Use of Artificial Intelligence (AI) for analyzing images of the peritoneal space to evaluate peritoneal carcinomatosis

In recent years, the use of artificial intelligence as a supportive tool in therapeutic decision-making has been increasingly explored. Concurrently, advancements in the management of peritoneal carcinomatosis have introduced new challenges. Multimodal therapies, the exploration of novel methods for delivering intraperitoneal therapeutic agents, and new drugs present significant challenges for tools that evaluate the behavior of peritoneal metastases. This short communication discusses the use of an innovative tool that enhances the detection and monitoring of peritoneal metastatic nodules. Our initial experience underscores the need for image standardization and highlights the difficulties faced in the first cases evaluated. The numerical data obtained through oncologic risk pixel analysis and the technological evolution are discussed and presented in this report, which includes the first images evaluated by this peritoneal assessment tool.

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Publication Details

Journal
Journal of Surgery and Research
Published
2025-01-01
DOI
https://doi.org/10.26502/jsr.10020474
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Use of Artificial Intelligence (AI) for analyzing images of the peritoneal space to evaluate peritoneal carcinomatosis

Rafael Seitenfus
Journal of Surgery and Research
Radiomics and Machine Learning in Medical Imaging
article

Use of Artificial Intelligence (AI) for analyzing images of the peritoneal space to evaluate peritoneal carcinomatosis

Rafael Seitenfus
article en

Abstract

In recent years, the use of artificial intelligence as a supportive tool in therapeutic decision-making has been increasingly explored. Concurrently, advancements in the management of peritoneal carcinomatosis have introduced new challenges. Multimodal therapies, the exploration of novel methods for delivering intraperitoneal therapeutic agents, and new drugs present significant challenges for tools that evaluate the behavior of peritoneal metastases. This short communication discusses the use of an innovative tool that enhances the detection and monitoring of peritoneal metastatic nodules. Our initial experience underscores the need for image standardization and highlights the difficulties faced in the first cases evaluated. The numerical data obtained through oncologic risk pixel analysis and the technological evolution are discussed and presented in this report, which includes the first images evaluated by this peritoneal assessment tool.

Journal of Surgery and ResearchVol. 8(3)
Openalex Percentile: Top 59%
Radiomics and Machine Learning in Medical Imaging
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Use of Artificial Intelligence (AI) for analyzing images of the peritoneal space to evaluate peritoneal carcinomatosis — Rafael Seitenfus · Journal of Surgery and Research (2025) | TGRS Research Map | TGRS