Ultrasonic attenuation can be used to characterize tissue properties of the human breast. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can provide attenuation estimation. However, limitations have been identified for both approaches. In QUS, the generation of attenuation maps involves separating the whole image into different data blocks. The optimal size of the data block is around 15 to 30 pulse lengths, which dramatically decreases the spatial resolution for attenuation imaging. In USCT, the attenuation is often estimated with a full wave inversion (FWI) method, which is affected by background noise. In order to achieve a high resolution attenuation image with low variance, a deep learning (DL) based method was proposed. In the approach, RF data from 60 angle views from the QTI Breast Acoustic CT^TM Scanner were acquired as the input and attenuation images as the output. To improve image quality for the DL method, the spatial correlation between speed of sound (SOS) and attenuation were used as a constraint in the model. The results indicated that by including the SOS structural information, the performance of the model was improved. With a higher spatial resolution attenuation image, further segmentation of the breast can be achieved. The structural information and actual attenuation values provided by DL-generated attenuation images were validated with the values from the literature and the SOS-based segmentation map. The information provided by DL-generated attenuation images can be used as an additional biomarker for breast cancer diagnosis.

To compare the predictive value of minimal ablative margin (MAM) quantification using tumor segmentation on intraprocedural contrast-enhanced hepatic arterial (HAP) versus portal venous phase (PVP) CT on local outcomes following percutaneous thermal ablation of colorectal liver metastases (CRLM). This dual-center retrospective study included patients undergoing thermal ablation of CRLM with intraprocedural preablation and postablation contrast-enhanced CT imaging between 2009 and 2021. Tumors were segmented in both HAP and PVP CT phases using an artificial intelligence-based auto-segmentation model and reviewed by a trained radiologist. The MAM was quantified using a biomechanical deformable image registration process. The area under the receiver operating characteristic curve (AUROC) was used to compare the prognostic value for predicting local tumor progression (LTP). Among 81 patients (60 y±13, 53 men), 151 CRLMs were included. During 29.4 months of median follow-up, LTP was noted in 24/151 (15.9%). Median tumor volumes on HAP and PVP CT were 1.7 mL and 1.2 mL, respectively, with respective median MAMs of 2.3 and 4.0 mm (both P< 0.001). The AUROC for 1-year LTP prediction was 0.78 (95% CI: 0.70-0.85) on HAP and 0.84 (95% CI: 0.78-0.91) on PVP (P= 0.002). During CT-guided percutaneous thermal ablation, MAM measured based on tumors segmented on PVP images conferred a higher predictive accuracy of ablation outcomes among CRLM patients than those segmented on HAP images, supporting the use of PVP rather than HAP images for segmentation during ablation of CRLMs.

Limited patient data availability presents a challenge for efficient machine learning (ML) model development. Recent studies have proposed methods to generate synthetic medical images but lack the corresponding prognostic information required for predicting outcomes. We present a cancer outcomes modelling approach that involves generating a comprehensive synthetic dataset which can accurately mimic a real dataset. A real public dataset containing computed tomography-based radiomic features and clinical information for 132 non-small cell lung cancer patients was used. A synthetic dataset of virtual patients was synthesized using a conditional tabular generative adversarial network. Models to predict two-year overall survival were trained on real or synthetic data using combinations of four feature selection methods (mutual information, ANOVA F-test, recursive feature elimination, random forest (RF) importance weights) and six ML algorithms (RF, k-nearest neighbours, logistic regression, support vector machine, XGBoost, Gaussian Naïve Bayes). Models were tested on withheld real data and externally validated. Real and synthetic datasets were similar, with an average one minus Kolmogorov-Smirnov test statistic of 0.871 for continuous features. Chi-square test confirmed agreement for discrete features (p < 0.001). XGBoost using RF importance-based features performed the most consistently for both datasets, with percent differences in balanced accuracy and area under the precision-recall curve of < 1.3%. Preliminary findings demonstrate the potential application of synthetic radiomic and clinical data augmentation for cancer outcomes modelling, although further validation with larger diverse datasets is crucial. While our approach was described in a lung context, it may be applied to other sites or endpoints.

Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes. We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning. The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 - outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions. Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.

The differentiation between multiple system atrophy (MSA) and Parkinson's disease (PD) based on clinical diagnostic criteria can be challenging, especially at an early stage. Leveraging deep learning methods and magnetic resonance imaging (MRI) data has shown great potential in aiding automatic diagnosis. The aim was to determine the feasibility of a three-dimensional convolutional neural network (3D CNN)-based approach using multimodal, multicentric MRI data for differentiating MSA and its variants from PD. MRI data were retrospectively collected from three MSA French reference centers. We computed quantitative maps of gray matter density (GD) from a T1-weighted sequence and mean diffusivity (MD) from diffusion tensor imaging. These maps were used as input to a 3D CNN, either individually ("monomodal," "GD" or "MD") or in combination ("bimodal," "GD-MD"). Classification tasks included the differentiation of PD and MSA patients. Model interpretability was investigated by analyzing misclassified patients and providing a visual interpretation of the most activated regions in CNN predictions. The study population included 92 patients with MSA (50 with MSA-P, parkinsonian variant; 33 with MSA-C, cerebellar variant; 9 with MSA-PC, mixed variant) and 64 with PD. The best accuracies were obtained for the PD/MSA (0.88 ± 0.03 with GD-MD), PD/MSA-C&PC (0.84 ± 0.08 with MD), and PD/MSA-P (0.78 ± 0.09 with GD) tasks. Patients misclassified by the CNN exhibited fewer and milder image alterations, as found using an image-based z score analysis. Activation maps highlighted regions involved in MSA pathophysiology, namely the putamen and cerebellum. Our findings hold promise for developing an efficient, MRI-based, and user-independent diagnostic tool suitable for differentiating parkinsonian syndromes in clinical practice. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Artificial intelligence (AI) is rapidly transforming health care, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication. This study aimed to examine patient perspectives on AI use in health care, particularly in diagnostic processes and communication, identifying key concerns, expectations, and opportunities to guide the development and implementation of AI tools. This study used a qualitative focus group methodology with co-design principles to explore patient and family member perspectives on AI in clinical practice. A single 2-hour session was conducted with 17 adult participants. The session included interactive activities and breakout sessions focused on five specific AI scenarios relevant to diagnosis and communication: (1) portal messaging, (2) radiology review, (3) digital scribe, (4) virtual human, and (5) decision support. The session was audio-recorded and transcribed, with facilitator notes and demographic questionnaires collected. Data were analyzed using inductive thematic analysis by 2 independent researchers (GF and JB), with discrepancies resolved via consensus. Participants reported varying comfort levels with AI applications contingent on the level of patient interaction, with digital scribe (average 4.24, range 2-5) and radiology review (average 4.00, range 2-5) being the highest, and virtual human (average 1.68, range 1-4) being the lowest. In total, five cross-cutting themes emerged: (1) validation (concerns about model reliability), (2) usability (impact on diagnostic processes), (3) transparency (expectations for disclosing AI usage), (4) opportunities (potential for AI to improve care), and (5) privacy (concerns about data security). Participants valued the co-design session and felt they had a significant say in the discussions. This study highlights the importance of incorporating patient perspectives in the design and implementation of AI tools in health care. Transparency, human oversight, clear communication, and data privacy are crucial for patient trust and acceptance of AI in diagnostic processes. These findings inform strategies for individual clinicians, health care organizations, and policy makers to ensure responsible and patient-centered AI deployment in health care.

Forearm fractures constitute a significant proportion of emergency department presentations in pediatric population. The treatment goal is to restore length and alignment between the distal and proximal bone fragments. While immobilization through splinting or casting is enough for non-displaced and minimally displaced fractures. However, moderately or severely displaced fractures often require reduction for realignment. However, appropriate treatment in current practices has challenges due to the lack of resources required for specialized pediatric care leading to delayed and unnecessary transfers between medical centers, which potentially create treatment complications and burdens. The purpose of this study is to build a machine learning model for analyzing forearm fractures to assist clinical centers that lack surgical expertise in pediatric orthopedics. X-ray scans from 1250 children were curated, preprocessed, and manually annotated at our clinical center. Several machine learning models were fine-tuned using a pretraining strategy leveraging self-supervised learning model with vision transformer backbone. We further employed strategies to identify the most important region related to fractures within the forearm X-ray. The model performance was evaluated with and without region of interest (ROI) detection to find an optimal model for forearm fracture analyses. Our proposed strategy leverages self-supervised pretraining (without labels) followed by supervised fine-tuning (with labels). The fine-tuned model using regions cropped with ROI identification resulted in the highest classification performance with a true-positive rate (TPR) of 0.79, true-negative rate (TNR) of 0.74, AUROC of 0.81, and AUPR of 0.86 when evaluated on the testing data. The results showed the feasibility of using machine learning models in predicting the appropriate treatment for forearm fractures in pediatric cases. With further improvement, the algorithm could potentially be used as a tool to assist non-specialized orthopedic providers in diagnosing and providing treatment.

The goal of this study was to develop a highly precise, dynamic machine learning model centered on daily transcranial Doppler ultrasound (TCD) data to predict angiographic vasospasm (AV) in the context of aneurysmal subarachnoid hemorrhage (aSAH). A retrospective review of patients with aSAH treated at a single institution was performed. The primary outcome was AV, defined as angiographic narrowing of any intracranial artery at any time point during admission from risk assessment. Standard demographic, clinical, and radiographic data were collected. Quantitative data including mean arterial pressure, cerebral perfusion pressure, daily serum sodium, and hourly ventriculostomy output were collected. Detailed daily TCD data of intracranial arteries including maximum velocities, pulsatility indices, and Lindegaard ratios were collected. Three predictive machine learning models were created and compared: A static multivariate logistics regression model based on data collected on the date of admission (Baseline Model; BM), a standard TCD model using middle cerebral artery flow velocity and Lindegaard ratio measurements (SM), and a machine learning long short term memory (LSTM) model using all data trended through the hospitalization. A total of 424 patients with aSAH were reviewed, 78 of whom developed AV. In predicting AV at any time point in the future, the LSTM model had the highest precision (0.571) and accuracy (0.776), whereas the SM model had the highest overall performance with an F1 score of 0.566. In predicting AV within 5 days, the LSTM continued to have the highest precision (0.488) and accuracy (0.803). After an ablation test removing all non-TCD elements, the LSTM model improved to a precision of 0.824. Longitudinal TCD data can be used to create a dynamic machine learning model with higher precision than static TCD measurements for predicting AV after aSAH.

Percutaneous transluminal angioplasty and stenting (PTAS) in patients with carotid stenosis may have potential effects on brain parenchyma. However, current studies on parenchymal changes are scarce due to the need for advanced imaging modalities. Consequently, the alterations in brain parenchyma following PTAS remain an unsolved issue. To investigate changes to the brain parenchyma using magnetic resonance elastography (MRE). Prospective. 13 patients (6 women and 7 men; 39 MRI imaging sessions) with severe unilateral carotid stenosis patients indicated for PTAS were recruited between 2021 and 2024. Noncontrast MRI sequences including MRE (spin echo) were acquired using 3 T scanners. All patients underwent MRE before (preprocedural), within 24 h (early postprocedural) and 3 months after (delayed postprocedural) PTAS. Preprocedural and delayed postprocedural ultrasonographic peak systolic velocity (PSV) was recorded. MRE stiffness and damping ratio were evaluated via neural network inversion of the whole brain, in 14 gray matter (GM) and 12 white matter (WM) regions. Stiffness and damping ratio differences between each pair of MR sessions for each subject were identified by paired sample t tests. The correlations of stiffness and damping ratio with stenosis grade and ultrasonographic PSV dynamics were evaluated by Pearson correlation coefficients. The statistical significance was defined as p < 0.05. The stiffness of lesion side insula, deep GM, and deep WM increased significantly from preprocedural to delayed postprocedural MRE. Increasing deep GM stiffness on the lesion side was positively correlated with the DSA stenosis grade significantly (r = 0.609). The lesion side insula stiffness increments were positively correlated with PSV decrements significantly (r = 0.664). Regional brain stiffness increased 3 months after PTAS. Lesion side stiffness was positively correlated with stenosis grades in deep GM and PSV decrements in the insula. EVIDENCE LEVEL: 2. Stage 2.

Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-glucose (18F-FDG) PET/computed tomography (PET/CT) scans. This study aims to investigate the role of noninvasive PET/CT-derived radiomic features in characterizing 18F-FDG PET/CT TIs and distinguishing benign from malignant thyroid lesions in oncological patients. We included 46 patients with PET/CT TIs who underwent thyroid ultrasound and thyroid surgery at our oncological referral hospital. Radiomic features extracted from regions of interest (ROI) in both PET and CT images and analyzed for their association with thyroid cancer and their predictive ability. The TIs were graded using the ultrasound TIRADS classification, and histopathological results served as the reference standard. Univariate and multivariate analyses were performed using features from each modality individually and combined. The performance of radiomic features was compared to the TIRADS classification. Among the 46 included patients, 36 patients (78%) had malignant thyroid lesions, while 10 patients (22%) had benign lesions. The combined run length nonuniformity radiomic feature from PET and CT cubical ROIs demonstrated the highest area under the curve (AUC) of 0.88 (P < 0.05), with a negative correlation with malignancy. This performance was comparable to the TIRADS classification (AUC: 0.84, P < 0.05), which showed a positive correlation with thyroid cancer. Multivariate analysis showed higher predictive performance using CT-derived radiomics (AUC: 0.86 ± 0.13) compared to TIRADS (AUC: 0.80 ± 0.08). This study highlights the potential of 18F-FDG PET/CT-derived radiomics to distinguish benign from malignant thyroid lesions. Further studies with larger cohorts and deep learning-based methods could obtain more robust results.