Publications by authors named "Todd Mcnutt"

Background: Radiation oncologists closely monitor patients during weekly on-treatment visits (OTVs). This study examines whether routine patient-reported outcome measures (PROMs) during OTVs change physicians' perceptions of treatment-toxicity and inform symptom-management.

Patient And Methods: IMPROVE is a single-arm prospective multicenter trial, conducted from 2020 to 2023.

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Purpose/objectives: Tracking patient dose in radiation oncology is challenging due to disparate electronic systems from various vendors. Treatment planning systems (TPS), radiation oncology information systems (ROIS), and electronic health records (EHR) lack uniformity, complicating dose tracking and reporting. To address this, we examined practices in multiple radiation oncology settings and proposed guidelines for current systems.

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Article Synopsis
  • - The study presents a unified treatment planning system (TPS) model for four matched Elekta VersaHD linacs, allowing flexible workflows in radiation therapy and ensuring quality assurance in intensity-modulated radiation therapy (IMRT).
  • - The TPS was validated using comprehensive tests based on established guidelines, demonstrating that the single RayStation model delivered accurate results within recommended tolerance limits when compared to individual models.
  • - The results showed that the single model maintained high agreement (within 1% PDD) across different radiation energies and produced consistent IMRT quality assurance outcomes, confirming its effectiveness for clinical use.
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Background: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains.

Purpose: To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans.

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Although standardization has been shown to improve patient safety and improve the efficiency of workflows, implementation of standards can take considerable effort and requires the engagement of all clinical stakeholders. Engaging team members includes increasing awareness of the proposed benefit of the standard, a clear implementation plan, monitoring for improvements, and open communication to support successful implementation. The benefits of standardization often focus on large institutions to improve research endeavors, yet all clinics can benefit from standardization to increase quality and implement more efficient or automated workflow.

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Article Synopsis
  • Image-guided interventional oncology enhances cancer treatment by improving the quality, effectiveness, and safety of smart material delivery, though accuracy in placement is crucial to avoid adverse effects.
  • The study introduces a new deep-learning platform called XIOSIS, which creates patient-specific 3D CT images from intraoperative X-ray radiographs, offering real-time feedback for smart material delivery.
  • XIOSIS was tested in a duodenal hydrogel spacer placement procedure on cadaver specimens, achieving a high level of accuracy with a structural similarity of 0.88 and a Dice coefficient of 0.63, demonstrating its clinical viability.
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Purpose: Although surgical decompression is the gold standard for metastatic epidural spinal cord compression (MESCC) from solid tumors, not all patients are candidates or undergo successful surgical Bilsky downgrading. We report oncologic and functional outcomes for patients treated with stereotactic body radiation therapy (SBRT) to high-grade MESCC.

Methods And Materials: Patients with Bilsky grade 2 to 3 MESCC from solid tumor metastases treated with SBRT at a single institution from 2009 to 2020 were retrospectively reviewed.

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Purpose: Lymphopenia is associated with poor survival outcomes in head and neck squamous cell carcinoma (HNSCC), yet there is no consensus on whether we should limit lymphopenia risks during treatment. To fully elucidate the prognostic role of baseline versus treatment-related lymphopenia, a robust analysis is necessary to investigate the relative importance of various lymphopenia metrics (LMs) in predicting survival outcomes.

Methods: In this prospective cohort study, 363 patients were eligible for analysis (patients with newly diagnosed, nonmetastatic HNSCC treated with neck radiation with or without chemotherapy in 2015-2019).

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Article Synopsis
  • - The study assessed pre-treatment peer review for stereotactic body radiation therapy (SBRT) cases, aimed at improving patient outcomes while balancing physician workload and avoiding treatment delays.
  • - Between March 2020 and August 2021, a quality checklist was used for 252 SBRT patients, leading to an increase in pre-treatment review completion rates from 19% to 79%, significantly enhancing compliance.
  • - The implementation of a structured workflow resulted in higher early review rates, demonstrating a successful model for thorough pre-treatment assessment in thoracic SBRT, ensuring better treatment planning without compromising efficiency.
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As cone-beam computed tomography (CBCT) has become the localization method for a majority of cases, the indications for diode-based confirmation of accurate patient set-up and treatment are now limited and must be balanced between proper resource allocation and optimizing efficiency without compromising safety. We undertook a de-implementation quality improvement project to discontinue routine diode use in non-intensity modulated radiotherapy (IMRT) cases in favor of tailored selection of scenarios where diodes may be useful. After analysis of safety reports from the last 5 years, literature review, and stakeholder discussions, our safety and quality (SAQ) committee introduced a recommendation to limit diode use to specific scenarios in which in vivo verification may add value to standard quality assurance (QA) processes.

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Purpose: The Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP) provides patient-specific survival predictions and evidence-based recommendations to guide multidisciplinary management for symptomatic bone metastases. We assessed the clinical utility of the BMETS-DSP through a pilot prepost design in a simulated clinical environment.

Methods: Ten Radiation Oncology physicians reviewed 55 patient cases at two time points: without and then with the use of BMETS-DSP.

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Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a physician peer-review process, which includes a review of the treatment plan's dose and fractionation. Potentially, physicians may not identify errors during this manual peer review due to time constraints and caseload.

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Purpose: Pancreatic cancer is the fourth leading cause of cancer-related death, with a very low 5-year overall survival rate (OS). Radiation therapy (RT) together with dose escalation significantly increases the OS at 2 and 3 years. However, dose escalation is very limited due to the proximity of the duodenum.

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Article Synopsis
  • Pancreatic cancer has a low survival rate and radiation therapy improves outcomes, but it often causes toxicity to the duodenum, complicating treatment.
  • A hydrogel spacer has been shown to reduce this toxicity, but its complex placement can lead to uncertainty in its effectiveness.
  • This study simulated different spacer injection scenarios to assess their impact on reducing duodenal exposure during radiation therapy, showing that ideal placements significantly decrease toxicity compared to pre-injection scenarios.
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  • Current guidelines for treating bone metastases with palliative radiotherapy lack clarity in selecting the right treatment based on individual patient factors, prompting a need for decision support tools.
  • The Bone Metastases Ensemble Trees for Survival-Decision Support Platform (BMETS-DSP) was developed to provide evidence-based, personalized treatment options for palliative radiotherapy regimens.
  • Utilizing a theoretical framework with stakeholder input, the BMETS-DSP collects specific patient data and offers tailored recommendations, while also meeting international decision aid quality standards.
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Importance: Radiotherapy combined with androgen deprivation therapy (ADT) is a standard of care for high-risk prostate cancer. However, the interplay between radiotherapy dose and the required minimum duration of ADT is uncertain.

Objective: To determine the specific ADT duration threshold that provides a distant metastasis-free survival (DMFS) benefit in patients with high-risk prostate cancer receiving external beam radiotherapy (EBRT) or EBRT with a brachytherapy boost (EBRT+BT).

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Importance: Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) can detect low-volume, nonlocalized (ie, regional or metastatic) prostate cancer that was occult on conventional imaging. However, the long-term clinical implications of PSMA PET/CT upstaging remain unclear.

Objectives: To evaluate the prognostic significance of a nomogram that models an individual's risk of nonlocalized upstaging on PSMA PET/CT and to compare its performance with existing risk-stratification tools.

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Background: Here, we investigated the relationship between clinical parameters, including the site of surgical anastomosis and radiation dose to the anastomotic region, and anastomotic complications in esophageal cancer patients treated with trimodality therapy.

Methods: Between 2007 and 2016, esophageal cancer patients treated with trimodality therapy at a tertiary academic cancer center were identified. Patient, treatment, and outcome parameters were collected.

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Purpose: Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients.

Materials And Methods: CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes.

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Article Synopsis
  • The study investigates treatment outcomes for men with high-risk prostate cancer, focusing on those exhibiting severe clinical features, between 2000 and 2014 at various medical centers.
  • It compares three treatment methods: radical prostatectomy (RP), external beam radiotherapy (EBRT) with androgen deprivation therapy (ADT), and EBRT with a brachytherapy boost (BT) combined with ADT.
  • Findings indicate that patients treated with EBRT (either alone or with BT) had significantly lower rates of prostate cancer-specific mortality compared to those who underwent RP.
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For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified.

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The natural history of radiorecurrent high-risk prostate cancer (HRPCa) is not well-described. To better understand its clinical course, we evaluated rates of distant metastases (DM) and prostate cancer-specific mortality (PCSM) in a cohort of 978 men with radiorecurrent HRPCa who previously received either external beam radiation therapy (EBRT, n = 654, 67%) or EBRT + brachytherapy (EBRT + BT, n = 324, 33%) across 15 institutions from 1997 to 2015. In men who did not die, median follow-up after treatment was 8.

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Article Synopsis
  • The BMETS model uses machine learning to predict survival times for patients receiving palliative radiation therapy for symptomatic bone metastases.
  • A study retrospectively analyzed data from 182 patients treated at two community clinics between May 2013 and May 2016 to validate the model's effectiveness outside its original training set.
  • Results showed that BMETS retained high predictive accuracy (tAUC values around 0.77-0.82) when applied to external data, confirming its reliability and potential for ongoing updates.
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Background: Cancer therapy is associated with severe financial burden. However, the magnitude and longitudinal patient relationship with financial toxicity (FT) in the initial course of therapy is unclear.

Methods: Patients with stage II-IV lung cancer were recruited in a prospective longitudinal study between July 2018 and March 2020.

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