Utility of circulating tumor DNA for detection and monitoring of endometrial cancer recurrence and progression

Gorsia, Diviya N.; Collins, Anna; Sandhu, Pavandeep; Foreman, Nalini; Gore, Anu; Wood, Joey; Kent, Christopher; Silcock, Lee; Guttery, David S.

doi:10.1101/2020.03.04.20030908

Cited by 20 publications

(23 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results have identified new insights that can be used to inform the design of future trials incorporating ctDNA monitoring into clinical practice. The accuracy of ctDNA to detect EC recurrence in the pilot data from our study was 100% [12] and other studies support our results, including a lead time of over 6 months and high negative predictive value, 100% overall survival in gynaecological cancer cases with undetectable ctDNA [16].…”

Section: Discussionsupporting

confidence: 88%

“…As a result there is a high prevalence of EC survivors in the community 6 and this is projected to increase from 757,190 in 2016 to 942,670 over the next decade 3 , The 6 value of HFU for early stage EC is been questioned given the low recurrence rate and that the 6 4 majority of recurrences are associated with symptoms [6]. This has led to the introduction of reduced schedule or alternative models of follow-up [7], such as telephone (TFU) or patient-7 initiated follow-up (PIFU), which appear to be well tolerated [8,9] and cost savings for both 7 the patient and the healthcare economy [10]. Concerns however, have been raised over the 7 psychological impact on patients, with fear of recurrence reported to be higher with PIFU Before trials determining the utility of remote monitoring with ctDNA in EC can by 8 undertaken, patient acceptability needs to be established.…”

Section: Introductionmentioning

confidence: 99%

“…ctDNA is reported to have 100% sensitivity in detecting EC recurrence and a lead time of 10 months compared to CT scan and up to 18 months over patient symptoms [12]. The high accuracy of ctDNA in identifying recurrence opens up the possibility of developing remote monitoring for EC by combining ctDNA monitoring with reduced schedule follow-up, such as a PIFU scheme.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patient acceptability of ctDNA testing in endometrial cancer follow-up

Relton

Collins

Guttery

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Objective: Circulating tumour DNA (ctDNA) is emerging as a potential option to detect disease recurrence in many cancer types however, ensuring patient acceptability of changing clinical practice and the introduction of new technology is paramount. This study aimed to explore womens opinions on the acceptability of ctDNA to monitor for endometrial cancer (EC) recurrence. Methods: Women enrolled on a non-intervention cohort study determining the ability of ctDNA to detect recurrent endometrial cancer were invited to participate in a semi-structured interview. Data was analysed using Template Analysis. Results: Eighteen women were interviewed. Participants represented a mix of cases, including early stage high-risk EC, metastatic disease at diagnosis and EC recurrence, to ensure a wide range of participant experiences were captured. A ctDNA blood test was viewed by participants as more physically and psychologically acceptable than clinical examination to monitor for EC recurrence. In particular, participants expressed overwhelming preference for a blood test rather than pelvic examination. Although participants acknowledged that an abnormal ctDNA result could cause anxiety, they expressed a preference to be informed of their results, even if a recurrence was too small to detect radiologically. Explanations for these opinions were a desire for certainty whether their cancer would recur or not, and knowledge would help them be more aware of symptoms that should be reported to their clinician. Conclusions: ctDNA monitoring to identify EC recurrence appears to be acceptable to patients, and for many, may be preferable to clinical examination.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Patient acceptability of ctDNA testing in endometrial cancer follow-up

Relton

Collins

Guttery

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“… 32 The development and adoption of prognostic and risk-stratifying biomarkers in the future may also inform decisions on the optimum route of surgery thereby enabling more personalised management. 33–35 …”

Section: Discussionmentioning

confidence: 99%

Economic evaluation of different routes of surgery for the management of endometrial cancer: a retrospective cohort study

Moss

Morgan²,

Martin³

et al. 2021

BMJ Open

View full text Add to dashboard Cite

ObjectivesThe benefits of minimally invasive surgery (MIS) for endometrial carcinoma (EC) are well established although the financial impact of robotic-assisted hysterectomy (RH) compared with laparoscopic hysterectomy (LH) is disputed.DesignRetrospective cohort study.SettingEnglish National Health Service hospitals 2011–2017/2018.Participants35 304 women having a hysterectomy for EC identified from Hospital Episode Statistics.Primary and secondary outcome measuresThe primary outcome was the association between route of surgery on cost at intervention, 30, 90 and 365 days for women undergoing an open hysterectomy (OH) or MIS (LH/RH) for EC in England. The average marginal effect was calculated to compare RH versus OH and RH versus LH which adjusted for any differences in the characteristics of the surgical approaches. Secondary outcomes were to analyse costing data for each surgical approach by age, Charlson Comorbidity Index (CCI) and hospital MIS rate classification.ResultsA total of 35 304 procedures were performed, 20 405 (57.8%) were MIS (LH: 18 604 and RH: 1801), 14 291 (40.5%) OH. Mean cost for LH was significantly less than RH, whereas RH was significantly less than OH at intervention, 30, 90 and 365 days (p<0.001). Over time, patients who underwent RH had increasing CCI scores and by the 2015/2016 year had a higher average CCI than LH. Comparing the cost of LH and RH against CCI score identified that the costs closely reflected the patients’ CCI. Increasing disparity was also seen between the MIS and OH costs with rising age. When exploring the association between provider volume, MIS rate and surgical costs, there was an association with the higher the MIS rate the lower the average cost.ConclusionsFurther research is needed to investigate costs in matched patient cohorts to determine the optimum surgical modality in different populations.

show abstract

“…Liquid biopsies, obtained via minimally invasive techniques such as a simple blood test, in theory allow for early detection of cancer, monitoring risk of relapse over time, and guiding treatment options. As an example, microsatellite instability (MSI) status can be predicted from ctDNA in patients with endometrial cancer in order to inform immunotherapy-based treatment (29). Chabon and colleagues developed a machine learning-based approach, Lung-CLiP (cancer likelihood in plasma), that predicts the likelihood of ctDNA in blood drawn from patients with lung cancer (30).…”

Section: On the Road To Early Cancer Detectionmentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

301

153

View full text Add to dashboard Cite

Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

show abstract

Utility of circulating tumor DNA for detection and monitoring of endometrial cancer recurrence and progression

Cited by 20 publications

References 36 publications

Patient acceptability of ctDNA testing in endometrial cancer follow-up

Patient acceptability of ctDNA testing in endometrial cancer follow-up

Economic evaluation of different routes of surgery for the management of endometrial cancer: a retrospective cohort study

Artificial Intelligence in Cancer Research and Precision Medicine

Contact Info

Product

Resources

About