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Related Experiment Video

Updated: Aug 25, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Differential Diagnosis of Hematologic and Solid Tumors Using Targeted Transcriptome and Artificial Intelligence.

Hong Zhang¹, Muhammad A Qureshi², Mohsin Wahid²

¹Genomic Testing Cooperative, Irvine, California.

The American Journal of Pathology

|October 15, 2022

Summary

This study shows that combining targeted RNA sequencing with artificial intelligence accurately diagnoses various hematologic and solid tumors. This approach offers a powerful tool for cancer classification and differential diagnosis.

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Accurate tumor diagnosis and classification are crucial and increasingly rely on biomarkers.
RNA expression profiling via next-generation sequencing offers reliable insights into cancer biology.

Purpose of the Study:

To investigate the utility of targeted transcriptome analysis combined with artificial intelligence for the differential diagnosis of hematologic and solid tumors.
To assess the accuracy of machine learning algorithms in cancer classification.

Main Methods:

Next-generation sequencing was performed on RNA samples from 2606 hematologic neoplasms, 2038 solid tumors, and controls using a 1408-gene panel.
Machine learning, specifically a geometric mean naïve Bayesian classifier, was employed for differential diagnosis across 45 entities.
The algorithm was trained on 3045 samples and validated on 1415 samples.

Main Results:

High accuracy was achieved in distinguishing between diagnoses, with area under the curve values ranging from 0.841 to 1.
The geometric mean naïve Bayesian algorithm demonstrated high first-choice diagnostic accuracy for various cancers, including 100% for acute lymphoblastic leukemia and 88% for acute myeloid leukemia.
Twenty hematologic and 24 solid tumor subtypes were identified.

Conclusions:

Targeted transcriptome profiling coupled with artificial intelligence provides a highly effective method for the diagnosis and classification of diverse cancer types.
Integrating mutation profiles and clinical data can further enhance algorithm performance and reduce diagnostic errors.