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Oncology Homepage

Community practices not abiding by breast cancer screening guidelines for MR High breast density isn't enough

Volpara brings quality control and better risk assessment to breast imaging Launches two new products as part of VolparaEnterprise platform at RSNA

Elekta delays order target for Unity MR linac Making 'difficult' short-term decisions to benefit long term

Romanian government to install Elekta linear accelerators at five locations throughout country Will replace obsolete cobalt systems

ACR's Data Science Institute to develop structured AI framework for rad practices Create a standardized platform to build AI use cases

Siemens unveils its MAMMOMAT Revelation mammography system at RSNA Provides automated breast density measurements at exam

Survey finds most patients prefer annual mammograms Challenges the USPSTF's recommendations

Carestream Health now shipping MyVue Center Self-Service Kiosk Provides patients with access to imaging exam records quicker

MEVION S250i Proton Therapy System scores CE mark First European installation to be in Netherlands

Researchers develop CT system for imaging extremely small objects Generates high-resolution 3-D images

Transpara deep learning computer
detection system on display at RSNA

Study finds Transpara deep learning CAD system performs as well as radiologists

by Lauren Dubinsky , Senior Reporter
CHICAGO — A study presented last Wednesday at the RSNA meeting found that ScreenPoint’s Transpara deep learning computer detection system can perform as well as experienced radiologists.

“The decision support is where we come in — [the radiologist] knows where the abnormality is, but they want to know if it’s abnormal enough to recall the patient,” Jan-Jurre Mordang, technical sales and support specialist and medical engineer at ScreenPoint, told HCB News.

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The researchers at Radbound University Medical Centre in Nijmegen, Netherlands compiled reader study data from multiple breast imaging centers in Europe. In four different studies, 24 radiologists reviewed over 1,400 mammograms from three vendors to measure their ability to detect breast cancer.

They found that there was no significant difference between Transpara’s automated reading and the radiologists’ reading. In two of the studies, the radiologists had a higher performance, and Transpara had higher performance in the other two.

Transpara, which was launched in February, is a computer-aided detection system, but with added decision support. It automatically identifies soft-tissue and calcification lesions and combines the findings of all available views to generate a cancer suspiciousness score on a scale from 0 to 100.

The system marks calcifications just like conventional CAD, but only a small number of soft-tissue lesion marks are shown and are proved to have extremely low false positive rates. The radiology can also leverage the decision support to probe any suspicious image region to determine if further investigation is required.

“It’s more about specificity — you want to have as few recalls as you can have,” said Mordang. “Considering that more than 95 percent of the screening population is normal, it’s better to eliminate those so the radiologist can look at the other five percent more closely.”

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