The liver is the most involved organ by distant metastasis in colon-rectal
cancer (CRC) patients and it comes necessary to be aware of the mutational
status of the lesions to correctly design the best individual treatment. So
far, efforts have been made in order to develop non-invasive and real-time
methods that permit the analysis of the whole tumor, using new artificial
intelligence tools to analyze the tumor's image obtained by Computed Tomography
(CT) scan. In order to address the current medical workflow, that is biopsy
analysis-based, we propose the first DeepLearning-based exploration, to our
knowledge, of such classification approach from the patient medical imaging. We
propose i) a solid pipeline for managing undersized datasets of available CT
scans and ii) a baseline study for genomics mutation diagnosis support for
preemptive patient follow-up. Our method is able to identify CRC RAS mutation
family from CT images with 0.73 F1 score.

该研究使用人工智能分析计算机断层扫描（CT 扫描）图像，并提出了一种基于深度学习的方法，以准确识别结直肠癌患者肝脏远处转移所涉及的突变和基因组突变诊断，其 F1 评分为 0.73。

利用肝脏 CT 扫描在结直肠癌基因突变分类中的应用

Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation  classification

Digitization of histopathology slides has led to several advances, from easy
data sharing and collaborations to the development of digital diagnostic tools.
Deep learning (DL) methods for classification and detection have shown great
potential, but often require large amounts of training data that are hard to
collect, and annotate. For many cancer types, the scarceness of data creates
barriers for training DL models. One such scenario relates to detecting tumor
metastasis in lymph node tissue, where the low ratio of tumor to non-tumor
cells makes the diagnostic task hard and time-consuming. DL-based tools can
allow faster diagnosis, with potentially increased quality. Unfortunately, due
to the sparsity of tumor cells, annotating this type of data demands a high
level of effort from pathologists. Using weak annotations from slide-level
images have shown great potential, but demand access to a substantial amount of
data as well. In this study, we investigate mitigation strategies for limited
data access scenarios. Particularly, we address whether it is possible to
exploit mutual structure between tissues to develop general techniques, wherein
data from one type of cancer in a particular tissue could have diagnostic value
for other cancers in other tissues. Our case is exemplified by a DL model for
metastatic colon cancer detection in lymph nodes. Could such a model be trained
with little or even no lymph node data? As alternative data sources, we
investigate 1) tumor cells taken from the primary colon tumor tissue, and 2)
cancer data from a different organ (breast), either as is or transformed to the
target domain (colon) using Cycle-GANs. We show that the suggested approaches
make it possible to detect cancer metastasis with no or very little lymph node
data, opening up for the possibility that existing, annotated histopathology
data could generalize to other domains.

该研究使用基于深度学习的模型探讨了在组织分类问题中，通过利用组织之间的结构特征解决数据不足的问题。研究进一步表明，借助少量或无淋巴节点数据，利用原始肿瘤组织或通过 Cycle-GANs 变换的乳腺癌数据训练的模型可以用于发现其他器官的癌症细胞扩散情况。

有限数据访问情境下的深度学习结肠癌检测研究

A Study of Deep Learning Colon Cancer Detection in Limited Data Access  Scenarios

Each year, the treatment decisions for more than 230,000 breast cancer
patients in the U.S. hinge on whether the cancer has metastasized away from the
breast. Metastasis detection is currently performed by pathologists reviewing
large expanses of biological tissues. This process is labor intensive and
error-prone. We present a framework to automatically detect and localize tumors
as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x
100,000 pixels. Our method leverages a convolutional neural network (CNN)
architecture and obtains state-of-the-art results on the Camelyon16 dataset in
the challenging lesion-level tumor detection task. At 8 false positives per
image, we detect 92.4% of the tumors, relative to 82.7% by the previous best
automated approach. For comparison, a human pathologist attempting exhaustive
search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97%
on both the Camelyon16 test set and an independent set of 110 slides. In
addition, we discover that two slides in the Camelyon16 training set were
erroneously labeled normal. Our approach could considerably reduce false
negative rates in metastasis detection.

该研究提出了一种使用卷积神经网络 (CNN) 框架以及基于细胞病理学图像的肿瘤检测，旨在自动地检测、定位肿瘤，并降低转移性癌症检测中的假阴性率。