Elastography helps liver transplant patients avoid biopsy.

By Kate Madden Yee, AuntMinnie.com staff writer

October 8, 2019 -- Ultrasound with a shear-wave elastography (SWE) technique can help liver transplant patients avoid biopsy on follow-up, according to a study published online October 7 in the American Journal of Roentgenology.

The findings are good news for a patient population often vulnerable to invasive procedures after transplant -- and can help save healthcare resources, wrote a team led by Dr. Corinne Deurdulian of the University of Southern California in Los Angeles.

"Given the significant resources allotted to perform liver biopsies (e.g., radiologist time, nursing staff, and hospital resources) and patient recovery time, as well as patient discomfort and the possibility of significant postbiopsy complications developing, utilization of a noninvasive tool to determine the degree of hepatic fibrosis would be useful in the initial assessment and follow-up of liver transplant patients," the group wrote.

After liver transplant, patients are monitored for both possible rejection of the new organ and hepatic fibrosis, and they often undergo liver biopsies as part of this follow-up, Deurdulian and colleagues noted.

The researchers sought to determine whether shear-wave elastography could offer a noninvasive alternative to biopsy to assess for liver fibrosis, helping clinicians quantify it in liver transplant recipients.

The study included 111 adult liver transplant patients who underwent 147 SWE exams of the right hepatic lobe followed by biopsies between May 2015 and December 2017. The researchers compared SWE values with fibrosis scores of biopsy samples using the Metavir system: Metavir scores are F0 (no fibrosis), F1 (portal fibrosis without septa), F2 (portal fibrosis with few septa), F3 (numerous septa without cirrhosis), and F4 (cirrhosis). The team tracked SWE's sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.

Of the 147 SWE exams and liver biopsies, the researchers found consistent threshold values for patients with Metavir scores of F0 and F1 (no or minimal fibrosis), compared with those with Metavir scores of F2, F3, or F4 (significant fibrosis).

SWE's performance in classifying fibrosis
Performance measure	SWE
SWE value
No or minimal fibrosis	≤ 1.76 m/sec
Significant fibrosis	> 1.76 m/sec
Other performance measures
Sensitivity	77%
Positive predictive value	33%
Negative predictive value	96%

The study results suggest that clinical decisions for liver transplant patients can be based on SWE results rather than biopsy, the researchers concluded.

"If the median SWE value is 1.76 [m/sec] or less, the patient can be classified as having no or minimal fibrosis ... and can avoid biopsy," the group concluded. "According to these results, which have a negative predictive value of 96%  liver biopsies may be obviated in most patients."

Radiofrequency ultrasound , AI predict thyroid cancer.

Radiofrequency ultrasound , AI predict thyroid cancer
By Kate Madden Yee, AuntMinnie.com staff writerRadiofrequency ultrasound and an artificial intelligence (AI) model can be used to effectively predict the malignancy of thyroid nodules, as well as stratify their risk, according to a study set for publication in the November issue of Ultrasonics.

The combination of radiofrequency ultrasound with an artificial neural network (ANN) could also avoid operator dependency issues and help prevent unnecessary thyroid biopsies, according to a group led by Dr. Chunrui Liu of the Affiliated Hospital of Nanjing University Medical School in China.

"The proposed method has no operator dependency; all of the analyses are performed by computer," the team noted (Ultrasonics, November 2019, Vol. 99, pp. 1-9). "Preliminary results indicated that the performance of ANN combined with radiofrequency ultrasound signals is better than that combined with conventional ultrasound images."

Common but not often malignant

Thyroid nodules are common, but only 8% to 16% are actually malignant, according to the researchers. Many ultrasound techniques are used to evaluate nodule malignancy, including strain elastography, acoustic radiation force impulse imaging, and contrast-enhanced ultrasound, but these methods' efficacy remains unclear, the group wrote.

That's where radiofrequency ultrasound comes in. The technique elicits more clinical information than conventional ultrasound by extracting radiofrequency signals from tissues. But how it performs with thyroid nodules has not been studied, Liu and colleagues noted.

"Preliminary studies of radiofrequency ultrasound have been promising, and the method has been shown to have broader prospective applications in identifying prostate and breast cancers and grading fatty liver," they wrote. "To date, few studies on radiofrequency ultrasound's thyroid cancer detection performance have been reported."

The researchers developed their method to predict suspicious thyroid nodules by first gathering radiofrequency data and then creating radiofrequency ultrasound images using Matlab software (MathWorks). After a radiologist outlined regions of interest on the images, textural features were then analyzed using the gray-level co-occurrence matrix (GLCM) algorithm and principal component analysis. The resulting characteristic values from the textural analysis were subsequently used to train the ANN.

The study included 131 pathologically proven thyroid nodules, of which 59 were benign and 72 were malignant. The nodules were randomly divided into training, validation, and testing cohorts. To test their hypothesis that radiofrequency ultrasound could provide more tissue characteristic information than conventional ultrasound, the researchers also performed the same texture and ANN analyses on the B-mode ultrasound images.

The ANN algorithm performed better with radiofrequency ultrasound than it did on conventional ultrasound in all categories except specificity, the group found.

ANN performance for predicting thyroid nodule malignancy
Performance measure	ANN on conventional ultrasound images	ANN on radiofrequency ultrasound images
Sensitivity	94.4%	100%
Specificity	93.2%	91.5%
Accuracy	93.9%	96.2%
AUC*	0.917	0.945

*AUC = Area under the curve

The group also used the ANN with radiofrequency ultrasound to characterize new malignancy risk groups for categories 3 (probably benign), 4 (suspicious), and 5 (probably malignant) thyroid nodules as established by the American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS). The new categories better distinguished malignant nodules compared with TI-RADS.

Parameter	Category 3	Category 4	Category 5
No. of samples	42	16	73
No. of malignant samples	0	3	69
Risk of malignancy
ANN plus radiofrequency ultrasound	0	18.8%	94.5%
TI-RADS	0	55.1%	88.2%

"The new categories allow for a selection of suspicious nodules to be submitted to fine-needle aspiration, thereby avoiding unnecessary thyroid biopsies," the group wrote.

More research to come

More research needs to be done to establish the benefits of using an ANN and radiofrequency ultrasound, according to Liu and colleagues.

"Of course, although these preliminary results suggested [the use of the ANN and radiofrequency ultrasound] could help sonographers to identify risky thyroid nodules and reduce the number of unnecessary thyroid biopsies, more data will be collected and analyzed in our future study to further confirm the feasibility and accuracy of the proposed method," they concluded.