Dataset Information

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer.

ABSTRACT:

Objective

To develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.

Methods

186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong's test.

Results

The clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711-0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717-0.915), P = 0.015).

Conclusion

Multiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.

SUBMITTER: Liu X

PROVIDER: S-EPMC7930475 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer.

Liu Xiangchun X Yang Qi Q Zhang Chunyu C Sun Jianqing J He Kan K Xie Yunming Y Zhang Yiying Y Fu Yu Y Zhang Huimao H

Frontiers in oncology 20210218

<h4>Objective</h4>To develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.<h4>Methods</h4>186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman's rank correlation coefficient and the least absolute shrinkage and selection opera ...[more]

PMID: 33680919

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Project description:Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Project description:BackgroundAdequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear.PurposeTo investigate whether magnetic resonance imaging (MRI)-based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer.Study typeRetrospective.PopulationTwo hundred and thirty-six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each).Field strength/sequence1.5 T; T2-weighted turbo spin-echo images.AssessmentRadiomics models (Rprim , Rprim+3 , Rprim+5 , Rprim+10 , Rprim+15 ) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI-reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim , CRprim+3 , CRprim+5 , CRprim+10 , CRprim+15 ). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion.Statistical testsChi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant.ResultsOf all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, -0.157 to -0.336). The CRprim+10 was an independent indicator for poor disease-free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set.Data conclusionRadiomics analysis with a 10-mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.

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Project description:PurposeThe aim of this study was to use 7 T ex vivo magnetic resonance imaging (MRI) scans to determine the size of lymph nodes (LNs) in total mesorectal excision (TME) specimens and to increase the pathological yield of LNs with MR-guided pathology.Materials and methodsTwenty-two fixated TME specimens containing adenocarcinoma were scanned on a 7 T preclinical MRI system with a T1-weighted 3-dimensional gradient echo sequence with frequency-selective lipid excitation (repetition time/echo time, 15/3 milliseconds; resolution, 0.293 mm) and a water-excited 3-dimensional multigradient echo (repetition time, 30 milliseconds; computed echo time, 6.2 milliseconds; resolution, 0.293 mm) pulse sequence.The first series of 11 TME specimens (S1) revealed the number and size of LNs on both ex vivo MRI and histopathology. The second series of 11 TME specimens (S2) was used to perform MR-guided pathology. The number, size, and percentages of yielded LNs of S1 and S2 were compared.ResultsIn all specimens (22/22), a median number of 34 LNs (interquartile range, 26-34) was revealed on ex vivo MRI compared with 14 LNs (interquartile range, 7.5-21.5) on histopathology (P = 0.003). Mean size of all LNs did not differ between the 2 series (ex vivo MRI: 2.4 vs 2.5 mm, P = 0.267; pathology: 3.6 vs 3.5 mm, P = 0.653). The median percentages of harvested LNs compared with nodes visible on ex vivo MRI per specimen for both series were not significantly different (40% vs 43%, P = 0.718). By using a size threshold of greater than 2 mm, the percentage improved to 71% (S1) and to 78% (S2, P = 0.895). The median number of harvested LNs per specimen did not increase by performing MR-guided pathology (S1, 14 LNs; S2, 20 LNs; P = 0.532).ConclusionsEx vivo MRI visualizes more LNs than (MR-guided) pathology is able to harvest. Current pathological examination was not further improved by MR guidance. The majority of LNs or LN-like structures visible on ex vivo MRI below 2 mm in size remain unexplained, which warrants a 3-dimensional approach for pathological reconstruction of specimens.

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Dataset Information

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer.

Objective

Methods

Results

Conclusion

Publications

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer.

Similar Datasets

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