Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images

Saul Fuster, Farbod Khoraminia, Umay Kiraz, Neel Kanwal, Vebjorn Kvikstad, Trygve Eftestol, Tahlita C.M. Zuiverloon, Emiel A.M. Janssen, Kjersti Engan

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

10 Citations (Scopus)

Abstract

Bladder cancer patients' stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0.

Original languageEnglish
Title of host publicationIVMSP 2022 - 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665478229
DOIs
Publication statusPublished - 29 Jun 2022
Event14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2022 - Nafplio, Greece
Duration: 26 Jun 202229 Jun 2022

Conference

Conference14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2022
Country/TerritoryGreece
CityNafplio
Period26/06/2229/06/22

Bibliographical note

Funding Information: This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 860627 (CLARIFY Project).

Publisher Copyright: © 2022 IEEE.

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