Literature Highlights

Zafar Sajjad



As diagnostic imaging becomes the single largest source of manmade radiation exposure to humans, (contributing more than all the nuclear power plants and atomic weapon tests put together) it is important for us radiologists to understand the implications of this exposure and to try and limit the potential harm that may come from it.  Mulvihill et al review the available data on risk of subsequent cancer development after childhood exposure to diagnostic ionizing radiation.  The data so far seems to suggest that the risk is small but the quality of the data is questionable and further work is needed to refine our understanding of this.  They suggest strategies to limit this exposure in the highest risk areas.

On a similar note Kithara et al followed over a 100,000 radiographers over almost a 30 year period to determine the incidence of malignant brain tumours in this cohort.  Despite absorbed doses as high as 290mGy the risk of malignant brain neoplasm was similar to the general population.  This is reassuring to the professionals for whom this is an occupational hazard.

Jia et al report the findings of the meta analysis of Radio Frequency Abalation (RFA) vs surgical resection for hepatocellular carcinoma.  Their findings that RFA has similar outcomes to surgical resection for the operators in Pakistan given the burden of viral hepatitis and the incidence of HCC in the country.  With the increasing awareness there is anecdotal evidence that more tumours are being picked up at an early stage because of the surveillance strategies.  RFA is increasingly becoming available throughout the country and is proving to be cost effective, safe and efficacious in this scenario.

 Another common scenario in every day radiology is an incidentally found complex renal cyst.  As soon as Bosniak grade of III or higher is assigned to them there is a tendency among urologists to excise them.  Mousassian et al’s findings seem to suggest that this may not be entirely justified.  In their series although the majority of the excised lesion were malignant, they were low grade lesions with a longitudinal stability during the follow up period.  It may be justified to just watch these lesions with periodic scans rather than offering surgery upfront.

Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionise radiology like no other technology has done in the past.  AI and ML have the potential of being truly disruptive technologies hen it comes to image interpretation.  This has led to pessimism regarding the future of radiologists in some circles.  I am of the belief that used appropriately AI will make radiologists better and more capable rather than redundant.  Dreyer and Geis also broadly agree with this view of the future.  AI and ML are here.  Radiology needs to embrace them rather than be afraid of them.


Zafar Sajjad



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