Acute Myeloid Leukemia (AML) is one of the most aggressive types of
hematological neoplasm. To support the specialists' decision about the
appropriate therapy, patients with AML receive a prognostic of outcomes
according to their cytogenetic and molecular characteristics, often divided
into three risk categories: favorable, intermediate, and adverse. However, the
current risk classification has known problems, such as the heterogeneity
between patients of the same risk group and no clear definition of the
intermediate risk category. Moreover, as most patients with AML receive an
intermediate-risk classification, specialists often demand other tests and
analyses, leading to delayed treatment and worsening of the patient's clinical
condition. This paper presents the data analysis and an explainable
machine-learning model to support the decision about the most appropriate
therapy protocol according to the patient's survival prediction. In addition to
the prediction model being explainable, the results obtained are promising and
indicate that it is possible to use it to support the specialists' decisions
safely. Most importantly, the findings offered in this study have the potential
to open new avenues of research toward better treatments and prognostic
markers.

根据病人的生存预测，本研究提供了数据分析和可解释的机器学习模型，以支持确定最适合的治疗方案；该研究的结果表明，这一模型能够安全地支持专家的决策，并有潜力开展新的研究方向，以改善治疗和预测标志物。

一个可解释的模型来支持治疗 AML 的决策协议

An explainable model to support the decision about the therapy protocol  for AML

Accurate survival prediction is crucial for development of precision cancer
medicine, creating the need for new sources of prognostic information.
Recently, there has been significant interest in exploiting routinely collected
clinical and medical imaging data to discover new prognostic markers in
multiple cancer types. However, most of the previous studies focus on
individual data modalities alone and do not make use of recent advances in
machine learning for survival prediction. We present Deep-CR MTLR -- a novel
machine learning approach for accurate cancer survival prediction from
multi-modal clinical and imaging data in the presence of competing risks based
on neural networks and an extension of the multi-task logistic regression
framework. We demonstrate improved prognostic performance of the multi-modal
approach over single modality predictors in a cohort of 2552 head and neck
cancer patients, particularly for cancer specific survival, where our approach
achieves 2-year AUROC of 0.774 and $C$-index of 0.788.

以神经网络和多任务逻辑回归为基础的深度多模态机器学习方法，可提高癌症患者特定生存率的准确预测，其二年 AUROC 为 0.774，C 指数为 0.788。

Deep-CR MTLR: 一个多模态方法用于癌症生存预测与竞争风险

Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with  Competing Risks

The proliferative activity of breast tumors, which is routinely estimated by
counting of mitotic figures in hematoxylin and eosin stained histology
sections, is considered to be one of the most important prognostic markers.
However, mitosis counting is laborious, subjective and may suffer from low
inter-observer agreement. With the wider acceptance of whole slide images in
pathology labs, automatic image analysis has been proposed as a potential
solution for these issues. In this paper, the results from the Assessment of
Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The
challenge was based on a data set consisting of 12 training and 11 testing
subjects, with more than one thousand annotated mitotic figures by multiple
observers. Short descriptions and results from the evaluation of eleven methods
are presented. The top performing method has an error rate that is comparable
to the inter-observer agreement among pathologists.

本研究介绍了自动图像分析在乳腺肿瘤组织分析中的应用，通过分析 AMIDA13 数据集和 11 种不同的算法，提出了一种性能可与病理学家间交互的标准相媲美的图像分析算法。