Sumeet Hindocha

Background: The number of novel cancer therapies approved for use in England by the National Institute for Health and Care Excellence is increasing. Monitoring the adoption of new therapies is important to assess equity of access and evaluate real-world prescribing practices. OpenPrescribing Hospitals has recently been launched to facilitate analysis of open secondary care medicines data in England. Using this platform, we set out to describe the use of cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, including the frequency of dose reductions, within National Health Service (NHS) hospitals in England between January 2019 and December 2024. Methods: The monthly proportion of each CDK4/6 inhibitor relative to total CDK4/6 inhibitor use was calculated at hospital level. Regional variation was assessed across Cancer Alliances by comparing the proportions of each CDK4/6 inhibitor used within each alliance in 2021 and 2024. Use of lower strength palbociclib and abemaciclib was used as a proxy for dose reductions. Findings: There was more than a 3-fold increase in the use of CDK4/6 inhibitors between 2019 and 2024. In 2019, 78.6%, 11.9% and 9.5% of CDK4/6 inhibitors used were palbociclib, abemaciclib and ribociclib, compared with 40.2%, 41.2% and 18.6% in 2024. There was variation in the relative percentage change in use of each agent by Cancer Alliance. Use of lower strengths was common for both palbociclib (60%) and abemaciclib (63%). Interpretation: Changes in usage appeared responsive to publication of key evidence and regulatory milestones. There was a higher apparent frequency of dose reductions than reported in clinical trials. OpenPrescribing Hospitals is an accessible, publicly available tool for understanding uptake and use of medicines in NHS hospitals in England.

Background and Objective: Brain metastases are common in lung cancer and increasingly treated using targeted radiotherapy techniques such as stereotactic radiosurgery (SRS). Using MRI, post-SRS changes may be difficult to distinguish from progressive brain metastasis. Contrast clearance analysis (CCA) uses T1-weighted MRI images to assess the clearance of gadolinium and can be thus used to assess vascularity and active tumours. Design and Methods: We retrospectively assessed CCAs in 62 patients with non-small cell lung cancer (NSCLC) undergoing 104 CCA scans in a single centre. Results: The initial CCA suggested the aetiology of equivocal changes on standard MRI in 80.6% of patients. In all patients whose initial CCA showed post-SRS changes and who underwent serial CCAs, the initial diagnosis was upheld with the serial imaging. In only two cases of a presumed progressive tumour on the initial CCA, subsequent treatment for radionecrosis was instigated; a retrospective review and re-evaluation of the CCAs show that progression was reported where a thin rim of rapid contrast clearance was seen, and this finding has been subsequently recognised as a feature of post-treatment change on CCAs. The lack of concordance with CCA findings in those who underwent surgical resection was also found to be due to the over-reporting of the thin blue rim as disease in the early cases of CCA use and, in three cases, potentially related to timelines longer than 7 days prior to surgery, both factors being unknown during the early implementation phase of CCA at our centre but subsequently learned. Conclusions: Our single-centre experience shows CCA to be feasible and useful in patients with NSCLC in cases of diagnostic uncertainty in MRI. It has helped guide treatment in the majority of patients, with subsequent outcomes following the implementation of the treatment based on the results, suggesting correct classification. Recommendations from our experience of the implementation include the careful consideration of the thin rim of the rapid contrast clearance and the timing of the CCA prior to surgery for suspected brain metastasis progression.

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients’ prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.