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Objective Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage versus host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes. Materials and methods A discovery cohort of 1,598 patients (10,485 lesions) and an external validation cohort of 2,440 patients (6,597 lesions) underwent portal-venous-phase CT. After manual segmentation, lesion-level radiomic features were standardized and embedded using t -distributed stochastic neighbor embedding. Bayesian-optimized agglomerative clustering defined morphology-based groups. Concordance with the primary tumor site (lineage) and anatomical environment was quantified using bootstrapped adjusted Rand indices (ARI); the silhouette score assessed clustering quality. Feature-class (shape, intensity, texture) and mask-erosion experiments probed mechanistic drivers. Results Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; p  < 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 versus 0.05; p  < 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64–0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core. Conclusion Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental “imprint” rather than the primary tumor site. Relevance statement Context-aware modeling is essential for reliable radiomic biomarkers and could motivate a two-step AI pipeline that first identifies the organ habitat and refines lineage-specific predictions. Key Points In a large, multicenter cohort, tumors exhibited distinct morphological clustering. These clusters did not align with primary tumor sites (ARI = 0.04). Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37). Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60). Graphical Abstract

Objective This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)—the esophagus, trachea, heart, and aorta—in CT images in the context of radiotherapy planning. Materials and methods We compare a multi-organ segmentation approach and the fusion of multiple single-organ models, each dedicated to one OAR. All were trained using nnU-Net with the default parameters and the full-resolution configuration. We evaluate their robustness with adversarial perturbations, and their generalizability on external datasets, and explore potential biases introduced by expert corrections compared to fully manual delineations. Results The two approaches show excellent performance with an average Dice score of 0.928 for the multi-class setting and 0.930 when fusing the four single-organ models. The evaluation of external datasets and common procedural adversarial noise demonstrates the good generalizability of these models. In addition, expert corrections of both models show significant bias to the original automated segmentation. The average Dice score between the two corrections is 0.93, ranging from 0.88 for the trachea to 0.98 for the heart. Conclusion Both approaches demonstrate excellent performance and generalizability in segmenting four thoracic OARs, potentially improving efficiency in radiotherapy planning. However, the multi-organ setting proves advantageous for its efficiency, requiring less training time and fewer resources, making it a preferable choice for this task. Moreover, corrections of AI segmentation by clinicians may lead to biases in the results of AI approaches. A test set, manually annotated, should be used to assess the performance of such methods. Key Points Question While manual delineation of thoracic organs at risk is labor-intensive, prone to errors, and time-consuming, evaluation of AI models performing this task lacks robustness . Findings The deep-learning model using the nnU-Net framework showed excellent performance, generalizability, and robustness in segmenting thoracic organs in CT, enhancing radiotherapy planning efficiency . Clinical relevance Automatic segmentation of thoracic organs at risk can save clinicians time without compromising the quality of the delineations, and extensive evaluation across diverse settings demonstrates the potential of integrating such models into clinical practice .

We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowing widespread adoption of AI in clinical practice. However, medical AI training paradigms are shifting towards federated learning settings in order to comply with data protection regulations. In a federated scenario, past data is inaccessible to the current user. Thus, we use a deep generative model to generate synthetic examples that protect privacy and explain decisions. Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data.