MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases

Shu, Zhenyu; Fang, Songhua; Ding, Zhongxiang; Mao, Dewang; Cai, Rui; Yuan-jun, Chen; Pang, Peipei; Gong, Xiangyang

doi:10.1038/s41598-019-39651-y

Cited by 56 publications

(33 citation statements)

References 39 publications

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“…Radiomics can be combined with the imaging appearance to further improve the differential diagnosis ability of the lesion (30,31). The AK, an imaging analytic software used in this study, has been used in many research reports (32,33). A previous study showed that radiomic feature-based CT imaging signatures allow the prediction of lymph node metastasis in cancer and could facilitate the preoperative individualized prediction of lymph node status (20).…”

Section: Discussionmentioning

confidence: 99%

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Zhang

et al. 2020

Front. Oncol.

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Background: Kimura disease may be easily misdiagnosed as malignant tumors such as lymph node metastases based on imaging and clinical symptoms. The aim of this article is to investigate whether the radiomic features and the model based on the features on venous-phase contrast-enhanced CT (CECT) images can distinguish Kimura disease from lymph node metastases in the head and neck. Methods: A retrospective analysis of 14 patients of head and neck Kimura disease (a total of 38 enlarged lymph nodes) and 39 patients with head and neck lymph node metastases (a total of 39 enlarged lymph nodes), confirmed by biopsy or surgery resection, was conducted. All patients accepted CECT within 10 days before biopsy or surgery resection. Radiomic features based on venous-phase CECT were generated automatically from Artificial-Intelligence Kit (AK) software. All lymph nodes were randomly divided into the training set (n = 54) and testing set (n = 23) in a ratio of 7:3. ANOVA + Mann-Whitney, Spearman correlation, least absolute shrinkage and selection operator, and Gradient Descent were introduced for the reduction of the highly redundant features. Binary logistic regression model was constructed based on the selected features. Receiver operating characteristic was used to evaluate the diagnostic performance of the features and the model. Finally, a nomogram was established for model application. Results : Seven features were screened out at the end. Significant difference was found between the two groups for all the features with area under the curves (AUCs) ranging from 0.759 to 0.915. The AUC of the model's identification performance was 0.970 in the training group and 0.977 in the testing group. The disease discrimination efficiency of the model was better than that of any single feature. Conclusions : The radiomic features and the model based on these features on venous-phase CECT images had very good performance for the discrimination between Kimura disease and lymph node metastases in the head and neck.

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Section: Discussionmentioning

confidence: 99%

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Zhang

et al. 2020

Front. Oncol.

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“…Among them, 52 cases were diagnosed with mMCAi. All patients were divided randomly into training ( n = 87) and validation ( n = 39) sets according to a 7:3 ratio ( Shu et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…The inter-rater reliability on ROI segmentation of MCA territory was computed by comparing the measurement from rater B (15 years of experience in interpreting neurological CT scans) and the measurement from rater A (10 years of experience in interpreting neurological CT scans) in 30 randomly selected patients by κ test. Images underwent preprocessing with AK software (Artificial Intelligence Kit V3.0.0.R, GE Healthcare), which included image interpolation, intensity normalization, and gray-level discretization as described previously ( Shu et al, 2019 ). Next, we calculated the texture features including histogram, formfactor, haralick, run-length matrix (RLM), gray-level co-occurrence matrix (GLCM), and gray-level size zone matrix (GLSZM) with AK software (Artificial Intelligence Kit V3.0.0.R, GE Healthcare).…”

Section: Methodsmentioning

confidence: 99%

“…Next, we calculated the texture features including histogram, formfactor, haralick, run-length matrix (RLM), gray-level co-occurrence matrix (GLCM), and gray-level size zone matrix (GLSZM) with AK software (Artificial Intelligence Kit V3.0.0.R, GE Healthcare). Prior to feature selection, a total of 396 imaging features were extracted for each patient and the extracted texture features were then standardized ( Shu et al, 2019 ). Dimension reduction was performed using analysis of variance and Mann–Whitney U test and then we performed a correlation test to reduce data redundancy.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Malignant Acute Middle Cerebral Artery Infarction via Computed Tomography Radiomics

Wen

et al. 2020

Front. Neurosci.

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Malignant middle cerebral artery infarction (mMCAi) is a serious complication of cerebral infarction usually associated with poor patient prognosis. In this retrospective study, we analyzed clinical information as well as non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) data from patients with cerebral infarction in the middle cerebral artery (MCA) territory acquired within 24 h from symptoms onset. Then, we aimed to develop a model based on the radiomics signature to predict the development of mMCAi in cerebral infarction patients. Patients were divided randomly into training ( n = 87) and validation ( n = 39) sets. A total of 396 texture features were extracted from each NCCT image from the 126 patients. The least absolute shrinkage and selection operator regression analysis was used to reduce the feature dimension and construct an accurate radiomics signature based on the remaining texture features. Subsequently, we developed a model based on the radiomics signature and Alberta Stroke Program Early CT Score (ASPECTS) based on NCCT to predict mMCAi. Our prediction model showed a good predictive performance with an AUC of 0.917 [95% confidence interval (CI), 0.863–0.972] and 0.913 [95% CI, 0.795–1] in the training and validation sets, respectively. Additionally, the decision curve analysis (DCA) validated the clinical efficacy of the combined risk factors of radiomics signature and ASPECTS based on NCCT in the prediction of mMCAi development in patients with acute stroke across a wide range of threshold probabilities. Our research indicates that radiomics signature can be an instrumental tool to predict the risk of mMCAi.

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“…These features can characterize the heterogeneity of cancer and reflect changes in the tumor microstructure (17). The most robust features were used for manual correction purposes to improve the usefulness of the model (18). The Spearman's rank test was used to assess the correlation coefficients between features of set-A (Radiologist A) and set-B (Radiologist B).…”

Section: Extraction Of Radiomic Featuresmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Chen

Shu

et al. 2020

Front. Oncol.

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The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, Chen et al. Radiomics Predicted Efficancy of NACT specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.

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MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases

Cited by 56 publications

References 39 publications

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Prediction of Malignant Acute Middle Cerebral Artery Infarction via Computed Tomography Radiomics

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

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