Consolidation Regimen and Cerebral Atrophy in Patients with Primary Central Nervous System Lymphoma.

Background: In primary central nervous system lymphoma (PCNSL), the extent to which post-methotrexate consolidation contributes to neurotoxicity is unclear. Concerns for neurotoxicity from standard-dose whole-brain radiotherapy (WBRT) have led to declining use. Cerebral atrophy is an established surrogate for neurotoxicity; however, the relative extent to which modern consolidation (i.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.

Background And Purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma.

Materials And Methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma.

A Phase II Study Assessing Long-term Response to Ibrutinib Monotherapy in Recurrent or Refractory CNS Lymphoma.

Purpose: Ibrutinib is a first-in-class inhibitor of Bruton tyrosine kinase. We previously reported the safety and short-term antitumor activity of ibrutinib in 20 patients with relapsed or refractory (r/r) primary central nervous system (CNS) lymphoma (PCNSL) or secondary CNS lymphoma (SCNSL).

Patients And Methods: We enrolled 26 additional patients with r/r PCNSL/SCNSL into the dose-expansion cohort of the trial into a combined cohort of 46 patients (31 with PCNSL and 15 with SCNSL).

Differentiating Low-Grade from High-Grade Intracranial Ependymomas: Comparison of Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging.

Background And Purpose: The aim of this study was to determine the diagnostic value of fractional plasma volume derived from dynamic contrast-enhanced perfusion MR imaging versus ADC, obtained from DWI in differentiating between grade 2 (low-grade) and grade 3 (high-grade) intracranial ependymomas.

Materials And Methods: A hospital database was created for the period from January 2013 through June 2022, including patients with histologically-proved ependymoma diagnosis with available dynamic contrast-enhanced MR imaging. Both dynamic contrast-enhanced perfusion and DWI were performed on each patient using 1.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.

Objectives: Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T-weighted (Tw) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients.

Methods: This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.

Artificial Intelligence in CT and MR Imaging for Oncological Applications.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.

Objectives: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

Methods: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients).

Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study.

The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]).

Qualitative and Quantitative Performance of Magnetic Resonance Image Compilation (MAGiC) Method: An Exploratory Analysis for Head and Neck Imaging.

The present exploratory study investigates the performance of a new, rapid, synthetic MRI method for diagnostic image quality assessment and measurement of relaxometry metric values in head and neck (HN) tumors and normal-appearing masseter muscle. The multi-dynamic multi-echo (MDME) sequence was used for data acquisition, followed by synthetic image reconstruction on a 3T MRI scanner for 14 patients (3 untreated and 11 treated). The MDME enables absolute quantification of physical tissue properties, including T1 and T2, with a shorter scan time than the current state-of-the-art methods used for relaxation measurements.

Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study.

The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model.

Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas.

The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including F-FDG-PET/CT, F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K, k, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [K, v, and τ]) were investigated using the CDA based on a "spin-glass model" coupled with the Spearman's rank, ρ, analysis.

Prognostic value of [F]FDG PET/CT in patients with CNS lymphoma receiving ibrutinib-based therapies.

Purpose: Current clinical and imaging tools remain suboptimal for predicting treatment response and prognosis in CNS lymphomas. We investigated the prognostic value of baseline [F]FDG PET in patients with CNS lymphoma receiving ibrutinib-based treatments.

Methods: Fifty-three patients enrolled in a prospective clinical trial and underwent brain PET before receiving single-agent ibrutinib or ibrutinib in combination with methotrexate with or without rituximab.

Reproducibility of radiomic features using network analysis and its application in Wasserstein -means clustering.

The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients.

Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure.

The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (, *, f, and ) models.

Precision Radiotherapy: Reduction in Radiation for Oropharyngeal Cancer in the 30 ROC Trial.

Background: Patients with human papillomavirus-related oropharyngeal cancers have excellent outcomes but experience clinically significant toxicities when treated with standard chemoradiotherapy (70 Gy). We hypothesized that functional imaging could identify patients who could be safely deescalated to 30 Gy of radiotherapy.

Methods: In 19 patients, pre- and intratreatment dynamic fluorine-18-labeled fluoromisonidazole positron emission tomography (PET) was used to assess tumor hypoxia.

F-FDG PET/CT versus anatomic imaging for evaluating disease extent and clinical trial eligibility in Erdheim-Chester disease: results from 50 patients in a registry study.

Objectives: The aim of this study was to [1] characterize distribution of Erdheim-Chester Disease (ECD) by F-FDG PET/CT and [2] determine the utility of metabolic (F-FDG PET/CT) imaging versus anatomic imaging (CT or MRI) in evaluating ECD patients for clinical trial eligibility.

Methods: F-FDG PET/CT and corresponding CT or MRI studies for ECD patients enrolled in a prospective registry study were reviewed. Sites of disease were classified as [1] detectable by F-FDG PET only, CT/MRI only, or both and as [2] measurable by modified PERCIST (mPERCIST) only, RECIST only, or both.