Automated Computational Pathology Analysis for Clinical Trial Retrospective Review
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Solution Overview
Problem
Clinical trials often face challenges in understanding why a Phase II trial is successful but fails in Phase III, due to issues like representative patient data, unclear target biology, and incorrect patient enrollment, especially when companion diagnostics are used.
Innovation Solution
An automated method for retrospectively analyzing clinical trial data by computing image feature metrics from patient biological samples, deriving diagnostic feature metrics, and applying statistical minimization to determine a diagnostic cut point, which helps in identifying patient cohorts and understanding trial outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companion diagnostics are used to select patients for clinical trials, then patient enrollment precision is improved, but understanding of trial failure reasons deteriorates due to lack of retrospective analysis capability
Solution Approach 1:
The patent applies preliminary action by performing retrospective analysis on historical clinical trial data before making future enrollment decisions. The system computes image feature metrics and derives diagnostic feature metrics from archived patient samples, allowing researchers to understand past trial outcomes and improve future patient selection strategies without waiting for new trials to complete.
2Adaptability or versatility
If Phase III trials use broader patient populations, then generalizability is improved, but trial success rate deteriorates due to inclusion of non-responsive patients
Solution Approach 1:
The patent implements feedback by using retrospective analysis results to inform and adjust future patient enrollment criteria. The system analyzes outcomes from Phase III trials, identifies patterns in patient response based on image and diagnostic features, and feeds this information back into companion diagnostic threshold optimization, creating a continuous improvement loop that maintains trial success rates while expanding population inclusivity.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting companion diagnostic thresholds based on retrospective analysis findings. The system computes diagnostic feature metrics from historical data, determines optimal cut-points that maximize trial success, and modifies enrollment criteria parameters accordingly, allowing the trial design to adapt to actual patient response patterns observed in broader populations.
3Measurement precision
If manual analysis of clinical trial data is performed, then diagnostic accuracy is improved, but analysis time and resource consumption deteriorate
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems. The system uses image analysis algorithms to compute image feature metrics from patient samples, automatically derives diagnostic feature metrics, and applies statistical methods to determine optimal diagnostic thresholds, eliminating the need for manual pathologist review while maintaining or improving diagnostic accuracy and dramatically reducing analysis time.
Solution Approach 2:
The patent creates digital copies of patient tissue samples through image scanning and processing. Instead of manually examining physical slides, the system generates digital image representations that can be computationally analyzed, allowing multiple analyses to be performed simultaneously without additional time cost, and enabling retrospective re-analysis of the same samples with different algorithms or criteria.
Data Source
AI summary
Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.