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Tytuł pozycji:

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer.

Tytuł :
Multimodal Deep Learning for Prognosis Prediction in Renal Cancer.
Autorzy :
Schulz S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Woerl AC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
Jungmann F; Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany.
Glasner C; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Stenzel P; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Strobl S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Fernandez A; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Wagner DC; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Haferkamp A; Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany.
Mildenberger P; Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany.
Roth W; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
Foersch S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
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Źródło :
Frontiers in oncology [Front Oncol] 2021 Nov 24; Vol. 11, pp. 788740. Date of Electronic Publication: 2021 Nov 24 (Print Publication: 2021).
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: [Lausanne : Frontiers Research Foundation]
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Contributed Indexing :
Keywords: artificial intelligence; deep learning; pathology; prognosis prediction; radiology; renal cancer
Entry Date(s) :
Date Created: 20211213 Latest Revision: 20220429
Update Code :
20220502
PubMed Central ID :
PMC8651560
DOI :
10.3389/fonc.2021.788740
PMID :
34900744
Czasopismo naukowe
Background: Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients.
Objective: In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC.
Design Setting and Participants: Two mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing.
Outcome Measurements and Statistical Analysis: Outcome measurements included Harrell's concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent.
Results: The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM's prediction was an independent prognostic factor outperforming other clinical parameters.
Interpretation: Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease.
Patient Summary: An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.
(Copyright © 2021 Schulz, Woerl, Jungmann, Glasner, Stenzel, Strobl, Fernandez, Wagner, Haferkamp, Mildenberger, Roth and Foersch.)

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