测序数据的数量和复杂性不断增加，以及药物反应和可测量的残留疾病等其他临床和诊断数据，给有效的临床理解和解释带来了挑战。以儿科 B 细胞前体急性淋巴细胞白血病 (BCP-ALL) 作为用例，我们提出了一个人工智能 (AI) 辅助的临床框架 clinALL，它将基因组和临床数据集成到用户友好的界面中，以支持常规诊断并揭示我们对 1365 例血液肿瘤病例进行了靶向 RNA 测序，主要是来自 AIEOP-BFM ALL 研究的小儿 B 细胞前体急性淋巴细胞白血病 (BCP-ALL)。作为常规诊断的一部分，我们进行了荧光原位杂交 (FISH)、核型分析和 arrayCGH。这些测定的分析结果以及其他临床信息被集成到使用 Bokeh 的交互式网络界面中，其中主图基于基因表达数据的统一流形逼近和投影 (UMAP) 分析。在clinALL的后端，我们分别使用Scikit-learn和PyTorch构建了浅层机器学习模型和深度神经网络。通过应用clinALL，对当前诊断方案下78％的未确定患者进行了分层，并对模糊病例进行了调查。发现了转化见解，包括 BCR::ABL1 阳性患者的 IKZF1plus 状态依赖性亚群，以及 ETV6::RUNX1 阳性患者中复发频率较高的亚群。我们最好的机器学习模型，LDA和类似PASNET的神经网络模型，在预测患者亚组方面实现了97%以上的F1分数。集成基因组和临床数据的人工智能辅助临床框架可以充分利用现有数据，改善即时决策并及时揭示临床相关见解。这种轻量级且易于转移的框架适用于整个转录组数据以及具有成本效益的靶向 RNA-seq，从而能够在发展中国家的小型诊所中高效、公平地提供个性化医疗。德国教育和研究部 (BMBF)，德国研究基金会 (DFG) 和波兰科学基金会。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia.We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively.By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups.An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries.German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.