Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature

Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT).Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index.Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905).Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

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Preoperative prediction of histologic grade in invasive breast cancer by using contrast-enhanced spectral mammography-based radiomics

Journal of X-Ray Science and Technology ◽

10.3233/xst-210886 ◽

2021 ◽

pp. 1-10

Author(s):

Ning Mao ◽

Zimei Jiao ◽

Shaofeng Duan ◽

Cong Xu ◽

Haizhu Xie

Keyword(s):

Breast Cancer ◽

Invasive Breast Cancer ◽

Low Energy ◽

Histologic Grade ◽

Low Grade ◽

Training Set ◽

Predictive Tool ◽

Combined Model ◽

Test Set ◽

Contrast Enhanced

OBJECTIVE: To develop and validate a radiomics model based on contrast-enhanced spectral mammography (CESM), and preoperatively discriminate low-grade (grade I/II) and high-grade (grade III) invasive breast cancer. METHOD: A total of 205 patients with CESM examination and pathologically confirmed invasive breast cancer were retrospectively enrolled. We randomly divided patients into two independent sets namely, training set (164 patients) and test set (41 patients) with a ratio of 8:2. Radiomics features were extracted from the low-energy and subtracted images. The least absolute shrinkage and selection operator (LASSO) logistic regression were established for feature selection, which were then utilized to construct three classification models namely, low energy, subtracted images and their combined model to discriminate high- and low-grade invasive breast cancer. Receiver operator characteristic (ROC) curves were used to confirm performance of three models in training set. The clinical usefulness was evaluated by using decision curve analysis (DCA). An independent test set was used to confirm the discriminatory power of the models. To test robustness of the result, we used 100 times LGOCV (leave group out cross validation) to validate three models. RESULTS: From initial radiomics feature pool, 17 and 11 features were selected for low-energy image and subtracted image, respectively. The combined model using 28 features showed the best performance for preoperatively evaluating the histologic grade of invasive breast cancer, with an area under the curve, AUC = 0.88, and 95%confidence interval [CI] 0.85 to 0.92 in the training set and AUC = 0.80 (95%CI 0.67 to 0.92) in the test set. The mean AUC of LGOCV is 0.82. CONCLUSIONS: CESM-based radiomics model is a non-invasive predictive tool that demonstrates good application prospects in preoperatively predicting histological grade of invasive breast cancer.

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Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

Scientific Reports ◽

10.1038/s41598-021-97796-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Zhu ◽

Yingfan Mao ◽

Jun Chen ◽

Yudong Qiu ◽

Yue Guan ◽

...

Keyword(s):

Regression Analysis ◽

Multivariate Logistic Regression Analysis ◽

Early Recurrence ◽

Prediction Performance ◽

Multivariate Logistic Regression ◽

Training Set ◽

Combined Model ◽

Test Set ◽

Pathological Model ◽

Radiomics Signature

AbstractTo investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

10.21203/rs.3.rs-16481/v1 ◽

2020 ◽

Author(s):

Yae Won Park ◽

Dongmin Choi ◽

Mina Park ◽

Sung Jun Ahn ◽

Sung Soo ahn ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Combined Model ◽

Test Set ◽

Clinical Model ◽

Model Based ◽

Clinical Profiles

Abstract Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC). Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models. Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.

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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

Journal of Alzheimer s Disease ◽

10.3233/jad-200734 ◽

2020 ◽

pp. 1-9

Author(s):

Yae Won Park ◽

Dongmin Choi ◽

Mina Park ◽

Sung Jun Ahn ◽

Sung Soo Ahn ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Combined Model ◽

Test Set ◽

Clinical Model ◽

Model Based ◽

Clinical Profiles

Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ 42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ 42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.

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Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma

Frontiers in Oncology ◽

10.3389/fonc.2021.709321 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhen Zhao ◽

Dongdong Xiao ◽

Chuansheng Nie ◽

Hao Zhang ◽

Xiaobing Jiang ◽

...

Keyword(s):

Logistic Regression ◽

Discrimination Performance ◽

Clinical Effectiveness ◽

Preoperative Imaging ◽

Classification Models ◽

Training Set ◽

Test Set ◽

Clinical Model ◽

Blood Indices ◽

Radiomics Signature

BackgroundGiven the similarities in clinical manifestations of cystic-solid pituitary adenomas (CS-PAs) and craniopharyngiomas (CPs), this study aims to establish and validate a nomogram based on preoperative imaging features and blood indices to differentiate between CS-PAs and CPs.MethodsA departmental database was searched to identify patients who had undergone tumor resection between January 2012 and December 2020, and those diagnosed with CS-PAs or CPs by histopathology were included. Preoperative magnetic resonance imaging (MRI) features as well as blood indices were retrieved and analyzed. Radiological features were extracted from the tumor on contrast-enhanced T1 (CE-T1) weighted and T2 weighted sequences. The two independent samples t-test and principal component analysis (PCA) were used for feature selection, data dimension reduction, and radiomics signature building. Next, the radiomics signature was put in five classification models for exploring the best classifier with superior identification performance. Multivariate logistic regression analysis was then used to establish a radiomic-clinical model containing radiomics and hematological features, and the model was presented as a nomogram. The performance of the radiomics-clinical model was assessed by calibration curve, clinical effectiveness as well as internal validation.ResultsA total of 272 patients were included in this study: 201 with CS-PAs and 71 with CPs. These patients were randomized into training set (n=182) and test set (n=90). The radiomics signature, which consisted of 18 features after dimensionality reduction, showed superior discrimination performance in 5 different classification models. The area under the curve (AUC) values of the training set and the test set obtained by the radiomics signature are 0.92 and 0.88 in the logistic regression model, 0.90 and 0.85 in the Ridge classifier, 0.88 and 0.82 in the stochastic gradient descent (SGD) classifier, 0.78 and 0.85 in the linear support vector classification (Linear SVC), 0.93 and 0.86 in the multilayers perceptron (MLP) classifier, respectively. The predictive factors of the nomogram included radiomic signature, age, WBC count, and FIB. The nomogram showed good discrimination performance (with an AUC of 0.93 in the training set and 0.90 in the test set) and good calibration. Moreover, decision curve analysis (DCA) demonstrated satisfactory clinical effectiveness of the proposed radiomic-clinical nomogram.ConclusionsA personalized nomogram containing radiomics signature and blood indices was proposed in this study. This nomogram is simple yet effective in differentiating between CS-PAs and CPs and thus can be used in routine clinical practice.

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Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.720110 ◽

2021 ◽

Vol 9 ◽

Author(s):

Linyan Chen ◽

Hao Zeng ◽

Yu Xiang ◽

Yeqian Huang ◽

Yuling Luo ◽

...

Keyword(s):

Machine Learning ◽

Lung Adenocarcinoma ◽

Image Features ◽

Molecular Characteristics ◽

Learning Models ◽

Training Set ◽

Test Set ◽

Genetic Aberrations ◽

Histopathological Images ◽

Machine Learning Models

Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64–2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

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An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

10.21203/rs.3.rs-159301/v1 ◽

2021 ◽

Author(s):

Zongren Ding ◽

Kongying Lin ◽

Jun Fu ◽

Qizhen Huang ◽

Guoxu Fang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Focal Nodular Hyperplasia ◽

Cirrhotic Liver ◽

Arterial Phase ◽

Training Set ◽

Combined Model ◽

Clinical Model ◽

Contrast Enhanced ◽

Nodular Hyperplasia ◽

Validation Set

Abstract Purpose:This study aimed to develop and validate a radiomics model for differentiating between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI).Methods:We retrospectively enrolled 149 HCC patients and 75 FNH patients seen between May 2015 and May 2019 at our center and randomly allocated patients to a training set (n = 156) and a validation set (n = 68). A total of 2,260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forests, and the least absolute shrinkage and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the three models was compared. Results:Eight radiomics features were chosen to build a radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen to build the clinical model. When evaluating the performance of three models, the clinical model that included clinical data and visual MRI findings achieved excellent performance in the training set (AUC, 0.937; 95% CI, 0.887–0.970) and the validation set (AUC, 0.903; 95% CI, 0.807–0.962), and there was no significant difference between the radiomics model and the clinical model. The AUC of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p = 0.002) and validation (0.972 vs. 0.903, p = 0.032) sets.Conclusions:The combined model based on clinical and radiomics features can well distinguish HCC from FNH in non-cirrhotic liver. Our model may assist clinicians in the clinical decision-making process.

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Individual prediction of non-adherence to oral mercaptopurine (6MP) in children with acute lymphoblastic leukemia (ALL): Results from COG AALL03N1 study.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.10530 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 10530-10530 ◽

Cited By ~ 1

Author(s):

Anna Lynn Hoppmann ◽

Yanjun Chen ◽

Wendy Landier ◽

Lindsey Hageman ◽

Mary V. Relling ◽

...

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Time Of Day ◽

Red Cell ◽

Training Set ◽

Test Set ◽

Predictive Values ◽

Final Model ◽

Clinical Model ◽

Race Ethnicity

10530 Background: Poor adherence to 6MP (measured electronically [MEMS]) increases relapse risk in children with ALL (Bhatia et al. JAMA Oncol 2015). Adherence is difficult to assess clinically and non-adherers are more likely to over-report 6MP intake (Landier et al. Blood 2017). Key sociodemographic/clinical factors (Bhatia et al. JCO 2012) and red cell methyl-mercaptopurine (MMP, a 6MP metabolite) levels (Hoppmann et al. ASCO 2017) are associated with non-adherence and could potentially identify non-adherers. Methods: We developed a prediction model for 6MP non-adherence (MEMS adherence rate < 90%), using receiver operating characteristic (ROC) analyses in 407 children with ALL receiving 6MP (mean age 7.7±4.4y; 68% males; 35% Caucasians, 34% Hispanics, 16% African Americans, 15% Asians). The cohort was divided into a training set (n = 250) and test set (n = 157) using stratified random sampling (stratified by race/ethnicity, gender, age and 6MP non-adherence). We used logistic regression with backward variable elimination, guided by change in area under ROC (AUC), to create a prediction model in the training set, using only clinical and sociodemographic variables (Clinical Model). We then generated a model that added 6MP dose-intensity (6MPDI)-adjusted red cell MMP levels to the Clinical Model (Final Model). All models were validated in the test set. Results: Predictors retained in the Training Clinical Model included: age, race/ethnicity, absolute neutrophil count, 6MPDI, family structure, and taking 6MP at the same vs varied time of day (AUC = 0.79; 95%CI 0.72-0.85). The Training Final Model (adding 6MPDI-adjusted MMP to the Clinical Model) yielded an AUC = 0.79 (95%CI 0.72-0.86). The Test Final Model (AUC = 0.79, 95%CI 0.69-0.88) showed significantly superior discrimination compared to the Test Clinical Model (AUC = 0.74, 95%CI 0.63-0.85; P = 0.002). Using a binary classifier with predicted probability of non-adherence ≥0.5, the Test Final Model had an accuracy of 79%, and positive and negative predictive values of 71% and 80%, respectively. Conclusions: We created, validated, and compared 2 risk-prediction models for 6MP non-adherence in children undergoing maintenance chemotherapy. While inclusion of red cell MMP levels provided superior discrimination in identifying non-adherent patients, the Clinical Model (without MMP levels) performed adequately well, and could be used in the clinical setting.

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Development and validation of an oxidative phosphorylation-related gene signature in lung adenocarcinoma

Epigenomics ◽

10.2217/epi-2020-0217 ◽

2020 ◽

Vol 12 (15) ◽

pp. 1333-1348

Author(s):

Zihao Xu ◽

Zilong Wu ◽

Jingtao Zhang ◽

Ruihao Zhou ◽

Ling Ye ◽

...

Keyword(s):

Oxidative Phosphorylation ◽

Lung Adenocarcinoma ◽

Risk Groups ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Diagnostic Model ◽

Related Gene ◽

Training Set ◽

Diagnostic Efficacy ◽

Test Set

Aim: To develop an oxidative phosphorylation (OXPHOS)-related gene signature of lung adenocarcinoma (LUAD). Materials & methods: We split The Cancer Genome Atlas LUAD cohort into a training set and a test set; we used the least absolute shrinkage and selection operator Cox method to structure the OXPHOS-related prognostic signature in the training set and verified in the test set and GSE30219 dataset. Meanwhile, the diagnostic model was constructed using the logistic Cox method. Results: The signature consisted of seven genes ( LDHA, CFTR, HSPD1, SNHG3, MAP1LC3C, COX6B2, and TWIST1). LUAD patients were divided into high- and low-risk groups, demonstrating good diagnostic and prognostic capabilities. Conclusion: We developed the first-ever OXPHOS-related signature with both prognostic predictive power and diagnostic efficacy.

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Study on the prognosis predictive model of COVID-19 patients based on CT radiomics

Scientific Reports ◽

10.1038/s41598-021-90991-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dandan Wang ◽

Chencui Huang ◽

Siyu Bao ◽

Tingting Fan ◽

Zhongqi Sun ◽

...

Keyword(s):

Receiver Operating Characteristic ◽

Disease Progression ◽

Operating Characteristic ◽

Prediction Models ◽

Predictive Performance ◽

Training Set ◽

Combined Model ◽

Test Set ◽

Receiver Operating Characteristic Curves ◽

Good Predictive Performance

AbstractMaking timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.

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