Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and
Xiaopeng He1, Yi Wei2, Hanmei Zhang2
1Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China; Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
Rationale And Objectives:
To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell carcinomas (ccRCCs).
Materials And Methods:
The multidetector computed tomography images of 227 ccRCCs were retrospectively analyzed. For each ccRCC, 14 conventional image features (CIFs) were extracted manually by two radiologists, and 556 texture features (TFs) were extracted by a free software application, MaZda (version 4.6). The high-dimensional dataset of these RSs was reduced using the least absolute shrinkage and selection operator. Five minimum mean squared error models (minMSEMs) for predicting the ccRCC histopathological grades were constructed from the CIFs, the TFs of the corticomedullary phase images (CMP), and the TFs of the parenchyma phase (PP) images and their combinations, respectively abbreviated as CIF-minMSEM, CMP-minMSEM, PP-minMSEM, CIF+CMP-minMSEM, and CIF+PP-minMSEM. The RSs of each model were fed 30 times consecutively into an ANN for machine learning, and the predictive accuracy of each time ML was recorded for the statistical analysis.
Results:
The five predictive models were constructed from 12, 19, and 10 features selected from the CIFs, the TFs of the CMP images, and that of PP images, respectively. On the basis of their accuracy across the whole cohort, the five models were ranked as follows: CIF+CMP-minMSEM (accuracy: 94.06% ± 1.14%), CIF + PP-minMSEM (accuracy: 93.32% ± 1.23%), CIF-minMSEM (accuracy: 92.26% ± 1.65%), CMP-minMSEM (accuracy: 91.76% ± 1.74%), and PP-minMSEM (accuracy: 90.89% ± 1.47%).
Conclusion:
Machine learning based on ANN helped establish an optimal predictive model, and TFs contributed to the development of high accuracy predictive models. The CIF+CMP-minMSEM showed the greatest accuracy for differentiating low- and high-grade ccRCCs.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
11:38Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
Related Concept Videos
01:27Imaging Studies III: Computed Tomography
01:10Computed Tomography
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
01:24Sequence Networks of Rotating Machines
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
01:29Renal Regulation of Acid-Base Balance
In the kidneys, cells within the proximal convoluted tubules (PCT) and the collecting ducts secrete hydrogen ions (H+) into the tubular fluid. Specifically, in the PCT, Na+/H+ antiporters secrete H+ while reabsorbing Na+.
However, the intercalated cells in...
01:19Graded Potential
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...