| アイテムタイプ |
アイテムタイプ（フル）(1) |
| 公開日 |
2025-06-11 |
| タイトル |
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|
タイトル |
Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma |
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言語 |
en |
| 作成者 |
Mayu Shibata
Akihiro Umezawa
Saki Aoto
Kohji Okamura
Michiyo Nasu
Ryuichi Mizuno
Mototsugu Oya
Kei Yura
Shuji Mikami
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| アクセス権 |
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|
アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
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|
権利情報 |
© 2025 by the authors |
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言語 |
en |
| 権利情報 |
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|
権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
This is an open access article under the CC BY-NC-ND license. |
|
言語 |
en |
| 内容記述 |
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|
内容記述タイプ |
Abstract |
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内容記述 |
The histological grading of carcinoma has been one of the central applications of task-specific deep learning in pathology. The deep learning method has pushed away the regression approach, which has been exploited for two-class classification, to address multi-class classification. However, the applicability of the regression approach on multi-class carcinoma grading has not been extensively investigated. Here, we show that the regression approach is sufficiently compatible with classification regarding the four-class grading of clear cell renal cell carcinoma using 11,826 histological image patches from 16 whole slide images. Using convolutional neural network models (DenseNet-121 and Inception-v3), we found that regression models predict as accurately as classification models, achieving an accuracy of 0.990 at the highest, with fewer prediction errors by two or more grades. Furthermore, we found that the predictions by the regression models qualitatively capture intra-tumor heterogeneity of grades using the composite image patches. Our results demonstrate that the regression approach offers advantages in making a core of the multi-class grade prediction tools for practice. |
|
言語 |
en |
| 出版者 |
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出版者 |
Elsevier |
|
言語 |
en |
| 日付 |
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|
日付 |
2025-03 |
|
日付タイプ |
Issued |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
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|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1016/j.reth.2025.01.011 |
| 収録物名 |
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|
収録物名 |
Regenerative Therapy |
|
言語 |
en |
| 巻 |
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|
巻 |
28 |
| 開始ページ |
|
|
開始ページ |
431 |
| 終了ページ |
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|
終了ページ |
437 |
1-s2.0-S2352320425000112-main.pdf (1.6 MB)
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