Association between high-resolution MRI-detected extramural vascular invasion and tumour microcirculation estimated by dynamic contrast-enhanced MRI in rectal cancer: preliminary results

Perfusion magnetic resonance imaging in Asherman syndrome

BACKGROUND

Microvascular scarring compromises the functionality of the endometrium, and vascular flow at the junctional zone (JZ) may be the key to understanding poor reproductive outcomes in women with Asherman syndrome (AS).

AIMS

To investigate whether vascular perfusion of the uterus, measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is impaired in women with intrauterine adhesions (IUA) and AS.

MATERIALS AND METHODS

A prospective observational cohort pilot study of 23 women with IUA treated with hysteroscopic synecholysis and a control group of two patients with cervix cancer were subject to DCE-MRI with gadolinium to assess uterine vascularity. Twelve regions of interest (ROIs) were allocated on the DCE-MRI image incorporating the JZ, with control ROI placed at the psoas muscle. Individual ROIs were compared to the mean total perfusion (TP) in the same uterus. Pre- and post-operative perfusion analyses were performed on five women. Receiver operator curves (ROC) were used to analyse MRI as a predictor of IUA.

RESULTS

There was no significant difference in perfusion; a trend toward reduced perfusion was observed in women with IUA compared to the controls. The ROC was predictive of higher-grade and inoperable IUA.

CONCLUSIONS

Reduced perfusion on DCE-MRI as assessed by ROC predicted higher-stage AS. The results of this study support further investigation of DCE-MRI as a prognostic tool for AS prior to surgical intervention to assist in providing prognostic guidance for women suffering from AS.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging parameters and serum tumor markers in rectal carcinoma prognosis

BACKGROUND

Rectal carcinoma (RC), one of the most common malignancies globally, presents an increasing incidence and mortality year by year, especially among young people, which seriously affects the prognosis and quality of life of patients. At present, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and serum carbohydrate antigen 19-9 (CA19-9) and CA125 Levels have been used in clinical practice to evaluate the T stage and differentiation of RC. However, the accuracy of these evaluation modalities still needs further research. This study explores the application and value of these methods in evaluating the T stage and differentiation degree of RC.

AIM

To analyze the diagnostic performance of DCE-MRI parameters combined with serum tumor markers (TMs) in assessing pathological processes and prognosis of RC patients.

METHODS

A retrospective analysis was performed on 104 RC patients treated at Yantai Yuhuangding Hospital from May 2018 to January 2022. Patients were categorized into stages T1, T2, T3, and T4, depending on their T stage and differentiation degree. In addition, they were assigned to low (L group) and moderate-high differentiation (M + H group) groups based on their differentiation degree. The levels of DCE-MRI parameters and serum CA19-9 and CA125 in different groups of patients were compared. In addition, the value of DCE-MRI parameters [volume transfer constant (Ktrans), rate constant (Kep), and extravascular extracellular volume fraction (Ve) in assessing the differentiation and T staging of RC patients was discussed. Furthermore, the usefulness of DCE-MRI parameters combined with serum CA19-9 and CA125 Levels in the evaluation of RC differentiation and T staging was analyzed.

RESULTS

Ktrans, Ve, CA19-9 and CA125 were higher in the high-stage group and L group than in the low-stage group and M + H Group, respectively (P < 0.05). The areas under the curve (AUCs) of the Ktran and Ve parameters were 0.638 and 0.694 in the diagnosis of high and low stages, respectively, and 0.672 and 0.725 in diagnosing moderate-high and low differentiation, respectively. The AUC of DCE-MRI parameters (Ktrans + Ve) in the diagnosis of high and low stages was 0.742, and the AUC in diagnosing moderate-high and low differentiation was 0.769. The AUCs of CA19-9 and CA-125 were 0.773 and 0.802 in the diagnosis of high and low stages, respectively, and 0.834 and 0.796 in diagnosing moderate-high and low differentiation, respectively. Then, we combined DCE-MRI (Ktrans + Ve) parameters with CA19-9 and CA-125 and found that the AUC of DCE-MRI parameters plus serum TMs was 0.836 in the diagnosis of high and low stages and 0.946 in the diagnosis of moderate-high and low differentiation. According to the Delong test, the AUC of DCE-MRI parameters plus serum TMs increased significantly compared with serum TMs alone in the diagnosis of T stage and differentiation degree (P < 0.001).

CONCLUSION

The levels of the DCE-MRI parameters Ktrans and Ve and the serum TMs CA19-9 and CA125 all increase with increasing T stage and decreasing differentiation degree of RC and can be used as indices to evaluate the differentiation degree of RC in clinical practice. Moreover, the combined evaluation of the above indices has a better effect and more obvious clinical value, providing important guiding importance for clinical condition judgment and treatment selection.

Progress of MRI in predicting the circumferential resection margin of rectal cancer: A narrative review

Rectal cancer (RC) is the third most frequently diagnosed cancer worldwide, and the status of its circumferential resection margin (CRM) is of paramount significance for treatment strategies and prognosis. CRM involvement is defined as tumor touching or within 1 mm from the outermost part of tumor or outer border of the mesorectal or lymph node deposits to the resection margin. The incidence of involved CRM varied from 5.4 % to 36 %, which may associate with an in consistent definition of CRM, the quality of surgeries, and the different examination modalities. Although T and N status are essential factors in determining whether a patient should receive neoadjuvant therapy before surgery, CRM status is a powerful predictor of local and distant recurrence as well as survival rate. This review explores the significance of CRM, the various assessment methods, and the role of magnetic resonance imaging (MRI) and artificial intelligence-based MRI in predicting CRM status. MRI showed potential advantage in predicting CRM status with a high sensitivity and specificity compared to computed tomography (CT). We also discuss MRI advancements in RC imaging, including conventional MRI with body coil, high-resolution MRI with phased-array coil, and endorectal MRI. Along with a discussion of artificial intelligence-based MRI techniques to predict the CRM status of RCs before and after treatments.

MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma

BACKGROUND

Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult.

PURPOSE

To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery.

MATERIAL AND METHODS

A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated.

RESULTS

The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively.

CONCLUSION

The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.

MRI restaging of rectal cancer: The RAC (Response-Anal canal-CRM) analysis joint consensus guidelines of the GRERCAR and GRECCAR groups

PURPOSE

To develop guidelines by international experts to standardize data acquisition, image interpretation, and reporting in rectal cancer restaging with magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Evidence-based data and experts' opinions were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts provided recommendations for reporting template and protocol for data acquisition were collected; responses were analysed and classified as "RECOMMENDED" versus "NOT RECOMMENDED" (if ≥ 80% consensus among experts) or uncertain (if < 80% consensus among experts).

RESULTS

Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed.

CONCLUSION

These consensus recommendations should be used as a guide for rectal cancer restaging with MRI.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer

BACKGROUND

To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients.

METHODS

This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (Ktrans, Kep and Ve) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive).

RESULTS

The mean values of Ktrans and Ve in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in Ktrans skewness, Ktrans entropy, Ktrans kurtosis, and Ve maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The Ktrans kurtosis and Ktrans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores.

CONCLUSIONS

Histogram Analysis of DCE-MRI Ktrans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Purpose

This study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.

Materials and methods

There were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P &lt; 0.05 was considered significant.

Result

Tumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.

Data conclusion

A nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study

OBJECTIVES

To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA).

METHODS

Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6 years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method.

RESULTS

Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006).

CONCLUSIONS

Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA.

KEY POINTS

• Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. • APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. • The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).

Association between extramural vascular invasion and iodine quantification using dual-energy computed tomography of rectal cancer: a preliminary study

OBJECTIVE

This study aimed to investigate whether histopathological confirmed extramural vascular invasion (EMVI) is associated with quantitative parameters derived from dual-energy computed tomography (DECT) of rectal cancer.

METHODS

This retrospective study included patients with rectal cancer who underwent rectal cancer surgery and DECT (including arterial-, venous-, and delay-phase scanning) between November 2019 and November 2020. The EMVI of rectal cancer was confirmed via postoperative pathological results. Iodine concentration (IC), IC normalized to the aorta (NIC), and CT attenuation values of the three phases were measured and compared between patients with and without EMVI. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of these DECT quantitative parameters.

RESULTS

Herein, 36 patients (22 men and 14 women) with a mean age of 62 [range, 43-77] years) with (n = 13) and without (n = 23) EMVI were included. Patients with EMVI exhibited significantly higher IC in the venous and delay phases (venous-phase: 2.92 ± 0.6 vs 2.34 ± 0.48; delay-phase: 2.46 ± 0.47 vs 1.88 ± 0.35) and NIC in all the three phases (arterial-phase: 0.31 ± 0.12 vs 0.24 ± 0.06; venous-phase: 0.58 ± 0.11 vs 0.41 ± 0.07; delay-phase: 0.68 ± 0.10 vs 0.46 ± 0.08) than patients without EMVI. Among them, the highest area under the ROC curve (AUC) was obtained in the delay-phase NIC (AUC = 0.983). IC in the arterial-phase and CT attenuation in all the three phases did not significantly differ between patients with and without EMVI (p = 0.205-0.869).

CONCLUSION

Iodine quantification using dual-energy CT, especially the NIC of the tumor, differs between the EMVI-positive and EMVI-negative groups and seems to help predict the EMVI of rectal cancer in this preliminary study; however, a larger sample size study is warranted in the future.

Value of texture analysis based on dynamic contrast-enhanced magnetic resonance imaging in preoperative assessment of extramural venous invasion in rectal cancer

Objective

Accurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI.

Materials and methods

This study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of Ktrans, Kep, Ve and Vp), and key features were selected by least absolute shrinkage and selection operator regression (LASSO). Finally, clinical independent risk factors, conventional MRI assessment, and T-score were incorporated to construct an assessment model using multivariable logistic regression.

Results

The T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively.

Conclusions

The assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND

Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients.

METHODS

A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K^trans, V_e, K_ep) and semiquantitative DCE-MRI parameters (W_- in, W_- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

LVI-positive patients had a higher K_ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K_ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K_ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence.

CONCLUSION

The quantitative parameter of K_ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

Quantitative Evaluation of Extramural Vascular Invasion of Rectal Cancer by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

This study was carried out to explore the preoperative predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in extramural vascular invasion (EMVI) in patients with rectal cancer. 124 patients with rectal cancer were randomly divided into two groups, with 62 groups in each group. One group used conventional magnetic resonance imaging (MRI) and was recorded as the control group. The other group used DCE-MRI and was recorded as the experimental group. The diagnostic value was evaluated by comparing the MRI quantitative parameters of EMVI positive and EMVI negative patients, as well as the area under the curve (AUC) of the receiver operating characteristic curve (ROC), diagnostic sensitivity, and specificity of the two groups. The results showed that the Ktrans and Ve values of EMVI positive patients in the experimental group and the control group were 1.08 ± 0.97 and 1.03 ± 0.93, and 0.68 ± 0.29 and 0.65 ± 0.31, respectively, which were significantly higher than those in EMVI negative patients (P < 0.05). The AUC of EMVI diagnosis in the experimental group and the control group were 0.732 and 0.534 (P < 0.05), the sensitivity was 0.913 and 0.765 (P < 0.05), and the specificity was 0.798 and 0.756 (P > 0.05), respectively. In conclusion, DCE-MRI has a higher diagnostic value than conventional MRI in predicting EMVI in patients with rectal cancer, which was worthy of further clinical promotion.

Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study

Background

To explore the value of the quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in assessing preoperative extramural venous invasion (EMVI) in rectal cancer.

Methods

Eighty-two rectal adenocarcinoma patients who had underwent MRI preoperatively were enrolled in this study. The differences in quantitative DCE-MRI and DWI parameters including Krans, Kep and ADC values were analyzed between MR-detected EMVI (mrEMVI)-positive and -negative groups. Multivariate logistic regression analysis was performed to build the combined prediction model for pathologic EMVI (pEMVI) with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) curve.

Results

Of the 82 patients, 24 were mrEMVI-positive and 58 were -negative. In the mrEMVI positive group, the Ktrans and Kep values were significantly higher than those in the mrEMVI negative group (P < 0.01), but the ADC values were significantly lower (P < 0.01). A negative correlation was observed between the Ktrans vs ADC values and Kep vs ADC values in patients with rectal cancer. Among the four quantitative parameters, Ktrans and ADC value were independently associated with mrEMVI by multivariate logistic regression analysis. ROC analysis showed that combined prediction model based on quantitative DCE parameters and ADC values had a good prediction efficiency for pEMVI in rectal cancer.

Conclusion

The quantitative DCE-MRI parameters, Krans, Kep and ADC values play important role in predicting EMVI of rectal cancer, with Ktrans and ADC value being independent predictors of EMVI in rectal cancer.

Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer

OBJECTIVES

To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model.

METHODS

We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T₂-weighted imaging, T₁-weighted imaging, diffusion-weighted imaging, and enhanced T₁-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed.

RESULTS

The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively.

CONCLUSIONS

The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction.

KEY POINTS

• Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.

Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.

Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest

OBJECTIVES

This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations.

METHODS

The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated.

RESULTS

Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models.

CONCLUSIONS

CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.

Risk of nodal disease in patients with MRI-detected extramural vascular invasion in rectal cancer: a systematic review and meta-analysis

BACKGROUND

The impact of magnetic resonance imaging-detected extramural vascular invasion (mrEMVI) in distant metastasis is well known but its correlation with prevalence of lymph node metastasis is less studied. The aim of this systematic review and meta-analysis was to assess the prevalence of nodal disease in mrEMVI-positive and negative cases in rectal cancer.

METHODS

Following guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a systematic literature search in PubMed, Web of Science, Cochrane Library, and EMBase was carried out to identify relevant studies published up to May 2019.

RESULTS

Our literature search generated 10 studies (863 and 1212 mrEMVI-positive and negative patients, respectively). The two groups (mrEMVI-positive and negative) were significantly different in terms of nodal disease status (odds ratio [OR] 3.15; 95% confidence interval [CI] 2.12-4.67; p < 0.001). The prevalence of nodal disease was 75.90% vs 52.56% in the positive mrEMVI vs negative mrEMVI group, respectively (p < 0.001). The prevalence of positive lymph node in positive mrEMVI patients treated with neoadjuvant/adjuvant chemoradiotherapy (nCRT/CRT) (OR 2.47; 95% CI 1.65-3.69; p < 0.001) was less compared with the patients who underwent surgery alone (OR 6.25; 95% CI 3.74-10.44; p < 0.001).

CONCLUSION

The probability of positive lymph nodes in cases of positive mrEMVI is distinctly greater compared with negative cases in rectal cancer. Positive mrEMVI indicates risk of nodal disease prevalence increased by threefold in rectal cancer.

Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram for predicting the EMVI status. Methods: In total, 106 rectal cancer patients were enrolled in our study. All patients under went preoperative rectal high-resolution MRI and DCE-MRI. We built five models based on the perfusion parameters of DCE-MRI (quantitative model), the radiomics of T₂-weighted (T₂W) CUBE imaging (R₁ model), DCE-MRI (R₂ model), clinical features (clinical model), and clinical-radiomics features. The predictive efficacy of the radiomics signature was assessed and internally verified. The area under the receiver operating curve (AUC) was used to compare the diagnostic performance of different radiomics models and DCE-MRI quantitative parameters. The radiomics score and clinical-pathologic risk factors were incorporated into an easy-to-use nomogram. Results: The quantitative parameters K ^trans and Ve were significantly higher in the EMVI-positive group than in the EMVI-negative group (both P =0.02). K ^trans combined with Ve showed a fair degree of accuracy (AUC 0.680 in the training cohort and AUC 0.715 in the validation cohort) compared with K ^trans or Ve alone. The AUCs of the R₁ and R₂ models were 0.826, 0.715 and 0.872, 0.812 in the training and validation cohorts, respectively. In addition, the R₂-C model yielded an AUC of 0.904 in the training cohort and 0.812 in the validation cohort. The nomogram was presented based on the clinical-radiomics model. The calibration curves showed good agreement. Conclusion: The radiomics nomogram that incorporates the radiomics score, histopathological grade and T stage demonstrated better diagnostic accuracy than the DCE-MRI quantitative parameters and may have significant clinical implications for the preoperative individualized prediction of EMVI in rectal cancer patients.