A5028460960- Radiomics and Machine Learning in Medical Imaging
- Ovarian cancer diagnosis and treatment
- MRI in cancer diagnosis
- Endometrial and Cervical Cancer Treatments
- Prostate Cancer Diagnosis and Treatment
- AI in cancer detection
- Prostate Cancer Treatment and Research
- Glioma Diagnosis and Treatment
- Artificial Intelligence in Healthcare and Education
- Medical Imaging Techniques and Applications
- Renal cell carcinoma treatment
- Cancer Genomics and Diagnostics
- Uterine Myomas and Treatments
- Advanced X-ray and CT Imaging
- Cancer-related molecular mechanisms research
- Advanced MRI Techniques and Applications
- Endometriosis Research and Treatment
- Urologic and reproductive health conditions
- COVID-19 diagnosis using AI
- Intraperitoneal and Appendiceal Malignancies
- Urinary and Genital Oncology Studies
- Health Systems, Economic Evaluations, Quality of Life
- Lung Cancer Diagnosis and Treatment
- Radiology practices and education
- Advanced NMR Techniques and Applications
Università Cattolica del Sacro Cuore
2023-2025
Agostino Gemelli University Polyclinic
2022-2025
Istituti di Ricovero e Cura a Carattere Scientifico
2022-2025
University of Cambridge
2014-2025
Addenbrooke's Hospital
2012-2023
Cambridge University Hospitals NHS Foundation Trust
2009-2023
Cancer Research UK Cambridge Center
2019-2023
Cancer Research UK
2012-2023
Memorial Sloan Kettering Cancer Center
2013-2022
Bridge University
2019-2021
Machine learning methods offer great promise for fast and accurate detection prognostication of COVID-19 from standard-of-care chest radiographs (CXR) computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models both these tasks, but it is unclear which are potential clinical utility. In this systematic review, we search EMBASE via OVID, MEDLINE PubMed, bioRxiv, medRxiv arXiv papers preprints uploaded January 1, to October 3,...
We present an exceptional case of a patient with high-grade serous ovarian cancer, treated multiple chemotherapy regimens, who exhibited regression some metastatic lesions concomitant progression other during treatment-free period. Using immunogenomic approaches, we found that progressing metastases were characterized by immune cell exclusion, whereas regressing and stable infiltrated CD8
Purpose: To retrospectively evaluate the accuracy of endorectal magnetic resonance (MR) imaging in detection and local staging transition zone prostate cancers, with pathologic analysis serving as reference standard, to assess MR features these cancers. Materials Methods: The institutional review board approved this HIPAA-compliant retrospective study waived informed consent requirement. An database 986 patients who underwent before radical prostatectomy yielded 148 consecutive at least one...
Background The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is development progressive resistance to platinum-based chemotherapy. objective this study was determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and emergence subclonal tumour populations HGSOC associated with resistant disease. Methods Findings Evolutionary inference phylogenetic quantification performed using MEDICC algorithm on high-resolution whole...
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks particular, the state-of-the-art for most tasks. Issues with class imbalance pose a significant challenge datasets, lesions often occupying considerably smaller volume relative to background. Loss functions used training of algorithms differ their robustness imbalance, direct consequences model convergence. The commonly loss based on either cross...
Significance Gleason scores and ultimately the aggressiveness of prostate cancers determined using transrectal ultrasound (TRUS)-guided biopsy procedures could result in incorrect diagnosis addition to patient discomfort. The from TRUS-guided biopsies often differ immediate repeat following whole excision prostate. Our approach presents a highly accurate automated method for differentiating between high ≥7 low score 6(3+3), as well 7(3+4) 7(4+3) through multiparametric MRI combined with...
Purpose To investigate the value of T2-weighted-based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) for diagnosis clinical complete response in patients rectal cancer after neoadjuvant chemotherapy-radiation therapy (CRT). Materials Methods This retrospective study included 114 who underwent magnetic resonance (MR) CRT between March 2012 February 2016. Median age among women (47 114, 41%) was 55.9 years (interquartile range, 45.4-66.7...
Comparative reviews of whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography/computed tomography (CT; with different radiotracers) have shown that metastasis detection in advanced cancers is more accurate than currently used CT bone scans. However, the ability WB-MRI tomography/CT to assess therapeutic benefits has not been comprehensively evaluated. There also considerable variability availability quality WB-MRI, which an impediment clinical development. Expert...
Circulating tumour DNA (ctDNA) carrying tumour-specific sequence alterations may provide a minimally invasive means to dynamically assess burden and response treatment in cancer patients. Somatic TP53 mutations are defining feature of high-grade serous ovarian carcinoma (HGSOC). We tested whether these could be used as personalised markers monitor early changes predictor time progression (TTP).
Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13 C magnetic resonance spectroscopic (MRSI) hyperpolarized label exchange between injected [1- C]pyruvate and endogenous lactate pool. Treatment-naïve were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that estrogen progesterone receptor-positive (ER/PR+) HER2/neu-negative (HER2−), one 2 3; ER/PR+ HER2− lobular carcinoma (ILC). Dynamic MRSI was...
Abstract Background Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed accumulation subtle anatomical anomalies, such as...
Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems support clinicians and reduce number of missed.In this work we introduce Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial channel-based attention into single Gate module encourage selective learning...
Abstract Objectives Multi-centre, multi-vendor validation of artificial intelligence (AI) software to detect clinically significant prostate cancer (PCa) using multiparametric magnetic resonance imaging (MRI) is lacking. We compared a new AI solution, validated on separate dataset from different UK hospitals, the original multidisciplinary team (MDT)-supported radiologist’s interpretations. Materials and methods A Conformité Européenne (CE)-marked deep-learning (DL) computer-aided detection...