儘管基於形態特徵的子分型對於子宮內膜癌（endometrial cancer, EC）的風險
預測和治療計劃是一種有用的工具，但這種方法往往高度主觀，難以驗證，且在
預測治療反應方面具有有限的效用。自從分子分類被應用於 EC 後，對於不同的
EC亞型，特別是微衛星不穩定性高（microsatellite instability MSI-high, MSI-H）的
免疫治療逐漸受到關注。MSI 是由於 DNA 不配對修復機制缺陷而導致某些腫瘤
易感突變的狀態。確定 EC 的 MSI 狀態很重要，因為它是一個重要的治療預測指
標。美國國家網絡結直腸癌指南（NCCN guidelines）推薦使用 pembrolizumab 和
nivolumab 治療晚期或復發的 MSI-H/mismatch repair deficient（dMMR）EC 患者。
儘管評估 MSI 對於免疫治療很重要，但由於額外的成本和時間，無法對所有癌症
進行 MSI 檢測。最近，基於深度學習（Deep learning, DL）的形態學分析從全玻
片影像（whole slide images, WSIs）中得到了發展。在這裡，我們旨在評估 DL 在
基於H&E染色的 WSIs 中預測 MSI 狀態的應用，使用了來自癌症基因組圖譜計畫
（The Cancer Genome Atlas）和三軍總醫院數據集中的與 MSI 相關的 EC 樣本。DL
的方法有潛力從 EC 的 H&E 染色的 WSIs 中直接預測 MSI 狀態，這有助於針對性
的免疫治療評估。DL方法在病理學中的應用不僅有助於提高診斷準確性，還可以
應用於個體化治療決策的精準醫學。
本研究引入了 Shuffle Attention-ResNet 作為第一個方法，改進了 create patches
的方法，採用了 modify-FCN 為第二個方法，將 MIL 網路進行了改進，引入了
varMIL 方法作為第三個方法，並將其與 MIL RNN [1], TOAD [2],CLAM [3] 做比
較，其分別測試在四個不同的測試集，TMA core 測試集、TCGA NGS WSIs 測試
集、TCGA PCR WSIs 測試集以及NDMC的 WSIs 測試集。

Although subtype classification based on morphological features is a useful tool for risk prediction and treatment planning in endometrial cancer (EC), it is often highly subjective, difficult to validate, and has limited utility in predicting treatment response. Since molecular classification has been applied to EC, the use of immune therapy, particularly for microsatellite instability-high (MSI-H) EC, has gained attention. MSI is a state of certain tumors prone to mutations due to defects in DNA mismatch repair mechanisms. Determining the MSI status in EC is crucial as it serves as an important predictive indicator for treatment. The National Comprehensive Cancer Network (NCCN) guidelines recommend the use of pembrolizumab and nivolumab for the treatment of advanced or recurrent MSI-H/mismatch repair-deficient (dMMR) EC patients. However, MSI testing cannot be performed on all cancers due to additional costs and time constraints.

Recently, morphological analysis based on deep learning (DL) has emerged for the analysis of whole-slide images (WSIs). In this study, our aim was to evaluate the application of DL in predicting MSI status from H\&E-stained WSIs in EC, using samples from The Cancer Genome Atlas and Tri-Service General Hospital dataset that are associated with MSI. DL methods have the potential to directly predict the MSI status from H\&E-stained WSIs of EC, which can aid in targeted immune therapy assessment. The application of DL methods in pathology not only improves diagnostic accuracy but also enables precision medicine in individualized treatment decision-making.

In conclusion, our study focuses on the application of DL in predicting MSI status from H\&E-stained WSIs of EC. This research demonstrates the potential of DL methods to provide valuable insights for personalized treatment decisions, particularly in the assessment of immune therapy. The integration of DL into pathology holds promise for enhancing diagnostic accuracy and advancing the field of precision medicine.

This study introduces three novel methods to improve the computational pathology on whole-slide images. The first method involves the incorporation of Shuffle Attention-ResNet, which enhances the network architecture to focus on crucial regions within the whole-slide images. The second method improves the creation of patches by employing modified-FCN, a contour-drawing technique for cells, instead of using the conventional OpenCV approach.

Furthermore, the third method addresses the MIL network by implementing the varMIL method from the DeepSMILE research, specifically for the prediction of MSI status in colorectal and breast cancer. These three methods were compared against existing approaches, namely MIL RNN\cite{campanella2019clinical}, TOAD\cite{lu2021ai},and CLAM\cite{lu2021data}, to evaluate their effectiveness.

To assess the performance of the proposed methods, four distinct test datasets were utilized, namely the TMA core test set, TCGA NGS WSIs test set, TCGA PCR WSIs test set, and NDMC's WSIs test set.

By conducting comprehensive evaluations on these datasets, the study aimed to demonstrate the potential advantages and contributions of the introduced methods in terms of data efficiency, weakly supervised learning, and accuracy for computational pathology on whole-slide images. The comparison against existing techniques further provides insights into the relative strengths and limitations of each approach in different testing scenarios.

Overall, the research contributes to the field of computational pathology by presenting novel methods that potentially enhance the accuracy and efficiency of whole-slide image analysis. The evaluation on diverse test datasets offers valuable insights into the generalization and applicability of these methods in real-world scenarios, paving the way for future advancements in computational pathology research and clinical applications.

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