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 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.ResultsRF 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.ConclusionsRadiomics 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.
Highlights
Prostate cancer (PCa) is the highest incidence cancer and the second leading cause of death among men according to the latest statistics in 2021 [1]
The exclusion criteria are as follows: [1] Preoperative dynamic contrast enhanced (DCE)-MRI were unavailable (n = 149); [2] No mass lesion found on MRI image (n = 9); [3] Missing diffusion-weighted imaging (DWI) (n = 3); [4] Poor imaging quality (n = 10); [5] Missing clinical information (n = 5); [6] Biopsy before MRI leading to unclear lesions (n = 32); and [7] Previous treatment before MRI examinations (n = 12)
The clinical and pathological information we collected in this study is as follows: age, prostate serum antigen (PSA), white blood cell (WBC), red blood cell (RBC), hemoglobin, lymphocyte, platelet, albumin, alkaline phosphatase (ALP), platelet-to-lymphocyte ratio (PLR), fibrinogen, surgical Gleason score, immunohistochemistry (Ki67, S100, AR), Surgical margin (SM), Extracapsular extension (ECE), perineural invasion (PNI), seminal vesicle invasion (SVI), and lymphatic vascular invasion (LVI)
Summary
Prostate cancer (PCa) is the highest incidence cancer and the second leading cause of death among men according to the latest statistics in 2021 [1]. Some tumor biomarkers and biological characteristics have been proved to be useful for evaluating the malignant potential of prostate cancer and may influence the treatment decision-making [4,5,6]. Numerous studies have disclosed that positive SM increased cancer-specific mortality and the likelihood of biochemical recurrence of patients significantly [12,13,14]. PNI has been documented to be associated with biochemical recurrence (BCR) and promoting tumor aggressiveness [16, 17]. Judging these biological characteristics before operation can better evaluate the invasiveness of tumor and may change the clinical decision-making patterns in the future
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