Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study

Background: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients.Methods and findings: Within the European Network for Individualized Treatment of Endometrial Cancer (ENI-TEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with Conclusions: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.</div

Improved left ventricular endocardial border delineation and opacification with OPTISON (FS069), a new echocardiographic contrast agent Results of a phase III multicenter trial11Principal Investigators for the OPTISON Multicenter Trial: David S. Bach, MD, University of Michigan Medical Center, Ann Arbor, MI; Jorge Cheirif, MD, Ochsner Heart and Vascular Institute, New Orleans, LA; Jerald L. Cohen, MD, New Jersey VA Medical Center, East Orange, NJ; Linda J. Crouse, MD, Mid-America Cardiology Associates, Kansas City, MO; John Dent, MD, University of Virginia Medical Center, Charlottesville, VA; Candace Dick, MD, Hennipin County Medical Center, Minneapolis, MN; Samer Ellahham, MD,

AbstractObjectives. The echocardiographic contrast-enhancing effects and safety profile of ALBUNEX (a suspension of air-filled albumin microspheres) were compared with the new contrast agent OPTISON (formerly FS069: a suspension of albumin microspheres containing the gas perfluoropropane) in 203 patients with inadequate noncontrast echocardiograms.Background. The efficacy of ALBUNEX has been limited by its short duration of action. By using perfluoropropane instead of air within the microsphere, its duration of action is increased.Methods. Each patient received ALBUNEX (0.8 and 0.22 mL/kg) and OPTISON (0.2, 0.5, 3.0, and 5.0 mL) on separate days a minimum of 48 hours apart. Echocardiograms were evaluated for increase in left ventricular (LV) endocardial border length, degree of LV opacification, number of LV endocardial border segments visualized, conversion from a nondiagnostic to a diagnostic echocardiogram, and duration of contrast enhancement. A thorough safety evaluation was conducted.Results. Compared with ALBUNEX, OPTISON more significantly improved every measure of contrast enhancement. OPTISON increased well-visualized LV endocardial border length by 6.0 ± 5.1, 6.9 ± 5.4, 7.5 ± 4.7, and 7.6 ± 4.8 cm, respectively, for each of the four doses, compared with only 2.2 ± 4.5 and 3.4 ± 4.6 cm, respectively, for the two ALBUNEX doses (p < 0.001). 100% LV opacification was achieved in 61%, 73%, 87%, and 87% of the patients with the four doses of OPTISON, but in only 16% and 36% of the patients with the two ALBUNEX doses (p < 0.001). Conversion of nondiagnostic to diagnostic echocardiograms with contrast occurred in 74% of patients with the optimal dose of OPTISON (3.0 mL) compared with only 26% with the optimal dose of ALBUNEX (0.22 mL/kg) (p < 0.001). The duration of contrast effect was also significantly greater with OPTISON than with ALBUNEX. In a subset of patients with potentially poor transpulmonary transit of contrast (patients with chronic lung disease or dilated cardiomyopathy), OPTISON more significantly improved the same measures of contrast enhancement compared with ALBUNEX and did so to the same extent as in the overall population. Side effects were similar and transient with the two agents.Conclusion. OPTISON appears to be a safe, well-tolerated echocardiographic contrast agent that is superior to ALBUNEX

The cytokeratin 17 expression in primary ovarian tumors has diagnostic but not prognostic significance

We assessed the value of cytokeratin 17 (CK17) expression for the differential diagnosis between primary ovarian mucinous tumors and metastases from the gastrointestinal tract (GIT) and the significance of CK17 expression in a broad spectrum of primary ovarian tumors with respect to their prognosis. The sample set consisted of 554 primary ovarian tumors and 255 GIT tumors. In the primary ovarian tumors, a higher CK17 expression (in > 10% of tumors cells) was present only in 0-11.4% of all tumors (including mucinous tumors, micropapillary serous borderline tumors, clear cell, endometrioid, and high-grade serous carcinomas). The only exception was low-grade serous carcinoma, where higher CK17 expression was present in 24% of cases. Concerning GIT tumors, the higher levels of CK 17 expression (in > 10% of tumor cells) were observed in the upper GIT tumors (68.5% of pancreatic ductal adenocarcinoma, 61.6% of gallbladder adenocarcinoma, and 46% of gastric adenocarcinoma), which differs substantially not only from most of the primary ovarian tumors, but also from colorectal carcinoma (3.7%; p < 0.001). The results of our study suggest that expression of CK17 can potentially be used as an adjunct marker in differential diagnosis between primary ovarian mucinous tumors and metastases from the upper GIT, but not from colorectal carcinoma. However, in GIT tumors, CK17 can be used in the differential diagnosis between adenocarcinomas of the upper and lower GIT. Statistical analysis did not reveal strong association of CK17 expression with clinicopathological variables or patient outcomes in any primary ovarian tumors

Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer

Objective: Pre-operative immunohistochemical (IHC) biomarkers are not incorporated in endometrial cancer (EC) risk classification. We aim to investigate the added prognostic relevance of IHC biomarkers to the ESMO-ESGO-ESTRO risk classification and lymph node (LN) status in EC. Methods: Retrospective multicenter study within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), analyzing pre-operative IHC expression of p53, L1 cell-adhesion molecule (L1CAM), estrogen receptor (ER) and progesterone receptor (PR), and relate to ESMO-ESGO-ESTRO risk groups, LN status and outcome. Results: A total of 763 EC patients were included with a median follow-up of 5.5-years. Abnormal IHC expression was present for p53 in 112 (14.7%), L1CAM in 79 (10.4%), ER- in 76 (10.0%), and PR- in 138 (18.1%) patients. Abnormal expression of p53/L1CAM/ER/PR was significantly related with higher risk classification groups, and combined associated with the worst outcome within the ‘high and advanced/metastatic’ risk group. In multivariate analysis p53-abn, ER/PR- and ESMO-ESGO-ESTRO ‘high and advanced/metastatic’ were independently associated with reduced disease-specific survival (DSS). Patients with abnormal IHC expression and lymph node metastasis (LNM) had the worst outcome. Patients with LNM and normal IHC expression had comparable outcome with patients without LNM and abnormal IHC expression. Conclusion: The use of pre-operative IHC biomarkers has important prognostic relevance in addition to the ESMO-ESGO-ESTRO risk classification and in addition to LN status. For daily clinical practice, p53/L1CAM/ER/PR expression could serve as indicator for surgical staging and refine selective adjuvant treatment by incorporation into the ESMO-ESGO-ESTRO risk classification

Time-Resolved Study of the Photo-Curing Process of Dental Resins with the NMR-MOUSE

The photo-curing reaction of dental resins has been examined with unilateral nuclear magnetic resonance (NMR-MOUSE) allowing nondestructive high-resolution measurement of depth profiles as a function of time and space. The NMR signal is sensitive to both the monomer concentration and changes in molecular mobility. Upon irradiation with blue light, it first increases due to molecular mobility enhanced by the reaction heat and then decreases exponentially with the monomer concentration as the polymer signal is lost in the dead time of the instrument upon curing. The space and time dependence of the NMR signal can be described by the photo-polymerization reaction kinetics together with a heuristic approximation of the temperature dependence. © 2013 Springer-Verlag Wien

Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: a development and validation study

Background: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. Methods and findings: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. Conclusions: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic