Development and validation of a CT-based radiomic model combined with margin-related radiomic features to distinguish precancerous lesions from early-stage lung adenocarcinoma.

e21044 Background: The purpose of this study was to investigate whether the combined radiomic model based on tumor-associated and margin-related (5mm) radiomic features can effectively improve prediction performance of distinguishing precancerous lesions from early stage lung adenocarcinoma. Methods: 264 patients underwent preoperative chest CT in Guangdong Provincial People’s hospital from March 1, 2015 to December 31,2019 were sorted by three cohorts. All lesions were pathologically confirmed as precancerous lesions or Stage I lung adenocarcinoma and a total of 861 analyzable radiomic features were extracted from two segmented lesions including pulmonary lesions and margins, using PyRadiomics by two senior radiologists. In training cohort, 145 patients (70%) are selected randomly from the single-nodular patients (N = 207). As for the validation cohorts, the models were validated using the resting 62 patients from single-nodular cohort and multi-nodular cohort (n = 57) respectively. Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination were used for feature selection. ROC analysis and AUC were used to evaluate the performance of three models which were developed by multiple logistic regression on distinguishing the precancerous lesions from early stage lung adenocarcinoma. Results: Selected features from pulmonary lesions and pericarcinous tissue were developed into two independent radiomic models and a combined model. Margin-related radiomic model performs well in three validation cohorts. The AUC Brock of single-nodular cohort in training cohort was 0.912 (95% CI: 0.876-0.948), while in single-nodular validation cohort was 0.93 (95% CI: 0.862-0.966). Multi-nodular validation cohort in this model shows an AUC of 0.891 (95% CI = 0.824–0.943). Comparing combined model and tumor-associated radiomic model, it is found that the AUC of combined model was improved from 0.865 (95% CI: 0.767-0.963) to 0.94 (95% CI: 0.767-0.963) for single-nodular validation cohort. Respectively, this combined model also performs well in multi-nodular validation cohort. Conclusions: This study demonstrated the potential of margin-related radiomic features based on preoperative CT scans to distinguish precancerous lesions from early stage lung adenocarcinoma. The constructed radiomic model provided an easy-to-use, preoperative tool for surgeons to develop accurate therapeutic strategies for multi-nodular patients.

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Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

Frontiers in Oncology ◽

10.3389/fonc.2021.628982 ◽

2021 ◽

Vol 11 ◽

Author(s):

Duo Hong ◽

Lina Zhang ◽

Ke Xu ◽

Xiaoting Wan ◽

Yan Guo

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Value ◽

Validation Cohort ◽

Clinical Factors ◽

Combined Model ◽

Training Cohort ◽

Clinical Model ◽

Pre Treatment ◽

Off Time ◽

Radiomics Signature

PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

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CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study

Journal of Clinical Oncology ◽

10.1200/jco.2016.68.2153 ◽

2017 ◽

Vol 35 (7) ◽

pp. 734-742 ◽

Cited By ~ 53

Author(s):

Jiliang Qiu ◽

Baogang Peng ◽

Yunqiang Tang ◽

Yeben Qian ◽

Pi Guo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Support Vector Machine ◽

Cox Regression ◽

Early Stage ◽

Cpg Methylation ◽

Recursive Feature Elimination ◽

Support Vector ◽

Training Phase ◽

Virus Surface ◽

Training Cohort

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG–based signature, is useful in predicting recurrence for patients with E-HCC.

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A Molecule Based Nomogram Optimized the Prediction of Relapse in Stage I NSCLC

10.21203/rs.3.rs-437687/v1 ◽

2021 ◽

Author(s):

Rongrong Bian ◽

Guorong Zhu ◽

Feng Zhao ◽

Rui Chen ◽

Wengji Xia ◽

...

Keyword(s):

Cox Regression ◽

Early Stage ◽

External Validation ◽

Stage I ◽

Recursive Feature Elimination ◽

Support Vector ◽

Recurrence Free Survival ◽

Free Survival ◽

Training Cohort ◽

Discovery Phase

Abstract Background: Early-stage non-small cell lung cancer (NSCLC) is being diagnosed increasingly, and in 30% of diagnosed patients, recurrence will develop within 5 years. Thus, it is urgent to identify recurrence-related markers in order to optimize the management of patient-tailored therapeutics. The aim of the study was to develop a feasible tool to optimize the recurrence prediction of stage I NSCLC. Methods: The eligible datasets were downloaded from TCGA and GEO. In discovery phase, two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to identify candidate genes. Recurrence associated signature was developed by penalized cox regression. The nomogram was constructed and further tested via two independent cohorts. Results: In this retrospective study, 14 eligible datasets and 7 published signatures were included. In discovery phase, 42 significant genes were highlighted as candidate predictors by two algorithms. A 13-gene based signature was generated by penalized cox regression categorized training cohort into high-risk and low-risk subgroups (HR = 8.873, 95% CI:4.228–18.480 P < 0.001). Furthermore, a nomogram integrating the recurrence related signature, age, and histology was developed to predict the recurrence-free survival in the training cohort, which performed well in the two external validation cohorts (concordance index: 0.737, 95%CI:0.732–0.742, P < 0.001; 0.666, 95%CI: 0.650–0.682, P < 0.001; 0.651, 95%CI:0.637–0.665, P < 0.001 respectively). Conclusions: The proposed nomogram is a promising tool for estimating recurrence free survival in stage I NSCLC, which might have tremendous value in guiding adjuvant therapy. Prospective studies are needed to test the clinical utility of the nomogram in individualized management of stage I NSCLC.

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Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

Recent Patents on Anti-Cancer Drug Discovery ◽

10.2174/1574892816666210211092108 ◽

2021 ◽

Vol 16 ◽

Author(s):

Jin-Guo Chen ◽

Jing-Quan Wang ◽

Tian-Wen Peng ◽

Zhe-Sheng Chen ◽

Shan-Chao Zhao

Keyword(s):

Prognostic Factors ◽

Validation Cohort ◽

Early Stage ◽

Predictive Ability ◽

Testicular Germ Cell Tumor ◽

Progression Free Survival ◽

Cell Tumor ◽

Testicular Germ Cell ◽

Free Survival ◽

Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

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Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.582788 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ying Zhao ◽

Nan Wang ◽

Jingjun Wu ◽

Qinhe Zhang ◽

Tao Lin ◽

...

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Therapeutic Response ◽

Validation Cohort ◽

Combined Model ◽

Training Cohort ◽

Contrast Enhanced ◽

Three Phase ◽

Radiological Model

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

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Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.675877 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qi Li ◽

Xiao-qun He ◽

Xiao Fan ◽

Chao-nan Zhu ◽

Jun-wei Lv ◽

...

Keyword(s):

Risk Factors ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Tumor Size ◽

Validation Cohort ◽

Morphological Features ◽

Combined Model ◽

Training Cohort ◽

Node Metastasis ◽

Peripheral Lung

BackgroundBased on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.ConclusionCT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

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The CXCL Family Contributes to Immunosuppressive Microenvironment in Gliomas and Assists in Gliomas Chemotherapy

Frontiers in Immunology ◽

10.3389/fimmu.2021.731751 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zeyu Wang ◽

Yuze Liu ◽

Yuyao Mo ◽

Hao Zhang ◽

Ziyu Dai ◽

...

Keyword(s):

High Risk ◽

Tumor Progression ◽

Validation Cohort ◽

Critical Role ◽

Low Risk ◽

Clinical Samples ◽

Support Vector ◽

Central Nervous System Tumor ◽

Tumor Immune Escape ◽

Training Cohort

Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient’s clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas’ potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis.

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Radiomics Model Based on Shear-Wave Elastography Can Predict the Axillary Lymph Node Status in Early-Stage Breast Cancer

10.21203/rs.3.rs-75554/v1 ◽

2020 ◽

Author(s):

Meng Jiang ◽

Chang-Li Li ◽

Rui-Xue Chen ◽

Shi-Chu Tang ◽

Xiao-Mao Luo ◽

...

Keyword(s):

Breast Cancer ◽

Lymph Node ◽

Shear Wave ◽

Axillary Lymph Node ◽

Validation Cohort ◽

Early Stage ◽

Shear Wave Elastography ◽

Early Stage Breast Cancer ◽

Axillary Lymph ◽

Training Cohort

Abstract Background: Accurate prediction of axillary lymph node (ALN) involvement in early-stage breast cancer is important for determining appropriate axillary treatment and therefore avoiding unnecessary axillary surgery and complications. This study aimed to develop and validate an ultrasound radiomics nomogram for preoperative evaluation of the ALN burden. Methods: Data of 303 patients from Wuhan Tongji Hospital (training cohort) and 130 cases from Hunan Provincial Tumour Hospital (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomic features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. Then, the minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select ALN status-related features and construct the SWE and BMUS radiomic signatures. Proportional odds ordinal logistic regression was performed using the radiomic signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated the performance of the nomogram using C-index, calibration, and compared it with clinical model.Results: Multivariate analysis indicated that SWE signature, US-reported LN status and molecular subtype were independent risk factors associated with ALN status. The radiomics nomogram based on these variables showed good calibration and discrimination in the training set (overall C-index: 0.842; 95%CI, 0.773–0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765–0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥1)), it achieved C-index of 0.845 (95%CI, 0.777–0.914) for the training cohort and 0.817 (95%CI, 0.769–0.865) for the validation cohort. The tool could also discriminate between low (N + (1–2)) and heavy metastatic burden of ALN (N + (≥3)), with C-index of 0.827 (95%CI, 0.742–0.913) for the training cohort and 0.810 (95%CI, 0.755–0.864) for the validation cohort. Conclusions: The presented radiomics nomogram shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for preoperative decision-making.

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Pathway analysis of hypoxia-related factors in early colorectal cancer patients with poor prognosis.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3613 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3613-3613

Author(s):

Yingxin Tan ◽

Yuming Rong ◽

Zhaoliang Yu ◽

Feng Gao ◽

Yufeng Chen ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Patients ◽

Validation Cohort ◽

Early Stage ◽

Microarray Dataset ◽

Gene Signature ◽

Stage I ◽

Training Cohort ◽

Cell Cycle Pathway ◽

Colorectal Cancer Patients

3613 Background: Colorectal cancer is one of the most common malignancies with a high mortality rate. Patients with stage I and stage II colorectal cancer have limited options for treatment. Hypoxia affects the activation and regulation of colorectal cancer cells and participates in its invasion and migration. However, there is lack of an accurate and non-invasive method for assessing tumor hypoxia. The aim of this study was developing and validating a hypoxia gene signature for predicting the outcome in stage I/II colorectal cancer patients. At the same time , we hypothesized that analysis of database of CIT microarray dataset could identify important biomarkers for stage I/II colorectal cancer patients. Methods: A total of 309 colorectal cancer patients of early stage with complete clinical information were enrolled for construction generation of hypoxia-related gene signature (HRGS) based on the CIT microarray dataset. 1877 colorectal cancer patients with complete prognostic information in 5 independent datasets were divided into a training cohort and two validation cohort (TCGA and meta-validation). Prognostic analysis was assessed in these cohort to evaluate the predictive value of HRGS. Results: A model of prognostic HRGS containing 14 hypoxia-related genes was developed. In training cohort and two validation cohorts, patients in hypoxia high-risk group satisfied by our HRGS had significant poor disease free survival compared with those in the in the low risk group (HR=4.35, 95% CI=2.30-8.23, P<0.001 in training cohort, HR=2.14, 95% CI=1.09-4.21, P=0.024 in TCGA cohort, HR=1.91, 95% CI=1.08-3.39, P=0.024 in meta-validation cohort). When compared with Oncotype DX, HRGS achieved an improved survival correlation in the training cohort (mean C-index, 0.80 vs 0.65, P<0.05) and the validation cohort (mean C-index, 0.70 vs 0.61 in the TCGA cohort, mean C-index, 0.68 vs 0.73 in the meta-validation cohort). Analysis of the data found that patients with low survival rates have significant relationships with genes regulated by the cell cycle pathway, such as mTROC1, E2F, G2-M, mitotic, oxidative phosphorylation, MYC, PI3K-AKT-mTOR (P<0.005). Conclusions: HRGS was a satisfactory prognostic prediction model for early stage colorectal patients. Hypoxia-related genes that regulate the cell cycle pathway were associated with prognosis in patients with stage I and stage II colorectal cancer. Further researches are needed to assess the clinical effectiveness of the system and the treatment options for biological targets.

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Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma

Frontiers in Oncology ◽

10.3389/fonc.2021.625220 ◽

2021 ◽

Vol 11 ◽

Author(s):

Bing Xiao ◽

Yanghua Fan ◽

Zhe Zhang ◽

Zilong Tan ◽

Huan Yang ◽

...

Keyword(s):

Cerebral Edema ◽

Clinical Characteristics ◽

Three Dimensional ◽

Recursive Feature Elimination ◽

Treatment Programs ◽

Support Vector ◽

Individual Treatment ◽

Combined Model ◽

Machine Method ◽

Radiomics Signature

BackgroundPostoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma.ObjectiveTo evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma.MethodsA total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction.ResultsThree significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction.ConclusionsBy integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma.

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