scholarly journals The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Hui-zhu Chen ◽  
Xin-rong Wang ◽  
Fu-min Zhao ◽  
Xi-jian Chen ◽  
Xue-sheng Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lymph Node ◽  
Clinical Decision Making ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Training Cohort ◽  
Portal Phase

PurposeTo develop and validate a radiomics model for predicting preoperative lymph node (LN) metastasis in high-grade serous ovarian cancer (HGSOC).Materials and MethodsFrom May 2008 to January 2018, a total of 256 eligible HGSOC patients who underwent tumor resection and LN dissection were divided into a training cohort (n=179) and a test cohort (n=77) in a 7:3 ratio. A Radiomics Model was developed based on a training cohort of 179 patients. A radiomics signature (defined as the Radscore) was selected by using the random forest method. Logistics regression was used as the classifier for modeling. An Integrated Model that incorporated the Radscore and CT_reported LN status (CT_LN_report) was developed and presented as a radiomics nomogram. Its performance was determined by the area under the curve (AUC), calibration, and decision curve. The radiomics nomogram was internally tested in an independent test cohort (n=77) and a CT-LN-report negative subgroup (n=179) using the formula derived from the training cohort.ResultsThe AUC value of the CT_LN_report was 0.688 (95% CI: 0.626, 0.759) in the training cohort and 0.717 (95% CI: 0.630, 0.804) in the test cohort. The Radiomics Model yielded an AUC of 0.767 (95% CI: 0.696, 0.837) in the training cohort and 0.753 (95% CI: 0.640, 0.866) in the test. The radiomics nomogram demonstrated favorable calibration and discrimination in the training cohort (AUC=0.821), test cohort (AUC=0.843), and CT-LN-report negative subgroup (AUC=0.82), outperforming the Radiomics Model and CT_LN_report alone.ConclusionsThe radiomics nomogram derived from portal phase CT images performed well in predicting LN metastasis in HGSOC and could be recommended as a new, convenient, and non-invasive method to aid in clinical decision-making.

scholarly journals Preclinical 89Zr Immuno-PET of High-Grade Serous Ovarian Cancer and Lymph Node Metastasis

2016 ◽  
Vol 57 (5) ◽  
pp. 771-776 ◽  
Author(s):  
S. K. Sharma ◽  
K. K. Sevak ◽  
S. Monette ◽  
S. D. Carlin ◽  
J. C. Knight ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Node Metastasis

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

scholarly journals Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery

2021 ◽  
Vol 10 ◽  
Author(s):  
Guofo Ma ◽  
Jie Kang ◽  
Ning Qiao ◽  
Bochao Zhang ◽  
Xuzhu Chen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Effective Means ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Support Vector ◽  
Benign Tumors ◽  
Incomplete Resection ◽  
Test Sets ◽  
Radiomics Signature

PurposeCraniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus. Hence, if a method can be developed to predict the invasiveness of CP preoperatively, it will help in the development of a more personalized surgical strategy. The aim of the study was to report a radiomics-clinical nomogram for the individualized preoperative prediction of the invasiveness of adamantinomatous CP (ACPs) before surgery.MethodsIn total, 1,874 radiomics features were extracted from whole tumors on contrast-enhanced T1-weighted images. A support vector machine trained a predictive model that was validated using receiver operating characteristic (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.ResultsEleven features associated with the invasiveness of ACPs were selected by using the least absolute shrinkage and selection operator (LASSO) method. These features yielded area under the curve (AUC) values of 79.09 and 73.5% for the training and test sets, respectively. The nomogram incorporating peritumoral edema and the radiomics signature yielded good calibration in the training and test sets with the AUCs of 84.79 and 76.48%, respectively.ConclusionThe developed model yields good performance, indicating that the invasiveness of APCs can be predicted using noninvasive radiological data. This reliable, noninvasive tool can help clinical decision making and improve patient prognosis.

scholarly journals Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics –Based Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Du ◽  
Yu Wang ◽  
Dongdong Li ◽  
Xueming Xia ◽  
Qiaoyue Tan ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Tumor Stage ◽  
Support Vector ◽  
Training Cohort ◽  
Clinical Model ◽  
Radiomics Signature

PurposeTo build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation.MethodThis study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models.ResultThe radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%).ConclusionRadiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.

scholarly journals Sphingosine-kinase-1 expression is associated with improved overall survival in high-grade serous ovarian cancer

2021 ◽  
Vol 147 (5) ◽  
pp. 1421-1430
Author(s):  
L. C. Hanker ◽  
A. El-Balat ◽  
Z. Drosos ◽  
S. Kommoss ◽  
T. Karn ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tumor Resection ◽  
Sphingosine Kinase ◽  
Sphingolipid Metabolism ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Sphingosine Kinase 1 ◽  
Cancer Pathogenesis

Abstract Purpose Sphingosine-kinase-1 (SPHK1) is a key enzyme of sphingolipid metabolism which is involved in ovarian cancer pathogenesis, progression and mechanisms of drug resistance. It is overexpressed in a variety of cancer subtypes. We investigated SPHK1 expression as a prognostic factor in epithelial ovarian cancer patients. Methods Expression analysis of SPHK1 was performed on formalin-fixed paraffin-embedded tissue from 1005 ovarian cancer patients with different histological subtypes using immunohistochemistry. Staining intensity of positive tumor cells was assessed semi-quantitatively, and results were correlated with clinicopathological characteristics and survival. Results In our ovarian cancer collective, high levels of SPHK1 expression correlated significantly with complete surgical tumor resection (p = 0.002) and lower FIGO stage (p = 0.04). Progression-free and overall survival were further significantly longer in patients with high-grade serous ovarian cancer and overexpression of SPHK1 (p = 0.002 and p = 0.006, respectively). Conclusion Our data identify high levels of SPHK1 expression as a potential favorable prognostic marker in ovarian cancer patients.

scholarly journals Expression of hormone receptors predicts survival and platinum sensitivity of high-grade serous ovarian cancer

2021 ◽  
Author(s):  
Jiahong Tan ◽  
Chunyan Song ◽  
Daoqi Wang ◽  
Yigang Hu ◽  
Dan Liu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Hormone Receptors ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Progression Free Survival ◽  
Immunohistochemical Analysis ◽  
Predictive Performance ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Platinum Sensitivity

High-grade serous ovarian cancer (HGSOC) has abundant expression of hormone receptors, including androgen receptor (AR), estrogen receptor α (ER), and progesterone receptor (PR). The effects of hormone receptors on prognosis of HGSOC were first evaluated in online databases. Their prognostic values were then explored and validated in our inhouse TJ-cohort (92 HGSOC patients) and in a validation cohort (33 HGSOC patients), wherein hormone receptors were detected immunohistochemically. High expression of hormone receptors denoted longer progression-free survival (PFS), overall survival (OS), and platinum-free interval (PFI). Platinum-sensitive patients had higher expression of hormone receptors than their counterparts. Correlation analysis revealed significant positive correlations between hormone receptors expression and survival. AR, ER, and PR had predictive and prognostic values, alone and in combination. By receiver operating characteristic curve (ROC) analysis, co-expression of AR, ER, and PR had an improved predictive performance with an area under the curve (AUC) value of 0.945. Expression of hormone receptors predicts survival and platinum sensitivity of HGSOC. AR, ER, and PR might be feasible prognostic biomarkers for HGSOC by immunohistochemical analysis.

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