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

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

▼ 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

Supplementary File 1:
PDF-Document (PDF, 13186 KiB)

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, Yoichiro Yamamoto, Tetsuro Sekine, Yasushi Numata, Hiromu Morikawa, Kotaro Tsutsumi, Masato Yanagi, Yuki Endo, Hayato Takeda, Tatsuro Hayashi, Masao Ueki, Gen Tamiya, Ichiro Maeda, Manabu Fukumoto, Akira Shimizu, Toyonori Tsuzuki, Go Kimura, and Yukihiro Kondo. 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

Abstract views Pdf views Html views

Article Menu

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

Abstract

Supplementary Material

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI