The accurate classification of lymphoma subtypes using hematoxylin and eosin
(H&E)-stained tissue is complicated by the wide range of morphological features
these cancers can exhibit. We present LymphoML - an interpretable machine
learning method that identifies morphologic features that correlate with
lymphoma subtypes. Our method applies steps to process H&E-stained tissue
microarray cores, segment nuclei and cells, compute features encompassing
morphology, texture, and architecture, and train gradient-boosted models to
make diagnostic predictions. LymphoML's interpretable models, developed on a
limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy
to pathologists using whole-slide images and outperform black box deep-learning
on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using
SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each
feature on model prediction and find that nuclear shape features are most
discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma
(F1-score: 74.5%). Finally, we provide the first demonstration that a model
combining features from H&E-stained tissue with features from a standardized
panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a
46-stain panel (86.1%).

利用 H&E 染色组织精确分类淋巴瘤亚型的方法是复杂的，本研究提出了 LymphoML - 一种可解释的机器学习方法，它识别与淋巴瘤亚型相关的形态特征，通过处理 H&E 染色组织微阵列核心、分割细胞核和细胞、计算包括形态学、纹理和结构的特征，并训练梯度增强模型进行诊断预测。LymphoML 的可解释模型在有限量的 H&E 染色组织上达到了与病理学家使用全层切片图像相当的诊断准确性，并在跨越 8 个淋巴瘤亚型的 670 个病例的危地马拉数据集上胜过黑匣子深度学习。通过 SHapley Additive exPlanation (SHAP) 分析，我们评估了每个特征对模型预测的影响，并发现核形状特征对 DLBCL（F1 得分：78.7%）和典型霍奇金淋巴瘤（F1 得分：74.5%）最具鉴别性。最后，我们首次证明了将 H&E 染色组织特征与标准化的 6 种免疫染色特征相结合的模型具有类似的诊断准确性（85.3%）与 46 种免疫染色特征的模型（86.1%）。

LymphoML: 一种可解释的基于人工智能的方法发现与淋巴瘤亚型相关的形态特征

LymphoML: An interpretable artificial intelligence-based method  identifies morphologic features that correlate with lymphoma subtype

Determining lymphoma subtypes is a crucial step for better patients treatment
targeting to potentially increase their survival chances. In this context, the
existing gold standard diagnosis method, which is based on gene expression
technology, is highly expensive and time-consuming making difficult its
accessibility. Although alternative diagnosis methods based on IHC
(immunohistochemistry) technologies exist (recommended by the WHO), they still
suffer from similar limitations and are less accurate. WSI (Whole Slide Image)
analysis by deep learning models showed promising new directions for cancer
diagnosis that would be cheaper and faster than existing alternative methods.
In this work, we propose a vision transformer-based framework for
distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from
high-resolution WSIs. To this end, we propose a multi-modal architecture to
train a classifier model from various WSI modalities. We then exploit this
model through a knowledge distillation mechanism for efficiently driving the
learning of a mono-modal classifier. Our experimental study conducted on a
dataset of 157 patients shows the promising performance of our mono-modal
classification model, outperforming six recent methods from the
state-of-the-art dedicated for cancer classification. Moreover, the power-law
curve, estimated on our experimental data, shows that our classification model
requires a reasonable number of additional patients for its training to
potentially reach identical diagnosis accuracy as IHC technologies.

通过视觉变换器为高分辨率 WSI 从研究多模态分类器训练的 DLBCL（弥漫性大 B 细胞淋巴瘤）癌症亚型提出了一个前景恶劣，比现有的替代方法更便宜，更快速。