublicly available audio data presents a unique opportunity for the development of digital health technologies with large language models (LLMs). In this study, YouTube was mined to collect audio data from individuals with self-declared positive COVID-19 tests as well as those with other upper respiratory infections (URI) and healthy subjects discussing a diverse range of topics. The resulting dataset was transcribed with the Whisper model and used to assess the capacity of LLMs for detecting self-reported COVID-19 cases and performing variant classification.
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