A I Can Accurately Diagnose Appendicitis.

By Erik L. Ridley, AuntMinnie staff writer

October 1, 2019 -- By analyzing lab values and ultrasound data, an artificial intelligence (AI) algorithm can be highly accurate for diagnosing acute appendicitis and could potentially help avoid unnecessary surgery in two-thirds of patients without appendicitis, according to research published online September 25 in PLOS One.

A team of researchers led by Josephine Reissmann of Charité Universitätsmedizin Berlin trained an AI algorithm to provide an automated diagnosis of appendicitis based on the analysis of full blood counts, C-reactive protein (CRP), and appendiceal diameters on ultrasound examinations. In testing, the algorithm was 90% accurate for diagnosing appendicitis.

"The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and [machine learning] to significantly improve diagnostics even based on routine diagnostic parameters," the authors wrote.

Acute appendicitis represents one of the major causes for emergency surgery but remains a challenging diagnosis. As a result, the researchers set out to establish a decision-making model for suspected acute appendicitis in children based on reliable nonclinical parameters that are unbiased from interpretation or expert opinion. They also focused on differentiation between uncomplicated (phlegmonous) and complicated (gangrenous/perforated) appendicitis.

"Early diagnosis of complicated inflammation is particularly important, because this severe type of disease primarily requires surgical treatment," they wrote. "In contrast, for uncomplicated appendicitis conservative strategies are under investigation and will most probably be primarily applied in the near future, as shown by a current multicenter randomized controlled trial."

Reissmann and colleagues first gathered data from 590 pediatric patients who had received surgery for suspected acute appendicitis at their institution between December 2006 and September 2016. Of the 590 patients, 473 had histopathologically proven appendicitis and 117 had negative histopathological findings. The classification model was trained on 35% of the patients, with the remaining 65% used for validation. The AI model found two distinct biomarker signatures for diagnosing appendicitis and complicated appendicitis, respectively.

"For the diagnosis of appendicitis, a selective biomarker signature was developed containing basophils, leukocytes, monocytes, neutrophils, CRP, and the appendiceal diameter," they wrote. "For the differential diagnosis of complicated versus uncomplicated appendicitis, a selective biomarker signature was developed including basophils, eosinophils, monocytes, thrombocytes, [and] CRP, supplemented by the appendiceal diameter."

Performance of AI model on pediatric patients with suspected appendicitis
	Sensitivity	Specificity	Accuracy
Diagnosing appendicitis	93%	67%	90%
Identifying complicated inflammation	95%	33%	51%

If used clinically, the model would be capable of avoiding unnecessary surgery in two out of three patients without appendicitis and one out of three patients with uncomplicated appendicitis, according to the researchers.

"Due to the retrospective nature of our study we do not present a ready-to-use clinical algorithm, but our approach demonstrates significant improvements compared to today's diagnosis and enables secure translation into clinical practice," they wrote. "Our approach also demonstrates significant value in ruling out complicated appendicitis with high sensitivity. Investigations on the [omics] level such as genome-wide gene expression profiling of specific cell compartments could be a path to increase the specificity.

Ultrasound apter DBT helpful in women with dense tissue.

By Kate Madden Yee, AuntMinnie.com staff writer

September 30, 2019 -- The cancer detection rate of screening breast ultrasound in women with dense tissue is comparable regardless of whether it's used as an adjunct to conventional digital mammography or digital breast tomosynthesis (DBT), according to a study published online September 25 in the American Journal of Roentgenology.

The study findings suggest that ultrasound is still a good supplemental option for women with dense tissue, whether they've been screened by digital mammography or DBT, wrote a team led by Dr. Elizabeth Dibble of Brown University in Providence, RI.

"Knowing that the cancer yield of screening ultrasound is similar after DBT versus digital mammography may help inform clinical practice, because questions [have abounded] about whether DBT is sufficient screening for women with dense breast tissue," the group noted.

Dense breast tissue is associated with increased risk of breast cancer, and can have a masking effect on mammography, causing some cancers to be missed. Since 2009, more than 30 U.S. states have passed breast density notification laws intended to inform women if they have dense tissue, and if so, to encourage them to discuss supplemental imaging with their doctor.

Ultrasound has been the primary supplemental imaging modality, Dibble's group noted, but its performance has only been assessed after digital mammography, not after DBT -- which due to its 3D nature has been shown to find more cancers than mammography.

"The value ultrasound screening adds in patients who have already undergone mammographic screening with DBT remains unclear," the team wrote. "Given the enhanced cancer detection with DBT, we hypothesize that fewer cancers will be identified by ultrasound after DBT relative to after digital mammography."

To compare the yield of dense breast ultrasound screening after digital mammography versus DBT, Dibble's group reviewed 3,183 breast ultrasound scans performed between October 2014 and September 2016. Of these, 1,434 (45.1%) were done after digital mammography and 1,672 (52.4%) after DBT. Of the 3,183 exams, 2.5% had no prior mammogram available.

Of 122 digital mammography and DBT patients who received recommendations for biopsy, 96.7% had results available, the group noted. Of 36 biopsies after digital mammography, 16.7% were malignant and 83.3% were benign; of 82 biopsies after DBT, 13.4% were malignant and 86.6% were benign.

The researchers found no significant difference in screening ultrasound's additional cancer detection rate following digital mammography versus DBT.

Cancers detected on ultrasound after digital mammography or DBT
	Digital mammography	DBT	p-value
No. of cancers detected	3.5	3	0.99

The results suggest the use of DBT doesn't necessarily eliminate the benefit of additional screening with ultrasound in women with dense tissue, the researchers concluded.

"Our study found no evidence to suggest a difference in additional cancer detection rate with screening ultrasound after digital mammography versus after DBT," the group wrote. "These findings suggest that patients who have undergone screening mammography with DBT maintain a similar benefit of detecting mammographically occult cancers on screening ultrasound compared with patients who have undergone screening mammography with digital mammography."