Background: Following chemo-radiotherapy (CRT) for human papilloma virus positive (HPV+) anal squamous cell carcinoma (ASCC), detection of residual/recurrent disease is challenging. Patients frequently undergo unnecessary repeated biopsies for abnormal MRI/clinical findings. In a pilot study we assessed the role of circulating HPV-DNA in identifying "true" residual disease.Methods: We prospectively collected plasma samples at baseline (n = 21) and 12 weeks post-CRT (n = 17). Circulating HPV-DNA (cHPV DNA) was measured using a novel next generation sequencing (NGS) assay, panHPV-detect, comprising of two primer pools covering distinct regions of eight high-risk HPV genomes (16, 18, 31, 33, 35, 45, 52, and 58) to detect circulating HPV-DNA (cHPV DNA). cHPV-DNA levels post-CRT were correlated to disease response.Results: In pre-CRT samples, panHPV-detect demonstrated 100% sensitivity and specificity for HPV associated ASCC. PanHPV-detect was able to demonstrate cHPV-DNA in 100% (9/9) patients with T1/T2N0 cancers. cHPV-DNA was detectable 12 weeks post CRT in just 2/17 patients, both of whom relapsed. 1/16 patients who had a clinical complete response (CR) at 3 months post-CRT but relapsed at 9 months and 1/1 patient with a partial response (PR). PanHPV-detect demonstrated 100% sensitivity and specificity in predicting response to CRT. Conclusion:We demonstrate that panHPV-detect, an NSG assay is a highly sensitive and specific test for the identification of cHPV-DNA in plasma at diagnosis. cHPV-DNA post-treatment may predict clinical response to CRT.
CT-based radiotherapy workflow is limited by poor soft tissue definition in the pelvis and reliance on rigid registration methods. Current image-guided radiotherapy and adaptive radiotherapy models therefore have limited ability to improve clinical outcomes. The advent of MRI-guided radiotherapy solutions provides the opportunity to overcome these limitations with the potential to deliver online real-time MRI-based plan adaptation on a daily basis, a true “plan of the day.” This review describes the application of MRI guided radiotherapy in two pelvic tumour sites likely to benefit from this approach.
Purpose3D ultrasound (US) images of the uterus may be used to adapt radiotherapy (RT) for cervical cancer patients based on changes in daily anatomy. This requires accurate on‐line segmentation of the uterus. The aim of this work was to assess the accuracy of Elekta's “Assisted Gyne Segmentation” (AGS) algorithm in semi‐automatically segmenting the uterus on 3D transabdominal ultrasound images by comparison with manual contours.Materials & methodsNine patients receiving RT for cervical cancer were imaged with the 3D Clarity® transabdominal probe at RT planning, and 1 to 7 times during treatment. Image quality was rated from unusable (0)–excellent (3). Four experts segmented the uterus (defined as the uterine body and cervix) manually and using AGS on images with a ranking > 0. Pairwise analysis between manual contours was evaluated to determine interobserver variability. The accuracy of the AGS method was assessed by measuring its agreement with manual contours via pairwise analysis.Results35/44 images acquired (79.5%) received a ranking > 0. For the manual contour variation, the median [interquartile range (IQR)] distance between centroids (DC) was 5.41 [5.0] mm, the Dice similarity coefficient (DSC) was 0.78 [0.11], the mean surface‐to‐surface distance (MSSD) was 3.20 [1.8] mm, and the uniform margin of 95% (UM95) was 4.04 [5.8] mm. There was no correlation between image quality and manual contour agreement. AGS failed to give a result in 19.3% of cases. For the remaining cases, the level of agreement between AGS contours and manual contours depended on image quality. There were no significant differences between the AGS segmentations and the manual segmentations on the images that received a quality rating of 3. However, the AGS algorithm had significantly worse agreement with manual contours on images with quality ratings of 1 and 2 compared with the corresponding interobserver manual variation. The overall median [IQR] DC, DSC, MSSD, and UM95 between AGS and manual contours was 5.48 [5.45] mm, 0.77 [0.14], 3.62 [2.7] mm, and 5.19 [8.1] mm, respectively.ConclusionsThe AGS tool was able to represent uterine shape of cervical cancer patients in agreement with manual contouring in cases where the image quality was excellent, but not in cases where image quality was degraded by common artifacts such as shadowing and signal attenuation. The AGS tool should be used with caution for adaptive RT purposes, as it is not reliable in accurately segmenting the uterus on ‘good’ or ‘poor’ quality images. The interobserver agreement between manual contours of the uterus drawn on 3D US was consistent with results of similar studies performed on CT and MRI images.
Purpose To fully automate CT‐based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. Methods We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4‐field‐box (4‐field‐box), 3D conformal radiotherapy (3D‐CRT), and volumetric modulated arc therapy (VMAT). These auto‐planning algorithms were combined with a previously developed auto‐contouring system. To improve the quality of the 4‐field‐box and 3D‐CRT plans, we used an in‐house, field‐in‐field (FIF) automation program. Thirty‐five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5‐point Likert scale. Results Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4‐field‐box, 3D‐CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. Conclusion Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource‐constrained radiotherapy departments in low‐ and middle‐income countries.
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