Thyroid cancer now diagnosed with machine learning-powered photoacoustic/ultrasound imaging

Date:
July 9, 2021
Source:
Pohang University of Science & Technology (POSTECH)
Summary:
A research team performs machine learning-powered photoacoustic/
ultrasound imaging for thyroid cancer classification.



FULL STORY ==========================================================================
A lump in the thyroid gland is called a thyroid nodule, and 5-10% of all thyroid nodules are diagnosed as thyroid cancer. Thyroid cancer has a
good prognosis, a high survival rate, and a low recurrence rate, so early diagnosis and treatment are crucial. Recently, a joint research team
in Korea has proposed a new non-invasive method to distinguish thyroid
nodules from cancer by combining photoacoustic (PA) and ultrasound image technology with artificial intelligence.


==========================================================================
The joint research team -- composed of Professor Chulhong Kim and
Dr. Byullee Park of POSTECH's Department of Electrical Engineering,
Department of Convergence IT Engineering and Department of Mechanical Engineering, Professor Dong-Jun Lim and Professor Jeonghoon Ha of Seoul
St. Mary's Hospital of Catholic University of Korea, and Professor Jeesu
Kim of Pusan National University -- conducted a research to acquire
PA images from patients with malignant and benign nodules and analyzed
them with artificial intelligence. In recognition of their significance,
the findings from this study were published in Cancer Research.

Currently, the diagnosis of a thyroid nodule is performed using a
fine-needle aspiration biopsy (FNAB) using an ultrasound image. But about
20% of FNABs are inaccurate which leads to repetitive and unnecessary
biopsies.

To overcome this problem, the joint research team explored the use of PA imaging to obtain an ultrasonic signal generated by light. When light
(laser) is irradiated on the patient's thyroid nodule, an ultrasound
signal called a PA signal is generated from the thyroid gland and the
nodule. By acquiring and processing this signal, PA images of both the
gland and the nodule are collected. At this time, if multispectral PA
signals are obtained, oxygen saturation information of the thyroid gland
and thyroid nodule can be calculated.

The researchers focused on the fact that the oxygen saturation of
malignant nodules is lower than that of normal nodules, and acquired PA
images of patients with malignant thyroid nodules (23 patients) and those
with benign nodules (29 patients). Performing in vivo multispectral PA
imaging on the patient's thyroid nodules, the researchers calculated
multiple parameters, including hemoglobin oxygen saturation level in
the nodule area. This was analyzed using machine learning techniques to successfully and automatically classify whether the thyroid nodule was malignant or benign. In the initial classification, the sensitivity to
classify malignancy as malignant was 78% and the specificity to classify
benign as benign was 93%.

The results of PA analysis obtained by machine learning techniques
in the second analysis were combined with the results of the initial examination based on ultrasound images normally used in hospitals. Again,
it was confirmed that the malignant thyroid nodules could be distinguished
with a sensitivity of 83% and a specificity of 93%.

Going a step further, when the researchers kept the sensitivity at 100%
in the third analysis, the specificity reached 55%. This was about three
times higher than the specificity of 17.3% (sensitivity of 98%) of the
initial examination of thyroid nodules using the conventional ultrasound.

As a result, the probability of correctly diagnosing benign, non-malignant nodules increased more than three times, which shows that overdiagnosis
and unnecessary biopsies and repeated tests can be dramatically reduced,
and thereby cut down on excessive medical costs.

"This study is significant in that it is the first to acquire
photoacoustic images of thyroid nodules and classify malignant
nodules using machine learning," remarked Professor Chulhong Kim
of POSTECH. "In addition to minimizing unnecessary biopsies in
thyroid cancer patients, this technique can also be applied to a
variety of other cancers, including breast cancer." "The ultrasonic
device based on photoacoustic imaging will be helpful in effectively
diagnosing thyroid cancer commonly found during health checkups and
in reducing the number of biopsies," explained Professor Dong-Jun
Lim of Seoul St. Mary's Hospital. "It can be developed into a
medical device that can be readily used on thyroid nodule patients." ========================================================================== Story Source: Materials provided by Pohang_University_of_Science_&_Technology_(POSTECH).

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jeesu Kim, Byullee Park, Jeonghoon Ha, Idan Steinberg, Sarah
M. Hooper,
Chaiho Jeong, Eun-Yeong Park, Wonseok Choi, Tie Liang, Ja-Seong
Bae, Ravi Managuli, Yongmin Kim, Sanjiv S. Gambhir, Dong-Jun Lim,
Chulhong Kim.

Multiparametric Photoacoustic Analysis of Human Thyroid Cancers
In Vivo.

Cancer Research, 2021; canres.CAN-20-3334-A.2020 DOI: 10.1158/0008-
5472.CAN-20-3334 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210709104241.htm

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