Ann-Christin Woerl1, Markus Eckstein2, Josephine Geiger1, Daniel C Wagner3, Tamas Daher3, Philipp Stenzel3, Aurélie Fernandez3, Arndt Hartmann2, Michael Wand4, Wilfried Roth3, Sebastian Foersch5. 1. Institute of Pathology, University Medical Center Mainz, Mainz, Germany; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany. 2. Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. 3. Institute of Pathology, University Medical Center Mainz, Mainz, Germany. 4. Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany. 5. Institute of Pathology, University Medical Center Mainz, Mainz, Germany. Electronic address: sebastian.foersch@unimedizin-mainz.de.
Abstract
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications. OBJECTIVE: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning. DESIGN, SETTING, AND PARTICIPANTS: Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naïve, primary resected MIBC. This resulted in a total of 423 digital whole slide images of tumor tissue to train, validate, and test the deep learning algorithm to predict the molecular subtype. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Various accuracy measurements including the area under the receiver operating characteristic curves were used to evaluate the deep learning model. A sliding window approach to visualize classification was used. Class activation maps were used to identify image features that are most relevant to call a specific class. RESULTS AND LIMITATIONS: The deep learning model showed great performance in the prediction of the molecular subtype of MIBC patients from hematoxylin and eosin (HE) slides alone-similar to or better than pathology experts. Using different visualization techniques, we identified new histopathological features that were most relevant to our model. CONCLUSIONS: Deep learning can be used to predict important molecular features in MIBC patients from HE slides alone, potentially improving the clinical management of this disease significantly. PATIENT SUMMARY: In patients with bladder cancer, a computer program found changes in the appearance of tumor tissue under the microscope and used these to predict genetic alterations. This could potentially benefit patients.
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications. OBJECTIVE: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning. DESIGN, SETTING, AND PARTICIPANTS: Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naïve, primary resected MIBC. This resulted in a total of 423 digital whole slide images of tumor tissue to train, validate, and test the deep learning algorithm to predict the molecular subtype. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Various accuracy measurements including the area under the receiver operating characteristic curves were used to evaluate the deep learning model. A sliding window approach to visualize classification was used. Class activation maps were used to identify image features that are most relevant to call a specific class. RESULTS AND LIMITATIONS: The deep learning model showed great performance in the prediction of the molecular subtype of MIBC patients from hematoxylin and eosin (HE) slides alone-similar to or better than pathology experts. Using different visualization techniques, we identified new histopathological features that were most relevant to our model. CONCLUSIONS: Deep learning can be used to predict important molecular features in MIBC patients from HE slides alone, potentially improving the clinical management of this disease significantly. PATIENT SUMMARY: In patients with bladder cancer, a computer program found changes in the appearance of tumor tissue under the microscope and used these to predict genetic alterations. This could potentially benefit patients.
Authors: Oliver Lester Saldanha; Philip Quirke; Nicholas P West; Jacqueline A James; Maurice B Loughrey; Heike I Grabsch; Manuel Salto-Tellez; Elizabeth Alwers; Didem Cifci; Narmin Ghaffari Laleh; Tobias Seibel; Richard Gray; Gordon G A Hutchins; Hermann Brenner; Marko van Treeck; Tanwei Yuan; Titus J Brinker; Jenny Chang-Claude; Firas Khader; Andreas Schuppert; Tom Luedde; Christian Trautwein; Hannah Sophie Muti; Sebastian Foersch; Michael Hoffmeister; Daniel Truhn; Jakob Nikolas Kather Journal: Nat Med Date: 2022-04-25 Impact factor: 87.241
Authors: Stefan Schulz; Ann-Christin Woerl; Florian Jungmann; Christina Glasner; Philipp Stenzel; Stephanie Strobl; Aurélie Fernandez; Daniel-Christoph Wagner; Axel Haferkamp; Peter Mildenberger; Wilfried Roth; Sebastian Foersch Journal: Front Oncol Date: 2021-11-24 Impact factor: 6.244
Authors: Khoa A Tran; Olga Kondrashova; Andrew Bradley; Elizabeth D Williams; John V Pearson; Nicola Waddell Journal: Genome Med Date: 2021-09-27 Impact factor: 11.117