A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy

2011 ◽  
Vol 38 (12) ◽  
pp. 6528-6538 ◽  
Author(s):  
Nishikant Mishra ◽  
Sanja Petrovic ◽  
Santhanam Sundar
Keyword(s):  
Prostate Cancer ◽  
Case Based Reasoning ◽  
Cancer Radiotherapy ◽  
Reasoning System ◽  
Dose Planning ◽  
Prostate Cancer Radiotherapy ◽  
Case Based ◽  
Self Adaptive

scholarly journals Proximal seminal vesicle displacement and margins for prostate cancer radiotherapy

2021 ◽  
Author(s):  
Daryl Lim Joon ◽  
Michael Chao ◽  
Angelina Piccolo ◽  
Michal Schneider ◽  
Nigel Anderson ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Seminal Vesicle ◽  
Cancer Radiotherapy ◽  
Prostate Cancer Radiotherapy

scholarly journals Management of prostate cancer radiotherapy during the COVID-19 pandemic: A necessary paradigm change

2021 ◽  
Vol 27 ◽  
pp. 100331
Author(s):  
Beatrice Detti ◽  
Gianluca Ingrosso ◽  
Carlotta Becherini ◽  
Andrea Lancia ◽  
Emanuela Olmetto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Paradigm Change ◽  
Cancer Radiotherapy ◽  
Prostate Cancer Radiotherapy

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

Evaluation of set-up errors and estimation of set-up margin during external beam radiation therapy of prostate cancer using electronic portal imaging device (EPID)

2021 ◽  
pp. 1-8
Author(s):  
Daryoush Khoramian ◽  
Soroush Sistani ◽  
Bagher Farhood
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Target Volume ◽  
External Beam Radiation ◽  
Beam Radiation ◽  
Cancer Radiotherapy ◽  
Imaging Device ◽  
Portal Images ◽  
Set Up ◽  
Prostate Cancer Radiotherapy

Abstract Aim: In radiation therapy, accurate dose distribution in target volume requires accurate treatment setup. The set-up errors are unwanted and inherent in the treatment process. By achieving these errors, a set-up margin (SM) of clinical target volume (CTV) to planning target volume (PTV) can be determined. In the current study, systematic and random set-up errors that occurred during prostate cancer radiotherapy were measured by an electronic portal imaging device (EPID). The obtained values were used to propose the optimum CTV-to-PTV margin in prostate cancer radiotherapy. Materials and methods: A total of 21 patients with prostate cancer treated with external beam radiation therapy (EBRT) participated in this study. A total of 280 portal images were acquired during 12 months. Gross, population systematic (Σ) and random (σ) errors were obtained based on the portal images in Anterior–Posterior (AP), Medio-Lateral (ML) and Superior–Inferior (SI) directions. The SM of CTV to PTV were then calculated and compared by using the formulas presented by the International Commission on Radiation Units and Measurements (ICRU) 62, Stroom and Heijmen and Van Herk et al. Results: The findings showed that the population systematic errors during prostate cancer radiotherapy in AP, ML and SI directions were 1·40, 1·95 and 1·94 mm, respectively. The population random errors in AP, ML and SI directions were 2·09, 1·85 and 2·29 mm, respectively. The SM of CTV to PTV calculated in accordance with the formula of ICRU 62 in AP, ML and SI directions were 2·51, 2·68 and 3·00 mm, respectively. And according to Stroom and Heijmen, formula were 4·23, 5·19 and 5·48 mm, respectively. And Van Herk et al. formula were 4·96, 6·17 and 6·45 mm, respectively. Findings: The SM of CTV to PTV in all directions, based on the formulas of ICRU 62, Stroom and Heijmen and van Herk et al., were equal to 2·73, 4·98 and 5·86 mm, respectively; these values were obtained by averaging the margins in all directions.

Dosimetric comparison of four target alignment methods for prostate cancer radiotherapy

2006 ◽  
Vol 66 (3) ◽  
pp. 883-891 ◽  
Author(s):  
Jennifer C. O’Daniel ◽  
Lei Dong ◽  
Lifei Zhang ◽  
Renaud de Crevoisier ◽  
He Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Radiotherapy ◽  
Dosimetric Comparison ◽  
Prostate Cancer Radiotherapy

scholarly journals Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy

2012 ◽  
Vol 53 (6) ◽  
pp. 961-972 ◽  
Author(s):  
Hidetaka Arimura ◽  
Wataru Itano ◽  
Yoshiyuki Shioyama ◽  
Norimasa Matsushita ◽  
Taiki Magome ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Radiotherapy ◽  
Setup Errors ◽  
Patient Setup ◽  
Portal Images ◽  
Prostate Cancer Radiotherapy
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