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Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

Updated: Dec 10, 2025

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
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Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method.

Shuichi Iwahashi1, A Ammar Ghaibeh2, Mitsuo Shimada1

  • 1Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan.

Molecular and Clinical Oncology
|September 3, 2020
PubMed
Summary

Data mining accurately predicts hepatocellular carcinoma (HCC) recurrence after surgery. Key factors like age and viral hepatitis help identify patients at high risk for cancer recurrence.

Keywords:
artificial intelligencedata mining methodhepatocellular carcinomapostoperative recurrenceslternating decision tree

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The Influence of Liver Resection on Intrahepatic Tumor Growth
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Area of Science:

  • Hepatocellular carcinoma (HCC) research
  • Medical data mining
  • Oncology

Background:

  • Hepatocellular carcinoma (HCC) is a deadly cancer with frequent postoperative recurrence.
  • Predicting recurrence is crucial for patient management.

Purpose of the Study:

  • To apply data mining for predicting postoperative recurrence in HCC patients.
  • To identify key factors influencing HCC recurrence after hepatic resection.

Main Methods:

  • Analysis of clinicopathological data from 323 HCC patients undergoing hepatic resection.
  • Utilizing an alternating decision tree (ADT) model.
  • Validation through 10-fold cross-validation.

Main Results:

  • The ADT model achieved an average accuracy of 69.0±8.2%, sensitivity of 59.7±14.5%, and specificity of 77.7±10.2%.
  • Identified recurrence predictors include age, viral hepatitis, stage, GOT, and T-cholesterol levels.

Conclusions:

  • Data mining effectively identifies significant factors for HCC postoperative recurrence.
  • These identified factors can aid in predicting HCC recurrence risk.