A5008606043- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- AI in cancer detection
- Lung Cancer Treatments and Mutations
- Colorectal Cancer Surgical Treatments
- Artificial Intelligence in Healthcare and Education
- Brain Metastases and Treatment
- Head and Neck Cancer Studies
- Radiation Dose and Imaging
- Medical Imaging and Analysis
- MRI in cancer diagnosis
- Pancreatic and Hepatic Oncology Research
- Machine Learning in Healthcare
- Advanced Radiotherapy Techniques
- Endometrial and Cervical Cancer Treatments
- Cancer Immunotherapy and Biomarkers
- Cancer Genomics and Diagnostics
- Lung Cancer Research Studies
- Glioma Diagnosis and Treatment
- Gastric Cancer Management and Outcomes
- Colorectal Cancer Screening and Detection
- Health Systems, Economic Evaluations, Quality of Life
- Cervical Cancer and HPV Research
Vita-Salute San Raffaele University
2023-2025
Maastro Clinic
2018-2024
Maastricht University Medical Centre
2018-2024
Maastricht University
2018-2024
Istituti di Ricovero e Cura a Carattere Scientifico
2024
Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele
2024
Umicore (Belgium)
2024
Daping Hospital
2022
Fudan University
2022
Army Medical University
2022
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research of radiomics studies. Methods We conducted an online modified Delphi study with group international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members identify the items be voted; Stage#3, four rounds exercise by panelists determine eligible for METRICS their weights. The...
Abstract The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated evaluate the temporal stability imaging with respect a controlled scenario (test–retest), as well their dependence on acquisition parameters such slice thickness, or tube current. Only robust stable should used in prognostication/prediction models improve generalizability across multiple institutions. In this study, we three different scanners, variable current, use intravenous...
Abstract With the continuous development of human life and society, medical field is constantly improving. However, modern medicine still faces many limitations, including challenging previously unsolvable problems. In these cases, artificial intelligence (AI) can provide solutions. The research application generative adversarial networks (GAN) are a clear example. While most researchers focus on image augmentation, there few one-dimensional data augmentation examples. radiomics feature...
As artificial intelligence (AI) becomes increasingly prevalent in the medical field, effectiveness of AI-generated reports disease diagnosis remains to be evaluated. ChatGPT is a large language model developed by open AI with notable capacity for text abstraction and comprehension. This study aimed explore capabilities, limitations, potential Generative Pre-trained Transformer (GPT)-4 analyzing thyroid cancer ultrasound reports, providing diagnoses, recommending treatment plans.
PurposeRadiomics are quantitative features extracted from medical images. Many radiomic depend not only on tumor properties, but also non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirable value variations in the reduces performance of prediction models. In this paper, we investigate whether can use phantom measurements to characterize correct for SNR dependence.MethodsWe used a with 17...
The aims of this study are to evaluate the stability radiomic features from Apparent Diffusion Coefficient (ADC) maps cervical cancer with respect to: (1) reproducibility in inter-observer delineation, and (2) image pre-processing (normalization/quantization) prior feature extraction.Two observers manually delineated tumor on ADC derived pre-treatment diffusion-weighted Magnetic Resonance imaging 81 patients FIGO stage IB-IVA cancer. First-order, shape, texture were extracted original...
Purpose Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients’ clinical data an appealing, low‐cost and noninvasive approach toward personalization. Machine learning algorithms could be trained “big data” build prediction models for personalized therapy. To reach this goal, a scalable architecture the medical domain becomes essential, based on standardization transform into FAIR (Findable, Accessible, Interoperable Reusable) data....
Abstract Prediction modelling with radiomics is a rapidly developing research topic that requires access to vast amounts of imaging data. Methods work on decentralized data are urgently needed, because concerns about patient privacy. Previously published computed tomography medical image sets gross tumour volume (GTV) outlines for non-small cell lung cancer have been updated extended follow-up. In previous study, these were referred as Lung1 (n = 421) and Lung2 221). The dataset made...
PurposeHighlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems clinic. In this study we use machine learning-based methods to identify presence volume-confounding effects radiomics features.Methods841 features were extracted from two retrospective publicly available datasets lung head neck cancers using open source software. Unsupervised hierarchical clustering principal component analysis (PCA) identified...
Abstract Objectives Stereotactic body radiotherapy ( SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit surgery. Some may experience distant metastasis. This study aimed to develop and validate radiomics model predicting metastasis in NSCLC treated SBRT. Methods Patients at five institutions were enrolled this study. Radiomics features extracted based on the PET/CT images. After feature selection training set (from Tianjin), CT-based...
Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying phenotype and predicting treatment response. The three major challenges of radiomics research clinical adoption are: (a) lack standardized methodology analyses, (b) a universal lexicon denote features that are semantically equivalent, (c) lists values alone do not sufficiently capture details might nonetheless strongly affect (e.g. image normalization or interpolation...
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since introduction deep neural networks, many AI-based methods have been proposed address challenges in different aspects Commercial vendors started release tools that can be readily integrated established workflow. To show recent progress AI-aided radiotherapy, we reviewed studies five major radiotherapy including image reconstruction, registration, segmentation, synthesis, and automatic...
Abstract Background Deterioration of neurocognitive function in adult patients with a primary brain tumor is the most concerning side effect radiotherapy. This study aimed to develop and evaluate normal-tissue complication probability (NTCP) models using clinical dose–volume measures for 6-month, 1-year, 2-year Neurocognitive Decline (ND) postradiotherapy. Methods A total 219 treated radical photon and/or proton radiotherapy (RT) between 2019 2022 were included. Controlled oral word...