HRS Improves Active Surveillance for Prostate Cancer by Timely Identification of Progression.

Rationale And Objectives: Active surveillance (AS) is the preferred management strategy for low-risk prostate cancer. This study aimed to evaluate the impact of Habitat Risk Score (HRS), an automated approach for mpMRI analysis, for early detection of progressors in a prospective AS clinical trial (MAST NCT02242773).

Materials And Methods: The MAST protocol includes Confirmatory mpMRI ultrasound fusion (MRI-US) biopsy and yearly surveillance MRI-US biopsies for up to 3 years.

Integrated framework for quantitative T2-weighted MRI analysis following prostate cancer radiotherapy.

Purpose: The aim of this study is to develop a framework for quantitative analysis of longitudinal T2-weighted MRIs (T2w) following radiotherapy (RT) for prostate cancer.

Materials And Methods: The developed methodology includes: deformable image registration of longitudinal series to pre-RT T2w for automated detection of prostate, peripheral zone (PZ), and gross tumor volume (GTV); and T2w signal-intensity harmonization based on three reference tissues. The gistration and onization () framework was applied on T2w acquired in a clinical trial consisting of two pre-RT and three post-RT MRI exams.

Dynamics of Daily Glioblastoma Evolution During Chemoradiation Therapy on the 0.35T Magnetic Resonance Imaging-Linear Accelerator.

Purpose: Glioblastoma changes during chemoradiation therapy are inferred from magnetic resonance imaging (MRI) before and after treatment but are rarely investigated due to logistics of frequent MRI. Using a combination MRI-linear accelerator (MRI-linac), we evaluated changes during daily chemoradiation therapy.

Methods And Materials: Patients with glioblastoma were prospectively imaged daily during chemoradiation therapy on 0.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities.

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma.

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system.

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.

We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint).

Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer.

Background/hypothesis: MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions' MRI scans.

Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.

Purpose: Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors.

Methods: This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220).

Classification of suspicious lesions on prostate multiparametric MRI using machine learning.

We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients).

Automatic Detection of Prostate Tumor Habitats using Diffusion MRI.

A procedure for identification of optimal Apparent Diffusion Coefficient (ADC) thresholds for automatic delineation of prostatic lesions with restricted diffusion at differing risk for cancer was developed. The relationship between the size of the identified Volumes of Interest (VOIs) and Gleason Score (GS) was evaluated. Patients with multiparametric (mp)MRI, acquired prior to radical prostatectomy (RP) (n = 18), mpMRI-ultrasound fused (MRI-US) (n = 21) or template biopsies (n = 139) were analyzed.

Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy.

In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT).

An Automated Multiparametric MRI Quantitative Imaging Prostate Habitat Risk Scoring System for Defining External Beam Radiation Therapy Boost Volumes.

Purpose: To develop a prostate tumor habitat risk scoring (HRS) system based on multiparametric magnetic resonance imaging (mpMRI) referenced to prostatectomy Gleason score (GS) for automatic delineation of gross tumor volumes. A workflow for integration of HRS into radiation therapy boost volume dose escalation was developed in the framework of a phase 2 randomized clinical trial (BLaStM).

Methods And Materials: An automated quantitative mpMRI-based 10-point pixel-by-pixel method was optimized to prostatectomy GSs and volumes using referenced dynamic contrast-enhanced and apparent diffusion coefficient sequences.

Analysis of Magnetic Resonance Image Signal Fluctuations Acquired During MR-Guided Radiotherapy.

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes.