Tuesday December 10, 2019

Here’s How Radiation From CT Scan Can Increase Risk Of Developing Thyroid Cancer

Radiations from CT Scan can increase the risks of developing thyroid cancer and leukemia

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CT scan
Radiations from CT scan can have harmful effects on the body. Pixabay

Researchers have found that exposure to radiation from CT scans is associated with higher risks of developing thyroid cancer and leukemia, but according to health experts here, the probability of CT scans inducing cancers is very minimal.

Published in the journal JNCI Cancer Spectrum, the study based on a National Health Insurance dataset in Taiwan between 2000 and 2013 followed 22,853 thyroid cancer, 13,040 leukemia and 20,157 non-Hodgkin lymphoma cases.

Results showed that patients who developed thyroid cancer and leukemia had significantly higher likelihood of having received CT scans.

‘The probability of CT scans inducing cancers is very minimal. Very long and prolonged radiation exposure can cause skin redness, but the chance of developing malignancy is extremely less,” Gaurav Dixit, Senior Consultant, Clinical Haematologist, Action Cancer Hospital in New Delhi told IANS.

“However we need to be careful in children, and number of scans should be restricted,” Dixit added.

The study also revealed that for patients between 36 and 45 years of age, there was a three-fold increased risk of non-Hodgkin lymphoma associated with CT scans.

Thyroid cancer due to CT scan
Thyroid cancer and leukemia can be caused by radiations from CT scan. Pixabay

Patients receiving CT scans had in general marked increases in the risk of developing thyroid cancer and leukemia, especially in female patients and patients younger than 45.

However, according to Nitin Leekha, Senior Consultant, Surgical Oncology, Jaypee Hospital, in Noida, radiation exposure of any kind is associated with cancer and the fact is already well established.

“Radiation exposure with single diagnostic CT is relatively harmless. It takes multiple CT scans for radiation exposure to reach a point where it can lead to cancers. Thyroid cancer is the most commonly associated cancer with radiation exposure among others,” Leekha told IANS.

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“One should avoid repeated radiation exposure in diagnostic tests unless it is absolutely necessary. There is usually a time lag of years before cancer develops,” Leekha stressed.

Leekha added that if a person had radiation exposure multiple times in the childhood they may be at an increased risk of developing cancer.

“Such individuals should visit an oncosurgeon for evaluation,” he concluded. (IANS)

Next Story

Artificial Intelligence offers Quick Approach to Thyroid Cancer Screening: Study

The overall accuracy of the algorithm was 77.4 per cent

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artificial intelligence, nobel prize
"Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society." VOA

Using Artificial Intelligence (AI) with thyroid ultrasound offers a quick and non-invasive approach to thyroid cancer screening, says a new study.

The study, published in the journal PLOS Pathogens, suggests that automated machine learning shows promise as an additional diagnostic tool that could improve the efficiency of thyroid cancer diagnosis.

“Machine learning is a low-cost and efficient tool that could help physicians arrive at a quicker decision as to how to approach an indeterminate nodule,” said the study’s lead author John Eisenbrey from Thomas Jefferson University in the US.

According to the researchers, at present ultrasounds can tell if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy, but fine-needle biopsies only act as a peephole, they don’t reveal the whole picture.

As a result, some biopsies return inconclusive results as to whether the nodule is malignant, or cancerous in other words.

In order to improve the predictive power of the first-line diagnostic, the ultrasound, researchers looked into machine learning or AI models developed by Google.

They applied a machine learning algorithm to ultrasound images of patients’ thyroid nodules to see if it could pick out distinguishing patterns.

The researchers trained the algorithm on images from 121 patients who underwent ultrasound-guided fine needle-biopsy with subsequent molecular testing.

Cancer
Cancer Ribbon. Pixabay

From 134 total lesions, 43 nodules were classified as high risk and 91 were classified as low risk, based on a panel of genes used in the molecular testing.

A preliminary set of images with known risk classifications was used to train the model or algorithm.

From this bank of labeled images, the algorithm utilised machine learning technology to pick out patterns associated with high and low risk nodules.

It used these patterns to form its own set of internal parameters that could be used to sort future sets of images; it essentially ‘trained’ itself on this new task.

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Then the investigators tested the trained model on a different set of unlabeled images to see how closely it could classify high and low genetic risk nodules, compared to molecular tests results.

The researchers found that their algorithm performed with 97 per cent specificity and 90 per cent predictive positive value, meaning that 97 per cent of patients who truly have benign nodules will have their ultrasound read as ‘benign’ by the algorithm, and 90 per cent of malignant or ‘positive’ nodules are truly positive as classified by the algorithm.

The overall accuracy of the algorithm was 77.4 per cent. (IANS)