An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

Development and validation of a novel echocardiography-based nomogram for the streamlined classification of cardiac tumors in cancer patients

Background

Differentiating cardiac tumors is crucial for treatment planning, but the specificity of echocardiography as a first-line screening tool is limited. This study aimed to develop a streamlined classification model for cardiac tumors in cancer patients using echocardiographic data.

Methods

A total of 215 echocardiographic clips representing cardiac tumors from 121 patients with extracardiac malignancies were selected and divided into training and testing cohorts. The cardiac neoplasms were classified as benign or malignant based on substantial evidence. Radiomics features were extracted utilizing PyRadiomics, and a radiomics score (Rad-score) was subsequently computed through an optimized machine learning (ML) framework tailored for tumor classification. Non-experience-dependent indicators (NDIs) derived from baseline and echocardiographic assessments were ascertained and integrated with the Rad-score to construct a classification model. A composite nomogram was developed, and its predictive accuracy was benchmarked against that of junior and senior physicians using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

Results

Significant differences in the Rad-scores and four NDIs [age, tumor location, and long and short diameters (SDs)] (all P<0.05) distinguished benign from malignant tumors. Patients with malignant cardiac tumors were more likely to be younger, for the tumor to be in the right cardiac circulatory system, be larger in size, and have a lower Rad-score. Among these indicators, the Rad-score, tumor location, and SD were shown to be independent predictors of malignancy. The integrated model demonstrated strong classification capability [area under the curve (AUC): 0.873; 95% confidence interval (CI): 0.820-0.914], which was substantiated in the test cohort (AUC: 0.861; 95% CI: 0.807-0.904). The classification performance of the generated nomogram was comparable to that of the senior doctor (AUC: 0.867 vs. 0.873, DeLong P=0.928) and surpassed that of the junior doctor (AUC: 0.867 vs. 0.669, DeLong P=0.029). DCA indicated that the nomogram was superior to the junior physician for classification tasks.

Conclusions

This study developed a nomogram that involved radiomics and objective indicators based on echocardiography to effectively distinguish between malignant and benign cardiac tumors, thereby improving classification practices and decision-making in diverse clinical settings.

A narrative review on the application of artificial intelligence in renal ultrasound

Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.