A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence

A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma

BACKGROUND

a low PNI in patients with NPC is linked to poor survival, but prior studies have focused on single-timepoint measurements. Our study aims to employ joint modeling to analyze longitudinal PNI data from each routine visit, exploring its relationship with overall survival.

METHODS

In this retrospective study using data from the Chang Gung Research Database (2007-2019), we enrolled patients with NPC undergoing curative treatment. We analyzed the correlation between patient characteristics, including the PNI, and overall survival. A joint model combining a longitudinal sub-model with a time-to-event sub-model was used to further evaluate the prognostic value of longitudinal PNI.

RESULTS

A total of 2332 patient were enrolled for the analysis. Separate survival analyses showed that longitudinal PNI was an independent indicator of a reduced mortality risk (adjusted HR 0.813; 95% CI, 0.805 to 0.821). Joint modeling confirmed longitudinal PNI as a consistent predictor of survival (HR 0.864; 95% CI, 0.850 to 0.879). An ROC analysis revealed that a PNI below 38.1 significantly increased the risk of 90-day mortality, with 90.0% sensitivity and 89.6% specificity.

CONCLUSIONS

Longitudinal PNI data independently predicted the overall survival in patients with NPC, significantly forecasting 90-day survival outcomes. We recommend routine PNI assessments during each clinic visit for these patients.

Dynamic Prediction of Resectability for Patients with Advanced Ovarian Cancer Undergoing Neo-Adjuvant Chemotherapy: Application of Joint Model for Longitudinal CA-125 Levels

In patients with advanced ovarian cancer (AOC) receiving neoadjuvant chemotherapy (NAC), predicting the feasibility of complete interval cytoreductive surgery (ICRS) is helpful and may avoid unnecessary laparotomy. A joint model (JM) is a dynamic individual predictive model. The aim of this study was to develop a predictive JM combining CA-125 kinetics during NAC with patients' and clinical factors to predict resectability after NAC in patients with AOC. A retrospective study included 77 patients with AOC treated with NAC. A linear mixed effect (LME) sub-model was used to describe the evolution of CA-125 during NAC considering factors influencing the biomarker levels. A Cox sub-model screened the covariates associated with resectability. The JM combined the LME sub-model with the Cox sub-model. Using the LME sub-model, we observed that CA-125 levels were influenced by the number of NAC cycles and the performance of paracentesis. In the Cox sub-model, complete resectability was associated with Performance Status (HR = 0.57, [0.34-0.95], p = 0.03) and the presence of peritoneal carcinomatosis in the epigastric region (HR = 0.39, [0.19-0.80], p = 0.01). The JM accuracy to predict complete ICRS was 88% [82-100] with a predictive error of 2.24% [0-2.32]. Using a JM of a longitudinal CA-125 level during NAC could be a reliable predictor of complete ICRS.

Establishment and verification of the nomogram that predicts the 3-year recurrence risk of epithelial ovarian carcinoma

BACKGROUND

As we all know, patients with epithelial ovarian carcinoma have poor prognosis and high recurrence rate. It is critical and challenging to screen out the patients with high risk of recurrence. At present, there are some models predicting the overall survival of epithelial ovarian carcinoma, however, there is no widely accepted tool or applicable model predicting the recurrence risk of epithelial ovarian carcinoma patients. The objective of this study was to establish and verify a nomogram to predict the recurrence risk of EOC.

METHODS

We reviewed the clinicopathological and prognostic data of 193 patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy between January 2003 and December 2013 in Peking University First Hospital. The nomogram was established with the risk factors selected by LASSO regression. The medical data of 187 EOC patients with 5-year standard follow-up in Peking University Third Hospital and Beijing Obstetrics and Gynecology Hospital were used for external validation of the nomogram. AUC curve and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration.

RESULTS

The nomogram for 3-year recurrence risk was established with FIGO stage, histological grade, histological type, lymph node metastasis status and serum CA125 level at diagnosis. The total score can be obtained by adding the grading values of these factors together. The C statistics was 0.828 [95% CI, 0.764-0.884] and the Chi-square value is 3.6 (P = 0.731 > 0.05) with the training group. When the threshold value was set at 198, the sensitivity, specificity, positive predictive value, negative predictive value and concordance index were 88.8, 67.0, 71.8, 86.3% and 0.558 respectively. In the external validation, the C statistics was 0.803 [95%CI, 0.738-0.867] and the Chi-square value is 11.04 (P = 0.135 > 0.05). With the threshold value of 198, the sensitivity, specificity, positive predictive value, negative predictive value and concordance index of the nomogram were 75.7, 77.0, 83.2, 67.9%, and 0.52 respectively.

CONCLUSIONS

We established and validated a nomogram to predict 3-year recurrence risk of patients with EOC who achieved clinical complete remission after cytoreductive surgery and chemotherapy. This nomogram with good discrimination and calibration might be useful for screening out the patients with high risk of recurrence.

CA-125 ELIMination Rate Constant K (KELIM) Is a Marker of Chemosensitivity in Patients with Ovarian Cancer: Results from the Phase II CHIVA Trial

PURPOSE

In patients with ovarian cancer receiving neoadjuvant chemotherapy, the first-line treatment success will depend on both the tumor-primary chemosensitivity and the completeness of interval debulking surgery (IDS). The modeled CA-125 ELIMination rate constant K (KELIM), calculated with the CA-125 longitudinal kinetics during the first 100 chemotherapy days, is a validated early marker of tumor chemosensitivity. The objective was to investigate the role of the chemosensitivity relative to the success of first-line medical-surgical treatment.

EXPERIMENTAL DESIGN

The CA-125 concentrations were prospectively measured in the randomized phase II trial CHIVA (NCT01583322, carboplatin-paclitaxel regimen ± nintedanib, and IDS, n = 188 patients). The KELIM predictive value regarding the tumor response rate, likelihood of complete IDS, risk of subsequent platinum-resistant relapse (PtRR), progression-free survival (PFS), and overall survival (OS) was assessed using univariate and multivariate tests.

RESULTS

The data from 134 patients were analyzed. KELIM was an independent and major predictor of subsequent PtRR risk, and of survivals. The final logistic regression model, including KELIM [OR = 0.13; 95% confidence interval (CI), 0.03-0.49] and complete IDS (no vs. yes, OR = 0.30; 95% CI, 0.11-0.76) highlights the preponderant role of chemosensitivity on the success of the first-line treatment. In patients with highly chemosensitive diseases, the patient prognosis was driven more by the chemotherapy-induced antitumor effects than by the surgery.

CONCLUSIONS

The tumor-primary chemosensitivity, assessed by the modeled CA-125 KELIM calculated during neoadjuvant chemotherapy (http://www.biomarker-kinetics.org/CA-125-neo), may be a major parameter to consider for decision-making regarding IDS attempt, and selecting patients for treatments meant to reverse the primary chemoresistance.See related commentary by May and Oza, p. 4432.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

OBJECTIVE

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use.

METHODS

A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks.

RESULTS

Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured.

CONCLUSION

Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.

Value of [18F]FDG-PET/CT and CA125, serum levels and kinetic parameters, in early detection of ovarian cancer recurrence: Influence of histological subtypes and tumor stages

To assess the diagnostic accuracy of CA125, its kinetic values and positron emission tomography/computed tomography with 2-deoxy-2-[F]fluoro-D-glucose ([F]FDG-PET/CT), in relation with tumor characteristics for suspected recurrence of ovarian cancer. To evaluate the performance of CA125-related parameters as a selection criteria to perform a [F]FDG-PET/CT.A retrospective analysis of 69 [F]FDG-PET/CT for suspected recurrence of ovarian cancer was performed. All patients had 2 measurements of CA125, before PET/CT, to calculate kinetic values, as CA125vel (CA125vel = [CA125a - CA125b]/time) and CA125dt (CA125dt = [log2 × time]/[logCA125a - CA125b]). Maximum standard uptake value (SUVmax) was calculated. The diagnostic accuracy was calculated for all the variables and the optimal cut-off value of each of them by the receiver-operating characteristics (ROC) analysis. All the tests were compared with tumor characteristics and clinical-radiological evolution during follow-up of at least 6 months.Fifty-five cases were diagnosed of recurrence (11 with CA125 <35 U/mL), while 14 showed no disease (11 with CA125 < 35 U/mL). All of them were correctly cataloged by PET/CT. CA125, CA125vel, and SUVmax showed higher levels in recurrent patients (mean 129.54 U/mL, 24.58 U/mL per mo, and 8.69 g/mL, respectively) than in nonrecurrent (mean 20.35 U/mL, 0.60 U/mL per mo, and 0.64 g/mL, respectively). No statistical differences in CA125dt were found. Patients with recurrence of high-grade serous carcinoma (HGSC) showed higher CA125 and CA125vel, without differences in the rest of subtypes and International Federation of Gynecology and Obstetrics stages. The ROC analyses for CA125, CA125vel, and CA125dt showed an area under the curve (AUC) of 0.873 (95% confidence interval [CI] 0.77-0.969), 0.903 (95% CI 0.813-0.994), and 0.727 (95% CI 0.542-0.913), respectively, with an optimal cut-off point of 23.95 U/mL, 4.49 U/mL per mo, and 3.36 months, respectively, while for the SUVmax the AUC was of 0.982 (95% CI 0.948-1.000), and the cut-off point of 2. Multivariate regression analysis identified CA125 and CA125vel as predictors of recurrence.[F]FDG-PET/CT is more accurate than the parameters obtained from the CA125 to detect early recurrence. CA125vel is the most suitable parameter, mainly in HGSC. Levels of CA125vel ≥ 4.49 U/mL per mo facilitate earlier detection by the execution of a [F]FDG-PET/CT. The calculation of these parameters is independent of tumor stage at diagnosis.