Effect of allogeneic intraoperative blood transfusion on survival in patients treated with radical cystectomy for nonmetastatic bladder cancer: Results from a single high-volume institution

Transfusion has been related to poor survival after surgery in several cancers. Recently, timing of transfusion has been proposed as crucial in the determination of poor survival expectanies after surgery, in fact, intra- operative but not postoperative transfusion were found to be related. We confirmed these findings in patients who underwent radical cystectomy because of bladder cancer; physicians should avoid use of transfusion intraoperatively. Background: Previous studies have demonstrated that perioperative blood transfusion (BT) is associated with a significantly increased risk of cancer recurrence and mortality after radical cystectomy (RC). Recently, it was shown for the first time that intraoperative transfusion has a detrimental effect on cancer survival. The aim of the current study was to validate this finding in a single European institution. Patients and Methods: The study focused on 1490 consecutive nonmetastatic bladder cancer patients treated with RC at a single tertiary care referral center between January 1990 and August 2013. KaplaneMeier analyses and Cox regression analyses were used to assess the effect of timing of BT administration (no transfusion vs. intraoperative transfusion vs. postoperative transfusion vs. intra- operative and postoperative transfusion) on cancer-specific mortality (CSM), overall mortality (OM), and disease recurrence. Results: Mean age at the time of RC was 67 years. Overall, 322 (21.6%) patients received intraoperative BT and 97 (6.5%) received postoperative BT. At a mean follow-up time of 125 months (median, 110 months), the 5- and 10-year CSM rate was 846 (58%) and 715 (48%), respectively. In multivariable analyses patients who received intraoperative BT had greater risk of disease recurrence (hazard ratio [HR], 1.24; P .2). Conclusion: Our study confirms that intraoperative, but not postoperative BT, are related to a detrimental effect on survival after RC. These results should be take into account by physicians to administer BT using the correct timing

Anti-EphA2 Antibodies with Distinct In Vitro Properties Have Equal In Vivo Efficacy in Pancreatic Cancer

The EphA2 receptor tyrosine kinase is overexpressed in a variety of human epithelial cancers and is a determinant of malignant cellular behavior in pancreatic adenocarcinoma cells. Moreover, it is expressed in tumor endothelium and its activation promotes angiogenesis. To better clarify the therapeutic potential of monoclonal antibodies (mAbs) directed to the EphA2 receptor, we generated a large number of mAbs by differential screening of phage-Ab libraries by oligonucleotide microarray technology and implemented a strategy for the rapid identification of antibodies with the desired properties. We selected two high-affinity and highly specific EphA2 monoclonal antibodies with different in vitro properties on the human pancreatic tumor cell line MiaPaCa2. One is a potent EphA2-agonistic antibody, IgG25, that promotes receptor endocytosis and subsequent degradation, and the second is a ligand antagonist, IgG28, that blocks the binding to ephrin A1 and is cross-reactive with the mouse EphA2 receptor. We measured the effect of antibody treatment on the growth of MiaPaCa2 cells orthotopically transplanted in nude mice. Both IgG25 and IgG28 had strong antitumor and antimetastatic efficacy. In vivo treatment with IgG25 determined the reduction of the EphA2 protein levels in the tumor and the phosphorylation of FAK on Tyr576 while administration of IgG28 caused a decrease in tumor vascularization as measured by immunohistochemical analysis of CD31 in tumor sections. These data show that in a pancreatic cancer model comparable therapeutic efficacy is obtained either by promoting receptor degradation or by blocking receptor activation

Molecular Imaging Diagnosis of Renal Cancer Using 99mTc-Sestamibi SPECT/CT and Girentuximab PET-CT-Current Evidence and Future Development of Novel Techniques

: Novel molecular imaging opportunities to preoperatively diagnose renal cell carcinoma is under development and will add more value in limiting the postoperative renal function loss and morbidity. We aimed to comprehensively review the research on single photon emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography computed tomography (PET-CT) molecular imaging and to enhance the urologists' and radiologists' knowledge of the current research pattern. We identified an increase in prospective and also retrospective studies that researched to distinguish between benign and malignant lesions and between different clear cell renal cell carcinoma subtypes, with small numbers of patients studied, nonetheless with excellent results on specificity, sensitivity and accuracy, especially for 99mTc-sestamibi SPECT/CT that delivers quick results compared to a long acquisition time for girentuximab PET-CT, which instead gives better image quality. Nuclear medicine has helped clinicians in evaluating primary and secondary lesions, and has lately returned with new and exciting insights with novel radiotracers to reinforce its diagnostic potential in renal carcinoma. To further limit the renal function loss and post-surgery morbidity, future research is mandatory to validate the results and to clinically implement the diagnostic techniques in the context of precision medicine

The emerging landscape of tumor marker panels for the identification of aggressive prostate cancer: the perspective through bibliometric analysis of an Italian translational working group in uro-oncology

Molecular heterogeneity and availability of different therapeutic strategies are relevant clinical features of prostate cancer. On this basis, there is an urgent need to identify prognostic and predictive biomarkers for an individualized therapeutic approach. In this context, researchers focused their attention on biomarkers able to discriminate potential life-threatening from organ-confined disease identify high-grade tumors. Such biomarker could provide aid in clinical decision making, helping in order to choose the treatment which ensures the best results in terms of patient survival and quality of life. To address this need, many new laboratory tests have been proposed, witha clear tendency to use panels of combined biomarkers. In this review we evaluate current data on the application in clinical practice for of the most promising laboratory tests: Phi, 4Kscore and Stockholm 3 as circulating biomarkers, and Mi-prostate score, Exo DX Prostate and Select MD-X as urinary biomarkers, Confirm MDx, Oncotype Dx, Prolaris and Decipher as tissue biomarkers. In particular, the ability of these tests in the identification of clinically significant PCa and their potential use for precision medicine have been explored in this review

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

: Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions

Three vs. Four Cycles of Neoadjuvant Chemotherapy for Localized Muscle Invasive Bladder Cancer Undergoing Radical Cystectomy: A Retrospective Multi-Institutional Analysis

Three or four cycles of cisplatin-based chemotherapy is the standard neoadjuvant treatment prior to cystectomy in patients with muscle-invasive bladder cancer. Although NCCN guidelines recommend 4 cycles of cisplatin-gemcitabine, three cycles are also commonly administered in clinical practice. In this multicenter retrospective study, we assessed a large and homogenous cohort of patients with urothelial bladder cancer (UBC) treated with three or four cycles of neoadjuvant cisplatin-gemcitabine followed by radical cystectomy, in order to explore whether three vs. four cycles were associated with different outcomes

Quality assurance for automatically generated contours with additional deep learning

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior