Amirhossein Jalali

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients’ outcomes and quality of life.

Background: Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. Materials and methods: A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran’s three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. Results: The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650–0.734), as well as 0.627 (0.670, 0.686) in the test data. Conclusion: We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.

Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate cancer. Peripheral blood serum obtained from 436 men undergoing transrectal ultrasound guided biopsy were assessed for a panel of 18 inflammatory serum biomarkers in addition to Total and Free Prostate Specific Antigen (PSA). This panel was integrated into a previously developed Irish clinical risk calculator (IPRC) for the detection of prostate cancer and high-grade prostate cancer (Gleason Score ≥ 7). Using logistic regression and multinomial regression methods, two models (Logst-RC and Multi-RC) were developed considering linear and nonlinear effects of the panel in conjunction with clinical and demographic parameters for determination of the two endpoints. Both models significantly improved the predictive ability of the clinical model for detection of prostate cancer (from 0.656 to 0.731 for Logst-RC and 0.713 for Multi-RC) and high-grade prostate cancer (from 0.716 to 0.785 for Logst-RC and 0.767 for Multi-RC) and demonstrated higher clinical net benefit. This improved discriminatory power and clinical utility may allow for individualised risk stratification improving clinical decision making.