Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules Article Swipe
Satoshi Hara
,
Emi Haneda
,
Masaki Kawakami
,
Kento Morita
,
Ryo Nishioka
,
Takeshi Zoshima
,
Mitsuhiro Kometani
,
Takashi Yoneda
,
Mitsuhiro Kawano
,
Shigehiro Karashima
,
Hidetaka Nambo
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0271161
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0271161
Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0271161
- OA Status
- gold
- Cited By
- 16
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285008463
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285008463Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0271161Digital Object Identifier
- Title
-
Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubulesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-11Full publication date if available
- Authors
-
Satoshi Hara, Emi Haneda, Masaki Kawakami, Kento Morita, Ryo Nishioka, Takeshi Zoshima, Mitsuhiro Kometani, Takashi Yoneda, Mitsuhiro Kawano, Shigehiro Karashima, Hidetaka NamboList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0271161Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1371/journal.pone.0271161Direct OA link when available
- Concepts
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Medicine, Renal pathology, Biopsy, Pathology, Kidney, Renal biopsy, Pathological, Concordance, Anatomical pathology, Medical diagnosis, Segmentation, Radiology, Immunohistochemistry, Internal medicine, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 4, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.glomerular | 216 |
| abstract_inverted_index.multiclass | 90 |
| abstract_inverted_index.pathology. | 27 |
| abstract_inverted_index.specimens, | 285 |
| abstract_inverted_index.specimens. | 66, 83 |
| abstract_inverted_index.tubulitis, | 249 |
| abstract_inverted_index.U-Net-based | 71 |
| abstract_inverted_index.appropriate | 295 |
| abstract_inverted_index.calculating | 93 |
| abstract_inverted_index.concordance | 213 |
| abstract_inverted_index.considered. | 149 |
| abstract_inverted_index.degenerated | 181, 252 |
| abstract_inverted_index.enhancement | 289 |
| abstract_inverted_index.evaluation. | 134 |
| abstract_inverted_index.performance | 88 |
| abstract_inverted_index.coefficients | 96 |
| abstract_inverted_index.consumption, | 244 |
| abstract_inverted_index.facilitating | 287 |
| abstract_inverted_index.intermediate | 166, 184 |
| abstract_inverted_index.interstitium | 153, 256 |
| abstract_inverted_index.pathological | 212 |
| abstract_inverted_index.pathologists | 59, 116, 128, 262, 273 |
| abstract_inverted_index.quantitative | 246 |
| abstract_inverted_index.segmentation | 72, 91, 147, 265 |
| abstract_inverted_index.Consequently, | 67 |
| abstract_inverted_index.applicability | 108 |
| abstract_inverted_index.compartments, | 175 |
| abstract_inverted_index.significantly | 259 |
| abstract_inverted_index.classification | 33, 226, 276 |
| abstract_inverted_index.learning-based | 16, 45 |
| abstract_inverted_index.tubulointerstitial | 103, 174 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5040874965, https://openalex.org/A5043628933, https://openalex.org/A5001200637, https://openalex.org/A5045000185, https://openalex.org/A5079635419, https://openalex.org/A5112721567, https://openalex.org/A5032007756, https://openalex.org/A5004255843, https://openalex.org/A5045375203, https://openalex.org/A5068174823, https://openalex.org/A5004000376 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I10091056 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.8936574 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |