Daniele Regge
YOU?
Author Swipe
View article: Self-supervised learning leads to improved performance in biparametric prostate MRI classification
Self-supervised learning leads to improved performance in biparametric prostate MRI classification Open
Data with no annotations was used to train SSL models which were more data efficient and performed better than FSL models, highlighting the importance of large-scale data collection efforts in biomedical imaging.
View article: Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI
Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI Open
Developing radiomic biomarkers remains challenging due to the variability in imaging protocols across centres and the lack of standardised methodologies. This study evaluates the impact of different technical decisions in radiomics pipelin…
View article: Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study
Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study Open
A multimodal machine learning model combining radiomic, radiologic, and clinical variables reduced unnecessary prostate biopsies while maintaining high sensitivity for clinically significant cancer detection.
View article: Rad-Path Correlation of Deep Learning Models for Prostate Cancer Detection on MRI
Rad-Path Correlation of Deep Learning Models for Prostate Cancer Detection on MRI Open
While Deep Learning (DL) models trained on Magnetic Resonance Imaging (MRI) have shown promise for prostate cancer detection, their lack of direct biological validation often undermines radiologists’ trust and hinders clinical adoption. Ra…
View article: Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector
Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector Open
View article: Rectal Cancer Segmentation: A Methodical Approach for Generalizable Deep Learning in a Multi‐Center Setting
Rectal Cancer Segmentation: A Methodical Approach for Generalizable Deep Learning in a Multi‐Center Setting Open
Noninvasive Artificial Intelligence (AI) techniques have shown great potential in assisting clinicians through the analysis of medical images. However, significant challenges remain in integrating these systems into clinical practice due t…
View article: Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning
Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning Open
Objectives To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML. Methods Retrospect…
View article: Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks
Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks Open
Prostate cancer (PCa) is currently the second most prevalent cancer among men. Accurate diagnosis of PCa can provide effective treatment for patients and reduce mortality. Previous works have merely focused on either lesion detection or le…
View article: Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer
Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer Open
Background: Prostate cancer (PCa) diagnosis using MRI is often challenged by lesion variability. Methods: This study introduces Simplatab, an open-source automated machine learning (AutoML) framework designed for, but not limited to, autom…
View article: Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning–assisted Prostate Cancer Classification Using Multiparametric MRI
Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning–assisted Prostate Cancer Classification Using Multiparametric MRI Open
Aggressiveness of prostate cancer could be predicted using biparametric MRI and deep learning with negligible expert input, but performance was affected by scanner manufacturer and scanning protocol.
View article: AI Model Passport: Data and system traceability framework for transparent AI in health
AI Model Passport: Data and system traceability framework for transparent AI in health Open
View article: Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences
Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences Open
View article: Corrigendum to “Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data” [Comput. Biol. Med. 17 (2024) 108216]
Corrigendum to “Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data” [Comput. Biol. Med. 17 (2024) 108216] Open
View article: Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data
Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data Open
View article: Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer
Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer Open
Objectives The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical sign…
View article: Self-Supervised Learning for Volumetric Imaging: A Prostate Cancer Biparametric Magnetic Resonance Imaging Case Study
Self-Supervised Learning for Volumetric Imaging: A Prostate Cancer Biparametric Magnetic Resonance Imaging Case Study Open
View article: Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI
Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI Open
In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and ext…
View article: Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing
Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing Open
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures…
View article: AI and machine learning in medical imaging: key points from development to translation
AI and machine learning in medical imaging: key points from development to translation Open
Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, …
View article: A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice
A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice Open
Objectives To present the results of a survey on the assessment of treatment response with imaging in oncologic patient, in routine clinical practice. The survey was promoted by the European Society of Oncologic Imaging to gather informati…
View article: 223P Artificial intelligence-based pathomics biomarker predict primary resistance to first-line treatment in metastatic colorectal cancers
223P Artificial intelligence-based pathomics biomarker predict primary resistance to first-line treatment in metastatic colorectal cancers Open
View article: MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes
MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes Open
PURPOSE The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to o…
View article: A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST)
A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST) Open
View article: Could normalization improve robustness of abdominal MRI radiomic features?
Could normalization improve robustness of abdominal MRI radiomic features? Open
Radiomics-based systems could improve the management of oncological patients by supporting cancer diagnosis, treatment planning, and response assessment. However, one of the main limitations of these systems is the generalizability and rep…
View article: Virtual biopsy in abdominal pathology: where do we stand?
Virtual biopsy in abdominal pathology: where do we stand? Open
In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared…
View article: Arthrogryposis multiplex congenita with maxillofacial involvement: a case report
Arthrogryposis multiplex congenita with maxillofacial involvement: a case report Open
View article: Development and Prospective Validation of a Fully Automatic Bi-Parametric MRI Radiomics Signature to Predict Prostate Cancer Disease Aggressiveness: A Multi-Centric Study Using Over 4000 Patients
Development and Prospective Validation of a Fully Automatic Bi-Parametric MRI Radiomics Signature to Predict Prostate Cancer Disease Aggressiveness: A Multi-Centric Study Using Over 4000 Patients Open
View article: AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging Open
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further inte…
View article: Use of information and communication technologies (ICTs) in cancer multidisciplinary team meetings: an explorative study based on EU healthcare professionals
Use of information and communication technologies (ICTs) in cancer multidisciplinary team meetings: an explorative study based on EU healthcare professionals Open
Objectives Multidisciplinary teams in cancer care are increasingly using information and communication technology (ICT), hospital health information system (HIS) functionalities and ICT-driven care components. We aimed to explore the use o…
View article: Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation
Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation Open