A5008017088- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Ovarian cancer diagnosis and treatment
- Endometrial and Cervical Cancer Treatments
- Prostate Cancer Diagnosis and Treatment
- Liver Disease and Transplantation
- Hepatocellular Carcinoma Treatment and Prognosis
- Uterine Myomas and Treatments
- Liver Disease Diagnosis and Treatment
- Breast Lesions and Carcinomas
- AI in cancer detection
- Pelvic and Acetabular Injuries
- Intraperitoneal and Appendiceal Malignancies
- Medical Imaging and Pathology Studies
- Ultrasound Imaging and Elastography
- Ultrasound in Clinical Applications
- Sarcoma Diagnosis and Treatment
- Radiology practices and education
- Bladder and Urothelial Cancer Treatments
- Prostate Cancer Treatment and Research
- Gastroesophageal reflux and treatments
- Anorectal Disease Treatments and Outcomes
- Colorectal Cancer Surgical Treatments
- Endometriosis Research and Treatment
- Cancer and Skin Lesions
University of Cambridge
2021-2023
Cancer Research UK Cambridge Center
2021-2023
Clinical Emergency Hospital Bucharest
2017-2023
Cambridge University Hospitals NHS Foundation Trust
2021-2023
Addenbrooke's Hospital
2021
Iuliu Hațieganu University of Medicine and Pharmacy
2021
Abstract High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC major obstacle to predicting response neoadjuvant chemotherapy (NACT) and understanding critical determinants response. Here we present framework predict the patients NACT integrating baseline clinical, blood-based, radiomic biomarkers extracted from all primary lesions. We use ensemble machine learning model...
To assess the multiparametric MRI (mpMRI) appearances of normal peripheral zone (PZ) across age groups in a biopsy-naïve population, where prostate cancer (PCa) was subsequently excluded, and propose scoring system for background PZ changes.
Background Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy score (CRS) omental tumor deposits. The main limitation of CRS that it requires surgical sampling after initial (NACT) treatment. Earlier and non-invasive predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures predict before NACT as a gold standard. Methods Omental CT-based radiomics...
Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A learning model for the two most common disease sites high-grade serous (pelvis/ovaries omentum) was developed compared against well-established “no-new-Net” framework unrevised trainee radiologist segmentations. total 451 CT scans collected from four different institutions were used training (...
To evaluate the inter- and intraobserver agreement of COVID-RADS CO-RADS reporting systems among differently experienced radiologists in a population with high estimated prevalence COVID-19.
Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Materials Methods A learning model for the two most common disease sites high grade serous (pelvis/ovaries omentum) was developed compared against well-established “no-new-Net” (nnU-Net) framework unrevised trainee radiologist segmentations. total 451 pre-treatment post neoadjuvant chemotherapy (NACT) CT...
Abstract Objectives Imaging guidelines could play an important role in the training of radiologists, but extent their adoption residency programs is unclear. With this survey, European Society Urogenital Radiology (ESUR) Junior Network aimed to assess dissemination ESUR on endometrial cancer MRI staging (EC-ESUR guidelines) among young radiologists. Methods An online questionnaire targeted last year radiology residents and radiologists first career was designed. It included 24 questions,...
High grade serous ovarian cancer (HGSOC) is a highly heterogeneous disease that often presents at an advanced, metastatic state. The multi-scale complexity of HGSOC major obstacle to measuring response neoadjuvant chemotherapy (NACT) and understanding its determinants. Here we propose radiogenomic framework integrating clinical, radiomic, blood-based biomarkers measure predict the patients NACT, showing how quantitative imaging data can serve as backbone integration. We developed validated...
High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity clinically-actionable biomarkers due to high degrees multi-level heterogeneity. Radiogenomics markers have potential improve prediction patient outcome treatment response, require accurate multimodal spatial registration between radiological imaging histopathological tissue samples. Previously published co-registration work not taken into account anatomical, biological...
Renal cell carcinoma (RCC) is notorious for its uncommon metastatic location.We present a rare case of goiter as RCC site.As particularities, besides the that made practically impossible ultrasound diagnosis metastasis, this seemed to be correlated with an unexplained high level alkaline phosphatase (AP).AP persisted from time kidney cancer diagnosis, after nephrectomy and reached normal value only thyroidectomy.Currently, there are no guidelines indicate FDG-PET in patients.Thereby, we will...
T2* relaxometry mapping has found diverse applications in neuroradiology but was insufficiently evaluated pelvis MRI. Further, staging of the uterine-related cancers may be sometimes a cumbersome. Our aim is to present maps respectively and proton density values patients with endometrial cervical cancer, together their postoperative outcomes. Three underwent 3T pelvic MRI examination. The protocol included large field-of-view (FOV = 28 cm), short (TE=30 ms) long (TE 200 echo time sequences....