Extramural venous invasion in rectal cancer: overview of imaging, histopathology, and clinical implications

Ali, Hamideh Ale; Kirsch, Richard; Razaz, Suzan; Jhaveri, Aaditeya; Thipphavong, Seng; Kennedy, Erin; Jhaveri, Kartik

doi:10.1007/s00261-018-1673-2

Cited by 44 publications

(31 citation statements)

References 48 publications

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“…Sohn et al reported that the sensitivity of MRI in the evaluation of EMVI was only 28.2% (8), as the smallest vessel diameter that 3.0-T MRI can distinguish is 3 mm. Theoretically speaking, even if high-resolution T2WI is used, it is difficult to identify vessels with a diameter less than 3 mm (10,35). In fact, it is very difficult to identify vessels on MRI, and it is usually necessary to compare different sequences at the same level in addition to intravenously using gadolinium contrast agent to confirm whether it is a vessel.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have shown that MRI has medium to high sensitivity and specificity in detecting EMVI compared with pathological evaluation (7)(8)(9). However, it should be noted that MRI may not be able to correctly identify the invasion of small extramural and intramural vessels, which leads to the low sensitivity of conventional MRI in the evaluation of EMVI (10,11). On the other hand, computed tomography (CT) can assess the entire abdomen, pelvis and chest, allowing for local staging and distant metastasis evaluation (12,13).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Liu

Yang

et al. 2021

Front. Oncol.

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ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Liu

Yang

et al. 2021

Front. Oncol.

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“…In this study, based on the mrEMVI scoring system of Chand et al [6], the EMVI (+) group was consisted of patients with mrEMVI scores of 3 and 4 (Figure 2). Lymphatic invasion was distinguished from venous invasion by the visualization of the course of the vein in mesorectal fat tissue on T2-weighted images [13]. An EMVI score of 0 was accepted as the absence of tumoural extension through the extramural tissue/adjacent vessel.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Extramural Venous Invasion by Diffusion-Weighted Magnetic Resonance Imaging and Computed Tomography in Rectal Adenocarcinoma

et al. 2019

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Purpose The aim of this study is to evaluate the diagnostic contribution of diffusion-weighted magnetic resonance imaging (MRI) and computed tomography (CT) to distinguish extramural venous invasion (EMVI) in rectal adenocarcinoma. Materials and Methods Fifty-eight patients who had been diagnosed with rectal adenocarcinoma (30 patients with EMVI and 28 patients without EMVI) were enrolled in the study. Apparent diffusion coefficient (ADC) values of the tumour and the EMVI (+) vein, the lengths of the tumours were measured on MRI. The diameters of the superior rectal vein (SRV)-inferior mesenteric vein (IMV) and distant metastatic spread were evaluated on CT. The ability of these findings to detect EMVI was assessed using receiver operating characteristic (ROC) analysis. Pathology was accepted as the reference test for EMVI. Results Mean diameters of the SRV (4.9 ± 0.9 mm vs 3.7 ± 0.8 mm) and IMV (6.9 ± 0.8 mm vs 5.4 ± 0.9 mm) were significantly larger ( P < .001) and tumour ADC values were significantly lower (0.926 ± 0.281 × 10−3 mm2/s vs 1.026 ± 0.246 × 10−3 mm2/s; P = .032) in EMVI (+) patients. Diameters of 3.95 mm for the SRV (area under the curve [AUC] ± standard error [SE]: 0.851 ± 0.051, P < .001, sensitivity: 93.3%, specificity: 67.9%) and 5.95 mm for the IMV (AUC ± SE: 0.893 ± 0.040, P < .001, sensitivity: 93.3%, specificity: 71.4%) and an ADC value of 0.929 × 10−3 mm2/s (AUC ± SE: 0.664 ± 0.072, P = .032 sensitivity: 76.7%, specificity: 57.1%) were found to be cutoff values, determined by ROC analysis, for detection of EMVI. Distant metastases were significantly more prevalent in EMVI (+) patients ( P < .001). Conclusion The measurement of ADC values and SRV-IMV diameters seems to have contribution for diagnosis of EMVI in rectal adenocarcinoma. EMVI (+) patients appear to have higher risks of distant metastases at diagnosis.

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“…According to another study by Chand et al (47), positive EMVI after CRT was more frequently detected on MRI than on histopathology (53% versus 19%) and both ymrEMVI (hazard ratio 1.97) and ypEMVI (hazard ratio 2.39) were significant predictors for decreased survival. Considering the advantages of MRI such as multiplanar imaging capability and visualization of the entire rectum (50), radiation-induced fibrosis and destruction of venous endothelium after CRT may be responsible for a higher false-negative rate of EMVI on histopathology (51). As persistent ymrEMVI after CRT may increase the risks of metastatic disease, it is an independent negative prognostic marker for disease-free survival and implicit potential benefits of adjuvant chemotherapy (47).…”

Section: Reported Items On Post-chemoradiotherapy Mrimentioning

confidence: 99%

Response Assessment with MRI after Chemoradiotherapy in Rectal Cancer: Current Evidences

et al. 2019

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Baseline magnetic resonance imaging (MRI) has become the primary staging modality for surgical plans and stratification of patient populations for more efficient neoadjuvant treatment. Patients who exhibit a complete response to chemoradiotherapy (CRT) may achieve excellent local tumor control and better quality of life with organ-preserving treatments such as local excision or even watch-and-wait management. Therefore, the evaluation of tumor response is a key factor for determining the appropriate treatment following CRT. Although post-CRT MRI is generally accepted as the first-choice method for evaluating treatment response after CRT, its application in the clinical decision process is not fully validated. In this review, we will discuss various oncologic treatment options from radical surgical technique to organ-preservation strategies for achieving better cancer control and improved quality of life following CRT. In addition, the current status of post-CRT MRI in restaging rectal cancer as well as the main imaging features that should be evaluated for treatment planning will also be described for the tailored treatment.

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Extramural venous invasion in rectal cancer: overview of imaging, histopathology, and clinical implications

Cited by 44 publications

References 48 publications

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Evaluation of Extramural Venous Invasion by Diffusion-Weighted Magnetic Resonance Imaging and Computed Tomography in Rectal Adenocarcinoma

Response Assessment with MRI after Chemoradiotherapy in Rectal Cancer: Current Evidences

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