Diagnostic imaging has gained prominence as potential biomarkers for early
detection and diagnosis in a diverse array of disorders including cancer.
However, existing methods routinely face challenges arising from various
factors such as image heterogeneity. We develop a novel imaging-based
distributional data analysis (DDA) approach that incorporates the probability
(quantile) distribution of the pixel-level features as covariates. The proposed
approach uses a smoothed quantile distribution (via a suitable basis
representation) as functional predictors in a scalar-on-functional quantile
regression model. Some distinctive features of the proposed approach include
the ability to: (i) account for heterogeneity within the image; (ii)
incorporate granular information spanning the entire distribution; and (iii)
tackle variability in image sizes for unregistered images in cancer
applications. Our primary goal is risk prediction in Hepatocellular carcinoma
that is achieved via predicting the change in tumor grades at post-diagnostic
visits using pre-diagnostic enhancement pattern mapping (EPM) images of the
liver. Along the way, the proposed DDA approach is also used for case versus
control diagnosis and risk stratification objectives. Our analysis reveals that
when coupled with global structural radiomics features derived from the
corresponding T1-MRI scans, the proposed smoothed quantile distributions
derived from EPM images showed considerable improvements in sensitivity and
comparable specificity in contrast to classification based on routinely used
summary measures that do not account for image heterogeneity. Given that there
are limited predictive modeling approaches based on heterogeneous images in
cancer, the proposed method is expected to provide considerable advantages in
image-based early detection and risk prediction.

通过开发新的基于图像的分布数据分析方法，结合概率分布和像素级特征的定量回归模型，该论文在肝癌中实现了预测肿瘤分级变化的风险预测，同时改善了敏感性和特异性，为基于图像的早期检测和风险预测提供了优势。

基于分布数据分析的异质图像分类

Heterogeneous Image-based Classification Using Distributional Data  Analysis

Many modern causal questions ask how treatments affect complex outcomes that
are measured using wearable devices and sensors. Current analysis approaches
require summarizing these data into scalar statistics (e.g., the mean), but
these summaries can be misleading. For example, disparate distributions can
have the same means, variances, and other statistics. Researchers can overcome
the loss of information by instead representing the data as distributions. We
develop an interpretable method for distributional data analysis that ensures
trustworthy and robust decision-making: Analyzing Distributional Data via
Matching After Learning to Stretch (ADD MALTS). We (i) provide analytical
guarantees of the correctness of our estimation strategy, (ii) demonstrate via
simulation that ADD MALTS outperforms other distributional data analysis
methods at estimating treatment effects, and (iii) illustrate ADD MALTS'
ability to verify whether there is enough cohesion between treatment and
control units within subpopulations to trustworthily estimate treatment
effects. We demonstrate ADD MALTS' utility by studying the effectiveness of
continuous glucose monitors in mitigating diabetes risks.

我们开发了一种解释性的分布数据分析方法 ADD MALTS，通过匹配和伸缩学习来确保可信和稳健的决策。我们通过模拟演示 ADD MALTS 在估计治疗效果方面的优越性，并且通过研究连续葡萄糖监测仪在缓解糖尿病风险方面的有效性，展示了 ADD MALTS 的实用性。