Recently, Computer-Aided Diagnosis (CAD) systems have emerged as
indispensable tools in clinical diagnostic workflows, significantly alleviating
the burden on radiologists. Nevertheless, despite their integration into
clinical settings, CAD systems encounter limitations. Specifically, while CAD
systems can achieve high performance in the detection of lung nodules, they
face challenges in accurately predicting multiple cancer types. This limitation
can be attributed to the scarcity of publicly available datasets annotated with
expert-level cancer type information. This research aims to bridge this gap by
providing publicly accessible datasets and reliable tools for medical
diagnosis, facilitating a finer categorization of different types of lung
diseases so as to offer precise treatment recommendations. To achieve this
objective, we curated a diverse dataset of lung Computed Tomography (CT)
images, comprising 330 annotated nodules (nodules are labeled as bounding
boxes) from 95 distinct patients. The quality of the dataset was evaluated
using a variety of classical classification and detection models, and these
promising results demonstrate that the dataset has a feasible application and
further facilitate intelligent auxiliary diagnosis.

提供公开数据集和可靠工具，用于精确分类不同类型的肺部疾病，以便提供精确的治疗建议。

基于组织病理学癌症类型注释的肺部结节数据集

A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation

Accurate screening of cancer types is crucial for effective cancer detection
and precise treatment selection. However, the association between gene
expression profiles and tumors is often limited to a small number of biomarker
genes. While computational methods using nature-inspired algorithms have shown
promise in selecting predictive genes, existing techniques are limited by
inefficient search and poor generalization across diverse datasets. This study
presents a framework termed Evolutionary Optimized Diverse Ensemble Learning
(EODE) to improve ensemble learning for cancer classification from gene
expression data. The EODE methodology combines an intelligent grey wolf
optimization algorithm for selective feature space reduction, guided random
injection modeling for ensemble diversity enhancement, and subset model
optimization for synergistic classifier combinations. Extensive experiments
were conducted across 35 gene expression benchmark datasets encompassing varied
cancer types. Results demonstrated that EODE obtained significantly improved
screening accuracy over individual and conventionally aggregated models. The
integrated optimization of advanced feature selection, directed specialized
modeling, and cooperative classifier ensembles helps address key challenges in
current nature-inspired approaches. This provides an effective framework for
robust and generalized ensemble learning with gene expression biomarkers.
Specifically, we have opened EODE source code on Github at
this https URL

准确筛选肿瘤类型对于有效的癌症检测和精确的治疗选择至关重要。本研究提出了一种名为进化优化的多样化集成学习（EODE）的框架，用于改进基因表达数据的癌症分类的集成学习。实验证明，EODE 相对于单独和传统聚合模型，在 35 个基因表达基准数据集上获得了显著提高的筛选准确性。此研究为使用基因表达生物标志物进行强大而广义的集成学习提供了有效的框架。

基于自然启发计算的癌症筛查集成方法的全面利用

Exhaustive Exploitation of Nature-inspired Computation for Cancer  Screening in an Ensemble Manner

Prognostic information at diagnosis has important implications for cancer
treatment and monitoring. Although cancer staging, histopathological
assessment, molecular features, and clinical variables can provide useful
prognostic insights, improving risk stratification remains an active research
area. We developed a deep learning system (DLS) to predict disease specific
survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a
weakly-supervised approach without pixel-level annotations, and tested three
different survival loss functions. The DLS was developed using 9,086 slides
from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In
multivariable Cox regression analysis of the combined cohort including all 10
cancers, the DLS was significantly associated with disease specific survival
(hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer
type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS
remained a significant predictor of survival in 5 of 10 cancer types. Compared
to a baseline model including stage, age, and sex, the c-index of the model
demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined
cohort. Additionally, our models stratified patients within individual cancer
stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing
and evaluating prognostic models across multiple cancer types, this work
represents one of the most comprehensive studies exploring the direct
prediction of clinical outcomes using deep learning and histopathology images.
Our analysis demonstrates the potential for this approach to provide prognostic
information in multiple cancer types, and even within specific pathologic
stages. However, given the relatively small number of clinical events, we
observed wide confidence intervals, suggesting that future work will benefit
from larger datasets.

通过开发深度学习系统，在多种癌症类型中利用组织病理图像预测疾病特异性生存，为癌症治疗和监测提供重要的预后信息。

利用组织病理学图像基于深度学习的多种癌症生存预测

Deep learning-based survival prediction for multiple cancer types using  histopathology images

Background Precise prediction of cancer types is vital for cancer diagnosis
and therapy. Important cancer marker genes can be inferred through predictive
model. Several studies have attempted to build machine learning models for this
task however none has taken into consideration the effects of tissue of origin
that can potentially bias the identification of cancer markers. Results In this
paper, we introduced several Convolutional Neural Network (CNN) models that
take unstructured gene expression inputs to classify tumor and non-tumor
samples into their designated cancer types or as normal. Based on different
designs of gene embeddings and convolution schemes, we implemented three CNN
models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and
tested on combined 10,340 samples of 33 cancer types and 731 matched normal
tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent
prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal).
Furthermore, we interpreted one of the models, known as 1D-CNN model, with a
guided saliency technique and identified a total of 2,090 cancer markers (108
per class). The concordance of differential expression of these markers between
the cancer type they represent and others is confirmed. In breast cancer, for
instance, our model identified well-known markers, such as GATA3 and ESR1.
Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes
and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be
found at this https URL

本研究使用卷积神经网络 (CNN) 模型对 10,340 个来自 33 种不同癌症类型和 731 个正常组织样品进行分类，并在 1D-CNN 模型中确定了 2,090 个癌症标记物，其中包括 GATA3 和 ESR1 等已知标记，并对乳腺癌亚型进行了预测，取得了 88.42% 的平均准确率。