QuantX

DEN170022

Quantitative Insights, Inc · granted 2017-07-19 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.1)
“QuantX is a computer-aided diagnosis (CADx) software device used to assist radiologists in the assessment and characterization of breast abnormalities using MR image data. The device is a software-only post-processing system for patient breast images that includes analysis of MR images, and viewing ultrasound and mammographic images.”
Algorithmartificial intelligence algorithm, machine learning algorithm, combined feature score algorithm
source quote (p.1)
“These imaging (or radiomic) features are then synthesized by an artificial intelligence algorithm into a single value, the QI score, which is analyzed relative to a database of reference abnormalities with known ground truth. The QI score is based on a machine learning algorithm, trained on a subset of features calculated on a segmented lesions. The QI Score is calculcated using a combined feature score algorithm based on literature described in detail within the submission.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.9)
“The cybersecurity documentation is consistent with the recommendations for information that should be included in premarket submissions outlined in the FDA guidance document Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff (issued October 2, 2014).”

Validation studies (2)

Standalone

n=652 images · 3 site(s)

endpoints: AUC

Reader study (MRMC)

n=111 images · 3 site(s)

endpoints: AUC difference; sensitivity difference; specificity difference

Reported performance (8 observations)

sensitivity94.2
source quote (p.18)
“Sensivity 2nd READ 94.2”
specificity27.6
source quote (p.18)
“Specificity 2nd READ 27.6”
aurocas written: “auc”0.7575CI [0.6889, 0.8261]
source quote (p.17)
“AUC2nd read 0.7575”
aurocas written: “AUC difference (2nd read - 1st read)”0.052CI [0.0022, 0.1018]
source quote (p.17)
“AUC2nd read - AUC1st read 0.0520”
sensitivityas written: “Sensitivity difference (2nd read - 1st read) (BI-RADS cut-point of 3)”3.8CI [0.8, 7.4]
source quote (p.18)
“Sensivity 2nd READ - 1st READ Difference 3.8”
specificityas written: “Specificity difference (2nd read - 1st read) (BI-RADS cut-point of 3)”-1CI [-6.5, 4.3]
source quote (p.18)
“Specificity 2nd READ - 1st READ Difference -1.0”
sensitivityas written: “Sensitivity difference (2nd read - 1st read) (BI-RADS cut-point of 4a)”5.1CI [-0.9, 10.9]
source quote (p.18)
“Sensitivity 2nd READ - 1st READ Difference 5.1”
specificityas written: “Specificity difference (2nd read - 1st read) (BI-RADS cut-point of 4a)”-0.5CI [-7.3, 6.0]
source quote (p.18)
“Specificity 2nd READ - 1st READ Difference -0.5”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: De Novo decision summary PDF.

Predicate network

Postmarket — what happened after clearance

Not yet tracked — the weekly postmarket refresh hasn't snapshotted this device.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to De Novo AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/DEN170022