Case-Based Decision Support System for Breast Cancer Management

Sekar, Booma Devi; Lamy, Jean-Baptiste; Larburu, Nekane; Séroussi, Brigitte; Guézennec, Gilles; Bouaud, Jacques; Muro, Naiara; Wang, Hui; Liu, Jun

doi:10.2991/ijcis.2018.25905180

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…CBR is also used to segment the images of the kidneys deformed by nephroblastoma (Marieusing et al, 2018). Sekar et al (2018) presented a CBR approach for decision support in breast cancer management.…”

Section: Case-based Reasoning Using Radiographic Imagesmentioning

confidence: 99%

Integrating Radiology into an AI System for Physician Decision Making

Paruchuri¹,

Granville²

2020

CIS

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Over time, physicians gather a vast amount of data such as radiographic images, medical procedures, treatments, and insurance coverage about the patients. A Case-Based Reasoning (CBR) system is developed to organize and retrieve such data to aid physicians in making a diagnosis and formulating a treatment plan for patients with similar traits. This research is an extension of the earlier work presented by the authors. Our earlier research did not consider radiographic images, although radiology plays a vital role in diagnosing and treating patients. Currently, a new algorithm is created to retrieve and organize information from the CBR system to add radiographic images to the patient’s case data. This system is tested and the performance results prove that the acceptance rate achieved by this system is higher than that of the earlier system.

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Section: Case-based Reasoning Using Radiographic Imagesmentioning

confidence: 99%

Integrating Radiology into an AI System for Physician Decision Making

Paruchuri¹,

Granville²

2020

CIS

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“…Le cycle du raisonnement à partir de cas est constitué de quatre processus consécutifs : la recherche de cas similaires, la réutilisation des solutions passées, la révision des solutions passées et l'apprentissage de nouvelles solutions. Dans le cadre de DESIREE, les cas sont des cas cliniques décrits sous la forme d'un vecteur de variables représentées par un couple attributs-valeurs et de la solution du cas qui représente la décision de prise en charge thérapeutique prise par la RCP (Sekar et al, 2018). Les couples attributsvaleurs sont pris dans le modèle de données du patient tel que décrit par le BCKM.…”

Section: Le Sadc-rcunclassified

“…Pour les nombres, il s'agit d'une distance Euclidienne classique. Pour les valeurs catégorielles, nous avons utilisé la distance sémantique cosinus qui s'appuie sur la position hiérarchique des attributs dans le BCKM (Sekar et al, 2018), et calcule la distance comme un degré de proximité taxonomique dans l'ontologie.…”

Section: Recherche De Cas Similairesunclassified

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

Séroussi¹,

Lamy²,

Prebet³

et al. 2018

Techniques et sciences informatiques

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“…Thus, DESIREE offers a second decision support module based on the experience gained from non-compliant TB decisions [32]. Finally, a third decision support module has been developed based on the implementation of a case-based reasoning process where the goal is to reuse past TB decisions made for patients similar to the new patient case discussed by TB clinicians [33].…”

Section: Introductionmentioning

confidence: 99%

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project

Bouaud

Pelayo

Lamy³

et al. 2020

Artificial Intelligence in Medicine

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The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guidelinebased decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.(consolidated figures in 2018), breast cancer mortality is declining in France and breast cancer is one of the best prognosis cancers with among the best five and ten-year survival rates (87 % and 76 %, respectively). However, it still remains a therapeutic challenge especially for triple negative breast cancers and HER2+ breast cancers, for which improvements are both possible and necessary [1].Clinical practice guidelines (CPGs) are free-text documents developed by National agencies or academic associations to provide the best recommendations for the management of a set of selected patient profiles. These recommendations are built from published clinical research results and represent the state of the art following evidencebased medicine principles [2,3]. Although studies have shown that implementing oncology CPGs does improve clinical outcomes in both overall and recurrence-free survivals of cancer patients [4][5][6][7][8][9][10][11], there

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Case-Based Decision Support System for Breast Cancer Management

Cited by 8 publications

References 11 publications

Integrating Radiology into an AI System for Physician Decision Making

Integrating Radiology into an AI System for Physician Decision Making

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project

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