The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.

Background: Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. Methods: Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. Results: Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. Conclusions: There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

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.