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Cancer risks associated with X-rays, CT scans are only theoretical: Study


New York: The widespread belief that radiations from X-rays and CT scans can cause cancer has flaws and is based on an unproven theoretical model, suggests a study.

To estimate cancer risk from low-dose radiation, scientists used a model known as linear no-threshold (LNT) in the study published in the American Journal of Clinical Oncology.

But risk estimates based on this model “are only theoretical and, as yet, have never been conclusively demonstrated by empirical evidence”, wrote researcher James Welsh from the Loyola University in Chicago.

The use of the LNT model drives unfounded fears and “excessive expenditures on putative but unneeded and wasteful safety measures”, Welsh noted.

In the LNT model, the well-established cancer-causing effects of high doses of radiation are extended downward in a straight line to very low doses.

The model dissuades many physicians from using appropriate imaging techniques and “discourages many in the public from getting proper and needed imaging, all in the name of avoiding any radiation exposure”, the researcher explained.

This model assumes there is no safe dose of radiation, no matter how small.

However, the human body has evolved the ability to repair damage from low-dose radiation that naturally occurs in the environment.

Studies of atomic bomb survivors and other epidemiological studies of human populations have never conclusively demonstrated that low-dose radiation exposure can cause cancer, according to the study.

Any claim that low-dose radiation from medical imaging procedures is known to cause cancer “should be vigorously challenged, because it serves to alarm and perhaps harm, rather than educate”, the scientists suggested.

The LNT model “should finally and decisively be abandoned”, the authors concluded. (IANS)(Photo:

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New AI system can diagnose prostate cancer just good as pathologists

"The system was programmed to learn and gradually improve how it interpreted the samples. Our result show that the diagnosis the AI reported was at a level comparable to that of a pathologist"

Chronic diseases can cause cancer as well.
Chronic diseases can cause cancer as well.
  • This new AI can identify cancer just as accurately as a pathology lab
  • This app can overcome the shortage of pathologists
  • The app is very useful and revolutionary

Researchers have developed a new learning artificial intelligence (AI) system which can diagnose and identify cancerous prostate samples as accurately as any pathologist.

Chronic diseases are not yet included in cancer prevention schemes.
New mobile app can detect cancer.

According to the researchers, this holds out the possibility of streamlining and eliminating variation in the process of cancer diagnosis. It may also help overcome any local shortage of trained pathologists.

“This is not going to replace a human pathologist. We still need an experienced pathologist to take responsibility for the final diagnosis,” said lead author Hongqian Guo from the Nanjing University in China.

Also Read: Girls may inherit ovarian cancer gene from fathers

“What it will do is help pathologists make better, faster diagnosis, as well as eliminating the day-to-day variation in judgement which can creep into human evaluations,” Guo added.

For the study, presented at the 33rd European Association of Urology Congress in Copenhagen, researchers took 918 prostate whole mount pathology section samples from 283 patients, and ran these through the analysis system, with the software gradually learning and improving diagnosis.

These pathology images were subdivided into 40,000 smaller samples; 30,000 of these samples were used to ‘train’ the software, the remaining 10,000 were used to test accuracy.

The results showed an accurate diagnosis in 99.38 percent of cases (using a human pathologist as a ‘gold standard’), which is effectively as accurate as the human pathologist.

The app is just as effective as a pathologist. Pixabay

They were also able to identify different Gleason Grades in the pathology sections using AI; ten whole mount prostate pathology sections have been tested so far, with similar Gleason Grade in the AI and human pathologist’s diagnosis. The group has not started testing the system with human patients.

“The system was programmed to learn and gradually improve how it interpreted the samples. Our result show that the diagnosis the AI reported was at a level comparable to that of a pathologist. “Furthermore, it could accurately classify the malignant levels of prostate cancer,” Guo added. IANS

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