scholarly journals Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3546
Author(s):  
Katarzyna Sylwia Dobruch-Sobczak ◽  
Hanna Piotrzkowska-Wróblewska ◽  
Piotr Karwat ◽  
Ziemowit Klimonda ◽  
Ewa Markiewicz-Grodzicka ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Quantitative Measure ◽  
Malignant Cell ◽  
Breast Cancer Patients ◽  
The Third ◽  
Leibler Divergence

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

Incorporation of Treatment Response, Tumor Grade and Receptor Status Improves Staging Quality in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

2017 ◽  
Vol 24 (12) ◽  
pp. 3510-3517 ◽  
Author(s):  
John R. Bergquist ◽  
Brittany L. Murphy ◽  
Curtis B. Storlie ◽  
Elizabeth B. Habermann ◽  
Judy C. Boughey
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Cancer Patients ◽  
Tumor Grade ◽  
Breast Cancer Patients ◽  
Receptor Status

scholarly journals Systemic Immune-Inflammation Index Is Superior to Neutrophil to Lymphocyte Ratio in Prognostic Assessment of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Cong Jiang ◽  
Yubo Lu ◽  
Shiyuan Zhang ◽  
Yuanxi Huang
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Regression Models ◽  
Roc Analysis ◽  
Neutrophil To Lymphocyte Ratio ◽  
Breast Cancer Patients ◽  
Lymphocyte Ratio ◽  
Immune Inflammation ◽  
The Impact

Background and Methods. As a parameter integrating neutrophil (N), lymphocyte (L), and platelet (P) levels, altered systemic immune-inflammation index (SII) has been investigated in a number of malignant tumor types. Here, we explore the impact of SII in a cohort of 249 breast cancer patients receiving neoadjuvant chemotherapy (NAC), investigating the prognostic value of SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). All patients had complete follow-up data and pathological confirmation of breast cancer by a core needle biopsy prior to NAC treatment and surgery. All blood samples were obtained within one week prior to NAC. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value for patient classification by SII, NLR, and PLR. Associations between clinicopathological variables by SII, NLR, and PLR were determined by a chi-squared test or Fisher’s exact test. Overall survival (OS) analysis was performed using Kaplan-Meier plots, log-rank tests, and Cox proportional hazards regression models. The Z test is used to compare the prognostic ability of SII, NLR, and PLR. Results. SII, NLR, and PLR did not define patient groups with distinct clinicopathological characteristics. SII, NLR, and PLR cut-off values were 547, 2.13, and 88.23, as determined by ROC analysis; the corresponding areas under the curve (AUCs) were 0.625, 0.555, and 0.571, respectively. Cox regression models identified SII as independently associated with OS. Patients with low SII had prolonged OS (65 vs. 41 months, P = 0.017 , HR: 3.24, 95% CI: 1.23-8.55). In the Z test, the difference in AUC between SII and NLR was statistically significant ( Z = 2.721 , 95% CI: 0.0194-0.119, P = 0.0065 ). Conclusion. Our study suggests that the pretreatment SII value is significantly correlated with OS in breast cancer patients undergoing NAC and that the prognostic utility of SII is superior to that of NLR and PLR.

Evaluation of keratoconus progression

2018 ◽  
Vol 103 (4) ◽  
pp. 551-557 ◽  
Author(s):  
Mehdi Shajari ◽  
Gernot Steinwender ◽  
Kim Herrmann ◽  
Kate Barbara Kubiak ◽  
Ivana Pavlovic ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Youden Index ◽  
Back Surface ◽  
Corneal Surface ◽  
Maximum Elevation ◽  
Index Index ◽  
Variance Index

AimTo define variables for the evaluation of keratoconus progression and to determine cut-off values.MethodsIn this retrospective cohort study (2010–2016), 265 eyes of 165 patients diagnosed with keratoconus underwent two Scheimpflug measurements (Pentacam) that took place 1 year apart ±3 months. Variables used for keratoconus detection were evaluated for progression and a correlation analysis was performed. By logistic regression analysis, a keratoconus progression index (KPI) was defined. Receiver-operating characteristic curve (ROC) analysis was performed and Youden Index calculated to determine cut-off values.ResultsVariables used for keratoconus detection showed a weak correlation with each other (eg, correlation r=0.245 between RPImin and Kmax, p<0.001). Therefore, we used parameters that took several variables into consideration (eg, D-index, index of surface variance, index for height asymmetry, KPI). KPI was defined by logistic regression and consisted of a Pachymin coefficient of −0.78 (p=0.001), a maximum elevation of back surface coefficient of 0.27 and coefficient of corneal curvature at the zone 3 mm away from the thinnest point on the posterior corneal surface of −12.44 (both p<0.001). The two variables with the highest Youden Index in the ROC analysis were D-index and KPI: D-index had a cut-off of 0.4175 (70.6% sensitivity) and Youden Index of 0.606. Cut-off for KPI was −0.78196 (84.7% sensitivity) and a Youden Index of 0.747; both 90% specificity.ConclusionsKeratoconus progression should be defined by evaluating parameters that consider several corneal changes; we suggest D-index and KPI to detect progression.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P &lt; 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Procalcitonin as a marker of bacterial infection in children undergoing cardiac surgery with cardiopulmonary bypass

2011 ◽  
Vol 21 (4) ◽  
pp. 392-399 ◽  
Author(s):  
Pierre-Emmanuel Séguéla ◽  
Nicolas Joram ◽  
Bénédicte Romefort ◽  
Céline Manteau ◽  
Jean-Luc Orsonneau ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Bacterial Infection ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Confidence Intervals ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Infected Group ◽  
The Third ◽  
Operating Characteristic Curve

AbstractBackgroundOwing to systemic inflammatory response syndrome, the diagnosis of post-operative infection after cardiopulmonary bypass is difficult to assess in children with the usual clinical and biological tools. Procalcitonin could be informative in this context.MethodsRetrospective study in a paediatric intensive care unit. Blood samples were collected as soon as infection was clinically suspected and a second assay was performed 24 hours later. Using referenced criteria, children were retrospectively classified into two groups: infected and non-infected.ResultsOut of the 95 children included, 14 were infected. Before the third post-operative day, procalcitonin median concentration was significantly higher in the infected group than in the non-infected group – 20.24 nanograms per millilitre with a 25th and 75th interquartile of 15.52–35.71 versus 0.72 nanograms per millilitre with a 25th and 75th interquartile of 0.28 to 5.44 (p = 0.008). The area under the receiver operating characteristic curve was 0.89 with 95% confidence intervals from 0.80 to 0.97. The best cut-off value to differentiate infected children from healthy children was 13 nanograms per millilitre with 100% sensitivity – 95% confidence intervals from 51 to 100 – and 85% specificity – 95% confidence intervals from 72 to 91. After the third post-operative day, procalcitonin was not significantly higher in infected children – 2 nanograms per millilitre with a 25th and 75th interquartile of 0.18 to 12.42 versus 0.37 nanograms per millilitre with a 25th and 75th interquartile of 0.24 to 1.32 (p = 0.26). The area under the receiver operating characteristic curve was 0.62 with 95% confidence intervals from 0.47 to 0.77. A procalcitonin value of 0.38 nanograms per millilitre provided a sensitivity of 70% with 95% confidence intervals from 39 to 89 for a specificity of 52% with 95% confidence intervals from 34 to 68. After the third post-operative day, a second assay at a 24-hour interval can improve the sensitivity of the test.ConclusionsProcalcitonin seems to be a discriminating marker of bacterial infection during the post-operative days following cardiopulmonary bypass in children.

scholarly journals Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
K. Dobruch-Sobczak ◽  
H. Piotrzkowska-Wróblewska ◽  
Z. Klimonda ◽  
P. Karwat ◽  
K. Roszkowska-Purska ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Characteristic Curve ◽  
Poor Response ◽  
Maximum Diameter ◽  
Breast Cancer Patients ◽  
Backscatter Coefficient ◽  
Linear Discriminant ◽  
The Third ◽  
Multiparametric Ultrasound

AbstractTo investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

scholarly journals Circulating exosomal mRNA profiling identifies novel signatures for the detection of prostate cancer

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jin Ji ◽  
Rui Chen ◽  
Lin Zhao ◽  
Yalong Xu ◽  
Zhi Cao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Messenger Rnas ◽  
Sequencing Data ◽  
Detection Strategy ◽  
Great Performance ◽  
Diagnostic Signature ◽  
Different Characteristics

AbstractThe landscape and characteristics of circulating exosomal messenger RNAs (emRNAs) are poorly understood, which hampered the accurate detection of circulating emRNAs. Through comparing RNA sequencing data of circulating exosomes with the corresponding data in tissues, we illustrated the different characteristics of emRNAs compared to tissue mRNAs. We then developed an improved strategy for emRNA detection based on the features of circulating emRNAs. Using the optimized detection strategy, we further validated prostate cancer (PCa) associated emRNAs discovered by emRNA-seq in a large cohort of patients and identified emRNA signatures for PCa screening and diagnosis using logistic regression analysis. The receiver operating characteristic curve (ROC) analysis showed that the circulating emRNA-based screening signature yielded an area under the ROC curve (AUC) of 0.948 in distinguishing PCa patients from healthy controls. The circulating emRNA-based diagnostic signature also showed a great performance in predicting prostate biopsy results (AUC: 0.851). In conclusion, our study developed an optimized emRNA detection strategy and identified novel emRNA signatures for the detection of PCa.

scholarly journals Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Lin Qi ◽  
Ke Xue ◽  
Yongjun Cai ◽  
Jinjuan Lu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Logistic Models ◽  
Glass Ribbon ◽  
Ground Glass ◽  
Ct Features ◽  
Spread Through Air Spaces

ObjectivesThis study aimed to explore the predictive CT features of spread through air spaces (STAS) in patients with small-sized lung adenocarcinoma.MethodsFrom January 2017 to May 2019, patients with confirmed pathology of small-sized lung adenocarcinoma (less than or equal to 2 cm) and who underwent surgery were retrospectively analyzed. The clinical, pathological, and surgical information and CT features were analyzed.ResultsA total of 47 patients with STAS (males, 61.7%; mean age, 56 ± 8years) and 143 patients without STAS (males, 58%; mean age, 53 ± 11 years) were included. Pathologically, papillary, micropapillary, solid predominant subtypes, and vascular and pleural invasion were most commonly observed features in the STAS group. Radiologically, higher consolidation tumor ratio (CTR), presence of spiculation, satellites, ground glass ribbon sign, pleural attachment, and unclear tumor–lung interface were more commonly observed features in the STAS group. CTR, presence of ground glass ribbons and pleural connection, and absence of cystic airspaces were considered as stable predictors of STAS in multivariate logistic models. The receiver operating characteristic curve (ROC) analysis for predicting STAS demonstrated higher area under the curve (AUC) in the model that used CTR (0.760, 95% confidence interval, 0.69–0.83) for predicting STAS than in the model that used long diameter of entire lesion (0.640).ConclusionsCTR is the best CT sign for predicting STAS in small-sized lung adenocarcinoma. The ground glass ribbon is a newly found indicator and has the potential for predicting STAS.

scholarly journals A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

2021 ◽  
Vol 10 ◽  
Author(s):  
Dong Song ◽  
Xiaxia Man ◽  
Meng Jin ◽  
Qian Li ◽  
Han Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Prediction Method ◽  
Clinical Information ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Svm Model ◽  
Patient Prognosis

Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients.

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