2022
DOI: 10.3389/fonc.2022.839621
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Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer

Abstract: ObjectivesThis 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.MethodsA 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 trainin… Show more

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Cited by 25 publications
(22 citation statements)
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“…In addition to the previously mentioned two tasks, machine learning methods have also been introduced to predict the biological characteristics of prostate cancer, which might be able to affect the treatment decision making. Extracapsular extension has been one frequently evaluated factor 209,212,218–220,227 . Other factors, including Ki‐67, S100, perineural invasion, and surgical margin, have also been investigated 218,227 .…”
Section: Machine Learning In Multiparametric Mrimentioning
confidence: 99%
See 2 more Smart Citations
“…In addition to the previously mentioned two tasks, machine learning methods have also been introduced to predict the biological characteristics of prostate cancer, which might be able to affect the treatment decision making. Extracapsular extension has been one frequently evaluated factor 209,212,218–220,227 . Other factors, including Ki‐67, S100, perineural invasion, and surgical margin, have also been investigated 218,227 .…”
Section: Machine Learning In Multiparametric Mrimentioning
confidence: 99%
“…Extracapsular extension has been one frequently evaluated factor. 209,212,[218][219][220]227 Other factors, including Ki-67, S100, perineural invasion, and surgical margin, have also been investigated. 218,227 Besides, there is one study predicting pelvic lymph node invasion for prostate cancer patients utilizing mpMRI-based radiomics models.…”
Section: Prostate Cancer Diagnosismentioning
confidence: 99%
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“…Our study's ROC curves indicated that the AP+VP model (without laboratory indices) was the best model for pathological grade prediction; therefore, radiomic features can be regarded as more important than other indices. As high-dimensional features, wavelet texture features cannot be easily deciphered by humans but can be used to detect tumor heterogeneity ( 34 , 35 ), whereas LoG-filtered texture features enhance image grayscale contrast and can also reflect tumor heterogeneity ( 36 , 37 ). Tumor heterogeneity reflected by wavelet texture features and LoG-filtered texture features explains why 12 wavelet texture features and 8 LoG-filtered texture features played leading roles in pathological grade prediction among the 24 features contained in the AP+VP model.…”
Section: Discussionmentioning
confidence: 99%
“…Other prognosis markers are currently being evaluated. For instance machine and deep learning [73] or radiomics, which consists in extracting data from imaging examination to lead to better diagnoses [74] might help to more precisely classify patients, in order to tailor the treatment strategy.…”
Section: Discussion and Future Directionsmentioning
confidence: 99%