scholarly journals An Improvement of Survival Stratification in Glioblastoma Patients via Combining Subregional Radiomics Signatures

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Yang ◽  
Yu Han ◽  
Xintao Hu ◽  
Wen Wang ◽  
Guangbin Cui ◽  
...  

PurposeTo investigate whether combining multiple radiomics signatures derived from the subregions of glioblastoma (GBM) can improve survival prediction of patients with GBM.MethodsIn total, 129 patients were included in this study and split into training (n = 99) and test (n = 30) cohorts. Radiomics features were extracted from each tumor region then radiomics scores were obtained separately using least absolute shrinkage and selection operator (LASSO) COX regression. A clinical nomogram was also constructed using various clinical risk factors. Radiomics nomograms were constructed by combing a single radiomics signature from the whole tumor region with clinical risk factors or combining three radiomics signatures from three tumor subregions with clinical risk factors. The performance of these models was assessed by the discrimination, calibration and clinical usefulness metrics, and was compared with that of the clinical nomogram.ResultsIncorporating the three radiomics signatures, i.e., Radscores for ET, NET, and ED, into the radiomics-based nomogram improved the performance in estimating survival (C-index: training/test cohort: 0.717/0.655) compared with that of the clinical nomogram (C-index: training/test cohort: 0.633/0.560) and that of the radiomics nomogram based on single region radiomics signatures (C-index: training/test cohort: 0.656/0.535).ConclusionThe multiregional radiomics nomogram exhibited a favorable survival stratification accuracy.

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zi-Qi Pan ◽  
Shu-Jun Zhang ◽  
Xiang-Lian Wang ◽  
Yu-Xin Jiao ◽  
Jian-Jian Qiu

Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.


2020 ◽  
Vol 11 ◽  
Author(s):  
Siqi Dai ◽  
Shuang Xu ◽  
Yao Ye ◽  
Kefeng Ding

BackgroundDespite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis.MethodsIRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors.ResultsThe three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p &lt; 0.001), which was later confirmed in the two validation groups (log-rank p &lt; 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p &lt; 0.05). When combined with clinical risk factors, the model showed robust prediction capability.ConclusionThe immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1182-1182 ◽  
Author(s):  
Marion K Mateos ◽  
Toby N. Trahair ◽  
Chelsea Mayoh ◽  
Pasquale M Barbaro ◽  
Rosemary Sutton ◽  
...  

Abstract Venous thromboembolism (VTE) is an unpredictable and life-threatening toxicity that occurs early in acute lymphoblastic leukemia (ALL) therapy. The incidence is approximately 5% in children diagnosed with ALL [Caruso et al. Blood. 2006;108(7):2216-22], which is higher than in other pediatric cancer types [Athale et al. Pediatric Blood & Cancer. 2008;51(6):792-7]. Clinical risk factors for VTE in children during ALL therapy include older age and the use of asparaginase. We hypothesized that there may be additional risk factors that can modify VTE risk, beyond those previously reported [Mitchell et al. Blood. 2010;115(24):4999-5004]. We sought to define early predictive clinical factors that could select a group of children at highest risk of VTE, with possible utility in an interventional trial of prophylactic anticoagulation. We conducted a retrospective study of 1021 Australian children, aged 1-18 years, treated between 1998-2013 on successive BFM-based ALL therapies. Patient records were reviewed to ascertain incidence of VTE; and to systematically document clinical variables present at diagnosis and during induction/consolidation phases of therapy. The CTCAE v4.03 system was used for grading of VTE events. Multivariate logistic and cox regression were used to determine significant clinical risk factors associated with VTE (SPSS v23.0). All P values were 2-tailed, significance level <.05. The incidence of on-treatment VTE was 5.09% [96% ≥Grade 2 (CTCAE v4.0)]. Age ≥10 years [P =.048, HR 1.96 (95% confidence interval= 1.01-3.82)], positive blood culture in induction/consolidation [ P =.009, HR 2.35 (1.24-4.46)], extreme weight at diagnosis <5th or >95th centile [ P =.028, HR 2.14 (1.09-4.20)] and elevated peak gamma-glutamyl transferase (GGT) >5 x upper limit normal in induction/consolidation [ P =.018, HR 2.24 (1.15-4.36)] were significantly associated with VTE in multivariate cox regression modeling. The cumulative incidence of VTE, if all 4 clinical risk factors in our model were present, was 33.33%, which is significantly greater than the incidence of VTE for a patient without any risk factors (1.62%, P <.001). These 4 clinical factors could be used as a basis for assigning thromboprophylaxis in children with ALL. Our model detected 80% (42/52) of all VTE events by incorporating one or more risk factors. An equal proportion of patients eventually developing VTE could be predicted by weight and age ≥10 years; or later bacteremia and elevated GGT. Bacteremia, when present as a risk factor, preceded VTE in 80% of cases (20/25 cases) at a median of 29 days before VTE (range 3-668 days). The negative predictive value (NPV), specificity and sensitivity for the 4 risk factor model were 98.38%, 98.70% and 28.57% respectively. If 3 specified risk factors were included in the algorithm, such as 2 baseline and one treatment-related variable, the incidence of VTE was ≥25%, NPV 98.38%, specificity ≥96.19% and sensitivity 80%. The high NPV and high specificity mean the model can successfully exclude children who are not at increased risk of VTE. The challenge is to balance unnecessary exposure to anticoagulation against the risk of development of VTE. We have identified novel clinical risk factors in induction/consolidation - positive blood culture, hepatic enzymatic elevation and extreme weight at diagnosis- that may highlight risk mechanisms related to VTE pathogenesis. Our predictive model can define a group at highest risk of VTE who may benefit from randomized trials of prophylactic anticoagulation in childhood ALL therapy. Acknowledgments: The authors acknowledge support from the Kids Cancer Alliance (a Translational Cancer Research Centre of Cancer Institute NSW), Cancer Institute New South Wales, Royal Australasian College of Physicians - Kids Cancer Project Research Entry Scholarship, Cancer Therapeutics CRC (CTx) PhD Clinician Research Top-Up Scholarship, The Kids Cancer Project, Australian and New Zealand Children's Haematology Oncology Group, ASSET study members, data managers and clinical research associates at each site. Disclosures No relevant conflicts of interest to declare.


Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Esther Nkuipou-Kenfack ◽  
◽  
Agnieszka Latosinska ◽  
Wen-Yi Yang ◽  
Marie-Céline Fournier ◽  
...  

Abstract Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.


2021 ◽  
Vol 11 ◽  
Author(s):  
Zhongyi Wang ◽  
Fan Lin ◽  
Heng Ma ◽  
Yinghong Shi ◽  
Jianjun Dong ◽  
...  

PurposeWe developed and validated a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict neoadjuvant chemotherapy (NAC)-insensitive breast cancers prior to treatment.MethodsWe enrolled 117 patients with breast cancer who underwent CESM examination and NAC treatment from July 2017 to April 2019. The patients were grouped randomly into a training set (n = 97) and a validation set (n = 20) in a ratio of 8:2. 792 radiomics features were extracted from CESM images including low-energy and recombined images for each patient. Optimal radiomics features were selected by using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, to develop a radiomics score in the training set. A radiomics nomogram incorporating the radiomics score and independent clinical risk factors was then developed using multivariate logistic regression analysis. With regard to discrimination and clinical usefulness, radiomics nomogram was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC) and decision curve analysis (DCA).ResultsThe radiomics nomogram that incorporates 11 radiomics features and 3 independent clinical risk factors, including Ki-67 index, background parenchymal enhancement (BPE) and human epidermal growth factor receptor-2 (HER-2) status, showed an encouraging discrimination power with AUCs of 0.877 [95% confidence interval (CI) 0.816 to 0.924] and 0.81 (95% CI 0.575 to 0.948) in the training and validation sets, respectively. DCA revealed the increased clinical usefulness of this nomogram.ConclusionThe proposed radiomics nomogram that integrates CESM-derived radiomics features and clinical parameters showed potential feasibility for predicting NAC-insensitive breast cancers.


2021 ◽  
Vol 12 ◽  
Author(s):  
Shaozhi Zhao ◽  
Qi Zhao ◽  
Yuming Jiao ◽  
Hao Li ◽  
Jiancong Weng ◽  
...  

Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy.Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness.Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness.Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy.


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