Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

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

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Case Report: Birth Outcome and Neurodevelopment in Placental Malaria Discordant Twins.

Maternal infection during pregnancy can have lasting effects on neurodevelopment, but the impact of malaria in pregnancy on child neurodevelopment is unknown. We present a case of a 24-year-old gravida three woman enrolled at 14 weeks 6 days of gestation in a clinical trial evaluating malaria prevention strategies in pregnancy. She had two blood samples test positive for Plasmodium falciparum using loop-mediated isothermal amplification before 20 weeks of gestation. At 31 weeks 4 days of gestation, the woman presented with preterm premature rupture of membranes, and the twins were delivered by cesarean section. Twin A was 1,920 g and Twin B was 1,320 g. Both placentas tested negative for malaria by microscopy, but the placenta of Twin B had evidence of past malaria by histology. The twins' development was assessed using the Bayley Scales of Infant and Toddler Development-Third Edition. At 1 year chronologic age, Twin B had lower scores across all domains (composite scores: cognitive, Twin A [100], Twin B [70]; motor, Twin A [88], Twin B [73]; language, Twin A [109], Twin B [86]). This effect persisted at 2 years chronologic age (composite scores: cognitive, Twin A [80], Twin B [60]; motor, Twin A [76], Twin B [67]; language, Twin A [77], Twin B [59]). Infant health was similar over the first 2 years of life. We report differences in neurodevelopmental outcomes in placental malaria-discordant dizygotic twins. Additional research is needed to evaluate the impact of placental malaria on neurodevelopmental complications. Trial registration number: ClinicalTrials.gov number, NCT02163447. Registered: June 2014, https://clinicaltrials.gov/ct2/show/NCT02163447

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure