Comparison of rigid and deformable coregistration between mpMRI and CT images in radiotherapy of prostate bed cancer recurrence

2021 ◽  
Vol 92 ◽  
pp. 32-39
Author(s):  
Marica Masi ◽  
Valeria Landoni ◽  
Adriana Faiella ◽  
Alessia Farneti ◽  
Simona Marzi ◽  
...  
Keyword(s):  
Cancer Recurrence ◽  
Ct Images ◽  
Prostate Bed

scholarly journals Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

2017 ◽  
Author(s):  
Daryoush Saeed-Vafa ◽  
Rafael Bravo ◽  
Jamie A. Dean ◽  
Asmaa El-Kenawi ◽  
Nathaniel Mon Père ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Checkpoint Inhibitor ◽  
Cancer Recurrence ◽  
Initial Response ◽  
Resistance Mechanisms ◽  
Ct Images ◽  
Immune Checkpoint Blockade ◽  
Imaging Features ◽  
Mechanisms Of Resistance ◽  
Number Of Patients

AbstractImmune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and “virtual CT” images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

3.0T MR-Guided Laser Ablation of a Prostate Cancer Recurrence in the Postsurgical Prostate Bed

2011 ◽  
Vol 22 (7) ◽  
pp. 929-934 ◽  
Author(s):  
David A. Woodrum ◽  
Lance A. Mynderse ◽  
Krzysztof R. Gorny ◽  
Kimberly K. Amrami ◽  
Roger J. McNichols ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Laser Ablation ◽  
Cancer Recurrence ◽  
Prostate Bed ◽  
Prostate Cancer Recurrence

Predictors of positive lesions in 18F-fluciclovine PET/CT.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 231-231
Author(s):  
Helen Y Hougen ◽  
Nadine Mallak ◽  
Yiyi Chen ◽  
Catherine Degnin ◽  
Ryan P Kopp ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Detection Rate ◽  
Cancer Recurrence ◽  
Predictive Ability ◽  
Clinical Information ◽  
Median Baseline ◽  
Prostate Bed ◽  
Pet Ct ◽  
Positive Scan ◽  
Baseline Psa

231 Background: The 18F-Fluciclovine (FACBC) PET/CT scan has higher sensitivity than traditional imaging modalities for detection of prostate cancer (PCa) recurrence. We aim to identify the PSA characteristics as predictors of positive FACBC scan. Methods: Seventy-eight patients who underwent FACBC scan in 2018 at our institution were identified. Patient demographics, prior PSA, clinical information including prior treatment was recorded. Scans were deemed positive if definitive lesions were noted at the prostate bed, pelvic lymph nodes, or skeletal level. Detection rate was the ratio of positive over total scans and was calculated for PSA ranges. PSA velocity (PSAV) was calculated for patients who did not initiate androgen-deprivation therapy 12 months prior to the scan. The baseline PSA and PSAV were modeled as predictors of having a positive scan using logistic regression. Results: The median baseline PSA is 2.7 (range 0.2 - 226.5). The rates of positivity increased with increasing baseline PSA (Table 1). Positive scans had higher median baseline PSA (3.0, range 0.2-226.5 vs. 1.2, range 0.2-26.0; p = 0.0015) and higher PSAV (median 2.3, range -0.6-1478.7 vs. 1.0, range -0.5-31.9; p = 0.025). Baseline PSA (AUC = 0.712) was better predictor than PSAV (AUC = 0.656) of a positive scan. Combining the two variables does not improve their predictive ability (AUC = 0.719). There is a 50% detection rate in post-radical prostatectomy (RP) patients (Table 2). Conclusions: FACBC PET’s detection rate increases with increasing baseline PSA. While higher PSAV is associated with higher rate of positive scan, it did not increase the predictive ability of baseline PSA for a positive scan in prostate cancer recurrence patients. [Table: see text][Table: see text]

scholarly journals Prostate Cancer Aggressiveness Prediction Using CT Images

Life ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1164
Author(s):  
Bruno Mendes ◽  
Inês Domingues ◽  
Augusto Silva ◽  
João Santos
Keyword(s):  
Early Stage ◽  
Locally Advanced ◽  
Ct Images ◽  
Sampling Errors ◽  
Prostate Bed ◽  
Use Of Data ◽  
Cancer Aggressiveness ◽  
Wide Availability ◽  
Recommended Guidelines ◽  
Principal Method

pca is mostly asymptomatic at an early stage and often painless requiring active surveillance screening. trus is the principal method to diagnose pca following a histological examination by observing cell pattern irregularities and assigning the gs according to the recommended guidelines. This procedure presents sampling errors and, being invasive may cause complications to the patients. ebrt is presented as curative option for localised and locally advanced disease, as a palliative option for metastatic low-volume disease or after prostatectomy for prostate bed and pelvic nodes salvage. In the ebrt worflow a ct scan is performed as the basis for dose calculations and volume delineations. In this work, we evaluated the use of data-characterization algorithms (radiomics) from ct images for pca aggressiveness assessment. The fundamental motivation relies on the wide availability of ct images and the need to provide tools to assess ebrt effectiveness. We used Pyradiomics and lifex to extract features and search for a radiomic signature within ct images. Finnaly, when applying pcan to the features, we were able to show promising results.

238: Combined Effect of Apoptotic Biomarkers on Bladder Cancer Recurrence and Mortality after Radical Cystectomy

2007 ◽  
Vol 177 (4S) ◽  
pp. 79-80
Author(s):  
Jose A. Karam ◽  
Yair Lotan ◽  
Raheela Ashfaq ◽  
Claus G. Roehrborn ◽  
Arthur I. Sagalowsky ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Cancer Recurrence ◽  
Combined Effect ◽  
Bladder Cancer Recurrence

206: The Impact of Radical Prostatectomy Positive Surgical Margins on Fear of Cancer Recurrence: Results from Capsure

2007 ◽  
Vol 177 (4S) ◽  
pp. 69-70
Author(s):  
Y. Mark Hong ◽  
Jim C. Hu ◽  
Alan T. Paciorek ◽  
Sara J. Knight ◽  
Peter R. Carroll
Keyword(s):  
Radical Prostatectomy ◽  
Cancer Recurrence ◽  
Surgical Margins ◽  
Fear Of Cancer Recurrence ◽  
Positive Surgical Margins ◽  
Fear Of Cancer ◽  
The Impact

After Mohs, Cancer Recurrence Less Than 2% in New Study

2010 ◽  
Vol 41 (7) ◽  
pp. 16
Author(s):  
BRUCE JANCIN
Keyword(s):  
Cancer Recurrence

Fear of Cancer Recurrence Inventory--Danish Version

2018 ◽  
Author(s):  
Ida Hovdenak Jakobsen ◽  
Mette Moustgaard Jeppesen ◽  
Sébastien Simard ◽  
Henriette Vind Thaysen ◽  
Søren Laurberg ◽  
...  
Keyword(s):  
Cancer Recurrence ◽  
Fear Of Cancer Recurrence ◽  
Danish Version ◽  
Fear Of Cancer
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