News | Artificial Intelligence | September 03, 2019

New Radiomics Model Uses Immunohistochemistry to Predict Thyroid Nodules

Machine learning-based model uses texture analysis to identify whether thyroid nodules are benign or malignant

New Radiomics Model Uses Immunohistochemistry to Predict Thyroid Nodules

Workflow of radiomics analysis for IHC indicators. Yellow lines denote area of analysis; red lines denote ROI for radiomic features extraction. X = original image, L = low-pass filter, H = high-pass filter. Image courtesy of Jiabing Gu, et al.

September 3, 2019 — Researchers have validated a first-of-its-kind machine learning–based model to evaluate immunohistochemical (IHC) characteristics in patients with suspected thyroid nodules, according to an ahead-of-print article published in the December issue of the American Journal of Roentgenology (AJR).1 The research team achieved “excellent performance” for individualized noninvasive prediction of the presence of cytokeratin 19, galectin 3 and thyroperoxidase based upon computed tomography (CT) images.

“When IHC information is hidden on CT images,” principal investigator Jiabing Gu explained, “it may be possible to discern the relation between this information and radiomics by use of texture analysis.” 

To assess whether texture analysis could be utilized to predict IHC characteristics of suspected thyroid nodules, Gu and colleagues from China’s University of Jinan enrolled 103 patients (training cohort–to-validation cohort ratio, ≈ 3:1) with suspected thyroid nodules who had undergone thyroidectomy and IHC analysis from January 2013 to January 2016. All 103 patients — 28 men, 75 women; median age, 58 years; age range, 33–70 years — underwent CT before surgery, and 3D Slicer v 4.8.1 was used to analyze images of the surgical specimen.

To facilitate test-retest methods, 20 patients were imaged in two sets of CT series within 10–15 minutes, using the same scanner (LightSpeed 16, Philips Healthcare) and protocols, without contrast administration. These images were used only to select reproducible and nonredundant features, not to establish or verify the radiomic model. 

The Kruskal-Wallis test (SPSS v 19, IBM) was employed to improve classification performance between texture feature and IHC characteristic. Gu et al. considered characteristics with p < 0.05 significant, and the feature-based model was trained via support vector machine methods, assessed with respect to accuracy, sensitivity, specificity, corresponding AUC and independent validation. From 828 total features, 86 reproducible and nonredundant features were selected to build the model. 

The best performance of the cytokeratin 19 radiomic model yielded accuracy of 84.4 percent in the training cohort and 80 percent in the validation cohort. Meanwhile, the thyroperoxidase and galectin 3 predictive models evidenced accuracies of 81.4 percent and 82.5 percent in the training cohort, and 84.2 percent and 85 percent in the validation cohort, respectively. 

Noting that cytokeratin 19 and galectin 3 levels are high in papillary carcinoma, Gu maintained that these models can help radiologists and oncologists to identify papillary thyroid cancers, “which is beneficial for diagnosing papillary thyroid cancers earlier and choosing treatment options in a timely manner.”

Ultimately, asserted Gu, “this model may be used to identify benign and malignant thyroid nodules.”

For more information: www.ajronline.org

 

Reference

1. Gu J., Zhu J., Qiu Q., et al. Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning–Based Radiomics. American Journal of Roentgenology, published online Aug. 28, 2019. DOI: 10.2214/AJR.19.21535

Related Content

#RSNA19 A sophisticated type of artificial intelligence (AI) can detect clinically meaningful chest X-ray findings as effectively as experienced radiologists, according to a study published in the journal Radiology.

Image courtesy of GE Healthcare

News | Artificial Intelligence | December 03, 2019
December 3, 2019 — A sophisticated type of...
REiLI AI platform auto segmentation.
News | Artificial Intelligence | November 30, 2019
December 1, 2019 — Fujifilm Medical Systems U.S.A.
#RSNA19 Dynamics enables a radiologist to compare X-rays and provide automatically generated reports specifically addressing the changes in images over the course of patient treatment. Initially Dynamics feature will support longitudinal comparison for pneumothorax, consolidation, mass, nodule, pleural effusion, pulmonary edema and lung congestion radiological findings where progress reports are of greatest importance
News | Artificial Intelligence | November 29, 2019
November 29, 2019 — At RSNA19 Oxipit will offer a first public p
XACT Robotics is advancing the field of radiology, pioneering the first hands-free robotic system, combining image-based planning and navigation with instrument insertion and steering capabilities
News | Artificial Intelligence | November 26, 2019
November 26, 2019 — XACT Robotics Ltd.
 Lunit INSIGHT CXR

A radiologist conducts an interpretation of a chest x-ray image with Lunit INSIGHT CXR analysis.

News | Digital Radiography (DR) | November 25, 2019
November 25, 2019 — Lunit announced the certification of CE mark for its most up-
With the HiRise, musculoskeletal radiologists and orthopedic specialists will be able to assess alignment of the total leg in three dimensions
News | Computed Tomography (CT) | November 25, 2019
November 25, 2019 — CurveBeam introduced the next level of weight-bearing...
The Food and Drug Administration (FDA) has cleared the new Somatom X.cite premium single-source computed tomography (CT) scanner from Siemens Healthineers together with the new myExam Companion intelligent user interface concept
News | Computed Tomography (CT) | November 21, 2019
November 21, 2019 — The Food and Drug Administration (FDA) has cleared the new Somatom X.cite premium single-source
Infinitt PACS 7.0 is a faster, more powerful viewer that was built from the ground up to support AI for image analysis and for operational/ workflow improvements in radiology
News | PACS | November 19, 2019
November 19, 2019 — Infinitt North America will be highlighting a next generation,...