AO_SCPLOWBSTRACTC_SCPLOWThe quantification of the Ki-67 labeling index (LI) is critical for assessing tumor proliferation and prognosis in tumors, yet manual scoring remains a common practice. This study presents an automated workflow for Ki-67 scoring in whole slide images (WSIs) using an Apache Groovy code script for QuPath, complemented by a Python-based post-processing script, providing cell density maps and summary tables. The tissue and cell segmentation are performed using StarDist, a deep learning model, and adaptive thresholding to classify Ki-67 positive and negative nuclei. The pipeline was applied to a cohort of 632 pediatric brain tumor cases with 734 Ki-67-stained WSIs from the Childrens Brain Tumor Network. Medulloblastoma showed the highest Ki-67 LI (median: 19.84), followed by atypical teratoid rhabdoid tumor (median: 19.36). Moderate values were observed in brainstem glioma-diffuse intrinsic pontine glioma (median: 11.50), high-grade glioma (grades 3 & 4) (median: 9.50), and ependymoma (median: 5.88). Lower indices were found in meningioma (median: 1.84), while the lowest were seen in low-grade glioma (grades 1 & 2) (median: 0.85), dysembryoplastic neuroepithelial tumor (median: 0.63), and ganglioglioma (median: 0.50). The results aligned with the consensus of the oncology, demonstrating a significant correlation in Ki-67 LI across most of the tumor families/types, with high malignancy tumors showing the highest proliferation indices and lower malignancy tumors exhibiting lower Ki-67 LI. The automated approach facilitates the assessment of large amounts of Ki-67 WSIs in research settings.

Endovascular Aneurysm Repair (EVAR) is a minimally invasive procedure crucial for treating abdominal aortic aneurysms (AAA), where precise pre-operative planning is essential. Current clinical methods rely on manual measurements, which are time-consuming and prone to errors. Although AI solutions are increasingly being developed to automate aspects of these processes, most existing approaches primarily focus on computing volumes and diameters, falling short of delivering a fully automated pre-operative analysis. This work presents BRAVE (Blood Vessels Recognition and Aneurysms Visualization Enhancement), the first comprehensive AI-driven solution for vascular segmentation and AAA analysis using pre-operative CTA scans. BRAVE offers exhaustive segmentation, identifying both the primary abdominal aorta and secondary vessels, often overlooked by existing methods, providing a complete view of the vascular structure. The pipeline performs advanced volumetric analysis of the aneurysm sac, quantifying thrombotic tissue and calcifications, and automatically identifies the proximal and distal sealing zones, critical for successful EVAR procedures. BRAVE enables fully automated processing, reducing manual intervention and improving clinical workflow efficiency. Trained on a multi-center open-access dataset, it demonstrates generalizability across different CTA protocols and patient populations, ensuring robustness in diverse clinical settings. This solution saves time, ensures precision, and standardizes the process, enhancing vascular surgeons' decision-making.

Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review serves as a practical guide for early-career radiologists and researchers seeking to integrate radiomics into their studies. It provides practical insights for clinical and research applications, addressing common challenges, limitations, and future directions in the field. This work offers a structured overview of the essential steps in the radiomics workflow, focusing on concrete aspects of each step, including indicative and practical examples. It covers the main steps such as dataset definition, image acquisition and preprocessing, segmentation, feature extraction and selection, and AI model training and validation. Different methods to be considered are discussed, accompanied by summary diagrams. This review equips readers with the knowledge necessary to approach radiomics and AI in medical imaging from a hands-on research perspective.

Deep learning networks map input data to output predictions by fitting network parameters using training data. However, applying a trained network to new, unseen inference data resembles an interpolation process, which may lead to inaccurate predictions if the training and inference data distributions differ significantly. This study aims to generally improve the prediction accuracy of deep learning networks on the inference case by bridging the gap between training and inference data. We propose an inference-specific learning strategy to enhance the network learning process without modifying the network structure. By aligning training data to closely match the specific inference data, we generate an inference-specific training dataset, enhancing the network optimization around the inference data point for more accurate predictions. Taking medical image auto-segmentation as an example, we develop an inference-specific auto-segmentation framework consisting of initial segmentation learning, inference-specific training data deformation, and inference-specific segmentation refinement. The framework is evaluated on public abdominal, head-neck, and pancreas CT datasets comprising 30, 42, and 210 cases, respectively, for medical image segmentation. Experimental results show that our method improves the organ-averaged mean Dice by 6.2% (p-value = 0.001), 1.5% (p-value = 0.003), and 3.7% (p-value < 0.001) on the three datasets, respectively, with a more notable increase for difficult-to-segment organs (such as a 21.7% increase for the gallbladder [p-value = 0.004]). By incorporating organ mask-based weak supervision into the training data alignment learning, the inference-specific auto-segmentation accuracy is generally improved compared with the image intensity-based alignment. Besides, a moving-averaged calculation of the inference organ mask during the learning process strengthens both the robustness and accuracy of the final inference segmentation. By leveraging inference data during training, the proposed inference-specific learning strategy consistently improves auto-segmentation accuracy and holds the potential to be broadly applied for enhanced deep learning decision-making.

The tangential field-in-field (FIF) technique is a widely used method in breast radiation therapy, known for its efficiency and the reduced number of fields required in treatment planning. However, it is labor-intensive, requiring manual shaping of the multileaf collimator (MLC) to minimize hot spots. This study aims to develop a novel automated FIF planning approach for tangential breast radiation therapy using Digitally Reconstructed Radiograph (DRR) images. A total of 78 patients were selected to train and test a fluence map prediction model based on U-Net architecture. DRR images were used as input data to predict the fluence maps. The predicted fluence maps for each treatment plan were then converted into MLC positions and exported as Digital Imaging and Communications in Medicine (DICOM) files. These files were used to recalculate the dose distribution and assess dosimetric parameters for both the PTV and OARs. The mean absolute error (MAE) between the predicted and original fluence map was 0.007 ± 0.002. The result of gamma analysis indicates strong agreement between the predicted and original fluence maps, with gamma passing rate values of 95.47 ± 4.27 for the 3 %/3 mm criteria, 94.65 ± 4.32 for the 3 %/2 mm criteria, and 83.4 ± 12.14 for the 2 %/2 mm criteria. The plan quality, in terms of tumor coverage and doses to organs at risk (OARs), showed no significant differences between the automated FIF and original plans. The automated plans yielded promising results, with plan quality comparable to the original.

CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification. We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves. The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<0·0001, 1·55, 1·26-1·90; p<0·0001, and 1·30, 1·06-1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62-0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44-0·71; p<0·0001). CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value. The National Heart, Lung, and Blood Institute, National Institutes of Health.

Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of clinical applications. However, traditional MMD typically requires spectral CT scanners and pre-measured X-ray energy spectra, significantly limiting clinical applicability. To this end, various methods have been developed to perform MMD using conventional (i.e., single-energy, SE) CT systems, commonly referred to as SEMMD. Despite promising progress, most SEMMD methods follow a two-step image decomposition pipeline, which first reconstructs monochromatic CT images using algorithms such as FBP, and then performs decomposition on these images. The initial reconstruction step, however, neglects the energy-dependent attenuation of human tissues, introducing severe nonlinear beam hardening artifacts and noise into the subsequent decomposition. This paper proposes JSover, a fundamentally reformulated one-step SEMMD framework that jointly reconstructs multi-material compositions and estimates the energy spectrum directly from SECT projections. By explicitly incorporating physics-informed spectral priors into the SEMMD process, JSover accurately simulates a virtual spectral CT system from SE acquisitions, thereby improving the reliability and accuracy of decomposition. Furthermore, we introduce implicit neural representation (INR) as an unsupervised deep learning solver for representing the underlying material maps. The inductive bias of INR toward continuous image patterns constrains the solution space and further enhances estimation quality. Extensive experiments on both simulated and real CT datasets show that JSover outperforms state-of-the-art SEMMD methods in accuracy and computational efficiency.

Accurate cortical surface parcellation is essential for elucidating brain organizational principles, functional mechanisms, and the neural substrates underlying higher cognitive and emotional processes. However, the cortical surface is a highly folded complex geometry, and large regional variations make the analysis of surface data challenging. Current methods rely on geometric simplification, such as spherical expansion, which takes hours for spherical mapping and registration, a popular but costly process that does not take full advantage of inherent structural information. In this study, we propose an Attention-guided Deep Graph Convolutional network (ADGCN) for end-to-end parcellation on primitive cortical surface manifolds. ADGCN consists of a deep graph convolutional layer with a symmetrical U-shaped structure, which enables it to effectively transmit detailed information of the original brain map and learn the complex graph structure, help the network enhance feature extraction capability. What's more, we introduce the Squeeze and Excitation (SE) module, which enables the network to better capture key features, suppress unimportant features, and significantly improve parcellation performance with a small amount of computation. We evaluated the model on a public dataset of 100 artificially labeled brain surfaces. Compared with other methods, the proposed network achieves Dice coefficient of 88.53% and an accuracy of 90.27%. The network can segment the cortex directly in the original domain, and has the advantages of high efficiency, simple operation and strong interpretability. This approach facilitates the investigation of cortical changes during development, aging, and disease progression, with the potential to enhance the accuracy of neurological disease diagnosis and the objectivity of treatment efficacy evaluation.

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities for improving stroke management by informing acute interventions and guiding personalized rehabilitation strategies. The location of the stroke is a key predictor of outcomes, yet no clinically deployed tools incorporate lesion location information for outcome prognostication. This study responds to this critical need by introducing a fully automated, three-stage neuroimaging processing and machine learning pipeline that predicts personalized outcomes from clinical imaging in adult ischemic stroke patients. In the first stage, our system automatically processes raw DICOM inputs, registers the brain to a standard template, and uses deep learning models to segment the stroke lesion. In the second stage, lesion location and automatically derived network features are input into statistical models trained to predict long-term impairments from a large independent cohort of lesion patients. In the third stage, a structured PDF report is generated using a large language model that describes the strokes location, the arterial distribution, and personalized prognostic information. We demonstrate the viability of this approach in a proof-of-concept application predicting select cognitive outcomes in a stroke cohort. Brain-behavior models were pre-trained to predict chronic impairment on 28 different cognitive outcomes in a large cohort of patients with focal brain lesions (N=604). The automated pipeline used these models to predict outcomes from clinically acquired MRIs in an independent ischemic stroke cohort (N=153). Starting from raw clinical DICOM images, we show that our pipeline can generate outcome predictions for individual patients in less than 3 minutes with 96% concordance relative to methods requiring manual processing. We also show that prediction accuracy is enhanced using models that incorporate lesion location, lesion-associated network information, and demographics. Our results provide a strong proof-of-concept and lay the groundwork for developing imaging-based clinical tools for stroke outcome prognostication.

BackgroundComputed tomography (CT) is widely used in clinical diagnosis of lung diseases. The automatic segmentation of lesions in CT images aids in the development of intelligent lung disease diagnosis.ObjectiveThis study aims to address the issue of imprecise segmentation in CT images due to the blurred detailed features of lesions, which can easily be confused with surrounding tissues.MethodsWe proposed a promptable segmentation method based on an improved U-Net and Segment Anything model (SAM) to improve segmentation accuracy of lung lesions in CT images. The improved U-Net incorporates a multi-scale attention module based on a channel attention mechanism ECA (Efficient Channel Attention) to improve recognition of detailed feature information at edge of lesions; and a promptable clipping module to incorporate physicians' prior knowledge into the model to reduce background interference. Segment Anything model (SAM) has a strong ability to recognize lesions and pulmonary atelectasis or organs. We combine the two to improve overall segmentation performances.ResultsOn the LUAN16 dataset and a lung CT dataset provided by the Shanghai Chest Hospital, the proposed method achieves Dice coefficients of 80.12% and 92.06%, and Positive Predictive Values of 81.25% and 91.91%, which are superior to most existing mainstream segmentation methods.ConclusionThe proposed method can be used to improve segmentation accuracy of lung lesions in CT images, enhance automation level of existing computer-aided diagnostic systems, and provide more effective assistance to radiologists in clinical practice.