PURPOSE: To measure shear wave velocities (SWVs) by acoustic radiation force impulse (ARFI) ultrasound elastography in normal kidneys and in hydronephrotic kidneys in young children and to compare SWVs between the hydronephrosis grades. METHODS: This study was approved by an institutional review board, and informed consent was obtained from the parents of all the children included. Children under the age of 24 months were prospectively enrolled. Hydronephrosis grade was evaluated on ultrasonography, and three valid ARFI measurements were attempted using a high-frequency transducer for both kidneys. Hydronephrosis was graded from 0 to 4, and high-grade hydronephrosis was defined as grades 3 and 4. RESULTS: Fifty-one children underwent ARFI measurements, and three valid measurements for both kidneys were obtained in 96% (49/51) of the patients. Nineteen children (38.8%) had no hydronephrosis. Twenty-three children (46.9%) had unilateral hydronephrosis, and seven children (14.3%) had bilateral hydronephrosis. Seven children had ureteropelvic junction obstruction (UPJO). Median SWVs in kidneys with high-grade hydronephrosis (2.02 m/sec) were higher than those in normal kidneys (1.75 m/sec; P=0.027). However, the presence of UPJO did not influence the median SWVs in hydronephrotic kidneys (P=0.362). CONCLUSION: Obtaining ARFI measurements of the kidney is feasible in young children with median SWVs of 1.75 m/sec in normal kidneys. Median SWVs increased in high-grade hydronephrotic kidneys but were not different between hydronephrotic kidneys with and without UPJO.
Acoustics* ; Child* ; Elasticity Imaging Techniques ; Ethics Committees, Research ; Humans ; Hydronephrosis ; Informed Consent ; Kidney* ; Parents ; Prospective Studies ; Transducers ; Ultrasonography

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.

Purpose: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients withOPSCC. Materials and Methods: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. Results: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564–0.959], 0.638 (95% CI, 0.404–0.871), and 0.823 (95% CI, 0.668–0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. Conclusion When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: Magnetic resonance imaging (MRI) can be used for assessing the morphology of the pituitary gland in children with short stature. The purposes of this study were: (1) to determine if pituitary volume (PV) can distinguish patients with growth hormone (GH) deficiency from those with idiopathic short stature (ISS), (2) to validate an association between PV and severity of GH deficiency, and (3) to compare PV between good and poor response groups in children with GH deficiency or ISS after 1 year of treatment. Methods: Data were collected from the medical records of 152 children with GH deficiency or ISS who underwent GH stimulation test, sella MRI, and GH treatment for at least 1 year. Estimated PVs were calculated using the formula of an ellipsoid. We compared the PVs in patients with GH deficiency with those of patients with ISS. In addition, we assessed the association between PV and severity of GH deficiency, and we assessed growth response after treatment. Results: No difference was observed in PV between patients with GH deficiency and those with ISS. The severity of the GH deficiency seemed to be associated with PV (P=0.082), and the height of the pituitary gland was associated with severity of GH deficiency (P<0.005). The PV in the good response group was less than that of the poor response group in patients with GH deficiency (P<0.005), and PV showed no association with responsiveness to GH treatment in patients with ISS (P=0.073). Conclusion The measurement of PV cannot be used for differential diagnosis between GH deficiency and ISS. In patients with GH deficiency, PV tended to be smaller as the severity of GH deficiency increased, but the difference was not significant. PV may be a good response predictor for GH treatment. Further studies, including a radiomics-based approach, will be helpful in elucidating the clinical implications of pituitary morphology in patients with short stature.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Objective: Putaminal iron deposition is an important feature that helps differentiate multiple system atrophy with predominant parkinsonism (MSA-p) from Parkinson’s disease (PD). Most previous studies used visual inspection or quantitative methods with manual manipulation to perform this differentiation. We investigated the value of a new semiautomated diagnostic algorithm using 3T-MR susceptibility-weighted imaging for MSA-p. Methods: This study included 26 MSA-p, 68 PD, and 41 normal control (NC) subjects. The algorithm was developed in 2 steps: 1) determine the image containing the remarkable putaminal margin and 2) calculate the phase-shift values, which reflect the iron concentration. The next step was to identify the best differentiating conditions among several combinations. The highest phaseshift value of each subject was used to assess the most effective diagnostic set. Results: The raw phase-shift values were present along the lateral margin of the putamen in each group. It demonstrates an anterior- to-posterior gradient that was identified most frequently in MSA-p. The average of anterior 5 phase shift values were used for normalization. The highest area under the receiver operating characteristic curve (0.874, 80.8% sensitivity, and 86.7% specificity) of MSA-p versus PD was obtained under the combination of 3 or 4 vertical pixels and one dominant side when the normalization methods were applied. In the subanalysis for the MSA-p patients with a longer disease duration, the performance of the algorithm improved. Conclusion This algorithm detected the putaminal lateral margin well, provided insight into the iron distribution of the putaminal rim of MSA-p, and demonstrated good performance in differentiating MSA-p from PD.