Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

2015 ◽  
Vol 34 (18) ◽  
pp. 2636-2661 ◽  
Author(s):  
Philipp Hermann ◽  
Tomáš Mrkvička ◽  
Torsten Mattfeldt ◽  
Mária Minárová ◽  
Kateřina Helisová ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Stochastic Geometry ◽  
Interaction Process ◽  
Fractal Models ◽  
Random Fractal

scholarly journals Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

2021 ◽  
Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Support System ◽  
Reading Time ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Screening Programs ◽  
The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

scholarly journals The existence phase transition for two Poisson random fractal models

2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Erik I. Broman ◽  
Johan Jonasson ◽  
Johan Tykesson
Keyword(s):  
Phase Transition ◽  
Fractal Models ◽  
Random Fractal

Adopting a knowledge translation approach in healthcare co-production. A case study

2020 ◽  
Vol 58 (9) ◽  
pp. 1841-1862 ◽  
Author(s):  
Francesca Dal Mas ◽  
Helena Biancuzzi ◽  
Maurizio Massaro ◽  
Luca Miceli
Keyword(s):  
Breast Cancer ◽  
Knowledge Translation ◽  
Cancer Patients ◽  
Healthcare Sector ◽  
Emotional Engagement ◽  
Breast Cancer Patients ◽  
Case Study Methodology ◽  
Content Type ◽  
Production Program

PurposeThe paper aims to contribute to the debate concerning the use of knowledge translation for implementing co-production processes in the healthcare sector. The study investigates a case study, in which design was used to trigger knowledge translation and foster co-production.Design/methodology/approachThe paper employs a case study methodology by analysing the experience of “Oncology in Motion”, a co-production program devoted to the recovery of breast cancer patients carried on by the IRCCS C.R.O. of Aviano, Italy.FindingsResults show how design could help to translate knowledge from various stakeholders with different skills (e.g. scientists, physicians, nurses) and emotional engagement (e.g. patients and patients' associations) during all the phases of a co-production project to support breast cancer patients in a recovery path. Stewardship theory is used to show that oncology represents a specific research context.Practical implicationsThe paper highlights the vast practical contribution that design can have in empowering knowledge translation at different levels and in a variety of co-production phases, among different stakeholders, facilitating their engagement and the achievement of the desired outcomes.Originality/valueThe paper contributes to the literature on knowledge translation in co-production projects in the healthcare sector showing how design can be effectively implemented.

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