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Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer
OBJECTIVES: This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. METHODS: A total of 252 patients were retrospectively included who underwent radical prostatecto...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859464/ https://www.ncbi.nlm.nih.gov/pubmed/35198452 http://dx.doi.org/10.3389/fonc.2022.839621 |
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author | Fan, Xuhui Xie, Ni Chen, Jingwen Li, Tiewen Cao, Rong Yu, Hongwei He, Meijuan Wang, Zilin Wang, Yihui Liu, Hao Wang, Han Yin, Xiaorui |
author_facet | Fan, Xuhui Xie, Ni Chen, Jingwen Li, Tiewen Cao, Rong Yu, Hongwei He, Meijuan Wang, Zilin Wang, Yihui Liu, Hao Wang, Han Yin, Xiaorui |
author_sort | Fan, Xuhui |
collection | PubMed |
description | OBJECTIVES: This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. METHODS: A total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC. RESULTS: RF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests. CONCLUSIONS: Radiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer. |
format | Online Article Text |
id | pubmed-8859464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88594642022-02-22 Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer Fan, Xuhui Xie, Ni Chen, Jingwen Li, Tiewen Cao, Rong Yu, Hongwei He, Meijuan Wang, Zilin Wang, Yihui Liu, Hao Wang, Han Yin, Xiaorui Front Oncol Oncology OBJECTIVES: This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. METHODS: A total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC. RESULTS: RF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests. CONCLUSIONS: Radiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer. Frontiers Media S.A. 2022-02-07 /pmc/articles/PMC8859464/ /pubmed/35198452 http://dx.doi.org/10.3389/fonc.2022.839621 Text en Copyright © 2022 Fan, Xie, Chen, Li, Cao, Yu, He, Wang, Wang, Liu, Wang and Yin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Fan, Xuhui Xie, Ni Chen, Jingwen Li, Tiewen Cao, Rong Yu, Hongwei He, Meijuan Wang, Zilin Wang, Yihui Liu, Hao Wang, Han Yin, Xiaorui Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title_full | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title_fullStr | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title_full_unstemmed | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title_short | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
title_sort | multiparametric mri and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859464/ https://www.ncbi.nlm.nih.gov/pubmed/35198452 http://dx.doi.org/10.3389/fonc.2022.839621 |
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