Next Article in Journal
Biochemical Characterization of a Novel α/β-Hydrolase/FSH from the White Shrimp Litopenaeus vannamei
Next Article in Special Issue
Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine
Previous Article in Journal
Characterization of the Ohmyungsamycin Biosynthetic Pathway and Generation of Derivatives with Improved Antituberculosis Activity
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Biomolecules
Volume 9
Issue 11
10.3390/biom9110673
biomolecules-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement thumb_up
...
textsms
...
Article

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

by
Jun Akatsuka
1,2,
Yoichiro Yamamoto
1,*,
Tetsuro Sekine
3,
Yasushi Numata
1,
Hiromu Morikawa
1,
Kotaro Tsutsumi
1,
Masato Yanagi
2,
Yuki Endo
2,
Hayato Takeda
2,
Tatsuro Hayashi
2,
Masao Ueki
4,
Gen Tamiya
4,5,
Ichiro Maeda
1,6,
Manabu Fukumoto
1,
Akira Shimizu
7,
Toyonori Tsuzuki
8,
Go Kimura
2 and
Yukihiro Kondo
2
1
Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
2
Department of Urology, Nippon Medical School Hospital, Tokyo 113-8603, Japan
3
Department of Radiology, Nippon Medical School Hospital, Tokyo 113-8603, Japan
4
Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
5
Tohoku Medical Megabank Organization, Tohoku University, Miyagi 980-8575, Japan
6
Department of Pathology, Kitasato University Kitasato Institute Hospital, Tokyo 108-8642, Japan
7
Department of Analytic Human Pathology, Nippon Medical School, Tokyo 113-8602, Japan
8
Department of Surgical Pathology, Aichi Medical University Hospital, Aichi 480-1195, Japan
*
Author to whom correspondence should be addressed.
Biomolecules 2019, 9(11), 673; https://doi.org/10.3390/biom9110673
Received: 25 September 2019 / Revised: 24 October 2019 / Accepted: 28 October 2019 / Published: 30 October 2019
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
View Full-Text Download PDF
Browse Figures

Abstract

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87–0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible. View Full-Text
Keywords: deep learning; black box; prostate cancer; MRI; pathology deep learning; black box; prostate cancer; MRI; pathology
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Supplementary Material

SciFeed

Share and Cite

MDPI and ACS Style

Akatsuka, J.; Yamamoto, Y.; Sekine, T.; Numata, Y.; Morikawa, H.; Tsutsumi, K.; Yanagi, M.; Endo, Y.; Takeda, H.; Hayashi, T.; Ueki, M.; Tamiya, G.; Maeda, I.; Fukumoto, M.; Shimizu, A.; Tsuzuki, T.; Kimura, G.; Kondo, Y. Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches. Biomolecules 2019, 9, 673. https://doi.org/10.3390/biom9110673

AMA Style

Akatsuka J, Yamamoto Y, Sekine T, Numata Y, Morikawa H, Tsutsumi K, Yanagi M, Endo Y, Takeda H, Hayashi T, Ueki M, Tamiya G, Maeda I, Fukumoto M, Shimizu A, Tsuzuki T, Kimura G, Kondo Y. Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches. Biomolecules. 2019; 9(11):673. https://doi.org/10.3390/biom9110673

Chicago/Turabian Style

Akatsuka, Jun; Yamamoto, Yoichiro; Sekine, Tetsuro; Numata, Yasushi; Morikawa, Hiromu; Tsutsumi, Kotaro; Yanagi, Masato; Endo, Yuki; Takeda, Hayato; Hayashi, Tatsuro; Ueki, Masao; Tamiya, Gen; Maeda, Ichiro; Fukumoto, Manabu; Shimizu, Akira; Tsuzuki, Toyonori; Kimura, Go; Kondo, Yukihiro. 2019. "Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches" Biomolecules 9, no. 11: 673. https://doi.org/10.3390/biom9110673

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop