Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

: Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions

Classification of small renal masses based on CT images and machine learning algorithms

Kidney tumor is among the leading causes of tumors and deaths worldwide. In all kidney tumor cases, an increasing number of small renal masses (SRMs) with a size smaller than 4 cm have been detected and they are becoming a typical problem for radiologists and surgeons. Most SRMs are either of renal angiomyolipoma (AML) or renal cell carcinoma (RCC), the former being benign and the latter being malignant. The malignant ones can be further classified into three types, clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (chRCC). Different kind of renal tumor requires varied treatment and management. In recent years, four-phase computer tomography (CT) has become the standard approach for kidney tumor examination. In most circumstances, classic AMLs and RCCs can be classified by a radiologist reading the CT images. While fat poor angiomyolipomas (fp-AML) set barriers to this classification method due to the loss of typical diagnosis characteristics. Radiologists are also incapable of differentiating malignant tumors. For now, SRM classification is mainly performed by pathological examination, which is time and resource consuming. Machine learning and one of its branch, deep learning, has been extended to medical image processing field. In this paper, support vector machine (SVM) and convolutional neural network (CNN) were respectively used to build models with the input of one of the last three phases of CT images and the combination of them. For the establishment of each model, at least 20% of overall patient cases were picked out randomly as independent testing subset and the rest undertook 10-fold cross validation for an objective and reliable evaluation of the models. It turned out that SVM algorithm using a linear kernel with phase 2 (corticomedullary) images as input acquired an accuracy of 0.93 and a sensitivity of 0.97 on patient’s tumor type prediction of fp-AML/RCC classification. CNN algorithm, consisting of 12 layers including 4 convolutional layers each followed by a max-pooling layer, one flatten layer, and three densely connected layers, with the help of activation functions, dropout strategy, and stochastic gradient descent (SGD) optimization method, achieved an accuracy of 0.85 on pRCC/chRCC/ccRCC categorization with phase 2 images as input. Images of corticomedullary stage were proved to be eligible for classifiers. This can be seen as a breakthrough since it is the first successful application of deep learning networks in renal tumor classification. Meanwhile, these two models were both balanced over different classes and they together provide a comprehensive solution to SRM classification. Given these findings, the two models can be a preliminary step for machine learning and especially deep learning algorithms to assist, improve, and finally revolutionize the conventional clinical decision making process to guide appropriate management and treatment

The relationship between visceral obesity and hepatic steatosis measured by controlled attenuation parameter.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is closely related with obesity. However, obese subjects, generally represented by high BMI, do not always develop NAFLD. A number of possible causes of NAFLD have been studied, but the exact mechanism has not yet been elucidated. METHODS: A total of 304 consecutive subjects who underwent general health examinations including abdominal ultrasonography, transient elastography and abdominal fat computed tomography were prospectively enrolled. Significant steatosis was diagnosed by ultrasonography and controlled attenuation parameter (CAP) assessed by transient elastography. RESULTS: Visceral fat area (VFA) was significantly related to hepatic steatosis assessed by CAP, whereas body mass index (BMI) was related to CAP only in univariate analysis. In multiple logistic regression analysis, VFA (odds ratio [OR], 1.010; 95% confidence interval [CI], 1.001-1.019; P = 0.028) and triglycerides (TG) (OR, 1.006; 95% CI, 1.001-1.011; P = 0.022) were independent risk factors for significant hepatic steatosis. The risk of significant hepatic steatosis was higher in patients with higher VFA: the OR was 4.838 (P200 cm2, compared to patients with a VFA ≤100 cm2. CONCLUSIONS: Our data demonstrated that VFA and TG is significantly related to hepatic steatosis assessed by CAP not BMI. This finding suggests that surveillance for subjects with NAFLD should incorporate an indicator of visceral obesity, and not simply rely on BMI.ope

Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. It has also advanced bone regeneration devices and neuroprosthetics, highlighting its innovation capabilities. While AI advancements in orthopedics are exciting, challenges remain, like the need for standardized surgical system validation protocols, assessing ethical consequences of AI-derived decision-making, and using AI with bioprinting for tissue engineering. Future research should focus on proving the reliability and predictability of the performance of AI-pivoted systems and their adoption within clinical practice. This review synthesizes recent developments and highlights the increasing impact of AI in orthopedic bioengineering and its potential future effectiveness in bone care and beyond

Continuous Measurement of Cerebral Oxygenation with Near-Infrared Spectroscopy after Spontaneous Subarachnoid Hemorrhage

Objective. The aim of our prospective study was to investigate the applicability and the diagnostic value of near-infrared spectroscopy (NIRS) in SAH patients using the cerebral oximeter INVOS 5100C. Methods. Measurement of cerebral oximetry was done continuously after spontaneous SAH. Decrease of regional oxygen saturation (rSO2) was analyzed and interpreted in view of the determined intrinsic and extrinsic factors. Changes of rSO2 values were matched with the values of ICP, tipO2, and TCD and the results of additional neuroimaging. Results. Continuous measurement of rSO2 was performed in nine patients with SAH (7 females and 2 males). Mean measurement time was 8.6 days (range 2–12 days). The clinical course was uneventful in 7 patients without occurrence of CVS. In these patients, NIRS measured constant and stable rSO2 values without relevant alterations. Special findings are demonstrated in 3 cases. Conclusion. Measurement of rSO2 with NIRS is a safe, easy to use, noninvasive additional measurement tool for cerebral oxygenation, which is used routinely during vascular and cardiac surgical procedures. NIRS is applicable over a long time period after SAH, especially in alert patients without invasive probes. Our observations were promising, whereby larger studies are needed to answer the open questions.</jats:p