Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

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Noninvasive prediction of future macrovascular invasion occurrence in hepatocellular carcinoma based on quantitative imaging analysis: A multi-center study.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e14623 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e14623-e14623

Author(s):

JingWei Wei ◽

Jie Tian ◽

Sirui Fu ◽

Ligong Lu

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Quantitative Imaging ◽

Integrated Model ◽

Preoperative Imaging ◽

Rank Test ◽

Imaging Analysis ◽

Occurrence Time ◽

Training Cohort

e14623 Background: To investigate whether preoperative imaging-based analysis could help to predict future macrovascular invasion (MaVI) occurrence in hepatocellular carcinoma (HCC). Methods: A cohort of 224 patients with HCC was enrolled from five independent medical centers (training cohort: n = 154; independent validation cohort: n = 70). Predictive clinical factors were primarily selected by uni- and multi-variable analysis. CT-based imaging analysis was performed based on extraction of 1217 radiomic features. Recursive feature elimination and random forest (RF) were chosen as the optimal radiomics modelling algorithms. A clinical-radiomics integrated model was constructed by RF modelling. Cox-regression analyses further selected risk independent factors. Risk stratification was explored by Kaplan-Meier analysis with log-rank test, regarding to MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS). Results: The clinical-radiomics integrated model could successfully predict MaVI occurrence with areas under curve of 0.920 (training cohort, 95% confidence index [CI]: 0.875-0.965) and 0.853 (validation cohort, 95% CI: 0.737-0.970). The radiomics signature added significant improvement to the integrated model in both training and validation cohorts with p-value of 0.009 and 0.008, respectively. Radiomic features: N25_ori_gldzm_IN (hazard ratio [HR]: 0.44; p = 0.001) and N25_Coif1_ngldm_DE (HR: 0.60; p = 0.016) were selected as independent risk factors associated with MaVI occurrence time. The cox-regression model could stratified patients into high-risk and low-risk groups in MOT (p < 0.001), PFS (p = 0.003), and OS (p = 0.007). Conclusions: The noninvasive quantitative imaging analysis could enable preoperative prediction of future MaVI occurrence in HCC with prognosis implication.

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Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study

BioMed Research International ◽

10.1155/2021/6674471 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jin Liu ◽

Tao Lian ◽

Haimei Chen ◽

Xiaohong Wang ◽

Xianyue Quan ◽

...

Keyword(s):

Risk Factors ◽

Multicenter Study ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Training Cohort ◽

Relapse Risk ◽

Significant Difference ◽

Prediction Of Relapse ◽

Radiomics Signature

Objective. To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts ( P < 0.001 and P = 0.015 , respectively). The radiomics nomogram achieved good discriminant results in the training cohort ( C -index: 0.779) and the validation cohort ( C -index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram ( P < 0.001 ). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

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Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Frontiers in Oncology ◽

10.3389/fonc.2021.644994 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yi Jiang ◽

Jingjing Sun ◽

Yuwei Xia ◽

Yan Cheng ◽

Linjun Xie ◽

...

Keyword(s):

Risk Factors ◽

Clinical Decision Making ◽

Validation Cohort ◽

External Validation ◽

Multivariable Analysis ◽

Tumor Diameter ◽

Event Free Survival ◽

Free Survival ◽

Training Cohort ◽

Radiomics Signature

Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model.Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a “radiomics signature” and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration.Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738–0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829–0.933).Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.

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Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Cancer Imaging ◽

10.1186/s40644-021-00395-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sijia Cui ◽

Tianyu Tang ◽

Qiuming Su ◽

Yajie Wang ◽

Zhenyu Shu ◽

...

Keyword(s):

Calibration Curve ◽

Main Pancreatic Duct ◽

Validation Cohort ◽

External Validation ◽

High Grade ◽

Intraductal Papillary Mucinous Neoplasms ◽

Training Cohort ◽

Independent Validation ◽

Medical Centers ◽

Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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Development of a Biomarker-Based Scoring System Predicting Early Recurrence of Resectable Pancreatic Duct Adenocarcinoma

Annals of Surgical Oncology ◽

10.1245/s10434-021-10866-6 ◽

2021 ◽

Author(s):

Keinosuke Ishido ◽

Norihisa Kimura ◽

Taiichi Wakiya ◽

Hayato Nagase ◽

Yutaro Hara ◽

...

Keyword(s):

Scoring System ◽

Radical Resection ◽

Postoperative Recurrence ◽

Early Recurrence ◽

University Hospital ◽

Ductal Adenocarcinoma ◽

Tumor Diameter ◽

Roc Curve Analysis ◽

R1 Resection ◽

Preoperative Serum

Abstract Background Resectable pancreatic ductal adenocarcinoma (R-PDAC) often recurs early after radical resection, which is associated with poor prognosis. Predicting early recurrence preoperatively is useful for determining the optimal treatment. Patients and methods One hundred and seventy-eight patients diagnosed with R-PDAC on computed tomography (CT) imaging and undergoing radical resection at Hirosaki University Hospital from 2005 to 2019 were retrospectively analyzed. Patients with recurrence within 6 months after resection formed the early recurrence (ER) group, while other patients constituted the non-early recurrence (non-ER) group. Early recurrence prediction score (ERP score) was developed using preoperative parameters. Results ER was observed in 45 patients (25.3%). The ER group had significantly higher preoperative CA19-9 (p = 0.03), serum SPan-1 (p = 0.006), and CT tumor diameter (p = 0.01) compared with the non-ER group. The receiver operating characteristic (ROC) curve analysis identified cutoff values for CA19-9 (133 U/mL), SPan-1 (78.2 U/mL), and preoperative tumor diameter (23 mm). When the parameter exceeded the cutoff level, 1 point was given, and the total score of the three factors was defined as the ERP score. The group with an ERP score of 3 had postoperative recurrence-free survival (RFS) of 5.5 months (95% CI 3.02–7.98). Multivariate analysis for ER-related perioperative and surgical factors identified ERP score of 3 [odds ratio (OR) 4.63 (95% CI 1.82–11.78), p = 0.0013] and R1 resection [OR 3.20 (95% CI 1.01–10.17), p = 0.049] as independent predictors of ER. Conclusions For R-PDAC, ER could be predicted by the scoring system using preoperative serum CA19-9 and SPan-1 levels and CT tumor diameter, which may have great significance in identifying patients with poor prognoses and avoiding unnecessary surgery.

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Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

10.21203/rs.3.rs-701172/v2 ◽

2021 ◽

Author(s):

Javid Azadbakht ◽

Sina Rashedi ◽

Soheil Kooraki ◽

Hamed Kowsari ◽

Elnaz Tabibian

Keyword(s):

Roc Curve ◽

Scoring System ◽

Prognostic Model ◽

Operating Characteristic ◽

Clinical Laboratory ◽

External Validation ◽

Clinical Deterioration ◽

Roc Curve Analysis ◽

Prospective Multicenter Study ◽

Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

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Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

Journal of Ovarian Research ◽

10.1186/s13048-020-00727-3 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-He Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05111-3 ◽

2020 ◽

Author(s):

Constantinos Zamboglou ◽

Alisa S. Bettermann ◽

Christian Gratzke ◽

Michael Mix ◽

Juri Ruf ◽

...

Keyword(s):

Prostate Cancer ◽

Validation Cohort ◽

External Validation ◽

Image Interpretation ◽

Receiver Operating Curve ◽

Training Cohort ◽

Primary Prostate Cancer ◽

Psma Pet ◽

Number Of Patients ◽

Clinically Significant

Abstract Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

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Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.4079 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 4079-4079

Author(s):

Hidetoshi Nitta ◽

Marc Antoine Allard ◽

Mylene Sebagh ◽

Gabriella Pittau ◽

Oriana Ciacio ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Predictive Model ◽

Validation Cohort ◽

Microvascular Invasion ◽

Roc Curve Analysis ◽

Training Cohort ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

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A prospective validation cohort study of baseline plasma cell-free DNA (cfDNA) as a prognostic biomarker in newly diagnosed glioblastoma (GBM).

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.2508 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 2508-2508

Author(s):

Stephen Joseph Bagley ◽

Seyed Ali Nabavizadeh ◽

Jacob Till ◽

Aseel Abdalla ◽

Hareena Sanga ◽

...

Keyword(s):

Cox Regression ◽

Validation Cohort ◽

Performance Status ◽

Survival Outcomes ◽

Prognostic Impact ◽

Newly Diagnosed ◽

Roc Curve Analysis ◽

Optimal Cutoff ◽

Derivation Cohort ◽

Mutational Status

2508 Background: Due to significant interpatient heterogeneity, survival outcomes vary widely in patients with GBM. Novel prognostic biomarkers are needed. We aimed to determine the prognostic impact of baseline plasma cfDNA concentration in patients with GBM. Methods: We analyzed 84 patients with newly diagnosed GBM and at least 7 months of follow-up time. The first 41 patients comprised a previously published derivation cohort (Bagley, Clin Cancer Res 2020). The subsequent 43 patients served as an independent validation cohort. cfDNA was extracted from plasma collected prior to initial surgical resection and quantified by qPCR for a 115 bp amplicon of the human ALU repeat element. Receiver operating characteristic (ROC) curve analysis was used in the derivation cohort to (1) assess the accuracy of plasma cfDNA concentration for predicting progression-free survival status at 7 months (PFS-7), a landmark based on the median PFS for newly diagnosed GBM (Stupp, N Engl J Med 2005), and (2) derive the optimal cutoff for dichotomizing patients into high- and low-cfDNA groups. In the validation cohort, logistic regression was used to measure the association of plasma cfDNA concentration (high vs. low) with PFS-7, adjusted for age, isocitrate dehydrogenase ( IDH) 1/2 mutational status, 0-6-methylguanine-methyltransferase ( MGMT) methylation, extent of resection, and performance status. Multivariate Cox regression was used for overall survival (OS) analysis. Results: In the derivation cohort, the optimal cutoff for plasma cfDNA was 25.0 ng/mL (area under the curve [AUC] = 0.663), with inferior PFS and OS in patients with cfDNA above this cutoff (PFS, median 4.9 vs. 9.5 months, log-rank p = 0.001; OS, median 8.5 vs. 15.5 months, log-rank p = 0.03). In the validation cohort, baseline plasma cfDNA concentration over the cutoff was independently associated with a lower likelihood of being alive and progression-free at 7 months (adjusted OR 0.13, 95% CI 0.02 – 0.75, p = 0.02). OS was also worse in in the validation cohort in patients with high plasma cfDNA (adjusted HR 3.0, 95% CI 1.1 – 8.0, p = 0.03). Conclusions: In patients with newly diagnosed GBM, high baseline plasma cfDNA concentration is associated with worse survival outcomes independent of other prognostic factors. Further validation in a larger, multicenter study is warranted.

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