scholarly journals Development and validation of circulating CA125 prediction models in postmenopausal women

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Naoko Sasamoto ◽  
Ana Babic ◽  
Bernard A. Rosner ◽  
Renée T. Fortner ◽  
Allison F. Vitonis ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Postmenopausal Women ◽  
Prediction Models ◽  
Smoking Status ◽  
Large Population ◽  
External Validation ◽  
Validation Dataset ◽  
Ovarian Cancer Screening

Abstract Background Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. Methods We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses’ Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. Result The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. Conclusions The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

scholarly journals Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1931
Author(s):  
Aleksandra Gentry-Maharaj ◽  
Oleg Blyuss ◽  
Andy Ryan ◽  
Matthew Burnell ◽  
Chloe Karpinskyj ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Area Under The Curve ◽  
Serum Samples ◽  
Ovarian Cancer Screening ◽  
Serum Ca125 ◽  
Line Test ◽  
Validation Set ◽  
Serial Samples

Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.

Ovarian cancer screening in asymptomatic postmenopausal women by transvaginal sonography

Cancer ◽  
1991 ◽  
Vol 68 (3) ◽  
pp. 458-462 ◽  
Author(s):  
J. R. Van Nagell ◽  
P. D. Depriest ◽  
L. E. Puls ◽  
E. S. Donaldson ◽  
H. H. Gallion ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Transvaginal Sonography ◽  
Ovarian Cancer Screening

91341916 Ovarian cancer screening in asymptomatic postmenopausal women by transviginal sonography

Maturitas ◽  
1992 ◽  
Vol 15 (1) ◽  
pp. 87
Author(s):  
J.R Van Nagell ◽  
P.D DePriest ◽  
L.E Puls ◽  
E.S Donaldson ◽  
H.H Gallion ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Ovarian Cancer Screening

Ovarian Cancer Screening in Asymptomatic Postmenopausal Women

1993 ◽  
Vol 51 (2) ◽  
pp. 205-209 ◽  
Author(s):  
P.D. DePriest ◽  
J.R. van Nagell ◽  
H.H. Gallion ◽  
D. Shenson ◽  
J.E. Hunter ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Ovarian Cancer Screening

Ovarian Cancer Screening in Asymptomatic Postmenopausal Women

1994 ◽  
Vol 49 (5) ◽  
pp. 330-331
Author(s):  
P. D. DePriest ◽  
J. R. van Nagell ◽  
H. H. Gallion ◽  
D. Shenson ◽  
J. E. Hunter ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Ovarian Cancer Screening

scholarly journals Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission

2020 ◽  
Vol 11 ◽  
pp. 374
Author(s):  
Masahito Katsuki ◽  
Yukinari Kakizawa ◽  
Akihiro Nishikawa ◽  
Yasunaga Yamamoto ◽  
Toshiya Uchiyama
Keyword(s):  
Deep Learning ◽  
Subarachnoid Hemorrhage ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Good Prediction ◽  
Network Communications ◽  
Learning Software

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.

scholarly journals Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Farzaneh Hamidi ◽  
Neda Gilani ◽  
Reza Arabi Belaghi ◽  
Parvin Sarbakhsh ◽  
Tuba Edgünlü ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Data ◽  
Prediction Models ◽  
Gynecologic Cancer ◽  
External Validation ◽  
Small Sample ◽  
Validation Dataset ◽  
Expression Data ◽  
Embedded Algorithms

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

scholarly journals Identifying Ovarian Cancer in Symptomatic Women: A Systematic Review of Clinical Tools

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3686
Author(s):  
Garth Funston ◽  
Victoria Hardy ◽  
Gary Abel ◽  
Emma J. Crosbie ◽  
Jon Emery ◽  
...  
Keyword(s):  
Systematic Review ◽  
Ovarian Cancer ◽  
Prediction Models ◽  
Meta Analysis ◽  
External Validation ◽  
Ovarian Cancer Screening ◽  
Symptom Index ◽  
Screening Programs ◽  
Index Sensitivity ◽  
The Impact

In the absence of effective ovarian cancer screening programs, most women are diagnosed following the onset of symptoms. Symptom-based tools, including symptom checklists and risk prediction models, have been developed to aid detection. The aim of this systematic review was to identify and compare the diagnostic performance of these tools. We searched MEDLINE, EMBASE and Cochrane CENTRAL, without language restriction, for relevant studies published between 1 January 2000 and 3 March 2020. We identified 1625 unique records and included 16 studies, evaluating 21 distinct tools in a range of settings. Fourteen tools included only symptoms; seven also included risk factors or blood tests. Four tools were externally validated—the Goff Symptom Index (sensitivity: 56.9–83.3%; specificity: 48.3–98.9%), a modified Goff Symptom Index (sensitivity: 71.6%; specificity: 88.5%), the Society of Gynaecologic Oncologists consensus criteria (sensitivity: 65.3–71.5%; specificity: 82.9–93.9%) and the QCancer Ovarian model (10% risk threshold—sensitivity: 64.1%; specificity: 90.1%). Study heterogeneity precluded meta-analysis. Given the moderate accuracy of several tools on external validation, they could be of use in helping to select women for ovarian cancer investigations. However, further research is needed to assess the impact of these tools on the timely detection of ovarian cancer and on patient survival.

A randomized study of screening for ovarian cancer: a multicenter study in Japan

2008 ◽  
Vol 18 (3) ◽  
pp. 414-420 ◽  
Author(s):  
H. Kobayashi ◽  
Y. Yamada ◽  
T. Sado ◽  
M. Sakata ◽  
S. Yoshida ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Postmenopausal Women ◽  
Statistical Significance ◽  
Intervention Group ◽  
Developed Countries ◽  
Control Group ◽  
Ovarian Cancer Screening ◽  
Gynecological Examination

Ovarian cancer is common in women from developed countries. We designed a prospective randomized controlled trial of ovarian cancer screening to establish an improved strategy for the early detection of cancers. Asymptomatic postmenopausal women were randomly assigned between 1985 and 1999 to either an intervention group (n= 41,688) or a control group (n= 40,799) in a ratio of 1:1, with follow-up of mean 9.2 years, in Shizuoka district, Japan. The original intention was to offer women in the intervention group annual screens by gynecological examination (sequential pelvic ultrasound [US] and serum CA125 test). Women with abnormal US findings and/or raised CA125 values were referred for surgical investigation by a gynecological oncologist. In December 2002, the code was broken and the Shizuoka Cohort Study of Ovarian Cancer Screening and Shizuoka Cancer Registry were searched to determine both malignant and nonmalignant diagnoses. Twenty-seven cancers were detected in the 41,688-screened women. Eight more cancers were diagnosed outside the screening program. Detection rates of ovarian cancer were 0.31 per 1000 at the prevalent screen and 0.38–0.74 per 1000 at subsequent screens; they increased with successive screening rounds. Among the 40,779 control women, 32 women developed ovarian cancer. The proportion of stage I ovarian cancer was higher in the screened group (63%) than in the control group (38%), which did not reach statistical significance (P= 0.2285). This is to our knowledge the first prospective randomized report of the ovarian cancer screening. The rise in the detection of early-stage ovarian cancer in asymptomatic postmenopausal women is not significant, but future decisions on screening policy should be informed by further follow-up from this trial.

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