Enhancing recurrent laryngeal nerve localization during transoral endoscopic thyroid surgery using augmented reality: a proof-of-concept study

Annals of Surgical Treatment and Research, Journal Year: 2025, Volume and Issue: 108(3), P. 135 - 135

Published: Jan. 1, 2025

During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this often obstructed by thyroid itself, increasing risk serious complications. This study explores application an augmented reality (AR) system to facilitate easier identification RLN during thyroidectomy. Three patients scheduled for thyroidectomy were enrolled in proof-of-concept study. Preoperative computed tomography scans used create AR model that included thyroid, trachea, veins, arteries, and RLN. The was overlaid onto real-time camera images live surgeries. Manual registration performed using customized controller. aligned with surgical landmarks such as trachea common carotid artery. accuracy assessed Dice similarity coefficient (DSC) evaluate alignment between real model. 3 female (mean age, 33.3 ± 15.7 years), mean tumor size 1.0 0.3 cm. All underwent right lobe. Final histopathological diagnoses comprised 2 papillary carcinomas one follicular adenoma. manual 0.60, 0.70, 0.57 1, 2, 3, respectively, value 0.6 0.1. proved feasible demonstrated potential improving localization anatomical structures, particularly RLN, indicated moderate DSC.

Language: Английский

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Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 215 - 215

Published: March 11, 2025

As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose to systematically DLAS product performances PCa RT their associated evaluation methodology. A literature search was performed with the use electronic databases 7 November 2024. Thirty-two articles were included as per selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Technologies Co. AccuContour (Jian Sheng, China), MIM Software ProtégéAI (Cleveland, OH, Mirada Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation AutoContour (New York, NY, RaySearch Laboratories RayStation Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via Image Suite DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), Varian Systems, Ethos (Palo Alto, CA, USA)). Their results illustrate can delineate organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) right (R) femurs, L R pelvis, proximal sacrum) four clinical target volumes (prostate, lymph nodes, bed, seminal vesicle bed) clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although recommended each centre perform its own prior implementation, seems more important due methodological respective single studies, e.g., small dataset used, etc.

Language: Английский

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Advances in Radiation Oncology Top Downloaded Articles of 2024

Advances in Radiation Oncology, Journal Year: 2025, Volume and Issue: 10(4), P. 101749 - 101749

Published: April 1, 2025

Language: Английский

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Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review

Cancers, Journal Year: 2024, Volume and Issue: 16(17), P. 2988 - 2988

Published: Aug. 28, 2024

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications CT for tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused detecting malignancies, 11 (33.3%) classification, 6 (18.2%) prognostication, 3 (9.1%) 1 (3.0%) both detection classification. Of the classification studies, 7 (21.2%) used machine to distinguish between benign malignant lesions, evaluated tumor stage or grade, 2 (6.1%) employed radiomics biomarker Prognostic studies included three that predicted complications such as pathological fractures AI's potential improving workflow efficiency, aiding decision-making, reducing is discussed, along its limitations generalizability, interpretability, clinical integration. Future directions AI oncology are also explored. conclusion, while technologies promising, further research necessary validate their effectiveness optimize integration into routine practice.

Language: Английский

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Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1)

Published: Jan. 1, 2024

Abstract Radiation therapy is a localized cancer treatment that relies on precise delineation of the target to be treated and healthy tissues guarantee optimal effect. This step, known as contouring or segmentation, involves identifying both volumes organs at risk imaging modalities like CT, PET, MRI guide radiation delivery. Manual however, time-consuming highly subjective, despite presence guidelines. In recent years, automated segmentation methods, particularly deep learning models, have shown promise in addressing this task. However, challenges persist their clinical use, including need for robust quality assurance (QA) processes risks associated with use models. review examines considerations adoption auto-segmentation radiotherapy, focused volume. We discuss potential (eg, over- under-segmentation, automation bias, appropriate trust), mitigation strategies human oversight, uncertainty quantification, education professionals), we highlight importance expanding QA include geometric, dose-volume, outcome-based performance monitoring. While offers significant benefits, careful attention rigorous measures are essential its successful integration practice.

Language: Английский

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A comparative study on automatic treatment planning for online adaptive proton therapy of esophageal cancer: Which combination of deformable registration and deep learning planning tools performs the best?

Physics in Medicine and Biology, Journal Year: 2024, Volume and Issue: 69(20), P. 205013 - 205013

Published: Sept. 27, 2024

To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These leverage existing deformable image registration (DIR) algorithms state-of-the-art deep learning (DL) networks for organ segmentation dose prediction.

Language: Английский

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Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Breast Cancer Radiation Therapy Planning: A Systematic Review

Multimodal Technologies and Interaction, Journal Year: 2024, Volume and Issue: 8(12), P. 114 - 114

Published: Dec. 20, 2024

As yet, no systematic review on commercial deep learning-based auto-segmentation (DLAS) software for breast cancer radiation therapy (RT) planning has been published, although NRG Oncology highlighted the necessity such. The purpose of this is to investigate performances DLAS packages RT and methods their performance evaluation. A literature search was conducted with use electronic databases. Fifteen papers met selection criteria were included. included studies evaluated eight (Limbus Contour, Manteia AccuLearning, Mirada DLCExpert, MVision.ai Contour+, Radformation AutoContour, RaySearch RayStation, Siemens syngo.via Image Suite/AI-Rad Companion Organs RT, Therapanacea Annotate). Their findings show that could contour ten organs at risk (body, contralateral breast, esophagus-overlapping area, heart, ipsilateral humeral head, left right lungs, liver, sternum trachea) three clinical target volumes (CTVp_breast, CTVp_chestwall, CTVn_L1) up clinically acceptable standard. This can contribute 45.4%–93.7% contouring time reduction per patient. Although NRO suggested every center should conduct its own evaluation before implementation, such testing appears particularly crucial Contour+ as a result methodological weaknesses corresponding studies, small datasets collected retrospectively from single centers

Language: Английский

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Künstliche Intelligenz in der Strahlentherapie

Forum, Journal Year: 2024, Volume and Issue: 39(4), P. 264 - 268

Published: Aug. 8, 2024

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Screening and risk analysis of atrial fibrillation after radiotherapy for breast cancer: Rationale and design for the Watch Your HeaRT cohort study (WATCH)

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Background Post-radiotherapy atrial fibrillation (AF) in breast cancer (BC) patients is a relatively new and understudied topic. AF can increase the risk of stroke other serious cardiovascular complications, compromising patients' quality life survival. Detection AF, both asymptomatic symptomatic forms, therefore essential for optimal management. The objective WATCH study to assess incidence (symptomatic or asymptomatic) occurring throughout 5-years follow-up after RT investigate whether cardiac radiation exposure associated with occurrence such events. Methods cohort that will include 200 over 65 years old, treated radiotherapy BC five before inclusion, without history AF. Cross-sectional screening at time scheduled five-year post-radiotherapy visit conducted by recording data from Withings ScanWatch smartwatch one month, confirmed an ECG, validated physician. In addition, transthoracic echocardiography performed, providing comprehensive assessment structures, allowing underlying etiology complications. Patient's medical record provides retrospective information on timing factors arrhythmias diseases during 5 following RT. development deep learning algorithms auto-segmentation analysis potentially critical sub-structures including chambers, sinoatrial node, atrioventricular coronary arteries, pulmonary veins, produce dosimetry linked previous treatment all contoured structures. inclusions started October 2023 continue until mid-2026 patients. results are expected end 2026. Discussion This contribute generating knowledge BC, help considering into routine clinical practice these Identifying dose-risk associations would improve delivery protocols limit and, if necessary, initiate appropriate treatment. Trial registration ClinicalTrials.gov:NCT06073509. Registration date: 10/09/2023

Language: Английский

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Screening and Risk Analysis of Atrial Fibrillation after Radiotherapy for Breast Cancer: Protocol for a Cross-Sectional Cohort Study (Watch Your HeaRT – WATCH study) (Preprint)

Published: Oct. 23, 2024

Post-radiotherapy atrial fibrillation (AF) in breast cancer (BC) patients is a relatively new and understudied topic. AF can increase the risk of stroke other serious cardiovascular complications, compromising patients' quality life survival. Detection AF, both asymptomatic symptomatic forms, therefore essential for optimal management. The aim WATCH study to assess incidence (symptomatic or asymptomatic) occurring throughout 5-years follow-up after RT investigate whether cardiac radiation exposure associated with occurrence such events. cohort that will include 200 over 65 years old, treated radiotherapy BC five before inclusion, without history AF. Cross-sectional screening at time scheduled five-year post-radiotherapy visit conducted by recording data from Withings ScanWatch smartwatch one month, confirmed an ECG, validated physician. In addition, transthoracic echocardiography performed, providing comprehensive assessment structures, allowing underlying etiology complications. Patient's medical record provides retrospective information on timing factors arrhythmias diseases during 5 following RT. development deep learning algorithms auto-segmentation analysis potentially critical sub-structures including chambers, sinoatrial node, atrioventricular coronary arteries, pulmonary veins, produce dosimetry linked previous treatment all contoured structures. inclusions started October 2023 continue until mid-2026 patients. results are expected end 2026. This contribute generating knowledge BC, help considering into routine clinical practice these Identifying dose-risk associations would improve delivery protocols limit and, if necessary, initiate appropriate treatment. ClinicalTrials.gov:NCT06073509. Registration date: 10/09/2023

Language: Английский

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