scholarly journals Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors

2021 ◽  
Vol 12 ◽  
Author(s):  
Lisha Qi ◽  
Dandan Chen ◽  
Chunxiang Li ◽  
Jinghan Li ◽  
Jingyi Wang ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Ultrasound Image ◽  
Model Performance ◽  
Subjective Assessment ◽  
Ovarian Tumors ◽  
Clinical Feature ◽  
Clinical Factors ◽  
Preoperative Diagnostic ◽  
Serous Tumors ◽  
Ccr Model

Objectives: To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors.Materials and methods: In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance.Results: The US-based radiomics models performed satisfactorily in both the tasks, showing especially higher accuracy in the second task by successfully discriminating borderline and malignant ovarian serous tumors compared to the evaluations by senior sonographers (AUC = 0.789 for seniors and 0.877 for radiomics models in task one; AUC = 0.612 for senior and 0.839 for radiomics model in task 2). We showed that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905–0.969 in task 1, AUC = 0.924, 95%CI 0.876–0.971 in task 2) in the training as well as in the validation cohorts (AUC = 0.914, 95%CI 0.851–0.976 in task 1, AUC = 0.890, 95%CI 0.794–0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of the tumors than the clinical features alone.Conclusion: This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between benign, borderline, and malignant ovarian serous tumors, thereby reducing unnecessary and risky biopsies and surgeries.

Evaluation of concomitant gynecological pathology in group of patients of reproductive age with tumors and tumor formations

2020 ◽  
pp. 10-14
Author(s):  
N. V. Spiridonova ◽  
A. A. Demura ◽  
V. Yu. Schukin
Keyword(s):  
Diagnostic Errors ◽  
Ovarian Tumors ◽  
Reproductive Age ◽  
Women Of Reproductive Age ◽  
Predisposing Factor ◽  
Ovarian Tumours ◽  
Preoperative Diagnostic ◽  
Neoplastic Process ◽  
History Of ◽  
The Moment

According to modern literature, the frequency of preoperative diagnostic errors for tumour-like formations is 30.9–45.6%, for malignant ovarian tumors is 25.0–51.0%. The complexity of this situation is asymptomatic tumor in the ovaries and failure to identify a neoplastic process, which is especially important for young women, as well as ease the transition of tumors from one category to another (evolution of the tumor) and the source of the aggressive behavior of the tumor. The purpose of our study was to evaluate the history of concomitant gynecological pathology in a group of patients of reproductive age with ovarian tumors and tumoroid formations, as a predisposing factor for the development of neoplastic process in the ovaries. In our work, we collected and processed complaints and data of obstetric and gynecological anamnesis of 168 patients of reproductive age (18–40 years), operated on the basis of the Department of oncogynecology for tumors and ovarian tumours in the Samara Regional Clinical Oncology Dispensary from 2012 to 2015. We can conclude that since the prognosis of neoplastic process in the ovaries is generally good with timely detection and this disease occurs mainly in women of reproductive age, doctors need to know that when assessing the parity and the presence of gynecological pathology at the moment or in anamnesis, it is not possible to identify alarming risk factors for the development of cancer in the ovaries.

Clinical-anamnestic analysis of the course and treatment of benign and borderline epithelial ovarian tumors

2016 ◽  
pp. 86-93
Author(s):  
M.Yu. Yegorov ◽  
◽  
A.A. Sukhanova ◽  
Keyword(s):  
Varicose Veins ◽  
Clinical Manifestations ◽  
Abnormal Uterine Bleeding ◽  
Abdominal Distension ◽  
Ovarian Tumors ◽  
Lower Limbs ◽  
Benign Tumors ◽  
Age Group ◽  
Venous Disorders ◽  
Serous Tumors

The objective: study the features of gynecological, physical history, diagnosis and treatment of patients with benign epithelial ovarian tumors (BeEOT) and borderline epithelial ovarian tumors (BEOT), determining the frequency of recurrence of ovarian tumors in the postoperative period. Patients and methods. According to a retrospective analysis of case histories of 112 women with epithelial ovarian tumors (EOT) underwent conservative or radical surgical treatment in a hospital, two groups were formed: I group – patients with benign epithelial ovarian tumors (BeEOT), which amounted to 85 (75.9%) women, and group II – patients with borderline epithelial ovarian tumors (BEOT), which amounted to 27 (24.1%) women. It was found that the main complaints of patients with EOT were pain (49.1%), abdominal distension (17%), and abnormal uterine bleeding (12.5%). The highest incidence of BeEOT (31.8%) observed in the age group of 41–50 years, while the peak incidence of BEOT (44.4%) corresponds to the age group of 51–60 years. Results. In BEOT endocrine pathology occurs significantly more frequently (p<0.05) than in BeEOT – 25.9% vs. 9.4%, respectively. Pathology of pancreatic-hepatobiliary system occurs significantly more frequently (p<0.05) in patients with BEOT compared with BeEOT – 81.5% versus 57.6%, respectively. Venous disorders (varicose veins of the pelvic organs, lower limbs, haemorrhoids) observed in BEOT significantly more frequently (p<0.05) than in BeEOT – 18.5% vs. 5.9%, respectively. EOT most often diagnosed in the period from 1 to 6 months after the first clinical manifestations with an average uptake of medical care 4.6±0.57 months. In assessing of peritoneal exudate cytogram the mesothelium cells are significantly more common for BeEOT (p<0.01) than BEOT – 79.4% versus 40.9%, respectively. Cervicitis is more likely significantly to occur in BeEOT (p<0.01) than in BEOT – 29.4% vs. 7.4%, respectively. The most common histological type among the benign tumors of the ovaries are endometriomas, which occurred in 48.2% of all BeEOT cases, and among the borderline tumors – serous tumors, which accounted for 59.3% of all BEOTs. Conclusion. The use of organ sparing surgery in EOT increases the risk of recurrence, especially in the case of endometrial histology or borderline variant of tumor. Key words: benign and borderline epithelial ovarian tumors, clinical-anamnestic analysis, diagnosis, treatment.

Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas

2021 ◽  
Author(s):  
Hassan Bagher Ebadian ◽  
Farzan Siddiqui ◽  
Ahmed Ghanem ◽  
Simeng Zhu ◽  
Mei Lu ◽  
...  
Keyword(s):  
Human Papilloma Virus ◽  
Squamous Cell ◽  
Ct Images ◽  
Smoking History ◽  
Clinical Feature ◽  
Clinical Factors ◽  
Predictive Values ◽  
Papilloma Virus ◽  
T Stage ◽  
Hpv Status

Abstract Purpose: To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC). Methods: One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using ‘in-house’ software (‘ROdiomiX’) developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers. Results: Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance. Conclusion: Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.

scholarly journals Performance of Selected Models for Predicting Malignancy in Ovarian Tumors in Relation to the Degree of Diagnostic Uncertainty by Subjective Assessment With Ultrasound

2019 ◽  
Vol 39 (5) ◽  
pp. 939-947
Author(s):  
Sebastian Szubert ◽  
Dariusz Szpurek ◽  
Andrzej Wójtowicz ◽  
Patryk Żywica ◽  
Maciej Stukan ◽  
...  
Keyword(s):  
Subjective Assessment ◽  
Ovarian Tumors ◽  
Diagnostic Uncertainty

scholarly journals Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

2021 ◽  
Author(s):  
Yaqian Mao ◽  
Lizhen Xu ◽  
Ting Xue ◽  
Jixing Liang ◽  
Wei Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Calibration Curve ◽  
Model Performance ◽  
Clinical Decision ◽  
Lasso Regression ◽  
Threshold Probability ◽  
Male Population ◽  
Incidence Risk ◽  
Chinese Male

Objective: To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40years) based on data mining technology. Materials and methods: A total of 1,834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. Results: The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95%CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1% and 100%, the nomogram had good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. Conclusions: This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population.

Controversial issues of clinical, laboratory and instrumental diagnostics of ovarian tumors of reproductive women (literature review)

GYNECOLOGY ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 7-13
Author(s):  
Nataliia M. Podzolkova ◽  
Vasilii B. Osadchev ◽  
Kirill V. Babkov ◽  
Natalia E. Safonova
Keyword(s):  
Clinical Laboratory ◽  
Malignant Tumors ◽  
Reproductive Function ◽  
Ovarian Tumors ◽  
Diagnostic Measures ◽  
Early Stages ◽  
Modern Methods ◽  
Preoperative Diagnostic ◽  
Women Literature ◽  
The Individual

The problem of differential diagnostic of benign and malignant tumors in the early stages is one of the most significant in practical gynecology. Now clear criteria for the diagnosis and screening of ovarian tumors are not developed. Early stages of disease are asymptomatic and even when first symptoms appeared, patients often dont consult a doctor or doctor doesnt recommend surgical treatment preferring dynamic observation. Modern diagnostic of ovarian tumors cant be based on one method of research and requires a whole complex of diagnostic measures which determines the individual plan of treatment in each case. The most modern methods for studying are complex using of biomarkers, including creation mathematical risk models of ovarian tumor malignancy, based on instrumental and laboratory techniques. Despite on the successes in the detection of ovarian tumors, it is needed to study new modern methods of early preoperative diagnostic in different age periods, and especially those women who planes realize reproductive function.

Borderline Ovarian Tumors

2021 ◽  
pp. 223-239
Author(s):  
George Pados ◽  
Dimitrios Zouzoulas
Keyword(s):  
Ovarian Cancer ◽  
Gold Standard ◽  
Early Stage ◽  
Ovarian Tumors ◽  
Microscopic Appearance ◽  
Borderline Tumors ◽  
Non Invasive ◽  
Borderline Ovarian Tumors ◽  
Specific Subgroup ◽  
Serous Tumors

Borderline ovarian tumors (BOTs) are a specific subgroup of ovarian tumors and are characterized by cell proliferation and nuclear atypia without invasion or stromal invasion. They are usually more present in younger people than the invasive ovarian cancer and are diagnosed at an early stage and thus have a better prognosis. Histologically, borderline tumors are divided into serous (50%), mucosal (46%), and mixed (4%). The serous tumors are bilateral in 30% of the cases and are accompanied by infiltrations outside the ovary in 35% of the cases. These infiltrations may be non-invasive or invasive depending on their microscopic appearance and may affect treatment. Surgery is the approach of choice, and laparoscopic surgery, with the undeniable advantages it offers today, is the “gold standard.” All the surgical steps required to properly treat borderline tumors, at both diagnostic and therapeutic levels, can be safely and successfully be applied laparoscopically. Manipulations during surgery should be limited, and biopsies for rapid biopsy should be done within an endoscopic bag.

scholarly journals Development of nomograms to assess the risk of clinical outcome

2019 ◽  
Vol 21 (2) ◽  
pp. 114-121
Author(s):  
A A Korneenkov ◽  
S G Kuzmin ◽  
V B Dergachev ◽  
D N Borisov
Keyword(s):  
Logistic Regression ◽  
Clinical Outcome ◽  
Calibration Curve ◽  
Bootstrap Method ◽  
Model Performance ◽  
Outcome Variable ◽  
Software Environment ◽  
R Software ◽  
Data Set ◽  
Virtual Data

A methodology is presented for developing nomograms for assessing and stratifying the risk of a clinical outcome based on the created virtual data set using the R software environment. The virtual data set included input numerical and factor variables (variable types correspond to the R software documentation) and outcome. For quantitative variables, descriptive statistics were calculated at all levels of the outcome variable, and mosaic diagrams were constructed for factor variables. As a model that describes the association of input variables with the outcome, a logistic regression model was used. A bootstrap method was applied to validate and evaluate the model performance. The calculated validity indicators showed an acceptable discriminatory ability of the predictive model. The statistical calibration demonstrated the proximity of the model’s calibration curve to the ideal calibration curve. Based on the logistic regression coefficients, a nomogram was constructed using which the risk value of a specific outcome was calculated for each subject (patient). It is shown that with the help of the presented technique it is possible to stratify patients effectively by the risk of an adverse outcome, thus adequately altering the diagnosis and treatment tactics. The use of a nomogram greatly simplifies risk assessment and can be used in paper form as a supplement to the patient examination protocol. The article contains the codes of the R programming language with explanations.

Sign in / Sign up

Export Citation Format

Share Document