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Related Concept Videos

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Related Experiment Video

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.

Guiying Du1,2, Lihua Chen3, Baole Wen4

  • 1Department of Radiology, The First Central Clinical College, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China.

International Urology and Nephrology
|December 13, 2024
PubMed
Summary

A novel deep learning model combining multiparametric MRI and clinical data accurately predicts renal cell carcinoma (RCC) aggressiveness. This fusion approach enhances non-invasive tumor grading for better patient management.

Keywords:
Deep learningMulti-parametric MRIPredictive modelingRenal cell carcinomaTumor aggressiveness

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Renal cell carcinoma (RCC) aggressiveness impacts treatment decisions.
  • Accurate non-invasive prediction of RCC aggressiveness is crucial.
  • Multiparametric MRI offers potential for non-invasive tumor assessment.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN) model for predicting RCC aggressiveness.
  • To integrate multiparametric MRI data with clinical characteristics for improved prediction.
  • To evaluate the diagnostic performance of the developed fusion model.

Main Methods:

  • Multiparametric abdominal MRI data from 47 RCC patients were analyzed.
  • A CNN model was developed using various MRI parameters (b values, ADC, IVIM, DKI).
  • Clinical features (tumor stage, size, fat invasion) were identified using LASSO regression and fused with MRI data.

Main Results:

  • The fusion model combining clinical features and b-values (0, 1000) achieved the highest Area Under the Curve (AUC) of 0.861.
  • This fusion model demonstrated superior predictive accuracy compared to models using only MRI parameters or clinical features alone.
  • Key clinical predictors identified included fat invasion, tumor stage, and size.

Conclusions:

  • A CNN-based fusion model integrating multiparametric MRI and clinical data effectively predicts RCC tumor aggressiveness.
  • This non-invasive approach holds significant promise for preoperative assessment of RCC.
  • Deep learning facilitates enhanced prediction of tumor behavior in RCC patients.