Major depressive disorder (MDD) is a heterogeneous condition; multiple
underlying neurobiological substrates could be associated with treatment
response variability. Understanding the sources of this variability and
predicting outcomes has been elusive. Machine learning has shown promise in
predicting treatment response in MDD, but one limitation has been the lack of
clinical interpretability of machine learning models. We analyzed data from six
clinical trials of pharmacological treatment for depression (total n = 5438)
using the Differential Prototypes Neural Network (DPNN), a neural network model
that derives patient prototypes which can be used to derive treatment-relevant
patient clusters while learning to generate probabilities for differential
treatment response. A model classifying remission and outputting individual
remission probabilities for five first-line monotherapies and three combination
treatments was trained using clinical and demographic data. Model validity and
clinical utility were measured based on area under the curve (AUC) and expected
improvement in sample remission rate with model-guided treatment, respectively.
Post-hoc analyses yielded clusters (subgroups) based on patient prototypes
learned during training. Prototypes were evaluated for interpretability by
assessing differences in feature distributions and treatment-specific outcomes.
A 3-prototype model achieved an AUC of 0.66 and an expected absolute
improvement in population remission rate compared to the sample remission rate.
We identified three treatment-relevant patient clusters which were clinically
interpretable. It is possible to produce novel treatment-relevant patient
profiles using machine learning models; doing so may improve precision medicine
for depression. Note: This model is not currently the subject of any active
clinical trials and is not intended for clinical use.

通过神经网络模型和机器学习技术，我们发现患者的原型可以用于识别与治疗反应相关的患者群体，并生成适当的治疗方案，这可能有助于精准医学治疗抑郁症。

朝着结果驱动的患者亚群：六项抑郁症治疗研究的机器学习分析

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies

Imaging biomarkers offer a non-invasive way to predict the response of
immunotherapy prior to treatment. In this work, we propose a novel type of deep
radiomic features (DRFs) computed from a convolutional neural network (CNN),
which capture tumor characteristics related to immune cell markers and overall
survival. Our study uses four MRI sequences (T1-weighted, T1-weighted
post-contrast, T2-weighted and FLAIR) with corresponding immune cell markers of
151 patients with brain tumor. The proposed method extracts a total of 180 DRFs
by aggregating the activation maps of a pre-trained 3D-CNN within labeled tumor
regions of MRI scans. These features offer a compact, yet powerful
representation of regional texture encoding tissue heterogeneity. A
comprehensive set of experiments is performed to assess the relationship
between the proposed DRFs and immune cell markers, and measure their
association with overall survival. Results show a high correlation between DRFs
and various markers, as well as significant differences between patients
grouped based on these markers. Moreover, combining DRFs, clinical features and
immune cell markers as input to a random forest classifier helps discriminate
between short and long survival outcomes, with AUC of 72\% and
p=2.36$\times$10$^{-5}$. These results demonstrate the usefulness of proposed
DRFs as non-invasive biomarker for predicting treatment response in patients
with brain tumors.

本研究提出了一种基于卷积神经网络的深度放射状特征（DRF），可以从 MRI 图像中捕捉与免疫治疗相关的肿瘤特征并预测治疗反应。将 DRF 与临床特征和免疫细胞标志物组合使用可以在患者病理分组中识别短期和长期生存结果。

带免疫细胞标记物的深度放射组学特征预测胶质瘤患者的生存率

Deep radiomic signature with immune cell markers predicts the survival of glioma patients

Estimation of treatment efficacy of real-world clinical interventions
involves working with continuous outcomes such as time-to-death,
re-hospitalization, or a composite event that may be subject to censoring.
Counterfactual reasoning in such scenarios requires decoupling the effects of
confounding physiological characteristics that affect baseline survival rates
from the effects of the interventions being assessed. In this paper, we present
a latent variable approach to model heterogeneous treatment effects by
proposing that an individual can belong to one of latent clusters with distinct
response characteristics. We show that this latent structure can mediate the
base survival rates and helps determine the effects of an intervention. We
demonstrate the ability of our approach to discover actionable phenotypes of
individuals based on their treatment response on multiple large randomized
clinical trials originally conducted to assess appropriate treatments to reduce
cardiovascular risk.

本文提出了一种潜变量方法，用于模拟异质性治疗效果，并演示其在多个大型随机对照试验中发现个体可操作表型的能力。