Coronary Artery Disease-Reporting and Data System (CAD-RADS), a standardized reporting system of stenosis severity from coronary computed tomography angiography (CCTA), is performed manually by expert radiologists, being time-consuming and prone to interobserver variability. While deep learning methods automating CAD-RADS scoring have been proposed, radiomics-based machine-learning approaches are lacking, despite their improved interpretability. This study aims to introduce a novel radiomics-based machine-learning approach for automating CAD-RADS scoring from CCTA images with multiplanar reconstruction. This retrospective monocentric study included 251 patients (male 70 %; mean age 60.5 ± 12.7) who underwent CCTA in 2016-2018 for clinical evaluation of CAD. Images were automatically segmented, and radiomic features were extracted. Clinical characteristics were collected. The image dataset was partitioned into training and test sets (90 %-10 %). The training phase encompassed feature scaling and selection, data balancing and model training within a 5-fold cross-validation. A cascade pipeline was implemented for both 6-class CAD-RADS scoring and 4-class therapy-oriented classification (0-1, 2, 3-4, 5), through consecutive sub-tasks. For each classification task the cascade pipeline was applied to develop clinical, radiomic, and combined models. The radiomic, combined and clinical models yielded AUC = 0.88 [0.86-0.88], AUC = 0.90 [0.88-0.90], and AUC = 0.66 [0.66-0.67] for the CAD-RADS scoring, and AUC = 0.93 [0.91-0.93], AUC = 0.97 [0.96-0.97], and AUC = 79 [0.78-0.79] for the therapy-oriented classification. The radiomic and combined models significantly outperformed (DeLong p-value < 0.05) the clinical one in class 1 and 2 (CAD-RADS cascade) and class 2 (therapy-oriented cascade). This study represents the first CAD-RADS classification radiomic model, guaranteeing higher explainability and providing a promising support system in coronary artery stenosis assessment.

Coronary artery calcium scoring (CACS), i.e. quantification of Agatston (AS) or volume score (VS), can be time consuming. The aim of this study was to compare automated, artificial intelligence (AI)-based CACS to manual scoring, in cardiac and chest CT for lung cancer screening. We selected 684 participants (59 ± 4.8 years; 48.8 % men) who underwent cardiac and non-ECG-triggered chest CT, including 484 participants with AS > 0 on cardiac CT. AI-based results were compared to manual AS and VS, by assessing sensitivity and accuracy, intraclass correlation coefficient (ICC), Bland-Altman analysis and Cohen's kappa for classification in AS strata (0;1-99;100-299;≥300). AI showed high CAC detection rate: 98.1% in cardiac CT (accuracy 97.1%) and 92.4% in chest CT (accuracy 92.1%). AI showed excellent agreement with manual AS (ICC:0.997 and 0.992) and manual VS (ICC:0.997 and 0.991), in cardiac CT and chest CT, respectively. In Bland-Altman analysis, there was a mean difference of 2.3 (limits of agreement (LoA):-42.7, 47.4) for AS on cardiac CT; 1.9 (LoA:-36.4, 40.2) for VS on cardiac CT; -0.3 (LoA:-74.8, 74.2) for AS on chest CT; and -0.6 (LoA:-65.7, 64.5) for VS on chest CT. Cohen's kappa was 0.952 (95%CI:0.934-0.970) for cardiac CT and 0.901 (95%CI:0.875-0.926) for chest CT, with concordance in 95.9 and 91.4% of cases, respectively. AI-based CACS shows high detection rate and strong correlation compared to manual CACS, with excellent risk classification agreement. AI may reduce evaluation time and enable opportunistic screening for CAC on low-dose chest CT.

ImportanceBrain MRI is the main imaging modality for pediatric brain tumors (PBTs); however, incomplete MRI exams are common in pediatric neuro-oncology settings and pose a barrier to the development and application of deep learning (DL) models, such as tumor segmentation and prognostic risk estimation. ObjectiveTo evaluate DL-based strategies (image-dropout training and generative image synthesis) and heuristic imputation approaches for handling missing MRI sequences in PBT imaging from clinical acquisition protocols, and to determine their impact on segmentation accuracy and prognostic risk estimation. DesignThis cohort study included 715 patients from the Childrens Brain Tumor Network (CBTN) and BraTS-PEDs, and 43 patients with longitudinal MRI (157 timepoints) from PNOC003/007 clinical trials. We developed a dropout-trained nnU-Net tumor segmentation model that randomly omitted FLAIR and/or T1w (no contrast) sequences during training to simulate missing inputs. We compared this against three imputation approaches: a generative model for image synthesis, copy-substitution heuristics, and zeroed missing inputs. Model-generated tumor volumes from each segmentation method were compared and evaluated against ground truth (expert manual segmentations) and incorporated into time-varying Cox regression models for survival analysis. SettingMulti-institutional PBT datasets and longitudinal clinical trial cohorts. ParticipantsAll patients had multi-parametric MRI and expert manual segmentations. The PNOC cohort had a median of three imaging timepoints and associated clinical data. Main Outcomes and MeasuresSegmentation accuracy (Dice scores), image quality metrics for synthesized scans (SSIM, PSNR, MSE), and survival discrimination (C-index, hazard ratios). ResultsThe dropout model achieved robust segmentation under missing MRI, with [&le;]0.04 Dice drop and a stable C-index of 0.65 compared to complete-input performance. DL-based MRI synthesis achieved high image quality (SSIM > 0.90) and removed artifacts, benefiting visual interpretability. Performance was consistent across cohorts and missing data scenarios. Conclusion and RelevanceModality-dropout training yields robust segmentation and risk-stratification on incomplete pediatric MRI without the computational and clinical complexity of synthesis approaches. Image synthesis, though less effective for these tasks, provides complementary benefits for artifact removal and qualitative assessment of missing or corrupted MRI scans. Together, these approaches can facilitate broader deployment of AI tools in real-world pediatric neuro-oncology settings.

Vision Mamba models promise transformer-level performance at linear computational cost, but their reliance on serializing 2D images into 1D sequences introduces a critical, yet overlooked, design choice: the patch scan order. In medical imaging, where modalities like brain MRI contain strong anatomical priors, this choice is non-trivial. This paper presents the first systematic study of how scan order impacts MRI segmentation. We introduce Multi-Scan 2D (MS2D), a parameter-free module for Mamba-based architectures that facilitates exploring diverse scan paths without additional computational cost. We conduct a large-scale benchmark of 21 scan strategies on three public datasets (BraTS 2020, ISLES 2022, LGG), covering over 70,000 slices. Our analysis shows conclusively that scan order is a statistically significant factor (Friedman test: $\chi^{2}_{20}=43.9, p=0.0016$), with performance varying by as much as 27 Dice points. Spatially contiguous paths -- simple horizontal and vertical rasters -- consistently outperform disjointed diagonal scans. We conclude that scan order is a powerful, cost-free hyperparameter, and provide an evidence-based shortlist of optimal paths to maximize the performance of Mamba models in medical imaging.

Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based approaches have been widely studied for deformable medical image registration and achieved promising results. However, existing deep learning image registration techniques do not theoretically guarantee topology-preserving transformations. This is a key property to preserve anatomical structures and achieve plausible transformations that can be used in real clinical settings. We propose a novel framework for deformable image registration. Firstly, we introduce a novel regulariser based on conformal-invariant properties in a nonlinear elasticity setting. Our regulariser enforces the deformation field to be mooth, invertible and orientation-preserving. More importantly, we strictly guarantee topology preservation yielding to a clinical meaningful registration. Secondly, we boost the performance of our regulariser through coordinate MLPs, where one can view the to-be-registered images as continuously differentiable entities. We demonstrate, through numerical and visual experiments, that our framework is able to outperform current techniques for image registration.

Immunotherapy has become a cornerstone in the treatment of advanced gastric cancer (GC). However, identifying reliable predictive biomarkers remains a considerable challenge. This study demonstrates the potential of integrating multimodal baseline data, including computed tomography scan images and digital H&E-stained pathology images, with biological interpretation to predict the response to immunotherapy-based combination therapy using a multicenter cohort of 298 GC patients. By employing seven machine learning approaches, we developed a radiopathomics signature (RPS) to predict treatment response and stratify prognostic risk in GC. The RPS demonstrated area under the receiver-operating-characteristic curves (AUCs) of 0.978 (95% CI, 0.950-1.000), 0.863 (95% CI, 0.744-0.982), and 0.822 (95% CI, 0.668-0.975) in the training, internal validation, and external validation cohorts, respectively, outperforming conventional biomarkers such as CPS, MSI-H, EBV, and HER-2. Kaplan-Meier analysis revealed significant differences of survival between high- and low-risk groups, especially in advanced-stage and non-surgical patients. Additionally, genetic analyses revealed that the RPS correlates with enhanced immune regulation pathways and increased infiltration of memory B cells. The interpretable RPS provides accurate predictions for treatment response and prognosis in GC and holds potential for guiding more precise, patient-specific treatment strategies while offering insights into immune-related mechanisms.

Accurate identification of bone marrow invasion (BMI) is critical for determining the prognosis of and treatment strategies for lymphoma. Although bone marrow biopsy (BMB) is the current gold standard, its invasive nature and sampling errors highlight the necessity for noninvasive alternatives. We aimed to develop and validate an interpretable machine learning model that integrates clinical data, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) parameters, radiomic features, and deep learning features to predict BMI in lymphoma patients. We included 159 newly diagnosed lymphoma patients (118 from Center I and 41 from Center II), excluding those with prior treatments, incomplete data, or under 18 years of age. Data from Center I were randomly allocated to training (n = 94) and internal test (n = 24) sets; Center II served as an external validation set (n = 41). Clinical parameters, PET/CT features, radiomic characteristics, and deep learning features were comprehensively analyzed and integrated into machine learning models. Model interpretability was elucidated via Shapley Additive exPlanations (SHAPs). Additionally, a comparative diagnostic study evaluated reader performance with and without model assistance. BMI was confirmed in 70 (44%) patients. The key clinical predictors included B symptoms and platelet count. Among the tested models, the ExtraTrees classifier achieved the best performance. For external validation, the combined model (clinical + PET/CT + radiomics + deep learning) achieved an area under the receiver operating characteristic curve (AUC) of 0.886, outperforming models that use only clinical (AUC 0.798), radiomic (AUC 0.708), or deep learning features (AUC 0.662). SHAP analysis revealed that PET radiomic features (especially PET_lbp_3D_m1_glcm_DependenceEntropy), platelet count, and B symptoms were significant predictors of BMI. Model assistance significantly enhanced junior reader performance (AUC improved from 0.663 to 0.818, p = 0.03) and improved senior reader accuracy, although not significantly (AUC 0.768 to 0.867, p = 0.10). Our interpretable machine learning model, which integrates clinical, imaging, radiomic, and deep learning features, demonstrated robust BMI prediction performance and notably enhanced physician diagnostic accuracy. These findings underscore the clinical potential of interpretable AI to complement medical expertise and potentially reduce the reliance on invasive BMB for lymphoma staging.

This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. 50 patients underwent pelvic T2w imaging at 3 Tesla using the following MR sequences in sagittal orientation without antiperistaltic premedication: T2-TSE (time of acquisition [TA]: 2.03-4.00 min), standard HASTE (TA: 0.65-1.10 min), and DL-HASTE (TA: 0.25-0.47 min), with a slice thickness of 3 mm and a varying number of slices (25-45). Three radiologists evaluated the image quality of the three sequences quantitatively and qualitatively. Overall image quality of DL-HASTE (average score: 5) was superior to HASTE and T2-TSE (p < .001). DL-HASTE provided the clearest bladder wall delineation, especially in the apical part of the bladder (p < .001). SNR (36.3 ± 6.3) and CNR (50.3 ± 19.7) were the highest on DL-HASTE, followed by T2-TSE (33.1 ± 6.3 and 44.3 ± 21.0, respectively; p < .05) and HASTE (21.7 ± 5.4 and 35.8 ± 17.5, respectively; p < .01). A limitation of DL-HASTE and HASTE was the susceptibility to urine flow artifact within the bladder, which was absent or only minimal on T2-TSE. Diagnostic confidence in assessment of the bladder was highest with the combination of DL-HASTE and T2-TSE (p < .05). DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising addition to T2-TSE.

To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance. Patients with NPC scheduled for MSI RT workflow were prospectively enrolled. This workflow integrates RT procedures from computed tomography (CT) scan to beam delivery, all performed with the patient on the treatment couch. Workflow performance, tumor response, patient-reported acute toxicities, and quality of life were evaluated. From March 2022 to October 2023, 120 newly diagnosed, nonmetastatic patients with NPC were enrolled. Of these, 117 completed the workflow with a median duration of 23.2 minutes (range, 16.3-45.8). Median translation errors were 0.2 mm (from CT scan to planning approval) and 0.1 mm (during beam delivery). AI-generated contours required minimal revision for the high-risk clinical target volume and organs at risk, minor revision for the involved cervical lymph nodes and low-risk clinical target volume (median Dice similarity coefficients (DSC), 0.98 and 0.94), and more revision for the gross tumor at the primary site and the involved retropharyngeal lymph nodes (median DSC, 0.84). Of 117 AI-generated plans, 108 (92.3%) passed after the first optimization, with ≥97.8% of target volumes receiving ≥100% of the prescribed dose. Dosimetric constraints were met for most organs at risk, except the thyroid and submandibular glands. One hundred and fifteen patients achieved a complete response at week 12 post-RT, while 14 patients reported any acute toxicity as "very severe" from the start of RT to week 12 post-RT. AI-empowered MSI RT workflow for patients with NPC is clinically feasible in a single institutional setting compared with standard, human-based RT workflow.

Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deformable registration between the patient-specific fan-beam CT, fan-beam computed tomography (FBCT), and daily CBCT. Ethos 2.0 allows for plan calculation directly on HyperSight CBCT and uses artificial intelligence-informed tools for daily contouring without the use of a priori information. This breaks an important link between daily adaptive sessions and initial reference plan preparation. This study explores adaptive radiation therapy for spine metastases without prior patient-specific imaging or treatment planning. We hypothesize that adaptive plans can be created when patient-specific positioning and anatomy is incorporated only once the patient has arrived at the treatment unit. An Ethos 2.0 emulator was used to create initial reference plans on 10 patient-specific FBCTs. Reference plans were also created using FBCTs of (1) a library patient with clinically acceptable contours and (2) a water-equivalent phantom with placeholder contours. Adaptive sessions were simulated for each patient using the 3 different starting points. Resulting adaptive plans were compared with determine the significance of patient-specific information prior to the start of treatment. The library patient and phantom reference plans did not generate adaptive plans that differed significantly from the standard workflow for all clinical constraints for target coverage and organ at risk sparing (P > .2). Gamma comparison between the 3 adaptive plans for each patient (3%/3 mm) demonstrated overall similarity of dose distributions (pass rate > 95%), for all but 2 cases. Failures occurred mainly in low-dose regions, highlighting difference in fluence used to achieve the same clinical goals. This study confirmed feasibility of a procedure for treatment of spine metastases that does not rely on previously acquired patient-specific imaging, contours or plan. Reference-free direct-to-treatment workflows are possible and can condense a multistep process to a single location with dedicated resources.