Artificial intelligence (AI) models based on pathological slides have great potential to assist pathologists in disease diagnosis and have become an important research direction in the field of medical image analysis. The aim of this study was to develop an AI model based on whole-slide images to predict the stage of colon cancer. In this study, a total of 100 pathological slides of colon cancer patients were collected as the training set, and 421 pathological slides of colon cancer were downloaded from The Cancer Genome Atlas (TCGA) database as the external validation set. Cellprofiler and CLAM tools were used to extract pathological features, and machine learning algorithms and deep learning algorithms were used to construct prediction models. The area under the curve (AUC) of the best machine learning model was 0.78 in the internal test set and 0.68 in the external test set. The AUC of the deep learning model in the internal test set was 0.889, and the accuracy of the model was 0.854. The AUC of the deep learning model in the external test set was 0.700. The prediction model has the potential to generalize in the process of combining pathological omics diagnosis. Compared with machine learning, deep learning has higher recognition and accuracy of images, and the performance of the model is better.

Objectives: Timely and accurate detection of colorectal polyps plays a crucial role in diagnosing and preventing colorectal cancer, a major cause of mortality worldwide. This study introduces a new, lightweight, and efficient framework for polyp detection that combines the Local Outlier Factor (LOF) algorithm for filtering noisy data with the YOLO-v11n deep learning model. Study design: An experimental study leveraging deep learning and outlier removal techniques across multiple public datasets. Methods: The proposed approach was tested on five diverse and publicly available datasets: CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG, ETIS, and EndoScene. Since these datasets originally lacked bounding box annotations, we converted their segmentation masks into suitable detection labels. To enhance the robustness and generalizability of our model, we apply 5-fold cross-validation and remove anomalous samples using the LOF method configured with 30 neighbors and a contamination ratio of 5%. Cleaned data are then fed into YOLO-v11n, a fast and resource-efficient object detection architecture optimized for real-time applications. We train the model using a combination of modern augmentation strategies to improve detection accuracy under diverse conditions. Results: Our approach significantly improves polyp localization performance, achieving a precision of 95.83%, recall of 91.85%, F1-score of 93.48%, [email protected] of 96.48%, and [email protected]:0.95 of 77.75%. Compared to previous YOLO-based methods, our model demonstrates enhanced accuracy and efficiency. Conclusions: These results suggest that the proposed method is well-suited for real-time colonoscopy support in clinical settings. Overall, the study underscores how crucial data preprocessing and model efficiency are when designing effective AI systems for medical imaging.

Renal cancer is amid the several reasons of increasing mortality rates globally, which can be reduced by early detection and diagnosis. The classification of lesions is based mostly on their characteristics, which include varied shape and texture properties. Computed tomography (CT) imaging is a regularly used imaging modality for study of the renal soft tissues. Furthermore, a radiologist's ability to assess a corpus of CT images is limited, which can lead to misdiagnosis of kidney lesions, which might lead to cancer progression or unnecessary chemotherapy. To address these challenges, this study presents a machine learning technique based on a novel feature vector for the automated classification of renal lesions using a multi-model texture-based feature extraction. The proposed feature vector could serve as an integral component in improving the accuracy of a computer aided diagnosis (CAD) system for identifying the texture of renal lesion and can assist physicians in order to provide more precise lesion interpretation. In this work, the authors employed different texture models for the analysis of CT scans, in order to classify benign and malignant kidney lesions. Texture analysis is performed using features such as first-order statistics (FoS), spatial gray level co-occurrence matrix (SGLCM), Fourier power spectrum (FPS), statistical feature matrix (SFM), Law's texture energy measures (TEM), gray level difference statistics (GLDS), fractal, and neighborhood gray tone difference matrix (NGTDM). Multiple texture models were utilized to quantify the renal texture patterns, which used image texture analysis on a selected region of interest (ROI) from the renal lesions. In addition, dimensionality reduction is employed to discover the most discriminative features for categorization of benign and malignant lesions, and a unique feature vector based on correlation-based feature selection, information gain, and gain ratio is proposed. Different machine learning-based classifiers were employed to test the performance of the proposed features, out of which the random forest (RF) model outperforms all other techniques to distinguish benign from malignant tumors in terms of distinct performance evaluation metrics. The final feature set is evaluated using various machine learning classifiers, with the RF model achieving the highest performance. The proposed system is validated on a dataset of 50 subjects, achieving a classification accuracy of 95.8%, outperforming other conventional models.

Accurate liver vessel segmentation is essential for effective liver surgery pre-planning, and reducing surgical risks since it enables the precise localization and extensive assessment of complex vessel structures. Manual liver vessel segmentation is a time-intensive process reliant on operator expertise and skill. The complex, tree-like architecture of hepatic and portal veins, which are interwoven and anatomically variable, further complicates this challenge. This study addresses these challenges by proposing the UNETR (U-Net Transformers) architecture for the multi-class segmentation of portal and hepatic veins in liver CT images. UNETR leverages a transformer-based encoder to effectively capture long-range dependencies, overcoming the limitations of convolutional neural networks (CNNs) in handling complex anatomical structures. The proposed method was evaluated on contrast-enhanced CT images from the IRCAD as well as a locally dataset developed from a hospital. On the local dataset, the UNETR model achieved Dice coefficients of 49.71% for portal veins, 69.39% for hepatic veins, and 76.74% for overall vessel segmentation, while reaching Dice coefficients of 62.54% for vessel segmentation on the IRCAD dataset. These results highlight the method's effectiveness in identifying complex vessel structures across diverse datasets. These findings underscore the critical role of advanced architectures and precise annotations in improving segmentation accuracy. This work provides a foundation for future advancements in automated liver surgery pre-planning, with the potential to enhance clinical outcomes significantly. The implementation code is available on GitHub: https://github.com/saharsarkar/Multiclass-Vessel-Segmentation .

Robot-assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade-off between performing a wider resection in order to reduce the risk of positive surgical margins (PSMs) and performing minimal resection of the nerve bundles that determine functional outcomes, such as incontinence and potency, which affect patients' quality of life. In order to achieve favourable outcomes, a precise understanding of the three-dimensional (3D) anatomy of the prostate, nerve bundles and tumour lesion is needed. This is the protocol for a single-centre feasibility study including a prospective two-arm interventional group (a 3D virtual and a 3D printed prostate model), and a prospective control group. The primary endpoint will be PSM status and the secondary endpoint will be functional outcomes, including incontinence and sexual function. The study will consist of a total of 270 patients: 54 patients will be included in each of the interventional groups (3D virtual, 3D printed models), 54 in the retrospective control group and 108 in the prospective control group. Automated segmentation of prostate gland and lesions will be conducted on multiparametric magnetic resonance imaging (mpMRI) using 'AutoProstate' and 'AutoLesion' deep learning approaches, while manual annotation of the neurovascular bundles, urethra and external sphincter will be conducted on mpMRI by a radiologist. This will result in masks that will be post-processed to generate 3D printed/virtual models. Patients will be allocated to either interventional arm and the surgeon will be given either a 3D printed or a 3D virtual model at the start of the RARP procedure. At the 6-week follow-up, the surgeon will meet with the patient to present PSM status and capture functional outcomes from the patient via questionnaires. We will capture these measures as endpoints for analysis. These questionnaires will be re-administered at 3, 6 and 12 months postoperatively.

To assess artificial intelligence (AI)'s added value in detecting prostate cancer lesions on MRI by comparing radiologists' performance with and without AI assistance. A fully-crossed multi-reader multi-case clinical trial was conducted across three institutions with 10 non-expert radiologists. Biparametric MRI cases comprising T2WI, diffusion-weighted images, and apparent diffusion coefficient were retrospectively collected. Three reading modes were evaluated: AI alone, radiologists alone (unaided), and radiologists with AI (aided). Aided and unaided readings were compared using the Dorfman-Berbaum-Metz method. Reference standards were established by senior radiologists based on pathological reports. Performance was quantified via sensitivity, specificity, and area under the alternative free-response receiver operating characteristic curve (AFROC-AUC). Among 407 eligible male patients (69.5±9.3years), aided reading significantly improved lesion-level sensitivity from 67.3% (95% confidence intervals [CI]: 58.8%, 75.8%) to 85.5% (95% CI: 81.3%, 89.7%), with a substantial difference of 18.2% (95% CI: 10.7%, 25.7%, p<0.001). Case-level specificity increased from 75.9% (95% CI: 68.7%, 83.1%) to 79.5% (95% CI: 74.1%, 84.8%), demonstrating non-inferiority (p<0.001). AFROC-AUC was also higher for aided than unaided reading (86.9% vs 76.1%, p<0.001). AI alone achieved robust performance (AFROC-AUC=83.1%, 95%CI: 79.7%, 86.6%), with lesion-level sensitivity of 88.4% (95% CI: 84.0%, 92.0%) and case-level specificity of 77.8% (95% CI: 71.5%, 83.3%). Subgroup analysis revealed improved detection for lesions with smaller size and lower prostate imaging reporting and data system scores. AI-aided reading significantly enhances lesion detection compared to unaided reading, while AI alone also demonstrates high diagnostic accuracy.

Develop a fusion model based on explainable machine learning, combining multiparametric MRI subregional radiomics and deep learning, to preoperatively predict the lymphovascular invasion status in rectal cancer. We collected data from RC patients with histopathological confirmation from two medical centers, with 301 patients used as a training set and 75 patients as an external validation set. Using K-means clustering techniques, we meticulously divided the tumor areas into multiple subregions and extracted crucial radiomic features from them. Additionally, we employed an advanced Vision Transformer (ViT) deep learning model to extract features. These features were integrated to construct the SubViT model. To better understand the decision-making process of the model, we used the Shapley Additive Properties (SHAP) tool to evaluate the model's interpretability. Finally, we comprehensively assessed the performance of the SubViT model through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the Delong test, comparing it with other models. In this study, the SubViT model demonstrated outstanding predictive performance in the training set, achieving an area under the curve (AUC) of 0.934 (95% confidence interval: 0.9074 to 0.9603). It also performed well in the external validation set, with an AUC of 0.884 (95% confidence interval: 0.8055 to 0.9616), outperforming both subregion radiomics and imaging-based models. Furthermore, decision curve analysis (DCA) indicated that the SubViT model provides higher clinical utility compared to other models. As an advanced composite model, the SubViT model demonstrated its efficiency in the non-invasive assessment of local vascular invasion (LVI) in rectal cancer.

While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F. A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.5). Scans were reconstructed with and without DDMC, and the spherical targets' maximal and mean recovery coefficient (RC) and contrast-to-noise ratio (CNR) were measured. DDMC results in higher RC values in the target spheres. CNR values increase for small, high-motion affected targets but decrease for larger spheres with smaller amplitudes. A sub-analysis shows that DDMC increases the contrast of the sphere along with a 36% increase in background noise. While DDMC significantly enhances contrast (RC), its impact on detectability (CNR) is less profound due to increased background noise. CNR improves for small targets with high motion amplitude, potentially enhancing the detectability of low-uptake lesions. Given that the increased background noise may reduce detectability for targets unaffected by motion, we suggest that DDMC reconstructions are used best in addition to non-DDMC reconstructions.

Diffusion-weighted imaging (DWI) has become a cornerstone of high-resolution rectal MRI, providing critical functional information that complements T2-weighted imaging (T2WI) throughout the management of rectal cancer. From baseline staging to restaging after neoadjuvant therapy and longitudinal surveillance during nonoperative management or post-surgical follow-up, DWI improves tumor detection, characterizes treatment response, and facilitates early identification of tumor regrowth or recurrence. This review offers a comprehensive overview of DWI in rectal cancer, emphasizing its technical characteristics, optimal acquisition strategies, and integration with qualitative and quantitative interpretive frameworks. The manuscript also addresses interpretive pitfalls, highlights emerging techniques such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and small field-of-view DWI, and explores the growing role of radiomics and artificial intelligence in advancing precision imaging. DWI, when rigorously implemented and interpreted, enhances the accuracy, reproducibility, and clinical utility of rectal MRI.

BackgroundHigher-resolution magnetic resonance imaging sequences are needed for the early detection of pancreatic cancer.PurposeTo compare the quality of our novel T2-weighted, high-contrast, thin-slice imaging sequence, with an improved spatial resolution and deep learning-based reconstruction (three-shot turbo spin-echo with deep learning-based reconstruction [3S-TSE-DLR]), for imaging the pancreas with imaging using three conventional sequences (half-Fourier acquisition single-shot turbo spin-echo [HASTE], fat-suppressed 3D T1-weighted [FS-3D-T1W] imaging, and magnetic resonance cholangiopancreatography [MRCP]).Material and MethodsPancreatic images of 50 healthy volunteers acquired with 3S-TSE-DLR, HASTE, FS-3D-T1W imaging, and MRCP were compared by two diagnostic radiologists. A 5-point scale was used for assessing motion artifacts, pancreatic margin sharpness, and the ability to identify the main pancreatic duct (MPD) on 3S-TSE-DLR, HASTE, and FS-3D-T1W imaging, respectively. The ability to identify MPD via MRCP was also evaluated.ResultsArtifact scores (the higher the score, the fewer the artifacts) were significantly higher for 3S-TSE-DLR than for HASTE, and significantly lower for 3S-TSE-DLR than for FS-3D-T1W imaging, for both radiologists. Sharpness scores were significantly higher for 3S-TSE-DLR than for HASTE and FS-3D-T1W imaging, for both radiologists. The rate of identification of MPD was significantly higher for 3S-TSE-DLR than for FS-3D-T1W imaging, for both radiologists, and significantly higher for 3S-TSE-DLR than for HASTE for one radiologist. The rate of identification of MPD was not significantly different between 3S-TSE-DLR and MRCP.Conclusion3S-TSE-DLR provides better image sharpness than conventional sequences, can identify MPD equally as well or better than HASTE, and shows identification performance comparable to that of MRCP.