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Oncology Homepage

Radiotherapy IT company CruxQS acquires RDS Will expand footprint for FlowBoard workflow management system

New model boosts flow efficiency of particle accelerators May lead to smaller accelerators, easier to operate at reduced costs

Why are breast cancer survivors foregoing recommended screening? Study finds poor adherence to annual mammogram guidelines

Is low cost Proton-to-Carbon Heavy Ion radiotherapy coming soon? Best Particle Therapy plans to bring smaller footprint system to market in two to three years

Researchers develop AI approach for high-risk clinical tumor volumes Assisting low- and middle-income countries that lack contouring expertise

Dr. Benjamin Movsas American Radium Society Names president-elect

Tennessee governor vetoes state employee proton therapy coverage Would require PT to be covered under the same aggregate amount as IMRT

AI approach used to study Mars now assessing tumor response to therapies Could reduce sample sizes and accelerate process for approval of cancer therapies

European medical center first to perform linac-based MR-guided RT using on-table adaptation Made possible with ViewRay's MRIdian Linac

Hospital in Italy performs radiosurgery with Brainlab's Elements Spine SRS software Automatically plans treatments for complex parts of the body

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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