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Abstract Background Pulmonary inflammatory myofibroblastic tumour (IMT) is a rare condition that usually presents in young individuals and is associated with anaplastic lymphoma kinase (ALK)-translocation. Case presentation We report a case of an 18-year-old Caucasian man with ALK-translocated pulmonary IMT treated with multimodality therapy. The patient presented with breathlessness and was found to have a collapsed left lung. Further investigations revealed an ALK-translocated pulmonary IMT. This is usually treated with an ALK-inhibitor but patient declined after discussing potential side-effects and had repeated rigid bronchoscopic interventions for local disease control. Due to persistent local recurrence, patient received radical external beam radiotherapy (EBRT) with pulse steroids, and one year later started on Ibuprofen, a non-steroidal anti-inflammatory agent (NSAID). Following multimodality treatment, he developed a complete response. He remains treatment-free for the past seven years. Eleven years on from his diagnosis, he remains in remission with a ECOG performance status of zero. Conclusions Achieving long-term local control in pulmonary IMT can be challenging. Multimodality treatment is sometimes needed but the overall outlook remains good.

Abstract Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss’ Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.

Background and purpose: In external beam radiotherapy for non-small cell lung cancer, dose to functioning lung should be minimised to reduce lung morbidity. This study aimed to develop a method for avoiding beam delivery through functional lung and to quantify the possible benefit to the patients. Materials and methods: Twelve patients that were treated as part of a clinical trial of single photon emission computed tomography (SPECT) functional lung avoidance were retrospectively studied. During treatment planning, the dose in the lung was weighted by the relative intensity of the functional image. A single conformal beam was scanned systematically around the planning target volume to find optimum orientations and the resulting map of functional dose variation with gantry and couch angle was used to select five non-coplanar intensity-modulated beams, taking into account directions prohibited due to collision risk. Expected reduction in pneumonitis risk was calculated using a logistic model. Results: The volume of lung irradiated to a functionally weighted dose of 5 Gy was 11.8 % (range 3.5 %–22.0 %) for functional plans, versus 20.9 % (range 4.9 %–33.3 %) for conventional plans (p = 0.002). Mean functionally weighted dose was 4.1 Gy (range 1.3 Gy-7.2 Gy) for functional plans, versus 4.5 Gy (range 1.5 Gy-8.3 Gy) for conventional plans (p = 0.002). Predicted pneumonitis risk was reduced by 4.3 % (range 0.4 %-15.6 %) (p = 0.002). Conclusions: By seeking the optimum non-coplanar beam orientations, it is possible to reduce dose/volume lung parameters by 10% or more, consistently in all patients, regardless of the pattern of lung perfusion. A prediction model indicates that this will improve radiation-associated lung injury.

Abstract Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.

Summary: Background: Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment. Methods: A retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed. Findings: Median follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575–0·788) and 0·681 (0·597–0·766), 2) Recurrence: 0·687 (0·582–0·793) and 0·722 (0·635–0·81), and 3) OS: 0·759 (0·663–0·855) and 0·717 (0·634–0·8). Our models were superior to TNM stage and performance status in predicting recurrence and OS. Interpretation: This robust and ready to use machine learning method, validated and externally tested, sets the stage for future clinical trials entailing quantitative personalised risk-stratification and surveillance following curative-intent radiotherapy for NSCLC. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Pneumonitis is a well-described, potentially life-threatening adverse effect of immune checkpoint inhibitors (ICI) and thoracic radiotherapy. It can require additional investigations, treatment, and interruption of cancer therapy. It is important for clinicians to have an awareness of its incidence and severity, however real-world data are lacking and do not always correlate with findings from clinical trials. Similarly, there is a dearth of information on cost impact of symptomatic pneumonitis. Informatics approaches are increasingly being applied to healthcare data for their ability to identify specific patient cohorts efficiently, at scale. We developed a Structured Query Language (SQL)-based informatics algorithm which we applied to CT report text to identify cases of ICI and radiotherapy pneumonitis between 1/1/2015 and 31/12/2020. Further data on severity, investigations, medical management were also acquired from the electronic health record. We identified 248 cases of pneumonitis attributable to ICI and/or radiotherapy, of which 139 were symptomatic with CTCAE severity grade 2 or more. The grade ≥2 ICI pneumonitis incidence in our cohort is 5.43%, greater than the all-grade 1.3–2.7% incidence reported in the literature. Time to onset of ICI pneumonitis was also longer in our cohort (mean 4.5 months, range 4 days-21 months), compared to the median 2.7 months (range 9 days−19.2 months) described in the literature. The estimated average healthcare cost of symptomatic pneumonitis is £3932.33 per patient. In this study we use an informatics approach to present new real-world data on the incidence, severity, management, and resource burden of ICI and radiotherapy pneumonitis. To our knowledge, this is the first study to look at real-world incidence and healthcare resource utilisation at the per-patient level in a UK cancer hospital. Improved management of pneumonitis may facilitate prompt continuation of cancer therapy, and improved outcomes for this not insubstantial cohort of patients.

Background: UK COVID-19 mortality rates are amongst the highest globally. Controversy exists on the vulnerability of thoracic cancer patients. We describe the characteristics and sequelae of patients with thoracic cancer treated at a UK cancer centre infected with COVID-19. Methods: Patients undergoing care for thoracic cancer diagnosed with COVID-19 (RT-PCR/radiology/clinically) between March-June 2020 were included. Data were extracted from patient records. Results: Thirty-two patients were included: 14 (43%) diagnosed by RT-PCR, 18 (57%) by radiology and/or convincing symptoms. 88% had advanced thoracic malignancies. Eleven of 14 (79%) patients diagnosed by RT-PCR and 12 of 18 (56%) patients diagnosed by radiology/clinically were hospitalised, of which four (29%) and 2 (11%) patients required high-dependency/intensive care respectively. Three (21%) patients diagnosed by RT-PCR and 2 (11%) patients diagnosed by radiology/clinically required non-invasive ventilation; none were intubated. Complications included pneumonia and sepsis (43% and 14% respectively in patients diagnosed by RT-PCR; 17% and 11% respectively in patients diagnosed by radiology/clinically). In patients receiving active cancer treatment, therapy was delayed/ceased in 10/12 (83%) and 7/11 (64%) patients diagnosed by RT-PCR and radiology/clinically respectively. Nine (28%) patients died; all were smokers. Median time from symptom onset to death was 7 days (range 3–37). Conclusions: The immediate morbidity from COVID-19 is high in thoracic cancer patients. Hospitalisation and treatment interruption rates were high. Improved risk-stratification models for UK cancer patients are urgently needed to guide safe cancer-care delivery without compromising efficacy.