Steve Jiang
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View article: Beyond the Podium: How Mega-Sporting Events Forge Social Solidarity and Collective Identity in a Multi-Regional Context
Beyond the Podium: How Mega-Sporting Events Forge Social Solidarity and Collective Identity in a Multi-Regional Context Open
View article: Exploring radiomic and topological features to evaluate treatment response in rectal cancer
Exploring radiomic and topological features to evaluate treatment response in rectal cancer Open
Objective. This study investigates the potential of topological data analysis (TDA) in conjunction with conventional radiomics applied to longitudinal T2-weighted magnetic resonance imaging (MRI) to evaluate treatment response following ne…
View article: Incorporating physicians’ contouring style into auto-segmentation of clinical target volume for post-operative prostate cancer radiotherapy using a language encoder
Incorporating physicians’ contouring style into auto-segmentation of clinical target volume for post-operative prostate cancer radiotherapy using a language encoder Open
View article: Adaptive radiotherapy dose prediction on head and neck cancer patients with a 3D multi-headed U-Net deep learning architecture
Adaptive radiotherapy dose prediction on head and neck cancer patients with a 3D multi-headed U-Net deep learning architecture Open
Online adaptive radiation therapy (ART) personalizes treatment plans by accounting for daily anatomical changes, requiring workflows distinct from conventional radiotherapy. Deep learning-based dose prediction models can enhance treatment …
View article: ChatGPT augmented clinical trial screening
ChatGPT augmented clinical trial screening Open
View article: Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy
Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy Open
View article: Strategies for Offline Adaptive Biology-Guided Radiotherapy (BgRT) on a PET-Linac Platform
Strategies for Offline Adaptive Biology-Guided Radiotherapy (BgRT) on a PET-Linac Platform Open
Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomica…
View article: Leveraging Compressed Sensing and Radiomics for Robust Feature Selection for Outcome Prediction in Personalized Ultra‐Fractionated Stereotactic Adaptive Radiotherapy
Leveraging Compressed Sensing and Radiomics for Robust Feature Selection for Outcome Prediction in Personalized Ultra‐Fractionated Stereotactic Adaptive Radiotherapy Open
Personalized ultra‐fractionated stereotactic adaptive is an innovative radiation treatment paradigm. To fully harness its benefits, transitioning decision‐making and plan adaptation from empirical judgment to a data‐driven approach is esse…
View article: PRMT1-catalyzed NUSAP1 methylation enhances Notch2 signaling and 5-FU resistance in gastric cancer
PRMT1-catalyzed NUSAP1 methylation enhances Notch2 signaling and 5-FU resistance in gastric cancer Open
5-Fluorouracil (5-FU) resistance remains a significant challenge in the treatment of gastric cancer, limiting its clinical efficacy. Our study identifies NUSAP1, a nucleolar and spindle-associated protein, as a key driver of 5-FU resistanc…
View article: Ubiquitin Specific Protease 9X Regulates the Activation of ARK5 and Promotes Progression of Fibrotic Remodeling
Ubiquitin Specific Protease 9X Regulates the Activation of ARK5 and Promotes Progression of Fibrotic Remodeling Open
View article: Prior guided deep difference meta-learner for fast adaptation to stylized segmentation
Prior guided deep difference meta-learner for fast adaptation to stylized segmentation Open
Radiotherapy treatment planning requires segmenting anatomical structures in various styles, influenced by guidelines, protocols, preferences, or dose planning needs. Deep learning-based auto-segmentation models, trained on anatomical defi…
View article: KDM1A-Driven RNF81 Downregulation Promotes Gastric Cancer Progression via KLF4 Destabilization
KDM1A-Driven RNF81 Downregulation Promotes Gastric Cancer Progression via KLF4 Destabilization Open
Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, especially in East Asia, with a low 5-year survival rate due to late-stage diagnosis. Identifying molecular mechanisms that regulate GC progression is critical for …
View article: Closing the gap in plan quality: Leveraging deep‐learning dose prediction for adaptive radiotherapy
Closing the gap in plan quality: Leveraging deep‐learning dose prediction for adaptive radiotherapy Open
Purpose Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re‐optimization with the original planning goals. While some systems allow planners to modify the planning goals, achi…
View article: Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data
Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data Open
Purpose Training deep learning dose prediction models for the latest cutting‐edge radiotherapy techniques, such as AI‐based nodal radiotherapy (AINRT) and Daily Adaptive AI‐based nodal radiotherapy (DA‐AINRT), is challenging due to limited…
View article: Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis
Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis Open
Background Standardised management of chronic sinusitis (CRS) is a challenging but vital area of research. Not only is accurate diagnosis and individualised treatment plans required, but post-treatment chronic disease management is also in…
View article: Deterministic Single-Photon Adder and Subtractor
Deterministic Single-Photon Adder and Subtractor Open
Single-photon addition and subtraction are fundamental operations in quantum information processing. Traditionally, the behavior of a single-photon adder (SPA) and single-photon subtractor (SPS) has been theoretically described using creat…
View article: Balancing Image Quality and Iodine Intake: Insights from CT Spectral Imaging of the Portal Vein
Balancing Image Quality and Iodine Intake: Insights from CT Spectral Imaging of the Portal Vein Open
BACKGROUND Portal vein tumor thrombus (PVTT) is a critical complication in hepatocellular carcinoma (HCC). Spectral computed tomography (CT) is increasingly used to enhance the diagnosis of such conditions. This study examines the effect o…
View article: PRMT1-Catalyzed NUSAP1 Methylation Enhances Notch2 Signaling and 5-FU Resistance in Gastric Cancer
PRMT1-Catalyzed NUSAP1 Methylation Enhances Notch2 Signaling and 5-FU Resistance in Gastric Cancer Open
5-Fluorouracil (5-FU) resistance remains a significant challenge in the treatment of gastric cancer, limiting its clinical efficacy. Our study identifies NUSAP1, a nucleolar and spindle-associated protein, as a key driver of 5-FU resistanc…
View article: Mathematical modeling of the synergetic effect between radiotherapy and immunotherapy
Mathematical modeling of the synergetic effect between radiotherapy and immunotherapy Open
The synergy between radiotherapy and immunotherapy plays a pivotal role in enhancing tumor control and treatment outcomes. To explore the underlying mechanisms of synergy, we investigated a novel treatment approach known as personalized ul…
View article: OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework Open
View article: Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study
Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study Open
This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathologi…
View article: Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning
Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning Open
Although even for 1000 training patients there was no convergence in obtained prediction accuracy yet, the accuracy for the 6-level model with 1000 training patients may be adequate for the pursued instantaneous planning, which is subject …
View article: Assessing population‐based to personalized planning strategies for head and neck adaptive radiotherapy
Assessing population‐based to personalized planning strategies for head and neck adaptive radiotherapy Open
Purpose Optimal head‐and‐neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x‐ray‐based adaptive radiotherapy (ART) t…
View article: High-speed dry hobbing geometry parameterization and hobbing simulation
High-speed dry hobbing geometry parameterization and hobbing simulation Open
View article: Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck
Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck Open
Objective . Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume…
View article: Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net
Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net Open
In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algo…
View article: Large Language Model Augmented Clinical Trial Screening
Large Language Model Augmented Clinical Trial Screening Open
Purpose Identifying potential participants for clinical trials using traditional manual screening methods is time-consuming and expensive. Structured data in electronic health records (EHR) are often insufficient to capture trial inclusion…
View article: Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR) Open
Purpose: We aimed to develop a data-driven multiomics approach integrating radiomics, dosiomics, and delta features to predict treatment response at an earlier stage (intra-treatment) for brain metastases (BMs) patients treated with PULSAR…
View article: Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy
Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy Open
Our proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with r…
View article: Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy
Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy Open
Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the c…