Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection.

PURPOSE: To construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ). METHODS: A prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model. RESULTS: A total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy. CONCLUSION: The prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

Radiomics analysis based on CT's greater omental caking for predicting pathological grading of pseudomyxoma peritonei.

The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater omental caking as the imaging observation index, a clinical model including clinical indexes, and a combined model of these two. A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and combined model) were used to predict PMP pathological grading. The models' diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP's different pathological grades was evaluated. The results showed that the radiomics model based on the CT's greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power for determining PMP pathological grading. The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grading, which is important in the treatment selection method and prognosis assessment.

Prognostic Value of CEA, CA19-9, CA125, CA724, and CA242 in Serum and Ascites in Pseudomyxoma Peritonei.

PURPOSE: To investigate the expression of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), CA19-9, CA724, and CA242 in serum and ascites of pseudomyxoma peritonei (PMP) patients and evaluate the predictive value of these elevated biomarkers in pathological grade, completeness of cytoreduction (CC), and survival. METHODS: From May 2009 to October 2019, a total of 512 patients diagnosed with PMP through pathology in Aerospace Center Hospital were enrolled. The serum and ascites tumor biomarkers were obtained. The diagnostic values between serum and ascites biomarkers in pathology and CC were compared by the receiver operating characteristic (ROC) curves. The correlation between pathology, cytoreduction, and biomarkers was calculated by univariate and multivariate logistic regression. The associations between different numbers of elevated biomarkers and survival status were examined using univariate and multivariate backward Cox proportional hazard regression models. RESULTS: The results showed that the areas under the ROC curves (AUROC) in the diagnosis of CC were 0.798 (95% CI: 0.760-0.836) and 0.632 (95% CI: 0.588-0.676) in serum and ascites biomarkers, respectively. The elevated serum and ascites biomarkers were independent risk factors for both pathology and CC. The 1-year, 3-year, and 5-year survival rates were 89.07%, 73.22%, and 66.94%, respectively. Longer survival was observed in patients who had less than two elevated serum biomarkers compared with those with 2-3 and 4-5 elevated serum biomarkers (p < 0.001). CONCLUSION: CEA, CA125, CA19-9, CA724, and CA242 in serum and ascites can be used to judge the severity and predict the resectability. Furthermore, different numbers of elevated biomarkers can help determine the prognosis of PMP.