# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
###### Abstract  
Prostate cancer is one of the most common cancers in the world. Due to the ageing of society and the extended life of the population, early diagnosis is a great challenge for healthcare. Unfortunately, the currently available diagnostic methods, in which magnetic resonance imaging (MRI) using the PIRADS protocol plays an increasingly important role, are imperfect, mostly in the inability to visualise small cancer foci and misinterpretation of the imagery data. Therefore, there is a great need to improve the methods currently applied and look for even better ones for the early detection of prostate cancer. In the presented research, anonymised MRI scans of 92 patients with evaluation in the PIRADS protocol were selected from the data routinely scanned for prostate cancer. Suspicious tissues were depicted manually under medical supervision. The texture features in the marked regions were calculated using the qMaZda software. The multiple-instance learning approach based on the SVM classifier allowed recognising between healthy and ill prostate tissue. The best F1 score equal to 0.77 with a very high recall equal to 0.70 and precision equal to 0.85 was recorded for the texture features describing the central zone. The research showed that the use of texture analysis in prostate MRI may allow for automation of the assessment of PIRADS scores. prostate cancer; MRI; PIRADS; texture analysis; multiple instance learning; support vector machine +
Footnote †: journal: Medical  
Attention should be paid to patients from the previously mentioned risk groups. A proper path of basic diagnostic methods that allows the early determination of prostate cancer is crucial. Currently, the level of prostate specific antigen (PSA) in blood is determined and a digital rectal examination is suggested. However, the diagnostic value of physical examination alone is low [7]. In addition, due to the imperfection of PSA blood concentration, which results in difficulties in introducing this marker to the general population, more accurate methods of early diagnosis of prostate cancer are sought. In analogy to other types of cancer where imaging is used as a screening, there is room for magnetic resonance imaging (MRI) to be introduced to a larger group of patients. Perhaps in a simplified form, for example, as noncontrast biparametric MRI (nbMRI), T2-weighted image (TW2-MRI), and diffusion-weighted imaging (DWI) [8; 9]. Performing MRI as a screening test results in higher detection of clinically significant cancer compared to PSA testing. Another modality considered may be the ultrasonography. Although the superiority of ultrasound over PSA has not been proven. Newer protocols, for example, elastography, require evaluation of effectiveness as a test more accessible than MRI [10]. Taking into account the high degree of genetic transmission, work is underway to better understand the molecular origin and search for genes responsible for the development of various types of prostate cancer [11; 12; 13]. Among the thousands of candidate genes, the development of prostate cancer at an early age and the worse course of the disease have been shown in the case of breast cancer gene mutations [14; 15]. Finding gene mutations in the group of patients with an aggressive course of prostate cancer allows the screening of family members, allowing early diagnosis and effective treatment [16]. PIRADS is an acronym for the Prostate Imaging Reporting and Data System. It was introduced in 2012 in a consensus document summarizing the methods of interpretation of the sequences used for prostate imaging and the assessment of the probability of tumour presence [17; 18]. After two years of practical experience with this approach, PIRADS 2.0 was introduced and it contained changes that allowed providing a more accurate data interpretation process [19; 20]. However, the observation of interpretational differences between different teams, and also technical development, led to the introduction of PIRADS 2.1 in 2018 [21; 22].