Colorectal liver metastases (CLM) significantly impact colon cancer patients,
influencing survival based on systemic chemotherapy response. Traditional
methods like tumor grading scores (e.g., tumor regression grade - TRG) for
prognosis suffer from subjectivity, time constraints, and expertise demands.
Current machine learning approaches often focus on radiological data, yet the
relevance of histological images for survival predictions, capturing intricate
tumor microenvironment characteristics, is gaining recognition. To address
these limitations, we propose an end-to-end approach for automated prognosis
prediction using histology slides stained with H&E and HPS. We first employ a
Generative Adversarial Network (GAN) for slide normalization to reduce staining
variations and improve the overall quality of the images that are used as input
to our prediction pipeline. We propose a semi-supervised model to perform
tissue classification from sparse annotations, producing feature maps. We use
an attention-based approach that weighs the importance of different slide
regions in producing the final classification results. We exploit the extracted
features for the metastatic nodules and surrounding tissue to train a prognosis
model. In parallel, we train a vision Transformer (ViT) in a knowledge
distillation framework to replicate and enhance the performance of the
prognosis prediction. In our evaluation on a clinical dataset of 258 patients,
our approach demonstrates superior performance with c-indexes of 0.804 (0.014)
for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in
predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG
classification task, our approach outperforms comparative methods. Our proposed
pipeline can provide automated prognosis for pathologists and oncologists, and
can greatly promote precision medicine progress in managing CLM patients.

我们提出了一个自动预测肠癌患者结直肠癌肝转移的病理预后的端到端方法，利用 H&E 和 HPS 染色的组织学切片；我们使用生成对抗网络对切片进行标准化，采用半监督模型进行组织分类，使用注意力机制加权不同区域进行分类，训练预后模型并在 258 名患者上进行了评估，结果证明我们的方法在预测生存和治疗时间反应方面具有优越性，可推动精准医学在管理结直肠癌肝转移患者中的进展。

半监督的 ViT 知识蒸馏网络与风格转移归一化用于结直肠肝转移生存预测

Semi-supervised ViT knowledge distillation network with style transfer  normalization for colorectal liver metastases survival prediction

Small liver lesions common to colorectal liver metastases (CRLMs) are
challenging for convolutional neural network (CNN) segmentation models,
especially when we have a wide range of slice thicknesses in the computed
tomography (CT) scans. Slice thickness of CT images may vary by clinical
indication. For example, thinner slices are used for presurgical planning when
fine anatomic details of small vessels are required. While keeping the
effective radiation dose in patients as low as possible, various slice
thicknesses are employed in CRLMs due to their limitations. However,
differences in slice thickness across CTs lead to significant performance
degradation in CT segmentation models based on CNNs. This paper proposes a
novel unsupervised attention-based interpolation model to generate intermediate
slices from consecutive triplet slices in CT scans. We integrate segmentation
loss during the interpolation model's training to leverage segmentation labels
in existing slices to generate middle ones. Unlike common interpolation
techniques in CT volumes, our model highlights the regions of interest (liver
and lesions) inside the abdominal CT scans in the interpolated slice. Moreover,
our model's outputs are consistent with the original input slices while
increasing the segmentation performance in two cutting-edge 3D segmentation
pipelines. We tested the proposed model on the CRLM dataset to upsample
subjects with thick slices and create isotropic volume for our segmentation
model. The produced isotropic dataset increases the Dice score in the
segmentation of lesions and outperforms other interpolation approaches in terms
of interpolation metrics.

本文提出了一种新颖的无监督注意力插值模型，用于生成计算机断层扫描中连续三重切片之间的中间切片，以改善基于卷积神经网络的计算机断层扫描分割模型在不同切片厚度上的性能降低问题，并且该模型在处理具有较厚切片的数据时，通过创建等各向异性的数据集，显著提高了病变分割的结果。