A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

Background: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.

AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer.

Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (V) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified.

Feasibility and clinical implementation of MRI-guided surface brachytherapy.

Purpose: Best practices for high-dose-rate surface applicator brachytherapy treatment (SABT) have long relied on computed tomography (CT)-based imaging to visualize diseased sites for treatment planning. Compared with magnetic resonance (MR)-based imaging, CT provides insufficient soft tissue contrast. This work described the feasibility of clinical implementation of MR-based imaging in SABT planning to provide individualized treatment optimization.

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency.

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.

Introduction: Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, auto-segmentations, and QA reporting.

Materials And Methods: We developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center.

Observer preference of artificial intelligence-generated versus clinical prostate contours for ultrasound-based high dose rate brachytherapy.

Background: For trans-rectal ultrasound (TRUS)-based high dose rate (HDR) prostate brachytherapy, prostate contouring can be challenging due to artifacts from implanted needles, bleeding, and calcifications.

Purpose: To evaluate the geometric accuracy and observer preference of an artificial intelligence (AI) algorithm for generating prostate contours on TRUS images with implanted needles.

Methods: We conducted a retrospective study of 150 patients, who underwent HDR brachytherapy.

Assessing the Feasibility of Using Artificial Intelligence-Segmented Dominant Intraprostatic Lesion for Focal Intraprostatic Boost With External Beam Radiation Therapy.

Purpose: The delineation of dominant intraprostatic gross tumor volumes (GTVs) on multiparametric magnetic resonance imaging (mpMRI) can be subject to interobserver variability. We evaluated whether deep learning artificial intelligence (AI)-segmented GTVs can provide a similar degree of intraprostatic boosting with external beam radiation therapy (EBRT) as radiation oncologist (RO)-delineated GTVs.

Methods And Materials: We identified 124 patients who underwent mpMRI followed by EBRT between 2010 and 2013.

Left Anterior Descending Coronary Artery Radiation Dose Association With All-Cause Mortality in NRG Oncology Trial RTOG 0617.

Purpose: A left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15 Gy) ≥10% has been recently observed to be an independent risk factor of major adverse cardiac events and all-cause mortality in patients with locally advanced non-small cell lung cancer treated with radiation therapy. However, this dose constraint has not been validated in independent or prospective data sets.

Methods And Materials: The NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 data set from the National Clinical Trials Network was used.

Image quality comparisons of coil setups in 3T MRI for brain and head and neck radiotherapy simulations.

Purpose: MRI is increasingly used for brain and head and neck radiotherapy treatment planning due to its superior soft tissue contrast. Flexible array coils can be arranged to encompass treatment immobilization devices, which do not fit in diagnostic head/neck coils. Selecting a flexible coil arrangement to replace a diagnostic coil should rely on image quality characteristics and patient comfort.

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

Background: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts.

Knowledge-based inverse treatment planning for low-dose-rate prostate brachytherapy.

Purpose: Permanent low-dose-rate brachytherapy is a widely used treatment modality for managing prostate cancer. In such interventions, treatment planning can be a challenging task and requires experience and skills of the planner. We developed a novel knowledge-based (KB) optimization method based on previous treatment plans.

Development and clinical implementation of semi-automated treatment planning including 3D printable applicator holders in complex skin brachytherapy.

Purpose: High-dose-rate brachytherapy (HDR-BT) is a treatment option for malignant skin diseases compared to external beam radiation therapy, HDR-BT provides improved target coverage, better organ sparing, and has comparable treatment times. This is especially true for large clinical targets with complex topologies. To standardize and improve the quality and efficacy of the treatments, a novel streamlined treatment approach in complex skin HDR-BT was developed and implemented.

A fast inverse treatment planning strategy facilitating optimized catheter selection in image-guided high-dose-rate interstitial gynecologic brachytherapy.

Purpose: Interstitial high-dose rate (HDR) brachytherapy is an important therapeutic strategy for the treatment of locally advanced gynecologic (GYN) cancers. The outcome of this therapy is determined by the quality of dose distribution achieved. This paper focuses on a novel yet simple heuristic for catheter selection for GYN HDR brachytherapy and their comparison against state of the art optimization strategies.

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high-dose-rate (HDR) brachytherapy.

Purpose: In this study, we introduce a novel, fast, inverse treatment planning strategy for interstitial high-dose-rate (HDR) brachytherapy with multiple regions of interest solely based on dose-volume-histogram-related dosimetric measures (DMs).

Methods: We present a new problem formulation of the objective function that approximates the indicator variables of the standard DM optimization problem with a smooth logistic function. This problem is optimized by standard gradient-based methods.

Feasibility of using single photon counting X-ray for lung tumor position estimation based on 4D-CT.

Purpose: In stereotactic body radiation therapy of lung tumors, reliable position estimation of the tumor is necessary in order to minimize normal tissue complication rate. While kV X-ray imaging is frequently used, continuous application during radiotherapy sessions is often not possible due to concerns about the additional dose. Thus, ultra low-dose (ULD) kV X-ray imaging based on a single photon counting detector is suggested.

Combined stereotactic biopsy and stepping-source interstitial irradiation of glioblastoma multiforme.

Patients diagnosed with glioblastoma multiforme receiving stereotactic biopsy only either due to tumor localization or impaired clinical status face a devastating prognosis with very short survival times. One strategy to provide an initial cytoreductive and palliative therapy at the time of the stereotactic biopsy is interstitial irradiation through the pre-defined trajectory of the biopsy channel. We designed a novel treatment planning system and evaluated the treatment potential of a fixed-source and a stepping-source algorithm for interstitial radiosurgery on non-spherical glioblastoma in direct adjacency to risk structures.