A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer

Zhang, Ke; Ren, Yiyue; Xu, Shufeng; Lu, Wei; Xie, Shengnan; Qu, Jiali; Wang, Xiaoyan; Shen, Bo; Pang, Peipei; Cai, Xiujun; Sun, Jihong

doi:10.1002/mp.15001

Cited by 15 publications

(11 citation statements)

References 51 publications

(99 reference statements)

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“…Second, the three-dimensional method can more accurately and fully characterize the voxel characteristics than the two-dimensional method, as shown in previous studies [ 26 , 27 ]. In recent years, there have been several studies similarly exploring the role of MRI based radiomics for LVI in patients with rectal cancer [ 28 , 29 ].But differently, we focused on those patients with negative statue of mrEMVI. As the previous evidence showed, MRI has high specificity but moderate sensitivity in the detection of EMVI.…”

Section: Discussionmentioning

confidence: 99%

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

et al. 2023

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Background Preoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI. Methods The data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong’s test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models. Results Radiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model. Conclusions The radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.

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Section: Discussionmentioning

confidence: 99%

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

et al. 2023

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“…When both systemic bone phenomena and CT cannot determine the existence of bone metastasis, mpMRI is usually feasible. Generally speaking, mpMRI includes conventional sequences (TIW1 and T2W1) and functional sequences (DWI, DCE-MRI and MRS) ( 38 , 39 ). Among them, DWI is more sensitive to monitoring bone metastasis of colorectal cancer than conventional sequences.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI

et al. 2023

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ObjectiveThe mortality of colorectal cancer patients with pelvic bone metastasis is imminent, and timely diagnosis and intervention to improve the prognosis is particularly important. Therefore, this study aimed to build a bone metastasis prediction model based on Gray level Co-occurrence Matrix (GLCM) - based Score to guide clinical diagnosis and treatment.MethodsWe retrospectively included 614 patients with colorectal cancer who underwent pelvic multiparameter magnetic resonance image(MRI) from January 2015 to January 2022 in the gastrointestinal surgery department of Gezhouba Central Hospital of Sinopharm. GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. The effectiveness evaluation of each model mainly included decision curve analysis(DCA), area under the receiver operating characteristic (AUROC) curve and clinical influence curve(CIC).ResultsWe captured fourteen categories of radiomics data based on GLCM for variable screening of bone metastasis prediction models. Among them, Haralick_90, IV_0, IG_90, Haralick_30, CSV, Entropy and Haralick_45 were significantly related to the risk of bone metastasis, and were listed as candidate variables of machine learning prediction models. Among them, the prediction efficiency of RFM in combination with Haralick_90, Haralick_all, IV_0, IG_90, IG_0, Haralick_30, CSV, Entropy and Haralick_45 in training set and internal verification set was [AUC: 0.926,95% CI: 0.873-0.979] and [AUC: 0.919,95% CI: 0.868-0.970] respectively. The prediction efficiency of the other four types of prediction models was between [AUC: 0.716,95% CI: 0.663-0.769] and [AUC: 0.912,95% CI: 0.859-0.965].ConclusionThe automatic segmentation model based on diffusion-weighted imaging(DWI) using depth learning method can accurately segment the pelvic bone structure, and the subsequently established radiomics model can effectively detect bone metastases within the pelvic scope, especially the RFM algorithm, which can provide a new method for automatically evaluating the pelvic bone turnover of colorectal cancer patients.

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“…The results showed that the LR (AUC 0.866, 95%CI: 0.808-0.925) and extreme gradient boosting models (AUC 0.903, 95%CI: 0.855-0.951) performed the best, outperforming F-18-FDG-PET/CT on both the training set and the test set[ 85 ]. Other studies have also shown that radiomics has a good ability to predict metastasis to distant organs, such as the liver and lung, as well as vascular and perineural invasion[ 86 , 87 ]. It is reported that the predictive power of CT-based radiomics for the preoperative staging of CRC.…”

Section: Radiomics Workflowmentioning

confidence: 99%

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Qi¹,

Zhou²,

Zhao³

et al. 2022

World J Gastroenterol

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Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

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A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer

Cited by 15 publications

References 51 publications

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

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