Shandong First Medical University and Shandong Academy of Medical Sciences

To develop and validate a combined model, based on multi-sequence magnetic resonance imaging (MRI), integrating immune checkpoint molecular markers and multiple independent prognostic factors, to predict overall survival (OS) in nasopharyngeal carcinoma (NPC)...

To investigate the value of CT-based radiomics for preoperative prediction lymph node status of the esophagogastric junction adenocarcinoma.

Contrast-enhanced CT (CECT) is routinely used in radiotherapy planning to improve visualization of tumors, lymph nodes, and vascular anatomy for accurate target delineation. However, contrast administration is contraindicated in a subset of patients (e.g., io...

Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of different deep learning mod...

This study designed SIB-IMRT plans based on pathologically confirmed positive LNs and compared them with conventional IMRT plans to evaluate the feasibility and dosimetric advantages of SIB-IMRT for rectal cancer patients with metastatic LNs.

To contour the PCMs utilizing MRI/CT image fusion technology and to evaluate the feasibility of PCM-sparing in proton therapy for NPC.

To establish an FMISO-PET–guided framework to estimate tumor control probability (TCP) and normal tissue complication probability (NTCP) in proton FLASH radiotherapy for efficacy prediction and clinical decision support.

This study evaluated the impact of interfractional rectal state changes on plan robustness in cervical cancer intensity-modulated proton therapy (IMPT) and established a robust optimization strategy incorporating multiple rectal conditions.

Radiotherapy has emerged as an effective noninvasive treatment for refractory ventricular tachycardia (VT). Given the high sensitivity of cardiac structures, proton therapy offers potential dosimetric advantages, but its robustness under cardiopulmonary motio...

To perform the first comprehensive dosimetric and radiobiological comparison of four modern delivery platforms for stereotactic body radiation therapy (SBRT) in pancreatic cancer: a conventional C-arm linac (TrueBeam), two established ring-gantry systems (Hal...

The purpose of this study was to explore the difference between the two plans of intensity modulated radiotherapy (IMRT) for head and neck tumors, which did not limit the dose of pharyngeal constrictor and protected the pharyngeal constrictor, the dose of pha...

The study aims to develop and validate a radiomics model using multiple sequence (MS) -MRI to predict the OS rate in patients diagnosed with NPC.

To investigate and improve the diagnostic performance of preoperative prediction of the Ki-67 expression index using CT-based radiomics.

The combination of FLASH and ion-beam therapy could potentially enhance the therapeutic index of radiation therapy. The goal of this study was to establish a FLASH effect modeling approach for ion beams based on microdosimetry.

To explore the value of radiological features extracted from T2-weighted imaging (T 2 WI) images of the parotid gland of patients with nasopharyngeal cancer (NPC) in predicting advanced radiation xerostomia after radiotherapy.

Integrated PET/CT-LINAC systems enable functional imaging and radiation delivery within a unified geometry, providing a unique platform for imaging-guided radiotherapy. Rigorous investigation of such systems requires a physics-consistent simulation framework...

Left-sided breast cancer radiotherapy poses a significant challenge in balancing target coverage with the sparing of critical cardiac substructures. RapidArc Dynamic (RAD) is a novel hybrid technique that integrates the continuous arc delivery of volumetric m...

To develop and validate a CT-based habitat radiomics approach for quantitative characterization of intratumoral heterogeneity and assessment of p40 protein expression derived from immunohistochemistry.

Severe radiation-induced lymphopenia (SRIL) is a detrimental prognostic factor in lung cancer. This study aimed to develop and validate normal tissue complication probability (NTCP) models for SRIL based on hematologic dose in patients receiving intensity-mod...

Spatially fractionated radiotherapy (SFRT) treats bulky tumors by creating heterogeneous dose distributions. Although both photon- and proton-based SFRT are accessible, various definitions of the peak-to-valley dose ratio (PVDR) may result in distinct dose di...

4D-MRI offers superior soft-tissue contrast for abdominal motion management but is limited by scan duration and image quality trade-offs. Moreover, existing techniques are restricted to anatomical imaging, lacking functional data. This study proposes a deep l...

Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive neuromodulation modality, offering unparalleled focal precision and depth penetration. Accurate treatment planning critically depends on the ability to predict acoustic propagation...

Concurrent chemoradiotherapy combined with image-guided brachytherapy is the standard for locally advanced cervical cancer, yet failures persist due to heterogeneous radioresistant sub-volumes. While Dose Painting by Numbers (DPbN) enables voxel-level modulat...

This study characterizes the spatial heterogeneity of non-small cell lung cancer (NSCLC) using intratumoral habitat radiomics and explores its predictive capability for overall survival in radiotherapy-treated patients by integrating it with peritumoral radio...

To advance sparse-information constrained respiratory modeling toward an information-augmentation driven paradigm by incorporating data augmentation, multimodality guidance, prior-informed representation, and improved optimization pipelines.

To develop a clinically adaptable 3D deep learning framework for synthesizing quantitative PET images from routine CT volumes, enabling reliable metabolic assessment for lung cancer while reducing reliance on additional PET scans.

To develop and validate a robust CT-radiomics framework using feature-level multicenter distribution adaptation (MDA) to differentiate pulmonary tuberculosis (TB) from fungal pneumonia (FP), explicitly addressing the challenge of inter-scanner variability and...

To propose a multimodal surrogates-guided, multi-task respiratory-modeling framework for simultaneous anatomical motion reconstruction and tumor-tracking.

Hepatocellular carcinoma (HCC) treatment with intensity-modulated proton therapy (IMPT) is challenged by respiratory motion, necessitating 4D robust optimization. This study aimed to optimize the number of breathing phases required for 4D robust IMPT planning...

To establish and validate a combined model based on multi-sequence MRI radiomics and clinical features for predicting LN metastasis in rectal adenocarcinoma patients.

With the number of proton centers increasing, each facing intermittent downtime or need for renovations. Developing optimal approaches to maintain uninterrupted treatment is essential to preserve clinical outcomes. This study outlines key risk factors, workfl...

This study systematically evaluated the robustness of radiomics features derived from photon-counting detector CT (PCD-CT) and three dual-energy CT (DECT) platforms under different reconstruction strategies and scanning conditions using chest tumor phantoms.

Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of different deep learning mod...

To determine the diagnostic performance of a machine learning model based on radiographic features of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) / computed tomography (CT) in distinguishing cervical adenocarcinoma (AC) from sq...

Inter-fractional anatomical changes in lung SBRT raise concerns about dosimetric accuracy and potential target under-dosage. Adaptive radiotherapy (ART) is a proposed strategy to correct these variations. This study aims to quantify the dosimetric degradation...

To develop a dual-mode radiation detector based on a novel perovskite scintillator for X‑ray and proton beam detection in clinical practice, addressing critical bottlenecks in long‑term stability, spatial resolution, and cost of current detectors, with the ul...

The Bragg peak of proton beam enables precise dose delivery and superior dose modulation in proton lattice radiotherapy (LRT), yet it remains susceptible to uncertainties. This study aims to investigate the impact of robust parameter settings and respiratory...

Liver biopsy is the gold standard for evaluating liver fibrosis, yet it is invasive and subject to sampling error and interobserver variability. We therefore developed a B-mode ultrasound-based deep learning model for noninvasive staging of liver fibrosis in...