Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRI.

Objectives: Small lesions are the limiting factor for reducing gadolinium-based contrast agents in brain magnetic resonance imaging (MRI). The purpose of this study was to compare the sensitivity and precision in metastasis detection on true contrast-enhanced T1-weighted (T1w) images and artificial images synthesized by a deep learning method using low-dose images.

Materials And Methods: In this prospective, multicenter study (5 centers, 12 scanners), 917 participants underwent brain MRI between October 2021 and March 2023 including T1w low-dose (0.

Whole-Brain Vascular Architecture Mapping Identifies Region-Specific Microvascular Profiles In Vivo.

Background And Purpose: The novel MR imaging technique of vascular architecture mapping allows in vivo characterization of local changes in cerebral microvasculature, but reference ranges for vascular architecture mapping parameters in healthy brain tissue are lacking, limiting its potential applicability as an MR imaging biomarker in clinical practice. We conducted whole-brain vascular architecture mapping in a large cohort to establish vascular architecture mapping parameter references ranges and identify region-specific cortical and subcortical microvascular profiles.

Materials And Methods: This was a single-center examination of adult patients with unifocal, stable low-grade gliomas with multiband spin- and gradient-echo EPI sequence at 3T using parallel imaging.

Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

Background: A recent innovation in computed tomography (CT) imaging has been the introduction of photon-counting detector CT (PCD-CT) systems, which are able to register the number and the energy level of incoming x‑ray photons and have smaller detector elements compared with conventional CT scanners that operate with energy-integrating detectors (EID-CT).

Objectives: The study aimed to evaluate the potential benefits of a novel, non-CE certified PCD-CT in detecting myeloma-associated osteolytic bone lesions (OL) compared with a state-of-the-art EID-CT.

Materials And Methods: Nine patients with multiple myeloma stage III (according to Durie and Salmon) underwent magnetic resonance imaging (MRI), EID-CT, and PCD-CT of the lower lumbar spine and pelvis.

Clinical Workflow of Cone Beam Computer Tomography-Based Daily Online Adaptive Radiotherapy with Offline Magnetic Resonance Guidance: The Modular Adaptive Radiotherapy System (MARS).

Purpose: The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present the clinical workflow of CBCT-based oART with shuttle-based offline MR guidance.

Whole-Brain Intracellular pH Mapping of Gliomas Using High-Resolution P MR Spectroscopic Imaging at 7.0 T.

Malignant tumors commonly exhibit a reversed pH gradient compared with normal tissue, with a more acidic extracellular pH and an alkaline intracellular pH (pH). In this prospective study, pH values in gliomas were quantified using high-resolution phosphorous 31 (P) spectroscopic MRI at 7.0 T and were used to correlate pH alterations with histopathologic findings.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

Objectives: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI).

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

Objectives: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone.

Materials And Methods: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone.

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.

Purpose: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms.

Materials And Methods: In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition.

In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Study.

Objectives: Despite the extensive number of publications in the field of radiomics, radiomics algorithms barely enter large-scale clinical application. Supposedly, the low external generalizability of radiomics models is one of the main reasons, which hinders the translation from research to clinical application. The objectives of this study were to investigate reproducibility of radiomics features (RFs) in vivo under variation of patient positioning, magnetic resonance imaging (MRI) sequence, and MRI scanners, and to identify a subgroup of RFs that shows acceptable reproducibility across all different acquisition scenarios.

Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer.

Background Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 requires multiparametric MRI of the prostate, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging sequences; however, the contribution of DCE imaging remains unclear. Purpose To assess whether DCE imaging in addition to apparent diffusion coefficient (ADC) and normalized T2 values improves PI-RADS version 2.

Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration.

Objectives: The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI).

Materials And Methods: The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey.

Repeatability and Reproducibility of ADC Measurements and MRI Signal Intensity Measurements of Bone Marrow in Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Multiscanner, Multiprotocol Study.

Background/objectives: Apparent diffusion coefficient (ADC) and signal intensity (SI) measurements play an increasing role in magnetic resonance imaging (MRI) of monoclonal plasma cell disorders. The purpose of this study was to assess interrater variability, repeatability, and reproducibility of ADC and SI measurements from bone marrow (BM) under variation of MRI protocols and scanners.

Patients And Methods: Fifty-five patients with suspected or confirmed monoclonal plasma cell disorder were prospectively included in this institutional review board-approved study and underwent several measurements after the standard clinical whole-body MR scan, including repeated scan after repositioning, scan with a second MRI protocol, scan at a second 1.

Dorsal Root Ganglion Morphometric Changes Under Oxaliplatin Treatment : Longitudinal Assessment by Computed Tomography.

Purpose: Magnetic resonance neurography (MRN) can detect dorsal root ganglia (DRG) hypertrophy in patients with oxaliplatin-induced peripheral neuropathy (OXIPN) but is difficult to apply in clinical daily practice. Aims of this study were (i) to assess whether DRG volume is reliably measurable by routine computed tomography (CT) scans, (ii) to measure longitudinal changes in DRG during and after oxaliplatin administration and (iii) to assess correlation between DRG morphometry and individual oxaliplatin dose.

Methods: For comparison of MRN and CT measurements, CT scans of 18 patients from a previous MRN study were analyzed.

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data.

Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens.

Fully Automatic Deep Learning in Bi-institutional Prostate Magnetic Resonance Imaging: Effects of Cohort Size and Heterogeneity.

Background: The potential of deep learning to support radiologist prostate magnetic resonance imaging (MRI) interpretation has been demonstrated.

Purpose: The aim of this study was to evaluate the effects of increased and diversified training data (TD) on deep learning performance for detection and segmentation of clinically significant prostate cancer-suspicious lesions.

Materials And Methods: In this retrospective study, biparametric (T2-weighted and diffusion-weighted) prostate MRI acquired with multiple 1.

MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology.

Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT.

Measured Multipoint Ultra-High b-Value Diffusion MRI in the Assessment of MRI-Detected Prostate Lesions.

Objectives: The aim of this study was to assess quantitative ultra-high b-value (UHB) diffusion magnetic resonance imaging (MRI)-derived parameters in comparison to standard clinical apparent diffusion coefficient (SD-ADC-2b-1000, SD-ADC-2b-1500) for the prediction of clinically significant prostate cancer, defined as Gleason Grade Group greater than or equal to 2.

Materials And Methods: Seventy-three patients who underwent 3-T prostate MRI with diffusion-weighted imaging acquired at b = 50/500/1000/1500s/mm2 and b = 100/500/1000/1500/2250/3000/4000 s/mm2 were included. Magnetic resonance lesions were segmented manually on individual sequences, then matched to targeted transrectal ultrasonography/MRI fusion biopsies.

Ultra-High--Value Kurtosis Imaging for Noninvasive Tissue Characterization of Ovarian Lesions.

Background MRI with contrast material enhancement is the imaging modality of choice to evaluate sonographically indeterminate adnexal masses. The role of diffusion-weighted MRI, however, remains controversial. Purpose To evaluate the diagnostic performance of ultra-high--value diffusion kurtosis MRI in discriminating benign and malignant ovarian lesions.

Quantitative Dynamic Oxygen 17 MRI at 7.0 T for the Cerebral Oxygen Metabolism in Glioma.

Background Altered metabolism is a characteristic of cancer. Because of a shift in glucose metabolism from oxidative phosphorylation to lactate production for energy generation, malignant tumors are characterized by increased glycolysis followed by lactic acid fermentation, even in the presence of abundant oxygen (the Warburg effect). Purpose To quantitatively investigate dynamic oxygen 17 (O) MRI in healthy participants and participants with untreated glioma to understand altered cerebral oxygen metabolism in glioma.

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC.

Automated brain extraction of multisequence MRI using artificial neural networks.

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations.

Guidelines for Acquisition, Interpretation, and Reporting of Whole-Body MRI in Myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS).

Acknowledging the increasingly important role of whole-body MRI for directing patient care in myeloma, a multidisciplinary, international, and expert panel of radiologists, medical physicists, and hematologists with specific expertise in whole-body MRI in myeloma convened to discuss the technical performance standards, merits, and limitations of currently available imaging methods. Following guidance from the International Myeloma Working Group and the National Institute for Clinical Excellence in the United Kingdom, the Myeloma Response Assessment and Diagnosis System (or MY-RADS) imaging recommendations are designed to promote standardization and diminish variations in the acquisition, interpretation, and reporting of whole-body MRI in myeloma and allow response assessment. This consensus proposes a core clinical protocol for whole-body MRI and an extended protocol for advanced assessments.

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.