Decision letter for "Follow‐up after oral cancer treatment transition to a personalised approach"

Keyword(s):  
2019 ◽  
Vol 8 (12) ◽  
pp. 2107 ◽  
Author(s):  
Davide B. Gissi ◽  
Achille Tarsitano ◽  
Andrea Gabusi ◽  
Roberto Rossi ◽  
Giuseppe Attardo ◽  
...  

Background: This study aimed to evaluate the prognostic value of a non-invasive sampling procedure based on 13-gene DNA methylation analysis in the follow-up of patients previously treated for oral squamous cell carcinoma (OSCC). Methods: The study population included 49 consecutive patients treated for OSCC. Oral brushing sample collection was performed at two different times: before any cancer treatment in the tumor mass and during patient follow-up almost 6 months after OSCC treatment, within the regenerative area after OSCC resection. Each sample was considered positive or negative in relation to a predefined cut-off value. Results: Before any cancer treatment, 47/49 specimens exceeded the score and were considered as positive. Six months after OSCC resection, 16/49 specimens also had positive scores in the samples collected from the regenerative area. During the follow-up period, 7/49 patients developed locoregional relapse: 6/7 patients had a positive score in the regenerative area after OSCC resection. The presence of a positive score after oral cancer treatment was the most powerful variable related to the appearance of locoregional relapse. Conclusion: 13-gene DNA methylation analysis by oral brushing may have a clinical application as a prognostic non-invasive tool in the follow-up of patients surgically treated for OSCC.


Author(s):  
Marieke Brands ◽  
André Verbeek ◽  
Sandra Geurts ◽  
Thijs Merkx
Keyword(s):  

2018 ◽  
Vol 29 ◽  
pp. viii685
Author(s):  
S. De Wilde ◽  
J. De Munter ◽  
M. Quaghebeur ◽  
D. Mazure ◽  
A. De Pauw ◽  
...  
Keyword(s):  

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3064
Author(s):  
Jean-Emmanuel Bibault ◽  
Steven Hancock ◽  
Mark K. Buyyounouski ◽  
Hilary Bagshaw ◽  
John T. Leppert ◽  
...  

Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.


1989 ◽  
Vol 33 (4) ◽  
pp. 441-448 ◽  
Author(s):  
Colin A. Espie ◽  
Eric Freedlander ◽  
Linda M. Campsie ◽  
David S. Soutar ◽  
A.G. Robertson

2016 ◽  
Vol 127 (4) ◽  
pp. E124-E131 ◽  
Author(s):  
Nathan Handley ◽  
Jacob Eide ◽  
Randall Taylor ◽  
Beverly Wuertz ◽  
Patrick Gaffney ◽  
...  
Keyword(s):  

2010 ◽  
Vol 78 (3) ◽  
pp. 689-695 ◽  
Author(s):  
Dominic A.X. Schinagl ◽  
Henri A.M. Marres ◽  
Arnoud C. Kappelle ◽  
Matthias A.W. Merkx ◽  
Lucas A.M. Pop ◽  
...  

Lung Cancer ◽  
2012 ◽  
Vol 77 ◽  
pp. S34-S35
Author(s):  
Viktors Kozirovskis ◽  
Vija Bērziņa ◽  
Aija Geriņa-Bērziņa ◽  
Elīna Skuja ◽  
Arturs Šorubalko ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document