research-article

Extending the boundaries of cancer therapeutic complexity with literature text mining

Authors:

Hillel Taub-Tabib,

Dana Meron-Azagury,

Maytal Avrashami,

Shaked Launer-Wachs,

Aryeh Tiktinsky,

Yosi ShamayAuthors Info & Claims

Volume 145, Issue C

https://doi.org/10.1016/j.artmed.2023.102681

Published: 01 November 2023 Publication History

Abstract

Drug combination therapy is a main pillar of cancer therapy. As the number of possible drug candidates for combinations grows, the development of optimal high complexity combination therapies (involving 4 or more drugs per treatment) such as RCHOP-I and FOLFIRINOX becomes increasingly challenging due to combinatorial explosion. In this paper, we propose a text mining (TM) based tool and workflow for rapid generation of high complexity combination treatments (HCCT) in order to extend the boundaries of complexity in cancer treatments. Our primary objectives were: (1) Characterize the existing limitations in combination therapy; (2) Develop and introduce the Plan Builder (PB) to utilize existing literature for drug combination effectively; (3) Evaluate PB's potential in accelerating the development of HCCT plans. Our results demonstrate that researchers and experts using PB are able to create HCCT plans at much greater speed and quality compared to conventional methods. By releasing PB, we hope to enable more researchers to engage with HCCT planning and demonstrate its clinical efficacy.

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Highlights

•

In cancer therapy complex drug combination is crucial and might be the only option.

•

Current complexity space was studied, the current clinical limit is six drugs.

•

A novel text data mining-tool was developed to generate of complex treatment plans.

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The tool's performance was validated and compared to the standard of care.

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A comparison of the tool and human designed plans shows it can outperforms humans.

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Index Terms

Extending the boundaries of cancer therapeutic complexity with literature text mining
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Volume 145, Issue C

Nov 2023

176 pages

ISSN:0933-3657

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 November 2023

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