Gland context networks: A novel approach for improving prostate cancer identification

Autor(es)

Mendes, Rodrigo de P. ^[1] ; Yuan, Xin ^[2] ; Genega, Elizabeth M. ^{[3, 4]} ; Xu, Xiaoyin ^[5] ; Costa, Luciano da F. ^[6] ; Comin, Cesar H. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp Sci, Sao Carlos, SP - Brazil

^[2] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Dept Med, Boston, MA 02115 - USA

^[3] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Dept Pathol, Boston, MA 02115 - USA

^[4] Tufts Med Ctr, Dept Pathol & Lab Med, Boston, MA 02111 - USA

^[5] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiol, Boston, MA 02115 - USA

^[6] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Computerized Medical Imaging and Graphics; v. 94, DEC 2021.
• Citações Web of Science: 0

Resumo

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading. (AU)

• Processo FAPESP: 21/12354-8 - Análise e refinamento de métodos de aprendizado de máquina para a morfometria de vasos sanguíneos
• Beneficiário: Cesar Henrique Comin
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/09125-4 - Representação, caracterização e modelagem de imagens biológicas utilizando redes complexas
• Beneficiário: Cesar Henrique Comin
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático