MRI-based Radiomics of Rectal Cancer: Preoperative Assessment of the Pathological Features

Ma, Xiaolu; Shen, Fu; Jia, Yan; Xia, Yuwei; Li, Yongling; Lü, Jianping

doi:10.21203/rs.2.12636/v2

Cited by 6 publications

(9 citation statements)

References 2 publications

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“…Radiomic analysis has been successfully utilized to predict survival outcomes in other diseases[ 12 - 14 ]. When integrated with the clinicopathologic features, the radiomics signature is superior to that of a single biomarker, in terms of prognostic value[ 15 - 17 ]. Hence, in the current study, the aim was to compare the predictive abilities, in terms of distant metastases and 3-year OS, of a radiomics signature and clinicoradiologic risk model in patients with RC.…”

Section: Introductionmentioning

confidence: 99%

Radiomics of rectal cancer for predicting distant metastasis and overall survival

Li¹,

Zhu²,

Zhang³

et al. 2020

WJG

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BACKGROUND Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients. AIM To build a novel model for predicting the presence of distant metastases and 3-year overall survival (OS) in RC patients. METHODS This was a retrospective analysis of 148 patients (76 males and 72 females) with RC treated with curative resection, without neoadjuvant or postoperative chemoradiotherapy, between October 2012 and December 2015. These patients were allocated to a training or validation set, with a ratio of 7:3. Radiomic features were extracted from portal venous phase computed tomography (CT) images of RC. The least absolute shrinkage and selection operator regression analysis was used for feature selection. Multivariate logistic regression analysis was used to develop the radiomics signature (Rad-score) and the clinicoradiologic risk model (the combined model). Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC. The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis. RESULTS A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score (consisted of three selected features) and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21 vs 0.32 ± 0.24 for the Rad-score, and 0.60 ± 0.23 vs 0.28 ± 0.26 for the combined model; P < 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological N-stage, and T-stage. The addition of histologic grade to the model failed to show incremental prognostic value. The combined model showed good discrimination, with areas under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups. CONCLUSION This study presents a clinicoradiologic risk model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.

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Section: Introductionmentioning

confidence: 99%

Radiomics of rectal cancer for predicting distant metastasis and overall survival

Li¹,

Zhu²,

Zhang³

et al. 2020

WJG

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“…In comparison with routine strategies using imaging methods, radiomics substantially improves disease diagnosis, tumor grading, and prognostic evaluation, providing a comprehensive guidance for treatment planning [16][17][18]. A previous study demonstrated that the SVM model showed good classification performance related to pathological features in patients with RC [13]. With continuous technology progress, the concept of "radiogenomics" has been widely applied in tumors recently.…”

Section: Discussionmentioning

confidence: 99%

“…All segmentations were checked by one senior radiologist (F.S., who had 12 years of experience in rectal MRI). and (4) higher-order statistics, encompassing transformed first-order statistics and texture features [13][14][15], e.g., logarithm, gradient, square, and wavelet transform. In the training cohort, inter-and intraclass correlation coefficients (ICCs) were determined to assess feature robustness.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Currently, radiomics, a novel noninvasive tool, has been widely used for pretreatment assessment as well as treatment outcome, distant metastasis, and local recurrence predictions in RC, providing important details of tissue characteristics inaccessible to human eyes [13][14][15][16]. The radiomics approach was inspired by the notion that medical images comprise considerable information reflecting potential pathophysiological properties through quantitative analysis of digital medical images for the whole tumor.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study

Jing

Chen

et al. 2022

BioMed Research International

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Detecting mismatch-repair (MMR) status is crucial for personalized treatment strategies and prognosis in rectal cancer (RC). A preoperative, noninvasive, and cost-efficient predictive tool for MMR is critically needed. Therefore, this study developed and validated machine learning radiomics models for predicting MMR status in patients directly on preoperative MRI scans. Pathologically confirmed RC cases administered surgical resection in two distinct hospitals were examined in this retrospective trial. Totally, 78 and 33 cases were included in the training and test sets, respectively. Then, 65 cases were enrolled as an external validation set. Radiomics features were obtained from preoperative rectal MR images comprising T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI), and combined multisequences. Four optimal features related to MMR status were selected by the least absolute shrinkage and selection operator (LASSO) method. Support vector machine (SVM) learning was adopted to establish four predictive models, i.e., ModelT2WI, ModelDWI, ModelCE-T1WI, and Modelcombination, whose diagnostic performances were determined and compared by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Modelcombination had better diagnostic performance compared with the other models in all datasets (all p < 0.05 ). The usefulness of the proposed model was confirmed by DCA. Therefore, the present pilot study showed the radiomics model combining multiple sequences derived from preoperative MRI is effective in predicting MMR status in RC cases.

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“…In recent years, radionics has been increasingly used in the evaluation of tumors. An MRI-based radiomics model has been used to distinguish tumors from benign tissues and reflect the histological characteristics of rectal cancer [ 30 , 31 ]. Hence, the present study explores the method of CRC LNM classification using MRI.…”

Section: Introductionmentioning

confidence: 99%

A novel classification method of lymph node metastasis in colorectal cancer

et al. 2021

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Colorectal cancer lymph node metastasis, which is highly associated with the patient's cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient's cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model.

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MRI-based Radiomics of Rectal Cancer: Preoperative Assessment of the Pathological Features

Cited by 6 publications

References 2 publications

Radiomics of rectal cancer for predicting distant metastasis and overall survival

Radiomics of rectal cancer for predicting distant metastasis and overall survival

Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study

A novel classification method of lymph node metastasis in colorectal cancer

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