CA125-related Measures of Tumor Kinetics and Outcome of Patients with Recurrent Ovarian Cancer Receiving Chemotherapy: A Retrospective Evaluation

Colloca, Giuseppe; Venturino, Antonella; Addamo, Gianfranco; Coccorullo, Zaira; Ratti, R.; Caltabiano, Graziano; Guarneri, Domenico

doi:10.1093/jjco/hyt139

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…1 CA-125 is the most widely cited and only clinically available biomarker for ovarian cancer. [2][3][4][5][6] While CA-125 can be used as a serum marker of ovarian cancer recurrence, it has a relatively low sensitivity and specificity for Abbreviations CTCs, circulating tumor cells; IRB, Institutional Review Board; PPV, positive predictive value; NPP, negative predictive value; RT-PCR, reverse transcriptase-polymerase chain reaction; OS, overall survival; EMT, epithelial-to-mesenchymal transmition the screening and diagnosis of ovarian cancer. 7 Only 50% of patients with early-stage ovarian cancers have elevated serum CA-125, 8 and a number of non-ovarian cancer conditions have falsely elevated CA-125.…”

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confidence: 99%

Assessment of Circulating Tumor Cells as a Predictive Biomarker of Histology in Women With Suspected Ovarian Cancer

Lou¹,

Teoh²,

Hoostal³

et al. 2018

Laboratory Medicine

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confidence: 99%

Assessment of Circulating Tumor Cells as a Predictive Biomarker of Histology in Women With Suspected Ovarian Cancer

Lou¹,

Teoh²,

Hoostal³

et al. 2018

Laboratory Medicine

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“…In addition, estimation of the natural growth rate of untreated tumour needs at least two tumour volume estimations prior to therapy, e.g., V d and V i . Furthermore, natural tumour growth rate is correlated with kinetic index [12] and patient survival [13,14] and is also valuable for evaluating therapeutic efficacy [1,2,6,[15][16][17][18][19][20][21]. Due to limited availability of diagnostic imaging, an examination close to treatment initiation is in general not possible, and indirect methods for estimation of V i must therefore be developed using, e.g., mathematical models.…”

Section: Discussionmentioning

confidence: 99%

“…We have previously shown that SGR is mathematically and biologically a more accurate measure for tumour growth rate than the widely used tumour volume doubling time [3,4]. Although the above equation is originally derived for tumour volume, it can also be used for tumour marker concentration levels for, e.g., prostate specific antigen, PSA [5], and CA125 [6]. Eq.…”

Section: Introductionmentioning

confidence: 99%

Estimation of tumour volume at therapy initiation by back-extrapolating the post-therapy regression curve of tumour volume

Mehrara

Forssell-Aronsson

2018

Appl Cancer Res

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Background: Tumour volume at therapy initiation, V i , is rarely available in cancer patients, and the last pre-treatment tumour volume available is from previous diagnostic imaging (V d ). Therapeutic efficacy is thus evaluated by comparing tumour volume after treatment with V d , instead of V i , which results in underestimation of treatment efficacy. V i , together with V d , can also be used for estimation of the natural growth rate of tumour valuable for, e.g., screening programs, prognostication and individualised treatment planning such as chemotherapy scheduling. The aim of this work was to study the feasibility of estimating V i by back-extrapolating the post-therapy regression of tumour volume, based on data from animal model. Methods: Nude mice bearing human neuroendocrine GOT1 tumour cell line were treated with 177 Lu-DOTA-TATE. Tumour volumes were measured regularly after therapy and V i was estimated by back-extrapolation of (a) linear and (b) exponential regression lines of the two earliest post-therapy tumour volumes and (c) the long-term exponential regression of tumour volume. The estimated V i values (V est ) were compared with the measured volume of tumour at therapy initiation. Results: The linear regression of the two earliest post-therapy tumour volumes gave the best estimate for V i (V est = 0.91 V i , p < 0.00001), compared with the exponential regression models either on short-term (V est = 2.30 V i , p < 0.01), or long-term (V est = 0.93 V i , non-significant) follow up of tumour volume after therapy. Conclusion: Back-extrapolation of the early linear regression of tumour volume after therapy gave the best estimate for tumour volume at time of therapy initiation. This estimate can be used as baseline for treatment efficacy evaluation or for estimation of the natural growth rate of tumour (together with the measured tumour volume at pre-treatment diagnostic imaging).

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“…Most patients are required to have CA‐125 testing every a month after their initial treatment for ovarian cancer. When CA‐125 increases above a value that is double the nadir, it suggests a similar performance of growth rate at progression versus Gynecologic Cancer Intergroup criteria and warrants further investigation 16 …”

Section: Introductionmentioning

confidence: 94%

Prediction of the survival of patients with advanced‐stage ovarian cancer patients undergoing interval cytoreduction with the use of computed tomography reevaluation after neoadjuvant chemotherapy

Li,

Wang

2023

J of Obstet and Gynaecol

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PurposeTo predict ovarian cancer patients' survival by computed tomography (CT) reevaluation after neoadjuvant chemotherapy.Patients and MethodsIn this retrospective single‐center cohort study, all patients with advanced epithelial ovarian cancer underwent platinum‐based chemotherapy followed by interval cytoreductive surgery. Assessment of abdominal and pelvic lesions before and after chemotherapy using CT scoring criteria. Meanwhile, the progression‐free survival and overall survival times were obtained. The Kaplan–Meier method was used to estimate survival curves. Univariate analysis of continuous and categorical variables was performed for prognostic significance using the Cox proportional hazards model. Variables with p < 0.10 on univariate analysis were then included in a multivariate forward stepwise Cox regression analysis.ResultsA total of 162 patients were included, with a median age of 52 years (range, 20–72 years). One hundred seven patients (66.0%) underwent suboptimal cytoreduction, and there was no statistically significant difference in patient survival between surgical procedures (log‐rank p = 0.092). Six radiographic features were hazard factors for suboptimal cytoreduction. Four features in the postchemotherapy CT images were assigned as predictive criteria by the stepwise regression model (area under the curve [AUC] = 0.689). As compared with a higher AUC (0.713) in the model involving two clinical variables (age and postsurgery CA‐125) and two postchemotherapy CT features, the model considering the CT score changes before and after chemotherapy had the highest diagnostic accuracy (AUC = 0.843).ConclusionCT reevaluation after neoadjuvant chemotherapy is essential for ovarian cancer, the changes of CT feature and score are potential great tools to predict patient survival.

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CA125-related Measures of Tumor Kinetics and Outcome of Patients with Recurrent Ovarian Cancer Receiving Chemotherapy: A Retrospective Evaluation

Cited by 6 publications

References 13 publications

Assessment of Circulating Tumor Cells as a Predictive Biomarker of Histology in Women With Suspected Ovarian Cancer

Assessment of Circulating Tumor Cells as a Predictive Biomarker of Histology in Women With Suspected Ovarian Cancer

Estimation of tumour volume at therapy initiation by back-extrapolating the post-therapy regression curve of tumour volume

Prediction of the survival of patients with advanced‐stage ovarian cancer patients undergoing interval cytoreduction with the use of computed tomography reevaluation after neoadjuvant chemotherapy

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