Reduces the Complexity of Reporting for Screening and Diagnostic MRI Exams to Deliver Time-Saving and Patient Safety Benefits
RICHARDSON, Texas, November 12, 2020 – Ikonopedia announced today the release of its newly updated next-generation breast MRI reporting module. The intuitive new interface is designed to reduce the complexity of reporting for screening and diagnostic MRI exams and is compliant to the ACR BI-RADS Atlas Fifth Edition.
The new breast MRI module leverages the intuitive icon-based interface of Ikonopedia’s Mammography and Ultrasound structured reporting modalities to deliver a variety of physician efficiency and patient safety benefits. Reporting capabilities have been expanded and instinctual organization guides radiologists through BI-RADS criteria to reach an accurate, BI-RADS-compliant, and natural sounding description of lesions. New functionality in the MRI diagnostic modality includes ten lesion assessment categories that adhere to BI-RADS. The MRI screening modality has been updated to include a new contrast selection dialog as well as to synchronize with the new MRI diagnostic modality.
The enhanced breast MRI module has also been optimized for AI input such as Qlarity Imaging’s QuantX, the first U.S. Food and Drug Administration (FDA)-cleared computer-aided diagnosis software for breast MRI analysis.
“We’ve been very pleased with the flexibility and efficiency gains from the intuitive user interface in the updated breast MRI reporting tools, particularly the ability to easily describe trackable entries while maintaining BI-RADS verbiage to create complex reports,” said Erica Guzalo, Section Chief, Breast Imaging, Sinai Health Chicago. “I also appreciate Ikonopedia’s dedication to continually help solve issues and implement new ideas that are beneficial to us, as users.”
“As we, as an industry, move towards more broadly adopting risk-based screening based on a women’s personal risk and breast density, the utilization of breast MRI will continue to grow,” said Michael Vendrell, MD, co-founder of Ikonopedia. “This new module streamlines reporting workflow to deliver more accurate diagnoses, reduces the risk of reporting errors, and save time as radiologists face increasing exam volume and data complexity. These are critical new capabilities to improve patient care and safety.”
Ikonopedia is an innovative structured breast reporting and MQSA management system designed to dramatically improve reporting efficiency, and optimize facility operations. All findings are saved as discrete data which allows Ikonopedia to prevent errors, maintain BI-RADS-compliant language and automate many time-consuming processes. Ikonopedia makes it possible to eliminate laterality errors, automatically choose exam-appropriate patient letters and pull forward findings from past exams along with many other time-saving features.
Ikonopedia’s integrated risk assessment tool is now available in dozens of languages and risk data is used to create alerts for the radiologist, populate the clinical section of the report, and automatically update the patient letter. A high-risk patient alert identifies patients with a 20% or greater lifetime risk and information about the score is instantly viewable.
Ikonopedia was founded by three expert breast imaging Radiologists: László Tabár, MD is the author of 6 books in 10 languages on mammography and a world renowned educator; A. Thomas Stavros, MD is the author of one of the most popular reference books in the field of breast ultrasound; and Michael J. Vendrell, MD is an expert in breast MRI and CAD design with extensive experience in breast-imaging software. For more information, visit www.ikonopedia.com.
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October 3, 2020 — Women who only speak Spanish have a 27% less likelihood of getting a screening mammogram than English speakers, according to a study presented at the American College of Surgeons (ACS) Clinical Congress 2020. And Spanish speakers aren’t the only ones — by and large, women who speak limited English are less likely to receive breast cancer screening.
The mammography rate of women with limited English-language proficiency was compared with English-speaking women by a research team led by Dr. Jose L. Cataneo, a general surgery resident at the University of Illinois at Chicago/Metropolitan Group Hospitals. They used data from the National Health Interview Survey for their analysis.
The researchers found that women who didn’t speak much English had much lower breast screening rates.
“The impact of language barriers on screening mammography was previously unknown from a national database,” Cataneo said. “It is important because approximately 67 million people in the United States speak a language other than English, and 41 million of those speak Spanish.”
Mammography screening by the numbers
Screening mammography is currently the gold standard for detecting breast cancer. The age range and the frequency vary depending on the governing body, but even still, most clinicians recommend screening mammography to catch cancer early when it’s most treatable.
In the U.S., does speaking exclusively or mostly a language besides English affect mammography screening uptake? That’s precisely what Cataneo and colleagues sought to find out. They used the National Health Interview Survey, an annual survey of U.S. civilian, noninstitutionalized residents. Only using the year 2015, the researchers included 9,653 women ages 40 to 75. Among those, 1,040 had limited English-language proficiency and 712 only spoke Spanish.
The researchers used statistical modeling to match the English speakers with the limited English speakers by age, race-ethnicity, insurance status, and family income. Of the group who spoke limited English, the overall rate of screening mammograms was 12% less than for proficient English speakers: 78% versus 90%. Dividing women into different age groups — 40 to 50, 45 to 75, and 50 to 75 — still resulted in women with limited English getting fewer screening mammograms.
Cataneo and colleagues also found speaking only Spanish produced a lower probability of getting a screening mammogram: for every 100 English-speaking women who get a screening mammogram, 73 Spanish-only speakers will get one.
On top of that, in the survey, 209 women reported never having a mammogram, which if extrapolated to the entire U.S. female population, equals 450,000 women in the country who are eligible for a screening mammogram but may not have had one, according to statistical software Cataneo used.
Why is it that limited English results in less screening uptake? It likely has to do with poverty, lack of health insurance, and fear, according to the researchers. Possible solutions include more education about breast health, the importance of mammography screening, and advancement in treatment options.
In addition to holding educational seminars in languages other than English, making online mammography scheduling available in other languages could also help, the researchers added.
August 27, 2020 –– The combination of an artificial intelligence (AI)-based computer-aided detection (CAD) algorithm with radiologist interpretation can detect more cases of breast cancer on screening mammograms than double reading by radiologists, according to research published online August 27 in JAMA: Oncology.
Researchers from the Karolinska University Hospital in Stockholm, Sweden, retrospectively compared three commercially available AI models in a case-control study involving nearly 9,000 women who had undergone screening mammography. They found that one of the models demonstrated sufficient diagnostic performance to merit further prospective evaluation as an independent reader.
What’s more, the best results — 88.6% sensitivity with 93% specificity — were achieved when utilizing that algorithm’s results along with the first radiologist interpretation.
“Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers,” wrote the authors, led by Dr. Mattie Salim. “No other examined combination of AI algorithms and radiologists surpassed this sensitivity level.”
The researchers used a study sample of 8,805 women ages 40 to 74 who had received screening mammography at their academic hospital from 2008 to 2015 and who did not have implants or prior breast cancer. All exams were performed on a full-field digital mammography system from Hologic.
Of these women from the public mammography screening program, 8,066 were a random sample of healthy controls and 739 were diagnosed with breast cancer. These 739 cancer cases included 618 actual screening-detected cancers and 121 clinically detected cancers. In order to mimic the 0.5% screening-detected cancer rate in the source screening cohort, a stratified bootstrapping method was used to increase the simulated number of screenings to 113,663.
The researchers then applied AI CAD software from three different vendors, who asked to remain anonymous. None of the algorithms had been trained on the mammograms in the study.
After processing the images, the CAD software provided a prediction score for each breast ranging from 0 (lowest suspicion) to 1 (highest suspicion). To enable comparison of the algorithm’s results with the recorded radiologist decisions, the researchers elected to choose an algorithm output cutpoint that corresponded as closely as possible to the specificity of that of the first-reader radiologists, i.e. 96.6%.
|Breast cancer detection performance|
|First-reader radiologists||Second-reader radiologists||Double reading consensus||AI algorithm #3||AI algorithm #2||AI algorithm #1||Combination of AI algorithm #1 and first-reader radiologists|
|Area under the curve||n/a||n/a||n/a||0.920||0.922||0.956||n/a|
The researchers noted that the differences in sensitivity between AI algorithm #1 and the other two algorithms and the first reader were statistically significant (p < 0.001 and p = 0.03, respectively).
In an accompanying commentary, Dr. Constance Lehman, PhD, of Harvard Medical School in Boston said that it’s now time to move beyond simulation and reader studies and enter the critical phase of rigorous, prospective clinical evaluation of AI.
“The need is great and a more rapid pace of research in this domain can be partnered with safe, careful, and effective testing in prospective clinical trials,” she wrote. “If AI models can sort women with cancer detected on their mammograms from those without cancer detected on their mammograms, the value of screening mammography can be made available and affordable to a large population of women globally who currently have no access to the life-saving potential of quality screening mammography.”
August 10, 2020 — Artificial intelligence (AI)-based software can reliably categorize a significant percentage of negative screening mammograms as normal, potentially decreasing mammography reading workload for radiologists by more than half, according to two presentations made at the recent ECR 2020 virtual meeting.
In separate studies that simulated outcomes from the use of AI algorithms to evaluate screening mammograms and set aside certain cases that are highly likely to be normal, Danish and U.S. researchers shared their experiences about how software can decrease the interpretation burden of radiologists without having a negative impact on cancer detection.
Pressure on radiologists
With the vast amount of women enrolled in breast cancer screening programs worldwide, there’s pressure on radiologists to read a substantial amount of mammograms — the majority of which are normal. The use of AI to help radiologists automatically detect a large number of these normal mammograms could possibly increase the performance of the screening program, while also making it more effective, explained Andreas Lauritzen of the University of Copenhagen in Denmark.
In a retrospective study, the investigators assessed the potential clinical impact of using AI software to reduce the screening mammography workload, specifically examining cases where an AI system could substitute for two radiologists when mammograms are very likely to be normal, he noted.
The team analyzed 53,948 mammography exams acquired in the Danish Capital Region breast cancer screening program from November 1, 2012, to December 31, 2013, in women ages 50-70. All exams included four full-field digital mammography (FFDM) images — two mediolateral oblique and two craniocaudal views — that were acquired on a Mammomat Inspiration FFDM system (Siemens Healthineers). Two radiologists read each of the exams, with agreement established in consensus.
The 53,948 exams included 418 screening-detected cancers, 150 interval cancers, and 812 long-term cancers that were confirmed by mammography, ultrasound, and biopsy. There were also 1,306 exams that were noncancer recalls.
The researchers then retrospectively applied version 1.6 of the Transpara AI-based software (ScreenPoint Medical) to these exams. Transpara provides a score of 1-10 to indicate the likelihood of a visible malignancy.
The experiment was set up to have radiologists not read exams deemed by the software to be very likely normal and then double-read the rest of the exams. The researchers then compared the outcome of these experiments with the original screening outcomes.
Lauritzen and colleagues found that the AI software yielded an area under the curve (AUC) of 0.95 for screening-detected cancers and 0.66 for interval cancers. If an AI software scoring threshold of 5 was used, 32,054 exams would be considered likely normal and not read by radiologists, reducing mammography workload by 59.42%.
|Screening mammography program outcomes in study of 53,948 screening mammograms from Danish Capital Region|
|Original screening outcomes||Screening outcomes if AI software scoring threshold of 5 was automatically used to determine a normal mammogram|
|Positive predictive value||24.34%||30.04%|
With this strategy, 16 (3.83%) of the screening-detected cancers that were detected during the normal double reading process would have been missed. However, if this AI strategy was expanded to recall all women with an exam score > 9.96, 16 new cancers would be detected, including five interval cancers and 11 long-term cancers. These added detections would come at the cost of only 91 new noncancer recalls, Lauritzen said.
This demonstrates that it’s possible to maintain a stable cancer detection rate and still avoid a large number of noncancer recalls, he pointed out.
“This study suggests that an AI system can be used to maintain safety of the breast screening program, possibly increase performance, while reducing the number of mammograms that have to be read by radiologists by a considerable amount,” Lauritzen said.
Dr. Peña describes herself as a coach’s wife and full-time working mom of three. She is also a clinical pharmacist, passionate public speaker, and breast cancer survivor.
She is founder and president of a non-profit organization; “The M.o.C.h.A.™️Tribe” (Mom Of a Child that Has Autism). She writes a blog called “The M.o.C.h.A Tribe Diaries” — a website devoted to squashing the idea that autism has a single story. She is also the Author of “Waiting for the Lightbulb” and a Today Show parenting team contributor.
Dr. Peña caught our attention with her video describing her personal experience with breast cancer beginning with consultation at the High-Risk Screening and Genetics Clinic at MD Anderson Center. We were so moved with the video and her insightful emphasis on the lifesaving importance of early cancer detection, we wanted to share it with our readers.
SOUND ON🔈This 5 min video could save your life or the life of someone you love..It saved mine..You will want to click this link when your done.https://ibis.ikonopedia.com/.You’re welcome for the grapefruit visual… there were other contenders… but they were inappropriate and not as comically subtle. Whatev..✌🏼&❤️#livinginthe5percent #cancersucks #purposeinpink #fightlikeagirl #survivor #breastcancerawareness The BreastiesSurvivorNet
Posted by Lisa A Peña on Wednesday, October 30, 2019
July 2, 2020 — Dr. László Tabár, a radiologist and oncologist from Falun, Sweden, is known worldwide as the “father” of mammography screening. He is certainly one of the key people behind the current national screening program for breast cancer in Sweden, but his research has also formed the basis for the introduction of screening programs in many other countries.
In this interview, Tabár shares the fascinating story of why he has dedicated the past 50 years of his life to introducing and improving mammography screening, and thereby reducing mortality in breast cancer — a journey colored by groundbreaking research results and success but also by opposition and a struggle to convince authorities of the benefits.
He gives us the story behind the introduction of mammography screening in Sweden and the intriguing results shown in two of his latest studies, published in 2018 and 2020, proving the benefits of screening independent of treatment regime — something he calls “one of the greatest accomplishments in clinical cancer research during the past 50 years.”
Dr. László Tabár in action by his breast imaging PACS workstation. Images courtesy of Dr. László Tabár.
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