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.

A I Can Accurately Diagnose Appendicitis.

By Erik L. Ridley, AuntMinnie staff writer

October 1, 2019 -- By analyzing lab values and ultrasound data, an artificial intelligence (AI) algorithm can be highly accurate for diagnosing acute appendicitis and could potentially help avoid unnecessary surgery in two-thirds of patients without appendicitis, according to research published online September 25 in PLOS One.

A team of researchers led by Josephine Reissmann of Charité Universitätsmedizin Berlin trained an AI algorithm to provide an automated diagnosis of appendicitis based on the analysis of full blood counts, C-reactive protein (CRP), and appendiceal diameters on ultrasound examinations. In testing, the algorithm was 90% accurate for diagnosing appendicitis.

"The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and [machine learning] to significantly improve diagnostics even based on routine diagnostic parameters," the authors wrote.

Acute appendicitis represents one of the major causes for emergency surgery but remains a challenging diagnosis. As a result, the researchers set out to establish a decision-making model for suspected acute appendicitis in children based on reliable nonclinical parameters that are unbiased from interpretation or expert opinion. They also focused on differentiation between uncomplicated (phlegmonous) and complicated (gangrenous/perforated) appendicitis.

"Early diagnosis of complicated inflammation is particularly important, because this severe type of disease primarily requires surgical treatment," they wrote. "In contrast, for uncomplicated appendicitis conservative strategies are under investigation and will most probably be primarily applied in the near future, as shown by a current multicenter randomized controlled trial."

Reissmann and colleagues first gathered data from 590 pediatric patients who had received surgery for suspected acute appendicitis at their institution between December 2006 and September 2016. Of the 590 patients, 473 had histopathologically proven appendicitis and 117 had negative histopathological findings. The classification model was trained on 35% of the patients, with the remaining 65% used for validation. The AI model found two distinct biomarker signatures for diagnosing appendicitis and complicated appendicitis, respectively.

"For the diagnosis of appendicitis, a selective biomarker signature was developed containing basophils, leukocytes, monocytes, neutrophils, CRP, and the appendiceal diameter," they wrote. "For the differential diagnosis of complicated versus uncomplicated appendicitis, a selective biomarker signature was developed including basophils, eosinophils, monocytes, thrombocytes, [and] CRP, supplemented by the appendiceal diameter."

Performance of AI model on pediatric patients with suspected appendicitis
	Sensitivity	Specificity	Accuracy
Diagnosing appendicitis	93%	67%	90%
Identifying complicated inflammation	95%	33%	51%

If used clinically, the model would be capable of avoiding unnecessary surgery in two out of three patients without appendicitis and one out of three patients with uncomplicated appendicitis, according to the researchers.

"Due to the retrospective nature of our study we do not present a ready-to-use clinical algorithm, but our approach demonstrates significant improvements compared to today's diagnosis and enables secure translation into clinical practice," they wrote. "Our approach also demonstrates significant value in ruling out complicated appendicitis with high sensitivity. Investigations on the [omics] level such as genome-wide gene expression profiling of specific cell compartments could be a path to increase the specificity.