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Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance. [ABSTRACT FROM AUTHOR]

Purpose: A workflow/planning strategy delivering low‐dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI‐based Ethos online adaptive RT (oART) platform is developed/evaluated. Methods: Using retrospective data for ten polymetastatic non‐small cell lung cancer patients (5‐52 lesions each) with PET/CTs, gross tumor volumes (GTVs) were delineated using PET standardized‐uptake‐value (SUV) thresholding. A 1 cm uniform expansion of GTVs to account for setup/contour uncertainty and organ motion‐generated planning target volumes (PTVs). Dose optimization/calculation used the diagnostic CT from PET/CT. Dosimetric objectives were: Dmin,0.03cc ≥ 95% (acceptable variation (Δ) ≥ 90%), V100% ≥ 95% (Δ ≥ 90%), and D0.03cc ≤ 120% (Δ ≤ 125%). Additionally, online adaptation was simulated. When available, subsequent diagnostic CT was used to represent on‐treatment CBCT. Otherwise, the CT from PET/CT used for initial planning was deformed to simulate clinically representative changes. Results: All initial plans generated, both for Raystation and Ethos, achieved clinical goals within acceptable variation. For all patients, Dmin,0.03cc ≥ 95%, V100% ≥ 95%, and D0.03cc ≤ 120% goals were achieved for 84.8%/99.5%, 97.7%/98.7%, 97.4%/92.3%, in conventional/Ethos plans, respectively. The ratio of 50% isodose volume to PTV volume (R50%), maximum dose at 2 cm from PTV (D2cm), and the ratio of the 100% isodose volume to PTV volume (conformity index) in Raystation/Ethos plans were 7.9/5.9; 102.3%/88.44%; and 0.99/1.01, respectively. In Ethos, online adapted plans maintained PTV coverage whereas scheduled plans often resulted in geographic misses due to changes in tumor size, patient position, and body habitus. The average total duration of the oART workflow was 26:15 (min:sec) ranging from 6:43 to 57:30. The duration of each oART workflow step as a function of a number of targets showed a low correlation coefficient for influencer generation and editing (R2 = 0.04 and 0.02, respectively) and high correlation coefficient for target generation, target editing and plan generation (R2 = 0.68, 0.63 and 0.69, respectively). Conclusions: This study demonstrates feasibility of conventional planning/treatment with Raystation/Halcyon and highlights efficiency gains when utilizing semi‐automated planning/online‐adaptive treatment with Ethos for immunostimulatory LDRT conformally delivered to all sites of polymetastatic disease. [ABSTRACT FROM AUTHOR]

Hypoxic tumors are resistant to radiotherapy, motivating the development of tools to image local oxygen concentrations. It is generally believed that stable or chronic hypoxia is the source of resistance, but more recent work suggests a role for transient hypoxia. Conventional EPR imaging (EPRI) is capable of imaging tissue pO2in vivo, with high pO2 resolution and 1mm spatial resolution but low imaging speed (10min temporal resolution for T1-based pO2 mapping), which makes it difficult to investigate the oxygen changes, e.g., transient hypoxia. Here we describe a new imaging method which accelerates dynamic EPR oxygen imaging, allowing 3D imaging at 2 frames per minute, fast enough to image transient hypoxia at the "speed limit" of observed pO2 change. The method centers on a low-rank tensor model that decouples the tradeoff between imaging speed, spatial coverage/resolution, and number of inversion times (pO2 accuracy). We present a specialized sparse sampling strategy and image reconstruction algorithm for use with this model. The quality and utility of the method is demonstrated in simulations and in vivo experiments in tumor bearing mice.

Purpose: A planning strategy was developed and the utility of online‐adaptation with the Ethos CBCT‐guided ring‐gantry adaptive radiotherapy (ART) system was evaluated using retrospective data from Head‐and‐neck (H&N) patients that required clinical offline adaptation during treatment. Methods: Clinical data were used to re‐plan 20 H&N patients (10 sequential boost (SEQ) with separate base and boost plans plus 10 simultaneous integrated boost (SIB)). An optimal approach, robust to online adaptation, for Ethos‐initial plans using clinical goal prioritization was developed. Anatomically‐derived isodose‐shaping helper structures, air‐density override, goals for controlling hotspot location(s), and plan normalization were investigated. Online adaptation was simulated using clinical offline adaptive simulation‐CTs to represent an on‐treatment CBCT. Dosimetric comparisons were based on institutional guidelines for Clinical‐initial versus Ethos‐initial plans and Ethos‐scheduled versus Ethos‐adapted plans. Timing for five components of the online adaptive workflow was analyzed. Results: The Ethos H&N planning approach generated Ethos‐initial SEQ plans with clinically comparable PTV coverage (average PTVHigh V100% = 98.3%, Dmin,0.03cc = 97.9% and D0.03cc = 105.5%) and OAR sparing. However, Ethos‐initial SIB plans were clinically inferior (average PTVHigh V100% = 96.4%, Dmin,0.03cc = 93.7%, D0.03cc = 110.6%). Fixed‐field IMRT was superior to VMAT for 93.3% of plans. Online adaptation succeeded in achieving conformal coverage to the new anatomy in both SEQ and SIB plans that was even superior to that achieved in the initial plans (which was due to the changes in anatomy that simplified the optimization). The average adaptive workflow duration for SIB, SEQ base and SEQ boost was 30:14, 22.56, and 14:03 (min: sec), respectively. Conclusions: With an optimal planning approach, Ethos efficiently auto‐generated dosimetrically comparable and clinically acceptable initial SEQ plans for H&N patients. Initial SIB plans were inferior and clinically unacceptable, but adapted SIB plans became clinically acceptable. Online adapted plans optimized dose to new anatomy and maintained target coverage/homogeneity with improved OAR sparing in a time‐efficient manner. [ABSTRACT FROM AUTHOR]