A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans.

Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.

COVID-19 and the multidisciplinary care of patients with lung cancer: an evidence-based review and commentary.

Delivering lung cancer care during the COVID-19 pandemic has posed significant and ongoing challenges. There is a lack of published COVID-19 and lung cancer evidence-based reviews, including for the whole patient pathway. We searched for COVID-19 and lung cancer publications and brought together a multidisciplinary group of stakeholders to review and comment on the evidence and challenges. A rapid review of the literature was undertaken up to 28 October 2020, producing 144 papers, with 113 full texts screened. We focused on new primary data collection (qualitative or quantitative evidence) and excluded case reports, editorials and commentaries. Following exclusions, 15 published papers were included in the review and are summarised. They included one qualitative paper and 14 quantitative studies (surveys or cohort studies), with a total of 2295 lung cancer patients data included (mean study size 153 patients; range 7-803). Review of current evidence and commentary included awareness and help-seeking; lung cancer screening; primary care assessment and referral; diagnosis and treatment in secondary care, including oncology and surgery; patient experience and palliative care. Cross-cutting themes and challenges were identified using qualitative methods for patients, healthcare professionals and service delivery, with a clear need for continued studies to guide evidence-based decision-making.