Search

Simple Summary: Diagnosis is the most important step in treating and managing kidney cancer, requiring accurate identification, localization, and classification of tumor regions. The selection of appropriate surgical procedures for malignant cases is further based on tumor volume and relative severity. In recent years, machine-learning-based approaches have been proposed to localize, quantify, and stratify kidney tumors using contrast-enhanced computed tomography (CT) images. However, previous studies have largely neglected the integration of patient metadata with clinical images to better diagnose and guide surgical interventions. In the current study, we developed a combined clinical and image-based approach to classify kidney cancers using a publicly available dataset. We show that the inclusion of clinical features alongside medical images improves the performance of kidney tumor classification. We further used clinical data together with a machine-learning approach to predict the expected surgical procedure employed in individual kidney cancer patients. In addition to cancer stage and tumor volume, some surprisingly common demographic features were revealed to be key determinants of the surgical procedure later selected for nephrectomy. Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are a leading cause of mortality worldwide. This study introduces an innovative end-to-end pipeline for automated measurement of Carotid Intima-Media Thickness (CIMT) from ultrasound images, a crucial step in assessing cardiovascular risk. The process begins with the localization of the Region of Interest (ROI) using the You Only Look Once (YOLO) v5, followed by the application of various image enhancement methods. For precise segmentation of the Intima-Media Complex (IMC), a Deep Learning model is employed, featuring an encoder-decoder architecture, Self-Organizing Neural Networks (Self-ONN), multi-stage feature fusion, and deep supervision. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm is then utilized to combine these segmented masks, producing a final mask for precise CIMT measurement. This method achieved a Dice score of 82.03% and an Intersection over Union of 69.55% on the public Carotid Ultrasound Binary Segmentation (CUBS) dataset. Furthermore, the novel CIMT calculation technique demonstrated a mean squared error of 0.049 mm and a mean average error of 0.166 mm. The utilization of YOLOv5 for ROI selection significantly improved the accuracy of CIMT measurements, ensuring the most relevant regions are considered for analysis. The application of the STAPLE algorithm for prediction consensus demonstrates significant promise in producing optimal segmentation masks. In conclusion, this research, along with its continual enhancements, holds promise for facilitating the integration of human expertise and Deep Learning technologies, thereby refining diagnostic processes and contributing to the advancement of healthcare standards. • Complete CIMT solution: ROI detection, IMC segmentation, mask post-processing. • Innovative IMC segmentation: deep supervised fusion in encoder-decoder. • Unique CIMT calculation from segmentation mask. • In-depth interobserver variability analysis on public dataset subset. • Extensive exploration of image enhancement's impact on automation. [ABSTRACT FROM AUTHOR]

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier's probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP 50 , sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP 50. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics. [ABSTRACT FROM AUTHOR]