scholarly journals Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Cuiping Li ◽  
Mingxue Zheng ◽  
Xiaomin Zheng ◽  
Xin Fang ◽  
Jiangning Dong ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Volume Fraction ◽  
Texture Features ◽  
Maximum Level ◽  
Cervical Squamous Cell Carcinoma ◽  
Proliferation Index ◽  
Imaging Biomarkers ◽  
Ki 67 ◽  
D Value

Purpose. This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods. A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D ∗ )) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results. The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group ( P < 0.05 ). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions. Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.

scholarly journals CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

2021 ◽  
Vol 10 (23) ◽  
pp. 5571
Author(s):  
Hans-Jonas Meyer ◽  
Jakob Leonhardi ◽  
Anne Kathrin Höhn ◽  
Johanna Pappisch ◽  
Hubert Wirtz ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Neuroendocrine Tumors ◽  
Texture Features ◽  
Positive Association ◽  
Proliferation Index ◽  
Imaging Biomarkers ◽  
Tumor Grading ◽  
Ki 67 ◽  
Discrimination Analysis ◽  
First Order

Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

scholarly journals The Value of MRI Findings Combined With Texture Analysis in the Differential Diagnosis of Primary Ovarian Granulosa Cell Tumors and Ovarian Thecoma–Fibrothecoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Nai-yu Li ◽  
Bin Shi ◽  
Yu-lan Chen ◽  
Pei-pei Wang ◽  
Chuan-bin Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Analysis ◽  
Texture Analysis ◽  
Granulosa Cell ◽  
Logistic Regression Analysis ◽  
Texture Features ◽  
Intratumoral Hemorrhage ◽  
Granulosa Cell Tumors ◽  
Ovarian Granulosa Cell

ObjectiveThis study aims to explore the value of magnetic resonance imaging (MRI) and texture analysis (TA) in the differential diagnosis of ovarian granulosa cell tumors (OGCTs) and thecoma-fibrothecoma (OTCA–FTCA).MethodsThe preoperative MRI data of 32 patients with OTCA–FTCA and 14 patients with OGCTs, confirmed by pathological examination between June 2013 and August 2020, were retrospectively analyzed. The texture data of three-dimensional MRI scans based on T2-weighted imaging and clinical and conventional MRI features were analyzed and compared between tumor types. The Mann–Whitney U-test, χ2 test/Fisher exact test, and multivariate logistic regression analysis were used to identify differences between the OTCA–FTCA and OGCTs groups. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic curve analysis was carried out to evaluate diagnostic efficiency.ResultsA multivariate analysis of the imaging-based features combined with TA revealed that intratumoral hemorrhage (OR = 0.037), log-sigma-20mm-3D_glszm_SmallAreaEmphasis (OR = 4.40), and log-sigma-2-0mm-3D_glszm_SmallAreaHighGrayLevelEmphasis (OR = 1.034) were independent features for discriminating between OGCTs and OTCA–FTCA (P &lt; 0.05). An imaging-based diagnosis model, TA-based model, and combination model were established. The areas under the curve of the three models in predicting OGCTs and OTCA–FTCA were 0.935, 0.944, and 0.969, respectively; the sensitivities were 93.75, 93.75, and 96.87%, respectively; and the specificities were 85.71, 92.86, and 92.86%, respectively. The DeLong test indicated that the combination model had the highest predictive efficiency (P &lt; 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA–FTCA (P &gt; 0.05).ConclusionsCompared with OTCA–FTCA, intratumoral hemorrhage may be characteristic MR imaging features with OGCTs. Texture features can reflect the microheterogeneity of OGCTs and OTCA–FTCA. MRI signs and texture features can help differentiate between OGCTs and OTCA–FTCA and provide a more comprehensive and accurate basis for clinical treatment.

Radiomics features on CT scans to predict response to HER2-targeted therapy of hepatic metastases from colorectal cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15086-e15086
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Arianna Defeudis ◽  
Giovanni Cappello ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Hepatic Metastases ◽  
Texture Features ◽  
Response To Therapy ◽  
Ct Scans ◽  
Imaging Biomarkers ◽  
Test Set ◽  
Extended Analysis ◽  
Ct Texture Analysis

e15086 Background: Metastatic Colorectal cancer (mCRC) is the 2nd cause of cancer death worldwide. Repeated cycles of therapies, combined with surgery in oligo-metastatic cases, are the therapeutic standard in mCRC. However, this strategy is seldom resolutive. Different lesions in in the same patient could have different responses to systemic therapy. Recently, CT texture analysis (CTTA) had been shown to potentially provide with prognostic and predictive markers, overcoming the limitations of biopsy sampling in defining tumor heterogeneity. The aim of this study is to use CT texture analysis (CTTA) to identify imaging biomarkers of HER2+ mCRC able to predict lesion response to therapy. Methods: The dataset is composed of 39 extended RAS wild type patients with amplified HER2 mCRC enrolled in the HERACLES trial (NCT03225937) that received either a lapatinib+trastuzumab treatment (n = 23) or a pertuzumab+ trastuzumab-emtansine treatment (n = 16). All patients underwent CT examination every 8 weeks, until disease progression. All mCRC on baseline CT were semi-automatically segmented and quantitative features extracted: size, mean, percentiles, 28 texture features. A logistic regression model was created using: (i) the whole dataset of mCRC as training and test set and (ii) 100 randomly generated training sets (with 70% of responder (R+) mCRC and an equal number of non-responder (R-) mCRC), and 100 test sets including the remaining mCRC. A mCRC was classified as R+ if size decreased (-10%) or was stable (±10%); as R- if size increased (+10%), during subsequent CT scans. Results: A total of 199 metastases were included (75R+ and 124R-). The training set was composed of 53R+ and 53R- mCRC and the test set of 22R+ and 71R- mCRC. Using the whole dataset, the model reached an AUC = 0.82 (sensitivity = 84%, specificity = 70%), while it reached a mean AUC of 0.70 (sensitivity = 68%, specificity = 67%) within the 100 repetitions. Conclusions: CTTA might help in stratifying different behaviors of mCRC, opening the way for lesion-specific therapies, with conceivable cost and life savings. Further extended analysis is needed to better characterize and validate predictive value of these biomarkers.

scholarly journals Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach

2021 ◽  
Vol 11 ◽  
Author(s):  
Antonino Guerrisi ◽  
Michelangelo Russillo ◽  
Emiliano Loi ◽  
Balaji Ganeshan ◽  
Sara Ungania ◽  
...  
Keyword(s):  
Pilot Study ◽  
Metastatic Melanoma ◽  
Texture Analysis ◽  
Quantitative Imaging ◽  
Texture Feature ◽  
Texture Features ◽  
Progression Free Survival ◽  
Imaging Biomarkers ◽  
Contrast Enhanced Ct ◽  
Medium Texture

In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.

scholarly journals PET/CT Radiomics in Breast Cancer: Promising Tool for the Prediction of the Ki67 Expression

2021 ◽  
Author(s):  
Cong Shen ◽  
Dong Han ◽  
Yunxuan Li ◽  
Anqi Zheng ◽  
Qi Nie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Fdg Pet ◽  
Roc Analysis ◽  
Binary Logistic Regression ◽  
Proliferation Index ◽  
Ki 67 ◽  
Ki67 Expression ◽  
Pet Ct ◽  
N Stage

Abstract Objective: This study aims to examine the values of radiomics parameters derived from 18-fluorine-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) imaging in the prediction of ki-67 expression in breast cancer patients.Patients and methods: A total of 115 patients diagnosed with breast cancer and examined by 18F-FDG PET/CT were included in this study. The Ki-67 proliferation index was determined from the pathological specimen as positive or negative. Radiomics features were extracted by pyRadiomics and reduced by Independent t-test and least absolute shrinkage selection operator. The radiomics risk score (RRS) was calculated with all the selected features. RRS incorporated with clinical-pathological features were used to construct a binary logistic regression and nomogram classifier. Receiver operating characteristic curve (ROC) analysis was used to predict the accuracy. Decision curve analysis (DCA) was performed to assess clinical utility. Results: Totally 944 features were reduced to 14 predictors. RRS were significantly differed between the ki67+ and ki67- groups (0.440 ± 0.473 and 1.039 ± 0.430; t = -6.663, p < 0.001). In the binary logistic regression, N stage (OR [95%CI], 5.752 [2.032, 16.286], p<0.001) and RRS (OR [95%CI], 20.540 [5.521, 76.423], p<0.001) were independent factors in predicting Ki67 expression. In ROC analysis, AUC was 0.866 (0.790, 0.922), (p<0.001), with sensitivity, specificity, Youden index and cutoff value of 82.50%, 80.00%, 0.6250 and 0.6672, respectively. DCA indicated that use of the clinical-radiomic nomogram had more benefit than utilizing either clinical or radiomic features alone.Conclusion: The radiomics-derived evaluation score combined with N stage could effectively predict Ki67 expression in breast cancer, enabling proper patient selection for treatment.

scholarly journals CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243181
Author(s):  
Eriko Koda ◽  
Tsuneo Yamashiro ◽  
Rintaro Onoe ◽  
Hiroshi Handa ◽  
Shinya Azagami ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Diagnostic Accuracy ◽  
Texture Analysis ◽  
Texture Features ◽  
Small Cell ◽  
Mediastinal Lymphadenopathy ◽  
Small Cell Lung

Objectives To investigate the potential of computed tomography (CT)-based texture analysis and elastographic data provided by endobronchial ultrasonography (EBUS) for differentiating the mediastinal lymphadenopathy by sarcoidosis and small cell lung cancer (SCLC) metastasis. Methods Sixteen patients with sarcoidosis and 14 with SCLC were enrolled. On CT images showing the largest mediastinal lymph node, a fixed region of interest was drawn on the node, and texture features were automatically measured. Among the 30 patients, 19 (12 sarcoidosis and 7 SCLC) underwent endobronchial ultrasound transbronchial needle aspiration, and the fat-to-lesion strain ratio (FLR) was recorded. Texture features and FLRs were compared between the 2 patient groups. Logistic regression analysis was performed to evaluate the diagnostic accuracy of these measurements. Results Of the 31 texture features, the differences between 11 texture features of CT ROIs in the patients with sarcoidosis versus patients with SCLC were significant. Among them, the grey-level run length matrix with high gray-level run emphasis (GLRLM-HGRE) showed the greatest difference (P<0.01). Differences between FLRs were significant (P<0.05). Logistic regression analysis together with receiver operating characteristic curve analysis demonstrated that the FLR combined with the GLRLM-HGRE showed a high diagnostic accuracy (100% sensitivity, 92% specificity, 0.988 area under the curve) for discriminating between sarcoidosis and SCLC. Conclusion Texture analysis, particularly combined with the FLR, is useful for discriminating between mediastinal lymphadenopathy caused by sarcoidosis from that caused by metastasis from SCLC.

scholarly journals Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaonan Mao ◽  
Yan Guo ◽  
Feng Wen ◽  
Hongyuan Liang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Texture Analysis ◽  
Transarterial Chemoembolization ◽  
Tumor Response ◽  
Region Of Interest ◽  
Texture Features ◽  
Discriminative Performance ◽  
Arterial Enhancement ◽  
Enhanced Computed Tomography

Abstract Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.

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