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Comput Methods Programs Biomed.2020 Jul;195:105637. S0169-2607(20)31470-X. doi: 10.1016/j.cmpb.2020.105637.Epub 2020-07-04.


Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection.

  • Julio Silva-Rodríguez
  • Adrián Colomer
  • María A Sales
  • Rafael Molina
  • Valery Naranjo
PMID: 32653747 DOI: 10.1016/j.cmpb.2020.105637.




BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Furthermore, recent reports indicate that the presence of patterns of the Gleason scale such as the cribriform pattern may also correlate with a worse prognosis compared to other patterns belonging to the Gleason grade 4. Current clinical guidelines have indicated the convenience of highlight its presence during the analysis of biopsies. All these requirements suppose a great workload for the pathologist during the analysis of each sample, which is based on the pathologist's visual analysis of the morphology and organisation of the glands in the tissue, a time-consuming and subjective task. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies. This analysis must include the Gleason grading of local structures, the detection of cribriform patterns, and the Gleason scoring of the whole biopsy.



METHODS: The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns based on the Gleason grading system. In particular, we train from scratch a simple self-design architecture with three filters and a top model with global-max pooling. The cribriform pattern is detected by retraining the set of filters of the last convolutional layer in the network. Subsequently, a biopsy-level prediction map is reconstructed by bi-linear interpolation of the patch-level prediction of the Gleason grades. In addition, from the reconstructed prediction map, we compute the percentage of each Gleason grade in the tissue to feed a multi-layer perceptron which provides a biopsy-level score.


182枚のアノテーションされた全スライド画像からなるSICAPv2データベースにおいて、ゼロから学習した提案アーキテクチャを用いてパッチレベルのグリソングレードのテストセットにおいて、コーエンの二次カッパが0.77であった。この結果は、文献で報告されている以前の結果を上回るものであった。さらに、このモデルは、患者ベースの4つのグループのクロスバリデーションにおいて、微調整された最先端のアーキテクチャのレベルに達している。cribriform pattern detection taskにおいて、ROC曲線下面積は0.82であった。生検グリソンスコアリングについては、テストサブセットで2次コーエンのカッパ0.81を達成した。ゼロから訓練された浅いCNNアーキテクチャは、グリーソン等級分類のための現在の最先端の手法を凌駕している。我々が提案するモデルは、3つの基本ブロック(すなわち、畳み込み層+最大プーリング)を通して低レベルの特徴を抽出することで、前立腺組織の異なるグリーソン等級を特徴づけることができる。各活性化マップを縮小するためにグローバル最大プーリングを使用することは、モデルの複雑さを軽減し、オーバーフィットを回避するための重要な要素であることが示されている。生検のグリーソンスコアリングに関しては、多層パーセプトロンは、文献で使用されている以前の単純なモデルよりも病理医の意思決定をより良くモデル化することが示されています。

RESULTS: In our SICAPv2 database, composed of 182 annotated whole slide images, we obtained a Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason grading with the proposed architecture trained from scratch. Our results outperform previous ones reported in the literature. Furthermore, this model reaches the level of fine-tuned state-of-the-art architectures in a patient-based four groups cross validation. In the cribriform pattern detection task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset. Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification. Our proposed model is capable of characterising the different Gleason grades in prostate tissue by extracting low-level features through three basic blocks (i.e. convolutional layer + max pooling). The use of global-max pooling to reduce each activation map has shown to be a key factor for reducing complexity in the model and avoiding overfitting. Regarding the Gleason scoring of biopsies, a multi-layer perceptron has shown to better model the decision-making of pathologists than previous simpler models used in the literature.

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