RESUMO
Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.
RESUMO
PURPOSE: To determine the readability of Internet-based patient education materials (IPEMs) created by United States hospitals and universities and clinical practices and miscellaneous health care-associated Web sites regarding uterine artery embolization (UAE) as a marker for IPEMs in general. METHODS AND METHODS: Two hundred unique Web sites were evaluated for patient-related articles on UAE. Web sites produced by US hospitals and universities and clinical practices, as well as miscellaneous health care-associated Web sites meeting the Health on the Net Foundation Code of Conduct criteria were included in the database. By using mathematical regression algorithms based on word and sentence length to quantitatively analyze reading materials for language intricacy, readability of 40 UAE-related IPEMs was assessed with four indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (GFOG). Scores were evaluated against national recommendations, and intergroup analysis was performed. RESULTS: None of the IPEMs were written at or below the sixth-grade reading level, based on FKGL. The mean readability scores were as follows: FRES, 43.98; FKGL, 10.76; SMOG, 13.63; and GFOG, 14.55. These scores indicate that the readability of UAE IPEMs is written at an advanced level, significantly above the recommended 6th grade reading level (P<.05) determined by the United States Department of Health and Human Services. CONCLUSIONS: IPEMs related to UAE generated by hospitals, clinical practices, and miscellaneous health care-associated Web sites are written above the recommended sixth grade level. IPEMs for other disease entities may also reflect similar results.