Submission Quality & Compliance Module

Submission Quality & Compliance

According to industry research, more than half of all image-related clinical trial query stoppages result from preventable human errors, causing an average delay of up to seven weeks.

AG Mednet recognizes that the current system is imperfect, and in response created its Submission Quality & Compliance module – an additional layer of automated quality assurance – to reduce submission error rates and bring preventable delays to zero. This module is the first quality-assurance software developed specifically to detect errors at the investigator site prior to data submission in order to accelerate clinical trial decision-making.

The AG Mednet Submission Quality & Compliance module features include:

  • The ability to confirm that all parameters in a medical image set are compliant with predetermined protocol ranges at the exam, series and instance level
  • The ability to identify and alert for missing information and instances
  • The ability to assure that specified views (e.g., coronal, sagittal, axial) are present
  • The flexibility to send specific image series so only the protocol-required data arrives at the trial repository
  • The ability to verify that the required series were taken in the proper sequence
  • The ability for the trial coordinator to acknowledge discrepancies, thus opening and closing potential queries concurrently with data submission
  • The ability to customize reporting to senders, core labs and sponsors including acknowledgements with electronic signatures

Longitudinal Analysis within SQC

AG Mednet’s Longitudinal Analysis is the only automated software that can check data across subject visits – at the source – and make determinations about consistency and validity of imaging data as a trial progresses. Longitudinal Analysis improves the quality and speed of clinical trials by automating imaging data collection and trial protocols to ensure images are consistently performed at the right time, with the right equipment, to the right specifications.

Key Features include:

  • Monitoring and Comparing of Image Thickness: AG Mednet tracks image thickness so radiologists can easily check if the next time point slice thickness is the same as the one previously acquired. This automated consistency of image slice thickness ensures adherence to trial protocols and removes any doubt of timing if something is detected in an image.
  • Automated Calendaring for Time-Dependent Timepoints: For trials that require patient imaging to occur at specific intervals, Longitudinal Analysis allows professionals to determine appropriate scanning timing – at the source.
  • Automated Tracking of Imaging Equipment: Longitudinal Analysis tracks the specific piece of equipment used to ensure images are consistent and checks if the same scanner and software release is used for all patients.

In addition to increased consistency that drives accurate conclusions, Longitudinal Analysis saves sponsors time and money, and helps core labs expand their bandwidth to take on more trials.

SQC for QIBA

The Quantitative Imaging Biomarkers Alliance (QIBA) is an RSNA-sponsored initiative to advance quantitative imaging and the use of imaging biomarkers in clinical trials by reducing variability.

This involves:

  • identifying needs, barriers, and solutions to develop and test consistent, reliable, valid, and achievable quantitative imaging results across imaging platforms, clinical sites, and time.
  • accelerating the development and adoption of hardware and software standards needed to achieve accurate and reproducible quantitative results from imaging methods.

In an effort to promote QIBA’s primary initiative, clinical trial sponsors now have an easy and effective way to ensure the protocols are being met.

AG Mednet’s SQC software for QIBA is the first technology to automate the process of verifying QIBA protocol compliance, saving sponsors time and money and making it easier for investigator sites to submit quality data.

While electronic transfer made the transporting of large imaging files faster, it did little to address the quality of the images being sent. SQC and the efforts of RSNA together with sponsor-driven QIBA protocols put the emphasis on improving quality at the source, before preventable human errors can cause queries and costly delays. Sponsors can utilize AG Mednet SQC for QIBA to cut quality control costs by approximately 50 percent at the core lab.

Current protocols implemented by SQC for QIBA include:

  • QIBA vCT 1C: Characterizing variability among multiple image sets of the same phantoms re-scanned across centers to isolate contributors to variability.
  • QIBA-CT Vol-Tumor Volume: Characterizing each designated tumor by its volume change relative to prior image sets. This is typically done by determining the boundary of the tumor, computing the volume of the segmented tumor and calculating the difference of the tumor volume in the current scan and in the baseline scan.
  • RECIST 1.1: Response Evaluation Criteria in Solid Tumors (RECIST) is a set of published rules that define when cancer patients improve, stay the same or worsen during treatments. The majority of clinical trials evaluating cancer treatments for objective response in solid tumors are using RECIST.