Non-small cell lung cancer (NSCLC) histological subtypes impact treatment decisions. While pre-surgical histopathological examination is ideal, it's not always possible. CT radiomic analysis shows promise in predicting NSCLC histological subtypes. To predict NSCLC histological subtypes using machine learning and deep learning models using Radiomic features. 422 lung CT scans from The Cancer Imaging Archive (TCIA) were analyzed. Primary neoplasms were segmented by expert radiologists. Using PyRadiomics, 2446 radiomic features were extracted; post-selection, 179 features remained. Machine learning models like logistic regression (LR), Support vector machine (SVM), Random Forest (RF), XGBoost, LightGBM, and CatBoost were employed, alongside a deep neural network (DNN) model. RF demonstrated the highest accuracy at 78 % (95 % CI: 70 %-84 %) and AUC-ROC at 94 % (95 % CI: 90 %-96 %). LightGBM, XGBoost, and CatBoost had AUC-ROC values of 95 %, 93 %, and 93 % respectively. The DNN's AUC was 94.4 % (95 % CI: 94.1 %-94.6 %). Logistic regression had the least efficacy. For histological subtype prediction, random forest, boosting models, and DNN were superior. Quantitative radiomic analysis with machine learning can accurately determine NSCLC histological subtypes. Random forest, ensemble models, and DNNs show significant promise for pre-operative NSCLC classification, which can streamline therapy decisions.

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. Therefore, it is imperative to develop methods that can perform robustly under varying diffusion gradient directions. In this paper, we propose a q-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on q-space sampling augmentation is introduced to fully explore the information between different gradient directions in q- space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying q-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks. SamRobNODDI was compared against seven state-of-the-art methods across 18 diverse q-space sampling schemes. Extensive experimental validations have been conducted under both identical and diverse sampling schemes for training and testing, as well as across varying sampling rates, different loss functions, and multiple network backbones. Results demonstrate that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility in the face of varying q-space sampling schemes.&#xD.

Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potential of CNNs for medical image analysis faces two limitations: they fail to process both local information and wide contextual data adequately, and do not provide explainable AI (XAI) operations that doctors need to accept them in clinics. The researcher developed the MammoFormer framework, which unites transformer-based architecture with multi-feature enhancement components and XAI functionalities within one framework. Seven different architectures consisting of CNNs, Vision Transformer, Swin Transformer, and ConvNext were tested alongside four enhancement techniques, including original images, negative transformation, adaptive histogram equalization, and histogram of oriented gradients. The MammoFormer framework addresses critical clinical adoption barriers of AI mammography systems through: (1) systematic optimization of transformer architectures via architecture-specific feature enhancement, achieving up to 13% performance improvement, (2) comprehensive explainable AI integration providing multi-perspective diagnostic interpretability, and (3) a clinically deployable ensemble system combining CNN reliability with transformer global context modeling. The combination of transformer models with suitable feature enhancements enables them to achieve equal or better results than CNN approaches. ViT achieves 98.3% accuracy alongside AHE while Swin Transformer gains a 13.0% advantage through HOG enhancements

To treat Trochlear Dysplasia (TD), current approaches rely mainly on low-resolution clinical Magnetic Resonance (MR) scans and surgical intuition. The surgeries are planned based on surgeons experience, have limited adoption of minimally invasive techniques, and lead to inconsistent outcomes. We propose a pipeline that generates super-resolved, patient-specific 3D pseudo-healthy target morphologies from conventional clinical MR scans. First, we compute an isotropic super-resolved MR volume using an Implicit Neural Representation (INR). Next, we segment femur, tibia, patella, and fibula with a multi-label custom-trained network. Finally, we train a Wavelet Diffusion Model (WDM) to generate pseudo-healthy target morphologies of the trochlear region. In contrast to prior work producing pseudo-healthy low-resolution 3D MR images, our approach enables the generation of sub-millimeter resolved 3D shapes compatible for pre- and intraoperative use. These can serve as preoperative blueprints for reshaping the femoral groove while preserving the native patella articulation. Furthermore, and in contrast to other work, we do not require a CT for our pipeline - reducing the amount of radiation. We evaluated our approach on 25 TD patients and could show that our target morphologies significantly improve the sulcus angle (SA) and trochlear groove depth (TGD). The code and interactive visualization are available at https://wehrlimi.github.io/sr-3d-planning/.

Accurate mediastinal tumor classification is crucial for treatment planning, but diagnostic performance varies with radiologists' experience and report quality. To evaluate GPT-4's diagnostic accuracy in classifying mediastinal tumors from radiological reports compared to radiologists of different experience levels using radiological reports of varying quality. We conducted a retrospective study of 1,494 patients from five tertiary hospitals with mediastinal tumors diagnosed via chest CT and pathology. Radiological reports were categorized into low-, medium-, and high-quality based on predefined criteria assessed by experienced radiologists. Six radiologists (two residents, two attending radiologists, and two associate senior radiologists) and GPT-4 evaluated the chest CT reports. Diagnostic performance was analyzed overall, by report quality, and by tumor type using Wald χ2 tests and 95% CIs calculated via the Wilson method. GPT-4 achieved an overall diagnostic accuracy of 73.3% (95% CI: 71.0-75.5), comparable to associate senior radiologists (74.3%, 95% CI: 72.0-76.5; p >0.05). For low-quality reports, GPT-4 outperformed associate senior radiologists (60.8% vs. 51.1%, p<0.001). In high-quality reports, GPT-4 was comparable to attending radiologists (80.6% vs.79.4%, p>0.05). Diagnostic performance varied by tumor type: GPT-4 was comparable to radiology residents for neurogenic tumors (44.9% vs. 50.3%, p>0.05), similar to associate senior radiologists for teratomas (68.1% vs. 65.9%, p>0.05), and superior in diagnosing lymphoma (75.4% vs. 60.4%, p<0.001). GPT-4 demonstrated interpretation accuracy comparable to Associate Senior Radiologists, excelling in low-quality reports and outperforming them in diagnosing lymphoma. These findings underscore GPT-4's potential to enhance diagnostic performance in challenging diagnostic scenarios.

Iron and myelin are key biomarkers for studying neurodegenerative and demyelinating brain diseases. Multi-contrast MRI techniques, such as R2* and QSM, are commonly used for iron assessment, with histology as the reference standard, but non-invasive myelin assessment remains challenging. To address this, we developed a deep learning model to generate iron and myelin staining images from in vivo multi-contrast MRI data, with a resolution comparable to ex vivo histology macro-scans. A cadaver head was scanned using a 7T MR scanner to acquire T1-weighted and multi-echo GRE data for R2*, and QSM processing, followed by histological staining for myelin and iron. To evaluate the generalizability of the model, a second cadaver head and two in vivo MRI datasets were included. After MRI-to-histology registration in the training subject, a self-attention generative adversarial network (GAN) was trained to synthesize myelin and iron staining images using various combinations of MRI contrast. The model achieved optimal myelin prediction when combining T1w, R2*, and QSM images. Incorporating the synthesized myelin images improved the subsequent prediction of iron staining. The generated images displayed fine details similar to those in histology data and demonstrated generalizability across healthy control subjects. Synthesized myelin images clearly differentiated myelin concentration between white and gray matter, while synthesized iron staining presented distinct patterns such as particularly high deposition in deep gray matter. This study shows that deep learning can transform MRI data into histological feature images, offering ex vivo insights from in vivo data and contributing to advancements in brain histology research.

To develop a highly accelerated MRI technique for simultaneous quantification of blood flow and T₁ of the brain tissue. A dual-flip-angle phase-contrast stack-of-stars (DFA PC-SOS) sequence was developed for simultaneous acquisition of highly-undersampled data for the quantification of velocity of arterial blood and T₁ mapping of brain tissue. A deep learning-based algorithm, combining hybrid-feature hash encoding implicit neural representation with explicit sparse prior knowledge (INRESP), was used for image reconstruction. Magnitude and phase images were used for T₁ mapping and velocity measurements, respectively. The accuracy of the measurements was assessed in a quantitative phantom and six healthy volunteers. T₁ mapping obtained with DFA PC-SOS showed high correlation and consistency with reference measurements in phantom experiments (y = 0.916× + 4.71, R² = 0.9953, ICC = 0.9963). Blood flow measurements in healthy volunteers demonstrated strong correlation and consistency with reference values measured by SFA PC-SOS (y = 1.04×-0.187, R² = 0.9918, ICC = 0.9967). The proposed technique enabled an acceleration of 16× with high correlation and consistency with fully sampled data in volunteers (T₁: y = 1.06× + 1.44, R² = 0.9815, ICC = 0.9818; flow: y = 1.01×-0.0525, R² = 0.9995, ICC = 0.9998). This study demonstrates the feasibility of 16-fold accelerated simultaneous acquisition for flow quantification and T₁ mapping in the brain. The proposed technique provides a rapid and comprehensive assessment of cerebrovascular diseases with both vascular hemodynamics and surrounding brain tissue characteristics, and has potential to be used in routine clinical applications.

To assess how artificial intelligence (AI) models perform in evaluating bone quality and quantity in the preoperative planning process for dental implants. This review included studies that utilized AI-based assessments of bone quality and/or quantity based on radiographic images in the preoperative phase. Studies published in English before April 2025 were used in this review, which were obtained from searches in PubMed/MEDLINE, Embase, Web of Science, Scopus, and the Cochrane Library, as well as from manual searches. Eleven studies met the inclusion criteria. Five studies focused on bone quality evaluation and six studies included volumetric assessments using AI models. The performance measures included accuracy, sensitivity, specificity, precision, F1 score, and Dice coefficient, and were compared with human expert evaluations. AI models demonstrated high accuracy (76.2%-99.84%), high sensitivity (78.9%-100%), and high specificity (66.2%-99%). AI models have potential for the evaluation of bone quality and quantity, although standardization and external validation studies are lacking. Future studies should propose multicenter datasets, integration into clinical workflows, and the development of refined models to better reflect real-life conditions. AI has the potential to offer clinicians with reliable automated evaluations of bone quality and quantity, with the promise of a fully automated system of implant planning. It may also support preoperative workflows for clinical decision-making based on evidence more efficiently.

Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial plaque segmentation and tissue characterization. The purpose of this study was to evaluate the feasibility and diagnostic accuracy of a deep learning model for plaque segmentation, tissue characterization and identification of LRP. A total of 1,098 IVUS images from 67 patients who underwent IVUS-guided percutaneous coronary intervention were selected for the training group, while 1,100 IVUS images from 100 vessels (88 patients) were used for the validation group. A 7-layer U-Net ++ was applied for automated coronary artery segmentation and tissue characterization. Segmentation and quantification of the external elastic membrane (EEM), lumen and guidewire artifact were performed and compared with manual measurements. Plaque tissue characterization was conducted using integrated backscatter (IB)-IVUS as the gold standard. LRP was defined as %lipid area of ≥65 %. The deep learning model accurately segmented EEM and lumen. AI-predicted %lipid area (R = 0.90, P < 0.001), % fibrosis area (R = 0.89, P < 0.001), %dense fibrosis area (R = 0.81, P < 0.001) and % calcification area (R = 0.89, P < 0.001), showed strong correlation with IB-IVUS measurements. The model predicted LRP with a sensitivity of 62 %, specificity of 94 %, positive predictive value of 69 %, negative predictive value of 92 % and an area under the receiver operating characteristic curve of 0.919 (95 % CI:0.902-0.934), respectively. The deep-learning model demonstrated accurate automatic segmentation and tissue characterization of human coronary arteries, showing promise for identifying LRP.

Computer-generated holography (CGH) is a promising method that modulates user-defined waveforms with digital holograms. An efficient and fast pipeline framework is proposed to synthesize CGH using initial point cloud and MRI data. This input data is reconstructed into volumetric objects that are then input into non-convex Fourier optics optimization algorithms for phase-only hologram (POH) and complex-hologram (CH) generation using alternating projection, SGD, and quasi-Netwton methods. Comparison of reconstruction performance of these algorithms as measured by MSE, RMSE, and PSNR is analyzed as well as to HoloNet deep learning CGH. Performance metrics are shown to be improved by using 2D median filtering to remove artifacts and speckled noise during optimization.