This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT). The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings-fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)-were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis. The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %). This combined ViT-CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.

Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive labeled training data. To address this limitation, our novel approach introduces a dual-branch transformer network operating on two scales, strategically encoding global contextual dependencies while preserving local information. To promote self-supervised learning, our method leverages semantic dependencies between different scales, generating a supervisory signal for inter-scale consistency. Additionally, it incorporates a spatial stability loss within each scale, fostering self-supervised content clustering. While intra-scale and inter-scale consistency losses enhance feature uniformity within clusters, we introduce a cross-entropy loss function atop the clustering score map to effectively model cluster distributions and refine decision boundaries. Furthermore, to account for pixel-level similarities between organ or lesion subpixels, we propose a selective kernel regional attention module as a plug and play component. This module adeptly captures and outlines organ or lesion regions, slightly enhancing the definition of object boundaries. Our experimental results on skin lesion, lung organ, and multiple myeloma plasma cell segmentation tasks demonstrate the superior performance of our method compared to state-of-the-art approaches.

To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols. The Catphan 600 phantom was scanned using a multidetector CT scanner under two protocols: 120 kV/100 mA (CT dose index volume = 3.4 mGy) to simulate low-dose CT, and 70 kV/40 mA (0.27 mGy) to simulate ultralow-dose CT. Raw data were reconstructed into standard 512-matrix images using three methods: filtered back projection (FBP), adaptive statistical iterative reconstruction at 40% intensity (ASIR-V), and deep learning image reconstruction at high intensity (DLIR-H). The Swin2SR super-resolution model was used to generate 2048-matrix images (Swin2SR-2048), while the super-resolution convolutional neural network (SRCNN) model generated 2048-matrix images (SRCNN-2048). The quality of 2048-matrix images generated by the two models (Swin2SR and SRCNN) was compared. Image quality was evaluated by ImQuest software (v7.2.0.0, Duke University) based on line pair clarity, task-based transfer function (TTF), image noise, and noise power spectrum (NPS). At equivalent radiation doses and reconstruction method, Swin2SR-2048 images identified more line pairs than both standard-512 and standard-1024 images. Except for the 0.27 mGy/DLIR-H/standard kernel sequence, TTF-50% of Teflon increased after super-resolution processing. Statistically significant differences in TTF-50% were observed between the standard 512, 1024, and Swin2SR-2048 images (all p < 0.05). Swin2SR-2048 images exhibited lower image noise and NPS_peak compared to both standard 512- and 1024-matrix images, with significant differences observed in all three matrix types (all p < 0.05). Swin2SR-2048 images also demonstrated superior quality compared to SRCNN-2048, with significant differences in image noise (p < 0.001), NPS_peak (p < 0.05), and TTF-50% for Teflon (p < 0.05). Transformer-enhanced 2048-matrix CT images improve spatial resolution and reduce image noise compared to standard-512 and -1024 matrix images.

Stereotactic magnetic resonance guided adaptive radiation therapy (SMART) is a new option for local treatment of unresectable pancreatic ductal adenocarcinoma, showing interesting survival and local control (LC) results. Despite this, some patients will experience early local and/or metastatic recurrence leading to death. We aimed to develop multiparametric prognostic models for these patients. All patients treated in our institution with SMART for an unresectable pancreatic ductal adenocarcinoma between October 21, 2019, and August 5, 2022 were included. Several initial clinical characteristics as well as dosimetric data of SMART were recorded. Radiomics data from 0.35-T simulation magnetic resonance imaging were extracted. All these data were combined to build prognostic models of overall survival (OS) and LC using machine learning algorithms. Eighty-three patients with a median age of 64.9 years were included. A majority of patients had a locally advanced pancreatic cancer (77%). The median OS was 21 months after SMART completion and 27 months after chemotherapy initiation. The 6- and 12-month post-SMART OS was 87.8% (IC95%, 78.2%-93.2%) and 70.9% (IC95%, 58.8%-80.0%), respectively. The best model for OS was the Cox proportional hazard survival analysis using clinical data, with a concordance index inverse probability of censoring weighted of 0.87. Tested on its 12-month OS prediction capacity, this model had good performance (sensitivity 67%, specificity 71%, and area under the curve 0.90). The median LC was not reached. The 6- and 12-month post-SMART LC was 92.4% [IC95%, 83.7%-96.6%] and 76.3% [IC95%, 62.6%-85.5%], respectively. The best model for LC was the component-wise gradient boosting survival analysis using clinical and radiomics data, with a concordance index inverse probability of censoring weighted of 0.80. Tested on its 9-month LC prediction capacity, this model had good performance (sensitivity 50%, specificity 97%, and area under the curve 0.78). Combining clinical and radiomics data in multiparametric prognostic models using machine learning algorithms showed good performance for the prediction of OS and LC. External validation of these models will be needed.

Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosis. The Peritoneal Cancer Index (PCI) is used to evaluate the extent of PM and to select Cytoreductive Surgery (CRS). However, PCI score is not accurate to guide patient's selection for CRS. We have developed a novel AI framework of decoupling feature alignment and fusion (DeAF) by deep learning to aid selection of PM patients and predict surgical completeness of CRS. 186 CRC patients with PM recruited from four tertiary hospitals were enrolled. In the training cohort, deep learning was used to train the DeAF model using Simsiam algorithms by contrast CT images and then fuse clinicopathological parameters to increase performance. The accuracy, sensitivity, specificity, and AUC by ROC were evaluated both in the internal validation cohort and three external cohorts. The DeAF model demonstrated a robust accuracy to predict the completeness of CRS with AUC of 0.9 (95 % CI: 0.793-1.000) in internal validation cohort. The model can guide selection of suitable patients and predict potential benefits from CRS. The high predictive performance in predicting CRS completeness were validated in three external cohorts with AUC values of 0.906(95 % CI: 0.812-1.000), 0.960(95 % CI: 0.885-1.000), and 0.933 (95 % CI: 0.791-1.000), respectively. The novel DeAF framework can aid surgeons to select suitable PM patients for CRS and predict the completeness of CRS. The model can change surgical decision-making and provide potential benefits for PM patients.

Information on deep learning (DL) tumor segmentation accuracy on a voxel and a structure level is essential for clinical introduction. In a previous study, a DL model was developed for oropharyngeal cancer (OPC) primary tumor (PT) segmentation in PET/CT images and voxel-level predicted probabilities (TPM) quantifying model certainty were introduced. This study extended the network to simultaneously generate TPMs for PT and pathologic lymph nodes (PL) and explored whether structure-level uncertainty in TPMs predicts segmentation model accuracy in an independent external cohort. We retrospectively gathered PET/CT images and manual delineations of gross tumor volume of the PT (GTVp) and PL (GTVln) of 407 OPC patients treated with (chemo)radiation in our institute. The HECKTOR 2022 challenge dataset served as external test set. The pre-existing architecture was modified for multi-label segmentation. Multiple models were trained, and the non-binarized ensemble average of TPMs was considered per patient. Segmentation accuracy was quantified by surface and aggregate DSC, model uncertainty by coefficient of variation (CV) of multiple predictions. Predicted GTVp and GTVln segmentations in the external test achieved 0.75 and 0.70 aggregate DSC. Patient-specific CV and surface DSC showed a significant correlation for both structures (-0.54 and -0.66 for GTVp and GTVln) in the external set, indicating significant calibration. Significant accuracy versus uncertainty calibration was achieved for TPMs in both internal and external test sets, indicating the potential use of quantified uncertainty from TPMs to identify cases with lower GTVp and GTVln segmentation accuracy, independently of the dataset.

The identification of early micro-lesions and adjacent blood vessels in CT scans plays a pivotal role in the clinical diagnosis of pancreatic cancer, considering its aggressive nature and high fatality rate. Despite the widespread application of deep learning methods for this task, several challenges persist: (1) the complex background environment in abdominal CT scans complicates the accurate localization of potential micro-tumors; (2) the subtle contrast between micro-lesions within pancreatic tissue and the surrounding tissues makes it challenging for models to capture these features accurately; and (3) tumors that invade adjacent blood vessels pose significant barriers to surgical procedures. To address these challenges, we propose LUNETR (Language-Infused UNETR), an advanced multimodal encoder model that combines textual and image information for precise medical image segmentation. The integration of an autoencoding language model with cross-attention enabling our model to effectively leverage semantic associations between textual and image data, thereby facilitating precise localization of potential pancreatic micro-tumors. Additionally, we designed a Multi-scale Aggregation Attention (MSAA) module to comprehensively capture both spatial and channel characteristics of global multi-scale image data, enhancing the model's capacity to extract features from micro-lesions embedded within pancreatic tissue. Furthermore, in order to facilitate precise segmentation of pancreatic tumors and nearby blood vessels and address the scarcity of multimodal medical datasets, we collaborated with Zhuzhou Central Hospital to construct a multimodal dataset comprising CT images and corresponding pathology reports from 135 pancreatic cancer patients. Our experimental results surpass current state-of-the-art models, with the incorporation of the semantic encoder improving the average Dice score for pancreatic tumor segmentation by 2.23 %. For the Medical Segmentation Decathlon (MSD) liver and lung cancer datasets, our model achieved an average Dice score improvement of 4.31 % and 3.67 %, respectively, demonstrating the efficacy of the LUNETR.

Accurate segmentation of three-dimensional (3D) clinical medical images is critical for the diagnosis and treatment of spinal diseases. However, the complexity of spinal anatomy and the inherent uncertainties of current imaging technologies pose significant challenges for the semantic segmentation of spinal images. Although convolutional neural networks (CNNs) and Transformer-based models have achieved remarkable progress in spinal segmentation, their limitations in modeling long-range dependencies hinder further improvements in segmentation accuracy. To address these challenges, we propose a novel framework, SpineMamba, which incorporates a residual visual Mamba layer capable of effectively capturing and modeling the deep semantic features and long-range spatial dependencies in 3D spinal data. To further enhance the structural semantic understanding of the vertebrae, we also propose a novel spinal shape prior module that captures specific anatomical information about the spine from medical images, significantly enhancing the model's ability to extract structural semantic information of the vertebrae. Extensive comparative and ablation experiments across three datasets demonstrate that SpineMamba outperforms existing state-of-the-art models. On two computed tomography (CT) datasets, the average Dice similarity coefficients achieved are 94.40±4% and 88.28±3%, respectively, while on a magnetic resonance (MR) dataset, the model achieves a Dice score of 86.95±10%. Notably, SpineMamba surpasses the widely recognized nnU-Net in segmentation accuracy, with a maximum improvement of 3.63 percentage points. These results highlight the precision, robustness, and exceptional generalization capability of SpineMamba.

This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium-zinc-telluride (CZT)-based photon-counting-detector-CT (PCD-CT) and comparing these super-high-resolution (SHR) images with conventional normal-resolution (NR) CT images. Twenty-five patients (median age 75 years, interquartile range 66-78 years, 18 men and 7 women) with 29 lung nodules overall (including two patients with 4 and 2 nodules, respectively) were enrolled to undergo PCD-CT. Three types of images were reconstructed: a 512 × 512 matrix with adaptive iterative dose reduction 3D (AIDR 3D) as the NR_AIDR3D image, a 1024 × 1024 matrix with AIDR 3D as the SHR_AIDR3D image, and a 1024 × 1024 matrix with deep-learning reconstruction (DLR) as the SHR_DLR image. For qualitative analysis, two radiologists evaluated the matched reconstructed series twice (NR_AIDR3D vs. SHR_AIDR3D and SHR_AIDR3D vs. SHR_DLR) and scored the presence of imaging findings, such as spiculation, lobulation, appearance of ground-glass opacity or air bronchiologram, image quality, and diagnostic confidence, using a 5-point Likert scale. For quantitative analysis, contrast-to-noise ratios (CNRs) of the three images were compared. In the qualitative analysis, compared to NR_AIDR3D, SHR_AIDR3D yielded higher image quality and diagnostic confidence, except for image noise (all P < 0.01). In comparison with SHR_AIDR3D, SHR_DLR yielded higher image quality and diagnostic confidence (all P < 0.01). In the quantitative analysis, CNRs in the modified NR_AIDR3D and SHR_DLR groups were higher than those in the SHR_AIDR3D group (P = 0.003, <0.001, respectively). In PCD-CT, SHR_DLR images provided the highest image quality and diagnostic confidence for lung tumor evaluation, followed by SHR_AIDR3D and NR_AIDR3D images. DLR demonstrated superior noise reduction compared to other reconstruction methods.

Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial brachytherapy cervical cancer. However, the prominent differences in gray values due to the interstitial needles bring great challenges on deep learning-based segmentation model. In this study, we proposed a novel interstitial-guided segmentation network termed advance reverse guided network (ARGNet) for cervical tumor segmentation with interstitial brachytherapy. Firstly, the location information of interstitial needles was integrated into the deep learning framework via multi-task by a cross-stitch way to share encoder feature learning. Secondly, a spatial reverse attention mechanism is introduced to mitigate the distraction characteristic of needles on tumor segmentation. Furthermore, an uncertainty area module is embedded between the skip connections and the encoder of the tumor segmentation task, which is to enhance the model's capability in discerning ambiguous boundaries between the tumor and the surrounding tissue. Comprehensive experiments were conducted retrospectively on 191 CT scans under multi-course interstitial brachytherapy. The experiment results demonstrated that the characteristics of interstitial needles play a role in enhancing the segmentation, achieving the state-of-the-art performance, which is anticipated to be beneficial in radiotherapy planning.