The next seminar will be held on the 30th of May 2023 at 11AM at the Seminar Room 12, School of Clinical Medicine, and will feature:
Using latent shape information to detect renal cancers automatically from CT scans – William McGough, PhD Student, Cancer Research UK Cambridge Institute
The 5-year survival rate of renal cell carcinoma (RCC) drops from 88% to 15% when detected at stage 1 vs stage 4. Detecting RCC at the earliest developmental stage possible is therefore preferable, but RCC early detection programmes present significant challenges: high staffing costs and a lack of a high-risk target population. Consequently, early detection screening has been considered too costly to be viable for RCC. However, recent developments in medical imaging have shown that fast RCC screening may be possible with a low-dose CT imaging technique. Given the emergent opportunity presented by this imaging modality, my work exploits modern artificial intelligence (AI) technologies to enable automated early RCC detection. We show that detecting renal cancers is possible using the kidney’s shape information latent within CT scans.
Machine-Assisted Triage of Histopathology Slides for Detecting Precursors of Oesophageal Adenocarcinoma – Dr. William Prew, Postdoctoral Research Associate, Cancer Research UK Cambridge Institute
The prognosis for Oesophageal Adenocarcinoma (EAC) is relatively poor, as the five-year survival rate is less than 20%. This high mortality rate is commonly caused by late presentation of symptoms, at which point it becomes difficult to treat effectively. Barrett’s Oesophagus (BE) is a clinically recognised precursor to EAC, and monitoring these patients for signs of progression can help pathologists detect cancers at an earlier and more treatable stage. Recently, the Cytosponge-TFF3 test was developed to help screen these individuals, which is a minimally invasive and cheaper alternative to endoscopy, and samples cells from across the length of the oesophagus. This sampling method results in histopathology slides which pathologists may be unfamiliar with because the spatial context between cells is not retained compared to regular biopsy data. We therefore train and present a machine learning model capable of performing quality control and triage of Cytosponge slides. Our approach mimics decision patterns of gastrointestinal pathologists to classify and present regions of interest for manual expert review. By substituting manual review with automated review in low-priority classes, we can reduce pathologist workload by 57% while matching the diagnostic performance of experienced pathologists.
Each session will involve two talks, followed by an interactive discussion with coffee and pastries! We hope that this seminar series will be a valuable platform for researchers, practitioners, and students to learn about the latest trends and explore collaborations in the exciting field of AI in Medicine.
This is a hybrid event so you can also join via Zoom: https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09
Meeting ID: 990 5046 7573 and Passcode: 617729
