Maintenance durvalumab after first-line chemotherapy in patients with HER2-negative advanced oesophago-gastric adenocarcinoma: results from the randomised PLATFORM study.

Background: PLAnning Treatment For Oesophago-gastric Cancer: a Randomised Maintenance Therapy Trial (PLATFORM) is an adaptive phase II study assessing the role of maintenance therapies in advanced oesophago-gastric (OG) adenocarcinoma. We evaluated the role of the anti-programmed death-ligand 1 (PD-L1) inhibitor durvalumab in these patients.

Patients And Methods: Patients with human epidermal growth factor receptor 2-negative locally advanced or metastatic OG adenocarcinoma with disease control or response to 18 weeks of platinum-based first-line chemotherapy were randomised to active surveillance or maintenance durvalumab.

Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.

Objectives: MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining "real-world" and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation.

Methods: Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods.

MicroRNAs as biomarkers for trastuzumab-based therapy in HER2-positive advanced oesophago-gastric cancer patients.

Background: This study aimed to identify microRNAs (miRs) as circulating biomarkers of resistance to first-line trastuzumab-based therapy in advanced HER2-positive oesophago-gastric cancer patients.

Methods: A high-throughput 1015 Exiqon miRCURY LNA™ microRNA inhibitor library screen was performed in trastuzumab-treated HER2-positive NCI-N87 and HER2-negative FLO-1 oesophago-gastric cancer cell lines. NanoString nCounter miR analysis was performed in NCI-N87, FLO-1, and MAGIC trial (ISRCTN93793971) formalin-fixed paraffin-embedded (FFPE) oesophago-gastric cancer patient samples.

The peritoneal cancer index as a predictor of complete cytoreduction at primary and interval cytoreductive surgery in advanced ovarian cancer.

Objective: The peritoneal cancer index quantitatively assesses cancer distribution and tumor burden in the peritoneal cavity. The aim of this study is to evaluate the association between the peritoneal cancer index and completeness of surgical cytoreduction for ovarian cancer and to identify a cut-off above which complete cytoreduction is unlikely.

Methods: This is a single-center prospective cohort observational study.

Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

Introduction: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness.