Sonographer vs. radiologist: What does it make?

• MoreBy Amerigo Allegretto, AuntMinnie.com staff writer

May 4, 2021 -- The clinical outcomes of children presenting with suspected acute appendicitis were similar regardless of whether they were scanned by sonographers or radiologists, according to research published April 29 in the Journal of the American College of Radiology.

In a study led by Dr. Leah Gilligan from Northwestern University in Illinois, researchers found that differences between radiologists and sonographers did not lead to clinically important outcomes in children undergoing ultrasound for suspected acute appendicitis. This also includes hospital readmission, surgical consultation, and appendectomy performance.

The team noted that this could also be because clinical care pathways could be sufficiently robust and that deviations in the performance of sonographers or radiologists are corrected by redundant safety nets.

"In other words, although sonographers and radiologists are known to vary in performance and interpretation skill, those differences do not seem to translate into meaningful differences in major clinical care outcomes for this indication," Gilligan colleagues wrote.

The team analyzed 9,283 appendix ultrasound scans with a mean patient age of 9.9 years; 58.2% of the patients were boys. For the study, ultrasound scans were performed by 31 sonographers and interpreted by another 31 radiologists.

The group found no statistically significant difference in outcomes between sonographers and radiologists. For example, children had the same appendectomy rate (20.3%) for both sonographers and radiologists, while the hospital admission frequency was also similar: 34% for sonographers and 33.5% for radiologists.

Despite the differences between sonographers and radiologists not being statistically significant, numerous other clinical and system factors do seem to be associated with these outcomes, the researchers found. Some of these were anticipated, such as ultrasound report impression, degree of abdominal tenderness, and white blood cell count.

One unanticipated factor that researchers noted was whether or not imaging was performed at the main hospital versus a satellite hospital.

They found that presentation to the satellite emergency department was associated with a decreased odds of hospital admission and surgical consultation, as well as an increased odds of hospital readmission within 30 days after adjusting for numerous clinical variables and system factors. The satellite hospital is staffed by the same sonographers and radiologists that work at the main hospital.

"As our study was not primarily designed to specifically investigate the impact of the location of imaging, the exact cause of these differences is unknown," they said.

The team also showed that assessing differences "probably" should not be used as a meaningful quality indicator in radiology department members performing and interpreting appendix ultrasound.

These results are potentially important because appendix ultrasound is widely performed and is a first-line test for suspected appendicitis at most dedicated pediatric hospitals, Gilligan and et al wrote.

AI can help to classify masses found on breast ultrasound

By Erik L. Ridley, AuntMinnie.com staff writer

May 3, 2021 -- Artificial intelligence (AI) software can aid radiologists in characterizing masses on screening breast ultrasound exams by improving cancer detection and reducing false positives, according to research from Yale University.

A research team led by Dr. Liane Philpotts retrospectively compared the performance of a commercial AI software algorithm with the original interpreting radiologist on over 200 lesions found on breast ultrasound. The group found that the software would have correctly classified all malignant cases and downgraded many lesions deemed initially to be suspicious.

"AI software appears to be a complementary tool for radiologists," Philpotts said during a presentation at the recent annual meeting of the American Roentgen Ray Society (ARRS). "Utilization of an AI decision support tool for whole-breast ultrasound findings could result in shifts away from the BI-RADS 3 category with the potential to increase the percentage of lesions characterized as benign, therefore increasing the sensitivity for malignant lesions."

Whole-breast screening ultrasound is becoming more commonplace across the U.S. and around the world, Philpotts said. Many states have passed laws regarding the notification of women with dense breasts, and in 2019, the U.S. Food and Drug Administration proposed national changes to the Mammography Quality Standards Act (MQSA) to require that women be notified of their breast density status.

"While these changes have increased the utilization of whole-breast screening ultrasound, the management of incidental solid masses found during these examinations is not well established," Philpotts added.

In their study, the researchers sought to establish a baseline performance for radiologists managing these masses and to determine whether an AI system -- Koios DS for Breast from Koios Medical -- could be used to improve diagnostic accuracy, Philpotts said. Lev Barinov, PhD, of Koios was also a co-author on the study.

Although the software is intended for use as an adjunct during radiologist interpretation, the researchers wanted to evaluate its theoretical benefit by retrospectively and independently assessing its potential impact, if any, on lesion management recommendations, Philpotts said.

"This type of analysis allows us to begin to set the bounds on the impact such systems will have on the interpretation of ultrasound studies," she said.

The researchers gathered cases from October 1, 2017, to September 30, 2018, of women with dense breasts that were interpreted as negative on digital breast tomosynthesis and who subsequently received whole-breast screening ultrasound. A total of 206 lesions of BI-RADS 3 or higher from 206 patients were included in the analysis. For the purposes of the study, ground truth was established via pathological results or an average of 15 months follow-up.

Of the 206 lesions, 162 were diagnosed as BI-RADS 3 (probably benign) by the radiologist and 44 were deemed to be BI-RADS 4 (suspicious). There were seven malignant lesions, two of which were classified by the original interpreting radiologist as BI-RADS 3 and five of which were categorized as BI-RADS 4. The remaining 109 lesions were benign. 

All identified lesions were anonymized and annotated with regions of interest by dedicated breast imagers in two orthogonal planes. The AI software then processed the two orthogonal B-mode views of each lesion to generate a likelihood of malignancy -- benign, probably benign, suspicious, and probably malignant -- that aligned to BI-RADS categories 2-5.

Each software assessment category can then be further subdivided by a confidence level indicator, which displays where within each risk category the lesion falls and provides a continuous probability of malignancy that can be used for subsequent data analysis, Philpotts noted.

Of the BI-RADS 3 lesions in the study that were actually benign, the AI software would have downgraded 41% to BI-RADS 2 and upgraded 32% to BI-RADS 4. The remaining 27% remained as BI-RADS 3. The software identified all malignant lesions, including the two lesions originally categorized as BI-RADS 3 by the initial interpreting radiologist.

Performance of AI software on assessing masses on screening breast ultrasound
	Radiologists	AI software
Sensitivity	71.4%	100%
Area under the curve	0.79	0.89

Larger and prospective studies will be needed, however, to assess how the software integrates into clinical workflow and influences patient management, according to Philpotts. 

She acknowledged the limitations of their study, including its use of only B-mode lesions. In addition, the group only examined the software's standalone output and didn't evaluate joint physician/AI decision-making.

"Additional clinical information, mammographic findings, or Doppler diagnostic evaluation would [also] be incorporated by radiologists when using the AI software in actual clinical practice," she said.