key: cord-0971655-0g15exm4 authors: Clough, A.; Sanders, J.; Banfill, K.; Faivre-Finn, C.; Price, G.; Eccles, C. L.; Aznar, M. C.; van Herk, M. title: A novel use for routine CBCT imaging during radiotherapy to detect COVID-19 date: 2021-07-23 journal: Radiography (Lond) DOI: 10.1016/j.radi.2021.07.011 sha: 4cc33343717b38a1d060b23ee20fdaf048c006b6 doc_id: 971655 cord_uid: 0g15exm4 Introduction Thoracic CT is a useful tool in the early diagnosis of patients with COVID-19. Typical appearances include patchy ground glass shadowing. Thoracic radiotherapy uses daily cone beam CT imaging (CBCT) to check for changes in patient positioning and anatomy prior to treatment through a qualitative assessment of lung appearance by radiographers. Observation of changes related to COVID-19 infection during this process may facilitate earlier testing improving patient management and staff protection. Methods A tool was developed to create overview reports for all CBCTs for each patient throughout their treatment. Reports contain coronal maximum intensity projection (MIP’s) of all CBCTs and plots of lung density over time. A single therapeutic radiographer undertook a blinded off-line audit that reviewed 150 patient datasets for tool optimisation in which medical notes were compared to image findings. This cohort included 75 patients treated during the pandemic and 75 patients treated between 2014-2017. The process was repeated retrospectively on a subset of the 285 thoracic radiotherapy patients treated between January-June 2020 to assess the efficiency of the tool and process. Results Three patients in the n=150 optimisation cohort had confirmed COVID-19 infections during their radiotherapy. Two of these were detected by the reported image assessment process. The third case was not detected on CBCT due to minimal density changes in the visible part of the lungs. Within the retrospective cohort four patients had confirmed COVID-19 based on RT-PCR tests, three of which were retrospectively detected by the reported process. Conclusion The preliminary results indicate that the presence of COVID-19 can be detected on CBCT by therapeutic radiographers. Implications for practice This process has now been extended to clinical service with daily assessments of all thoracic CBCTs. Changes noted are referred for oncologist review. In December 2019, initial cases of COVID-19 were reported in Wuhan China, and infections rapidly 2 spread internationally 1 . On the 30th January 2020 the World Health Organisation (WHO) declared 3 the outbreak as a public health emergency of international concern. Advanced age and co-4 morbidities have both been linked with poor outcomes of COVID-19 2 . 5 Clinical presentation of the infection varies greatly, with early predication indicating up to 80% 6 patients could be asymptomatic, a figure since revised to 17-20% 3, 4 . Symptomatic patients 7 experience a range of symptoms including fever, dry cough, and dyspnoea while more severe effects 8 include respiratory failure, multi-organ failure and death 5 . Patients who are elderly or have 9 underlying co-morbidities are disproportionally affected by COVID-19 and at increased risk of 10 adverse outcomes 6, 7 . Cancer patients are particularly vulnerable with data indicating a higher risk of 11 intensive care unit (ICU) admissions, ventilator requirements and death compared with the general 12 population 2, 8, 9, 10 . Fatality rates ranging from 4-11% have been reported for patients of an advanced 13 age or with comorbidities. These factors are associated with higher cancer incidence and could 14 explain the link between COVID-19 and cancer 10,12,14 . 15 Chest computed tomography (CT) can be a useful tool for early diagnosis of COVID-19 with the 16 majority of patients displaying similar CT findings 11 . The most common observations on CT are 17 ground-glass opacification (GGO's), air bronchograms, crazy-paving patterns, and thickening of the 18 adjacent pleura 12 . The disease distribution in the initial chest CT is typically confined to the middle 19 and lower lobes of the lung and is pre-dominantly peripheral. As disease advances, follow up CT 20 shows consolidation and coalescing infiltrates as the central and upper lungs become affected 13 . 21 Cancer patients receiving radiotherapy need to attend daily for treatment without any delays, 22 interruptions or premature termination to avoid suboptimal oncologic outcomes [15] [16] [17] The project was undertaken in two phases with two patient cohorts. The first, the "optimisation 45 cohort", retrospectively included a arbitrary convenient sample of 150 patients treated with curative 46 intent radiotherapy and daily online CBCT imaging. Seventy-five patients were treated during the 47 pandemic (i.e., after March 2020) and 75 were treated between 2014-2017. This cohort was used to 48 optimise the semi-automatic reporting tool. The optimisation cohort also included 75 patients 49 treated prior to the pandemic (i.e. between 2014 and 2017) as controls, to test it false positives 50 could be reported. The second cohort, the "evaluation cohort", retrospectively included all radical 51 lung and oesophagus patients receiving radiotherapy at our institution between January and June 52 2020 including the 75 from the optimisation cohort. 53 Thoracic treatment at the authors institute involves daily CBCT's, with treatment fractionation 54 ranging from 3-33 fractions. 55 56 Semi-automatic reporting tool development 57 CBCT scans were acquired with Elekta XVI (Elekta Oncology systems, Copenhagen, Sweden), version 58 5.01 to 5.03. Each daily CBCT was converted into a coronal maximum intensity projection (MIP). MIP 59 rendering is a standard CT image analysis technique that produces a single 2D image that is a 60 projection of the highest attenuating voxels of the image encountered by X-ray beam and 61 approximates a planar radiograph 22, 17, 9 . We create the MIP images using the standard software 62 libraries from the Elekta XVI system (the MIP operation is available in most common image 63 processing libraries). The tool reads the planning CT (pCT) and all available CBCTs for a patient and 64 sequentially plots the MIP images in date order and generates a visual timeline of lung appearance. 65 For that purpose CBCTs were registered to the pCT using a rectangular region of interest (ROI) drawn 66 to fit tightly around both lungs. The pCT lung contours with a small negative margin were then used 67 to mask the lungs for density analysis and MIP generation in the pCT and all CBCTs (i.e., MIP images 68 were restricted to the lung tissues alone). Pixel values outside the lungs were used for normalisation 69 of CBCT image intensities and all images were presented with a fixed level and window. Density 70 values were also plotted. Figure 1 shows an example of the tool's output. 71 72 Rigid image registration was chosen over non-rigid, because the latter tended to be affected by 73 COVID-19 lesions, driving these higher density regions towards the chest walls and reducing their 74 visibility. The margin used to mask the lungs during the analysis was shrunk by 3mm as a 75 compromise between sensitivity for small changes occurring in the peripheral lungs and resilience to 76 lung contour changes that could lead to tissues outside the lungs (e.g. the ribs) being mistaken for 77 changes in lung density. Prior to MIP generation, images were blurred in the A-P direction (σ=1cm) 78 with a 2 cm triangular kernel to reduce contrast from normal lung structures while maintaining 79 infection contrast. Typically, computation time was less than 1 minute per report. 80 81 Using the optimisation cohort, a therapeutic radiographer assessed all images and MIP summary 82 reports blindly for potential COVID related changes. This involved reviewing density change graphs 83 for any significant change followed by a review of each MIP for any significant density changes 84 throughout the treatment, with particular focus on changes suspicious for COVID-19. Algorithm 85 settings were optimised during this phase and fixed thereafter. 86 87 The observer categorised suspicious density changes following indications identified by previous 88 research, including GGO's distributed primarily in the lower and middle lobes of the lung that 89 progresses superiorly 13 . Medical and radiotherapy records were reviewed for COVID diagnosis, 90 known symptoms or documented COVID typical changes and compared to image findings. Records 91 were stored locally in MOSAIQ, the integrated information system used to collect patient imaging 92 and radiotherapy treatment information 23 . Within this initial cohort RT-PCR (Reverse Transcriptase 93 Polymerase Chain Reaction) test was not routinely carried out, therefore asymptomatic patients 94 were not identified and not all symptomatic patients were tested meaning many patients could have 95 been COVID-19 positive without detection. 96 97 Semi-automatic reporting tool evaluation 98 The second phase repeated this process retrospectively on the evaluation cohort to assess the 99 efficiency of the tool and process. Two radiographers independently assessed the reports generated 100 by the tools with the radiotherapy imaging record (Rad1), and evaluated medical notes (Rad2). Rad1 101 was blinded to patients' medical notes and COVID status. The results of potential COVID changes on 102 CBCT or reported in the radiotherapy notes were later correlated to the medical notes. 103 104 Results 105 Optimisation Cohort 106 Three of 150 patients had confirmed COVID-19 based on RT-PCR test. Two were diagnosed 107 retrospectively using the reported CBCT assessment tool and process. The other was not detected 108 on CBCT, as there were minimal lung parenchymal changes observed. Figure 2 shows the report of 109 two of those COVID-19 positive patients. 110 111 The observer identified suspicious lung density changes over the course of radiotherapy on CBCT 112 images from a further 32 patients. Fifteen were treated since the start of the COVID-19 pandemic 113 and 17 were treated prior to 2020. This research confirmed lung density changes during the course 114 of radiotherapy treatment are not specific to COVID-19, occurring in 23% of patients. Observers identified 23 patients that if seen prospectively would have been highlighted for further 133 evaluation. Unfortunately, none of these patients were tested for COVID-19 given the limited 134 testing capacity at the time. Details are summarised in Figure 4 . Our tool was designed to facilitate rapid review of thoracic CBCT by therapeutic radiographers to 145 detect COVID-19 infections. This tool allows radiographer to asses each CBCT without having to 146 review each image slice, and typically less than 1 minute is spent on reviewing a patient. 147 148 This work reports the first systematic screening of CBCT scans acquired during RT for COVID-19. The 149 ability to assess COVID-19 and non COVID-19 pneumonia on CT by radiologists has been investigated 150 with promising results for accuracy with 83%, 80% and 60% reported 24,25 . In a study comparing 151 repeat RT-PCR testing with CT-based diagnosis, 75% of patients with positive CT but negative RT-PCR 152 findings later tested positive on RT-PCR 14 . Similar results were found within He et al (2020) This work was limited by the use of patient screening measures, resulting in a low prevalence of 171 COVID-19 in patients treated during the recruitment period, reducing the statistical confidence in 172 our findings. These results should therefore be considered as proof of principle. We have now 173 implemented the assessment as part of the local daily workflow, enabling continuous, on-going 174 evaluation. A multi-centre study would allow faster accumulation of COVID-19 positive events; the 175 CATCH study is a multicentre study currently being established for this purpose. Another limitation 176 was the retrospective nature of this work, during a period with limited COVID-19 RT-PCR testing 177 capacity. As such it is unknown if asymptomatic patients were missed, and conversely, if the 23 178 patients identified who would have been highlighted for further investigation were infected or not. 179 180 The radiographer assessment team found the use of MIPs in the daily reports, with the images 181 aligned to a common frame of reference useful. The main advantage of using this technique is that it 182 simplifies visualisation regions of highest density change 28,14 and reducing the volume of data to be 183 reviewed, reducing the review time required per patient. Systematic review of the time required for 184 assessment was beyond the scope of this work. 185 186 One disadvantage of MIPs is the loss in spatial resolution and amount of data available, 29 which may 187 lead to missing information indicative of COVID-19. However, Jabeen et al. (2019) demonstrated a 188 MIP technique has a high sensitivity and specificity in the thorax, detecting even small pulmonary 189 nodules missed on conventional axial images 29 . 190 191 The observers recognised from the comparison of the overview reports and corresponding image 192 review notes that not all changes observed on the real-time CBCT were observed on the 193 reconstructed MIPs. One example included a patient whose image notes indicated lung density 194 changes in the left upper lobe that was not apparent on the MIP's or the density graphs. This could 195 be due to loss in data as images are projected or the margins and image processing used in the 196 analysis. 197 The use of a more automated method was proposed to reduce resources, with automation being 198 well established in reducing the rates of medical errors through removing the human involvement 199 within checks and procedures 31 . The initial plan for a clinical prospective review was to use the 200 density graphs produced to indicate any density variation. Only the MIP's of patients with density 201 changes demonstrated would be reviewed by radiographers. However, the observers determined 202 that a combined auto-manual approach would be required as graphs were not accurate enough as a 203 single decision point. In practice, treatment radiographers would have to assess all MIP images and 204 use the graphs as more of an aid. Within a prospective clinical assessment radiographers could also 205 review the CBCT data should any changes appear suspicious and the patient's clinical notes provided 206 a complete picture of the patient's situation. 207 Fast daily reviews of the evolution of changes in patients' lung densities, aided by tools similar to 208 those we report, could permit the identification of asymptomatic COVID-19 and other infections, 209 enabling appropriate management steps to be taken. Research has shown that up to 46% of lung 210 cancer patients experience an infection during their radiotherapy 32 , with early diagnosis meaning 211 earlier medication, reducing probability of gaps in treatment. 212 213 Following this initial retrospective work, prospective reviews have begun by two therapeutic 214 radiographers who access on-treatment lung and oesophagus patients daily. Changes noted by 215 radiographers that are deemed as suspicious are referred for oncologist review. 216 217 Conclusion 218 Lung density changes on CBCT suggestive of COVID-19 can be identified by Therapeutic 219 Radiographers. With the current wave of COVID-19, it is likely that many more cases will be detected 220 shortly. This work is particularly pertinent in clinical set-up where frontline RT-PCR testing for COVID-221 19 is not available routinely, but may also find applications in the monitoring of lung tissue changes 222 beyond the current pandemic. 223 The tool provides a single report containing sequentially positioned lung MIP images, multiple slices of the last CBCT to show the most up-to-date anatomy in more detail (typically of use when the tool is used for online daily assessment), an image indicating the location of the tumour, and a longitudinal plot of the right and left lung mean density over time. All images are labelled with the day since the planning CT scan. Lung density values (mean in 'HU') are also shown in the MIP images. Because of scatter, the apparent mean HU in the CBCTs always is higher than in the planning CT. Top -these images show lung density changes identified by the observer at fraction 9 of 25 (arrow). This patient became symptomatic on fraction 11 of 25 with a cough and received a RT-PCR test. The results were established on fraction 12 of 25 as positive. Bottom -CBCT images were acquired in this patient for whom COVID-19 was not detected through CBCT analysis. The changes in lung density over time are minimal. Treatment has paused for this patient following COVID-19 diagnosis until longer symptomatic (for 17 days) in line with Trust COVID response protocols at the time. Density changes may have occurred during the pause in treatment where no CBCTs were made. This is an unavoidable limitation of retrospective evaluation. Patient MIP's reviewed retrospectively demonstrating COVID-19 like lung density changes, start of change shown by orange symbol. Patient was asymptomatic and therefore not RT-PCT tested. Part of the observed HU variation demonstrated in the graph is due to differences in imaging systems used, as the patient was moved between treatment units. 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