Abstract
PurposeTo determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions.Materials and MethodsA retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set.ResultsA total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: p < 0.001; testing set: p = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical–radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75–1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52–0.90) (p = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68–1.00) (p < 0.001). The decision curve analysis implies that the clinical–radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions.ConclusionThe clinical–radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.
Highlights
Prostate cancer (PCa) is one of the most common cancer and the second leading cause of cancer deaths among men [1]
Since considered as positive MRI finding, Prostate Imaging Reporting and Data System (PI-RADS) 3 lesion should always be biopsied according to European Association of Urology guidelines, which results in a diagnosis of clinically significant prostatic cancer (csPCa) in 3%–50% of the patients [4, 5]
Among the 103 lesions with a score of PI-RADS 3 were 28 cases of csPCa (27.2%), 70 cases of benign hyperplasia (67.9%), and 5 cases of clinically insignificant prostate cancer (ciPCa) (4.9%); 44.7% (46/103) lesions were located in transitional zone (TZ), and 55.3% (57/103) lesions were located in peripheral zone (PZ)
Summary
Prostate cancer (PCa) is one of the most common cancer and the second leading cause of cancer deaths among men [1]. The Prostate Imaging Reporting and Data System (PI-RADS) aims to standardize the interpretation and reporting of prostate MRI, which develop a 5point assessment to assist in identifying suspicious lesions and reflect their relative possibility of a clinically significant prostatic cancer (csPCa) [2]. The PI-RADS has an inability to resolve some ambiguity and uncertainty associated with some reporting criteria and lesion descriptors. Since considered as positive MRI finding, PI-RADS 3 lesion should always be biopsied according to European Association of Urology guidelines, which results in a diagnosis of csPCa in 3%–50% of the patients [4, 5]. Other studies have reported that cancer diagnosis rates range from 2% to 23% in PI-RADS 3 lesions and suggested that mostly they are benign lesions or nonsignificative cancers [6–8]. Determining which lesions are csPCa will help improve patients’ quality of life via avoiding unnecessary biopsies and overtreatment
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