Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study

Bi, Shucheng; Li, Jie; Wang, Tongyu; Man, Fengyuan; Zhang, Peng; Hou, Feng; Wang, Hexiang; Hao, Dapeng

doi:10.1007/s00330-022-08780-w

Cited by 14 publications

(7 citation statements)

References 36 publications

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“…In general, entropy is positively correlated with signal dispersion, and the more scattered the tumor signals are, the stronger the uncertainty of the signals and the higher the entropy value in the ROI of the tumor ( 40 ). Entropy can make up for the deficiency of the resolution of the human eyes, which is not sufficiently high for identifying tumor heterogeneity in CE-T1WI images ( 41 ). The remaining features are texture-based features, including GLCM and GLRLM.…”

Section: Discussionmentioning

confidence: 99%

Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study

Ouyang¹,

He²,

Zeng³

et al. 2023

Quant Imaging Med Surg

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Background: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma.Methods: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma.Results: Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. Conclusions:The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.

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Section: Discussionmentioning

confidence: 99%

Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study

Ouyang¹,

He²,

Zeng³

et al. 2023

Quant Imaging Med Surg

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“…This could extend hypoxia and cause necrosis, reducing cellular density [30]. Moreover, mpMRI can provide additional knowledge concerning tumor heterogeneity [31] and improve the predictive function of the model [23]. Our study has certain limitations that should be noted.…”

Section: Discussionmentioning

confidence: 94%

Machine Learning with Multiparametric MRI-based Radiomics Models for Preoperative Prediction of Ki-67 Status in Luminal Breast Cancer

Gao,

Lu,

Xie

et al. 2023

Preprint

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Background The main objective of the study was to determine whether multiparametric MRI (mpMRI) radiomics models supported by machine learning could preoperatively predict Ki-67 status in luminalbreast carcinoma. Methods Between 2018 and 2021, patients with luminal breast cancer who underwent mpMRI in our institution were retrospectively enrolled. The Ki-67 status was analyzed by biopsy preoperatively. Radiomics features were extracted from the T2WI, DCE, DWI, and ADC images, and mpMRI features were derived from four MRI sequences. A prediction model was developed by training the logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) machine learning classifiersaccording to the radiomic characteristics. A clinical-radiomic nomogram was constructed by integrating mpMRI radiomic features and routine clinical MRI variables, followed by calibration and decision curve analyses. Results A total of 140 patients (85 with high and 55 with low Ki-67 expression) were enrolled. Compared to the DCE-, DWI-, and ADC-based radiomic signatures, the T2WI-based radiomic signature exhibited high prediction quality with AUCs of 0.87, 0.92, 0.92, and 0.89 for the four classification algorithms (LG, RF, MLP, SVM), respectively (all p<0.05). The mpMRI radiomic signature also showed high quality with AUCs of 0.92, 0.89, 0.92, and 0.92 for the four algorithms (all p<0.05). A prediction clinical-radiomicnomogram was constructed with training and validation set AUCs of 0.93 (0.90-0.96) and 0.92 (0.89-0.95), respectively. Conclusion T2-based and mpMRI-based radiomics models combined with advanced machine learning classifiers could assist in the preoperative individual-specific prediction of Ki-67 status in luminalbreast carcinoma.

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“…Radiomics has emerged as a significant non-invasive modality for assessing the heterogeneity of malignant tumors. This technique has the ability to identify subtle structures and uncover potential image information that may not be discernible to the unaided eye, thereby enhancing the accuracy of diagnosis and prognosis evaluation [ 17 , 18 ]. Recent investigations have explored the utility of CT-based radiomics in differential diagnosis, treatment prognosis, recurrence risk, and assessment of tumor heterogeneity [ 19 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

Leng,

Kan,

Wang

et al. 2024

BMC Cancer

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Background Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. Methods A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. Results Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). Conclusions The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

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Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study

Cited by 14 publications

References 36 publications

Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study

Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study

Machine Learning with Multiparametric MRI-based Radiomics Models for Preoperative Prediction of Ki-67 Status in Luminal Breast Cancer

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

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