Polycyclic aromatic hydrocarbons (PAHs) are typical and ubiquitous organic pollutants. Vapor pressures, which can be classified as solid vapor pressure (P(S)) and (subcooled) liquid vapor pressure (P(L)), are key physicochemical properties governing the environmental fate of organic pollutants. It is of great importance to develop predictive models of vapor pressures. In the present study, partial least squares (PLS) regression together with 15 theoretical molecular structural descriptors was used to develop quantitative predictive models for vapor pressures of PAHs at different temperatures. Two procedures were adopted to develop the optimal predictive models by eliminating redundant molecular structural descriptors. The cross-validated Q2(cum) values for the obtained models have been found higher than 0.975, indicating good predictive ability and robustness of the models. It has been shown that the intermolecular dispersive interactions played a leading role in governing the values of log P(L). In addition to dispersive interactions, dipole-dipole interactions also played a secondary role in determining the magnitude of log P(S). In view of the scarceness of chemical standards for some PAHs, the difficulty in experimental determinations, and the high cost involved in experimental determinations, the obtained models should serve as a fast and simple first approximation of the vapor pressure values for PAHs at different environmental temperatures.
Controlled ovarian hyperstimulation (COH) using a gonadotrophin-releasing
Background: Accurate intraoperative diagnosis of sentinel lymph node (SLN) metastases enables the selection of patients for axillary lymph node dissection during the same operation and reduces the need for re-operation. Touch imprint cytology (TIC) serves as a main intraoperative assessment of SLNs in our institute for over five years. The purpose of the present study is to evaluate the clinical value of TIC as an intraoperative assessment for the diagnosis of SLN. Methods: Patients treated for early-stage breast cancer between Feb-2005 and May-2010 enrolled in the study. TIC was routinely performed intraoperatively, the result of which was correlated with definitive histological assessments of serial section with Hematoxylin-Eosin staining. Subsequent immunohistochemistry staining with CK-19 and MUC-1 were performed for research purposes. The Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram was applied in this retrospective study to estimate probability of SLN involvement of each case. Results: A total of 1,077 patients with early-stage breast cancer enrolled in the study, and 3,048 SLNs were successfully harvested during the surgeries. Among those, 265 (24.6%) patients proved to have at least one SLN that was positive for carcinoma. Altogether, 397 (13.0%) involved nodes were removed from patients in the aforementioned patient pool. Based on the final pathology report, the sensitivity, specificity and overall accuracy of TIC was 83.4%, 99.0% and 95.2%, respectively on a per patient basis, and 78.3%, 99.4% and 96.7%, respectively on a per node basis. The sensitivity for macrometastasis and micrometastasis are 88.6% and 39.3%, respectively on a per patient basis, while 87.4% and 31. 3%, respectively on a per node basis. Of the patients included in this study, 98.7% had a positive SLN within their first three harvested SLNs. All the patients who were at a <10% chance of SLNs metastases according to the MSKCC nomogram were proved to be node negative by the final pathology. Conclusion: TIC is feasible and is able to detect macrometastasis in SLNs with an acceptable accuracy for clinical use in early-stage breast cancer patients while its ability to detect micrometastasis is limited. Limiting intraoperative TIC to the first three harvested SLNs in the diagnosis of SLN metastasis may make this diagnostic procedure significantly cheaper and easier for pathologists to perform. The MSKCC nomogram could be applied as a screening tool. Intraoperative assessment for SLNs, such as TIC, could be spared for patients with extremely low MSKCC scores (<10%). Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P1-01-07.
Background: The Memorial Sloan-Kettering Cancer Center (MSKCC) developed a nomogram to predict the presence of sentinel lymph node (SLN) metastasis in breast cancer patients. In our study, The MSKCC nomogram performance for prediction of SLN metastases was assessed in Chinese breast cancer population. A new model (Shanghai Cancer Center Nomogram, SCC nomogram ) was developed with clinically relevant variables and possible advantages. Methods: Data were collected from 771 patients with successful SLN biopsy who were treated during March 2005 to June 2010. Touch imprint cytology (TIC) and serial section with H&E staining were performed routinely on each sentinel node. 580 SLN biopsy procedures from March 2005to November 2009were used as training group to validate the MSKCC nomogram and assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The predictive accuracy of MSKCC nomogram was assessed by calculating the area under the receiver-operating characteristic (ROC) curve (AUC). The SCC nomogram was created from the logistic regression model. The new model was subsequently applied to 191 sequential SLN biopsies from January 2010 to June 2010 as the validation group. Results: It was shown that age, tumor size, tumor type, histological grade, lymphovascular invasion and neural invasion was correlated with the probability of SLN metastasis by univariate analysis (P<0.05). By multivariate analysis, tumor size, histological grade and lymphovascular invasion were identified as independent predictors of SLN metastasis. The SCC nomogram was then developed with four variables associated with SLN metastasis: age, tumor size, histological grade and lymphovascular invasion. The new model was accurate and discriminating, with AUC of 0.773 when applied to the validation group, as compared to the MSKCC nomogram with AUC of 0.754 in the modeling group. The trend of actual probability in various decile groups was comparable to the predicted probability. For predicted probability cut-off points of 7% and 15%, the false-negative rates of SCC nomogram were 0% and 8.1%. Conclusion: As far as we know, this is the first study designed to evaluate the MSKCC nomogram and develop a new nomogram in Chinese early breast cancer population. Compared to the MSKCC nomogram, the SCC nomogram was developed with similar AUC but less variables and lower false-negative rates for low-probability subgroups. It could provide a more acceptable clinical accessory in the preoperative discussion with patients, especially in the very low risk of patients. For those patients, the SCC nomogram could be used to safely avoid a SLN procedure, thereby reducing postoperative morbidity, whereas the rate could be as high as 7% in the literature. Although the SCC nomogram that predicts metastasis of breast cancer in the sentinel lymph node performed well in Chinese breast cancer population, it is imperfect. The SCC nomogram was developed and validated in the single instite. The SCC model should be validated in different patient groups before it is demonstrated to be reproducible and would be applied widely. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-07-36.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.