Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics

Chen, Li-Da; Liang, Jin-Yu; Wang, Hui; Zhu, Weiming; Li, Shurong; Li, Wei; Zhang, Xinhua; Chen, Jianhui; Ye, Jinning; Li, Xin; Xie, Xiaoyan; Lü, Ming-De; Kuang, Ming; Xu, Jianbo; Wang, Wei

doi:10.1016/j.lfs.2018.07.007

Cited by 48 publications

(31 citation statements)

References 24 publications

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“…Meanwhile, radiomics is a new method of medical image analysis that further characterizes the phenotype of tumor by transforming conventional medical images into quantitative, high-dimensional, and exploitable radiology data (17)(18)(19)(20). Currently, many studies have applied radiomics characteristics to predict lymph node metastasis and perineural invasion (PNI) in colorectal cancer (21,22), evaluate the response to neoadjuvant therapy (23), determine preoperative synchronous distant metastasis (24), and predict staging of rectal cancer (25). However, its use in LVI prediction is still rare.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Zhang

Guo

et al. 2020

Front. Oncol.

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Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods: A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann-Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts. Results: An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803-0.964) and validation (AUC, 0.876; 95% CI, 0.721-1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits. Conclusion: Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions.

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Section: Introductionmentioning

confidence: 99%

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Zhang

Guo

et al. 2020

Front. Oncol.

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“…Hence, we investigated whether multi-regional radiomics, including both tumor and mesorectum, could provide more features to discriminate LVI positive from LVI-negative lesions. When the current multi-regional radiomics signature was introduced into the prediction model, the performance improved than that of the single-regional model [34]. This suggests that the multiregional radiomic signature could enhance the prediction of LVI in rectal cancer patients.…”

Section: Discussionmentioning

confidence: 96%

“…LVI, which is defined as the presence of tumor cells in the lymphatic vessels or blood vessels or both, include intramural, extramural blood vessels, and lymphatic vessels. In addition to the region of the tumor, the surrounding mesorectal tissues may also exhibit abnormal microscopic changes in the microvascular and lymphatic networks, extracellular matrix, and interstitial pressure, which cannot be ignored [33,34]. Hence, we investigated whether multi-regional radiomics, including both tumor and mesorectum, could provide more features to discriminate LVI positive from LVI-negative lesions.…”

Section: Discussionmentioning

confidence: 99%

Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer

Liu

Yang

et al. 2019

Chin J Acad Radiol

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Purpose To investigate the value of radiomics in predicting lymphovascular invasion (LVI) status of rectal cancer based on MRI. Materials and methods The retrospective study included 188 patients based on MRI with histologically confirmed rectal cancer and evaluated LVI status. Clinical factors and image data were collected, and radiomics features were extracted from multi-region (tumor and mesorectum) and single region (tumor), respectively, on T2WI and DWI. Spearman correlation analysis and the LASSO algorithm were used for radiomic feature extraction and selection; preliminarily selection of an optimal classifier by the results of the fivefold cross-validation performance in the six preselected specific machine learning classifier. Multi-regional and single-regional predictive models were both built and evaluated by calculating the area under the ROC curve (AUC) and corresponding accuracy, specificity, sensitivity, etc.Results A Ridge Classification model was constructed with 21 features (2 clinical features, 10 radiomics features from mesorectum region, and 9 radiomics features from tumor region) selected by Spearman correlation and LASSO analysis. The multi-regional model shows a good performance in the differentiation of the status of LVI in training data sets (AUC = 0.87, accuracy = 0.79). The model was further validated in the testing data sets, giving an AUC and an accuracy of 0.74 and 0.68, respectively. Furthermore, the performance of single-regional model (AUC = 0.72, accuracy = 0.67) is lower compared to the values given by the multi-regional model. Conclusion The radiomics model which we developed demonstrates that multi-regional radiomics features based on multiparametric MRI are useful for preoperatively predicting lymphovascular invasion in patients with rectal cancer. KeywordsRectal cancer · Machine learning · Radiomics · Lymphovascular · MRI Abbreviations AUC Area under the ROC curve EMVI Extramural venous invasion LASSO Least absolute shrinkage and selection operator LVI Lymphovascular invasion MRF Mesorectal fascia ROC Receiver-operating characteristic

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“…Nevertheless, our report is noteworthy because this is the first study to evaluate biplane TRUS plus UE and CEUS in T staging of rectal cancer after primary TME. Ultrasonography based radiomics has been used to improve prediction of lymph node metastasis of rectal cancer [20]. The role of UE and CEUS based radiomics for rectal cancer should be elucidated in the future.…”

Section: Discussionmentioning

confidence: 99%

Biplane transrectal ultrasonography plus ultrasonic elastosonography and contrast-enhanced ultrasonography in T staging of rectal cancer

et al. 2020

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Background The aim of this study is to assess biplane transrectal ultrasonography (TRUS) plus ultrasonic elastosonography (UE) and contrast-enhanced ultrasonography (CEUS) in T staging of rectal cancer. Methods Between March 2016 and January 2019, 66 rectal cancer patients who completed biplane TRUS plus UE and CEUS for preoperative workup and were treated by primary total mesorectal excision (TME) were retrospectively analyzed. Results The accuracy of TRUS plus UE and CEUS in all T staging of rectal cancer was 69.7%. The highest accuracy was achieved in the T3 stage (87.5%), while it was 71.4 and 50.0% in the T1 and T2 stage, respectively. The mean sizes of uT1-T2 lesions and uT3-T4 lesions were 30.0 ± 10.6 mm (range, 10.0–55.0) and 40.2 ± 11.2 mm (range, 14.0–57.0), respectively (p < 0.001). According to the receiver operating characteristic (ROC) curve to predict pT stages (pT1,2 vs. pT3), the optimal cut-off value of lesions in greatest dimension was 28.5 mm by TRUS with areas under the curve (AUC) of 0.769, and the optimal cut-off values of peak systolic velocity (PSV) and resistive index (RI) were 18.8 cm/sec and 0.645, respectively. The AUCs of PSV and RI were 0.588 and 0.555, respectively. Conclusions Diagnostic accuracy of TRUS plus UE and CEUS in T staging of rectal cancer does not reach the excellent published study results, especially for patients with early rectal cancer. Tumor sizes, PSV and RI are useful additions for TRUS in T staging of rectal cancer.

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Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics

Cited by 48 publications

References 24 publications

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer

Biplane transrectal ultrasonography plus ultrasonic elastosonography and contrast-enhanced ultrasonography in T staging of rectal cancer

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