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Related Concept Videos

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Using Consensus-Based Reasoning and Large Language Models to Extract Structured Data From Surgical Pathology Reports.

Aakash Tripathi1, Asim Waqas2, Kavya Venkatesan1

  • 1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|December 18, 2025
PubMed
Summary
This summary is machine-generated.

A new framework using multiple large language models (LLMs) accurately extracts cancer data from pathology reports. This approach improves data analysis for cancer staging and treatment planning.

Keywords:
cancer registryextractionlarge language modelsnatural language processingreasoningsurgical pathology reports

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Area of Science:

  • Medical Informatics
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Surgical pathology reports contain critical cancer diagnostic information but vary widely in format and style.
  • The unstructured nature of these reports hinders automated data extraction for large-scale analysis.
  • Variability across tumor types and institutions presents significant challenges for consistent data retrieval.

Purpose of the Study:

  • To develop a consensus-driven, reasoning-based framework for extracting standard diagnostic variables and biomarkers from pathology reports.
  • To adapt locally deployed large language models (LLMs) for accurate and reliable data extraction.
  • To evaluate the framework's performance across diverse organ systems and cancer types.

Main Methods:

  • Utilized multiple locally deployed large language models (LLMs) to extract diagnostic variables (site, histology, stage, grade, behavior) and biomarkers.
  • Employed three separate reasoning models for accuracy and coherence evaluation of LLM-generated outputs.
  • Aggregated outputs to determine final consensus values and conducted expert validation by board-certified pathologists.

Main Results:

  • The framework achieved high accuracy in extracting standard variables from over 6,100 The Cancer Genome Atlas (TCGA) reports (mean 84.9%±7.3%) and 510 Moffitt Cancer Center reports (mean 88.2%±7.2%).
  • Histology, site, and behavior showed the highest extraction accuracy, with expert review confirming strong agreement across key variables.
  • Biomarker extraction achieved 70.6%±7.9% overall accuracy, with specific biomarkers showing high performance in relevant tumor types.

Conclusions:

  • Locally deployed LLMs, within a consensus-based framework, offer a transparent, accurate, and auditable solution for pathology data extraction.
  • The framework demonstrates potential for integration into real-world workflows like synoptic reporting and cancer registry abstraction.
  • Stratified, multi-organ evaluation frameworks with multi-evaluator consensus are crucial for benchmarking LLMs in clinical applications.