Size and distribution of lymph nodes in rectal cancer resection specimens

Preoperative scoring system for the prediction of risk of lymph node metastasis in cervical cancer

The study aimed to develop and validate a preoperative scoring system to predict the risk of lymph node metastasis (LNM) in cervical cancer (CC). A total of 426 stage IB1-IIA1 CC patients were randomly divided into two sets. A logistic regression model was used to determine independent factors that contribute to LNM. A preoperative scoring system was developed based on beta (β) coefficients. An area under the receiver operating curve (AUC) was used to test for model discrimination. Five-year overall survival (OS) rate was 91.7%. Multivariable logistic regression analysis showed that FIGO stage, tumor size, depth of invasion on MRI, and squamous cell carcinoma antigen levels were independent risk factors in the development set (all P < 0.05). The AUCs of the scoring system for the development and validation sets were 0.833 (95% CI = 0.757-0.909) and 0.767 (95% CI = 0.634-0.891), respectively. Patients who scored 0-2, 3-5, and 6-8 were classified into low-risk, medium-risk, and high-risk groups. Predicted rates were in accord with observed rates in both sets. The 5-year OS rates of the new groups were also significantly different for the entire group, development set, and validation set (all P < 0.05). LNM affects the prognosis of CC patients. The scoring system can be used to assist in evaluating the risk of LNM in CC patients preoperatively. It is easy to obtain and can provide reference for clinical treatment decision-making.

Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study

PURPOSE

To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer.

METHODS

A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis.

RESULTS

The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model.

CONCLUSION

The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.

R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer

The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.

Can we apply the concept of sentinel lymph node in rectal cancer surgery?

INTRODUCTION

Colorectal cancer remains one of the most common causes of cancer-related mortality worldwide, and lymph node staging is crucial in the diagnostic and therapeutic process. Sentinel lymph nodes are the first involved in this process, but their validity in colorectal surgery has not yet been established. Following the emergence of new imaging instrumentation, some authors have attempted to propose different techniques for lymph node identification. However, a clear pattern of mesorectal lymph node distribution relative to the primary lesion site has yet to be defined.

MATERIAL AND METHODS

Our analysis retrospectively reviewed suspicious mesorectal pathological lymph nodes on pre-operative magnetic resonance imaging (MRI) of rectal cancer patients, in order to assess the distribution patterns of possible tumour-related rectal lymph nodes. Mesorectal space was subdivided into quadrants and levels, and morphological features and distances from the lymph node to the primary rectal tumour were recorded.

RESULTS

Two hundred and fifty-five mesorectal lymph nodes distributed among 60 patients were collected. Results show that in 92.1% of cases, nodes were distributed in the same mesorectal quadrant as the rectal primary tumour, and in 88.5% of cases, they were found at the same level as the rectal primary tumour.

CONCLUSIONS

Although a clear node distribution pattern was not established, these results may suggest at least a lymphatic drainage preference lane, worthy of further investigation.

Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis

AIM

The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.

MATERIALS AND METHODS

In this study, we analyzed data from 119 CRC patients who underwent F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scanning. The patient cohort was divided into training and validation subsets in an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep-learning features, radiomics features, and clinical features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis.

RESULTS

The deep learning radiomics (DLR) nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC = 0.934, 95% confidence interval [CI]: 0.884-0.983) in the training cohort, (AUC = 0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC = 0.836, 95% CI: 0.673-0.998) in the test cohort.

CONCLUSION

We developed a preoperative predictive machine-learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in CRC, aiding in treatment decision-making for patients.

FDG-PET/MRI in colorectal cancer care: an updated systematic review

PURPOSE

Since its introduction in 2011, FDG-PET/MRI has been advocated as a useful adjunct in colorectal cancer care. However, gaps and limitations in current research remain. This systematic review aims to review the current literature to quantify the utility of FDG-PET/MRI in colorectal cancer care.

METHODS

An up-to-date review was performed on the available literature between 2000 and 2023 on PubMed, EMBASE, Medline, databases. All studies reporting on the use of FDG-PET/MRI in colorectal cancer care were analyzed. The main outcome measures were accuracy in initial staging, restaging, and detection of metastatic disease in both rectal as well as colon cancers. The secondary outcome was comparing the performance of FDG-PET/MRI versus Standard of Care Imaging (SCI). Finally, the clinical significance of FDG-PET/MRI was measured in the change in management resulting from imaging findings.

RESULTS

A total of 22 observational studies were included, accounting for 988 patients. When individually compared to current Standard of Care Imaging (SCI)-MRI pelvis for rectal cancer and thoraco-abdominal contrast CT, PET/MRI proved superior in terms of distant metastatic disease detection. This led to as much as 21.0% change in management. However, the technological limitations of PET/MRI were once again highlighted, suggesting SCI should retain its place as first-line imaging.

CONCLUSION

FDG-PET/MRI appears to be a promising adjunct in staging and restaging of colorectal cancer in carefully selected patients.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT

BACKGROUND

To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment.

METHODS

Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated.

RESULTS

Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05).

CONCLUSIONS

The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer

OBJECTIVE

We aim to construct a magnetic resonance imaging (MRI)-based multi-sequence multi-regional radiomics model that will improve the preoperative prediction ability of lymph node metastasis (LNM) in T3 rectal cancer.

METHODS

Multi-sequence MRI data from 190 patients with T3 rectal cancer were retrospectively analyzed, with 94 patients in the LNM group and 96 patients in the non-LNM group. The clinical factors, subjective imaging features, and the radiomic features of tumor and peritumoral mesorectum region of patients were extracted from T2WI and ADC images. Spearman's rank correlation coefficient, Mann-Whitney's U test, and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Logistic regression was used to construct six models. The predictive performance of each model was evaluated by the receiver operating characteristic curve (ROC). The differences of each model were characterized by area under the curve (AUC) via the DeLong test.

RESULTS

The AUCs of T2WI, ADC single-sequence radiomics model and multi-sequence radiomics model were 0.73, 0.75, and 0.78, respectively. The multi-sequence multi-regional radiomics model with improved performance was created by combining the radiomics characteristics of the peritumoral mesorectum region with the multi-sequence radiomics model (AUC, 0.87; p < 0.01). The AUC of the clinical model was 0.68, and the MRI-clinical composite evaluation model was obtained by incorporating the clinical data with the multi-sequence multi-regional radiomics features, with an AUC of 0.89.

CONCLUSION

The MRI-based multi-sequence multi-regional radiomics model significantly improved the prediction ability of LNM for T3 rectal cancer and could be applied to guide surgical decision-making in patients with T3 rectal cancer.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer

BACKGROUND

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge.

AIM

To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.

METHODS

In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis.

RESULTS

The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively.

CONCLUSION

The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

New findings in prognostic factor assessment for adenocarcinoma of transverse colon: a comparison study between competing-risk and COX regression analysis

Purpose

Competing-risk analysis was used to accurately assess prognostic factors for cancer-specific death in patients with adenocarcinoma of transverse colon (ATC), and the results were compared with those from a conventional Cox regression analysis.

Materials and Methods

Patients diagnosed with ATC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results database. The crude mortality rates of patients with ATC were calculated and their differences were tested using the Gray's test, respectively. In performing multivariate analysis, the Cox regression model and the subdistribution hazard function (SD) in competing risk analysis were utilized, respectively.

Results

This study included 21,477 eligible patients. The SD model indicated that age, etc. are actual independent prognostic factors. In contrast to previous recognition, the results of the Cox regression showed false-positives for sex and Carcinoembryonic antigen, and underestimated point-estimates in the stage and American Joint Committee on Cancer stage due to competing events. A detailed comparison of treatment revealed that the larger surgical scopes were prognostic risk factors compared with the smaller scope of local tumor excision, partial colectomy, or segmental resection. Patients treated with external proton beam radiotherapy had an increased risk compared with those with no radiotherapy and internal radiotherapy.

Conclusions

After comparing the results of the two methods and mitigating the significant bias introduced by Cox regression, we found independent factors that really affect the prognosis of ATC. On the other hand, in terms of ATC, a larger surgical scope and external proton beam radiotherapy may not improve the long-term survival of patients. Therefore, when faced with ATC patients, these differences should be noted and treated differently from common colorectal cancer patients. Thus, clinicians are able to give more targeted treatment plans and prognostic assessments.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Objective

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods

This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results

Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion

T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

Predictive value of infiltrating tumor border configuration of rectal cancer on MRI

BACKGROUND

Infiltrating tumor border configuration (iTBC) is assessed by postoperative pathological examination, thus, is not helpful for preoperative treatment strategies. The study aimed to detect iTBC by magnetic resonance imaging (MRI) and evaluate its predictive value.

MATERIALS AND METHODS

A total of 153 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (MEMVI), tumor length, tumor growth pattern, maximal extramural depth, pathology-proven lymph node metastasis (PLN) and pathology-proven extramural vascular invasion (PEMVI) were analyzed. The correlation of MRI factors with PEMVI and PLN was analyzed by univariate and multivariate logistic regression analyses. The nomograms were established based on multivariate logistic regression analysis and were confirmed by Bootstrap self-sampling. The receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to evaluate the diagnostic efficiency.

RESULTS

Fifty cases of PEMVI and 48 cases of PLN were found. Forty cases of PEMVI and 34 cases of PLN in 62 cases of iTBC were also found. iTBC, MEMVI and maximal extramural depth were significantly associated with PEMVI and PLN (P < 0.05). iTBC (odds ratio = 3.84 and 3.02) and MEMVI (odds ratio = 7.27 and 3.22) were independent risk factors for PEMVI and PLN. The C-indices of the two nomograms for predicting PEMVI and PLN were 0.863 and 0.752, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PEMVI and PLN was good. The AUCs of iTBC for predicting PEMVI and PLN were 0.793 (95% CI: 0.714-0.872) and 0.721 (95% CI: 0.632-0.810), respectively. The DeLong test showed that the predictive efficiency of the nomogram in predicting PEMVI was better than that of iTBC (P = 0.0009) and MEMVI (P = 0.0095).

CONCLUSION

iTBC and MEMVI are risk factors for PEMVI and pelvic lymph node metastasis. The nomograms based on iTBC show a good performance in predicting PEMVI and pelvic lymph node metastasis, possessing a certain clinical reference value.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of Beijing Friendship Hospital, and individual consent was waived for this retrospective analysis.

CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer

OBJECTIVES

The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC).

METHODS

A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index.

RESULTS

LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89.

CONCLUSIONS

There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection.

KEY POINTS

• LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.

Imaging of Gastrointestinal Tract Ailments

Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

OBJECTIVES

Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.

METHODS

A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis.

RESULTS

The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively.

CONCLUSION

The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.

Required distal mesorectal resection margin in partial mesorectal excision: a systematic review on distal mesorectal spread

BACKGROUND

The required distal margin in partial mesorectal excision (PME) is controversial. The aim of this systematic review was to determine incidence and distance of distal mesorectal spread (DMS).

METHODS

A systematic search was performed using PubMed, Embase and Google Scholar databases. Articles eligible for inclusion were studies reporting on the presence of distal mesorectal spread in patients with rectal cancer who underwent radical resection.

RESULTS

Out of 2493 articles, 22 studies with a total of 1921 patients were included, of whom 340 underwent long-course neoadjuvant chemoradiotherapy (CRT). DMS was reported in 207 of 1921 (10.8%) specimens (1.2% in CRT group and 12.8% in non-CRT group), with specified distance of DMS relative to the tumor in 84 (40.6%) of the cases. Mean and median DMS were 20.2 and 20.0 mm, respectively. Distal margins of 40 mm and 30 mm would result in 10% and 32% residual tumor, respectively, which translates into 1% and 4% overall residual cancer risk given 11% incidence of DMS. The maximum reported DMS was 50 mm in 1 of 84 cases. In subgroup analysis, for T3, the mean DMS was 18.8 mm (range 8-40 mm) and 27.2 mm (range 10-40 mm) for T4 rectal cancer.

CONCLUSIONS

DMS occurred in 11% of cases, with a maximum of 50 mm in less than 1% of the DMS cases. For PME, substantial overtreatment is present if a distal margin of 5 cm is routinely utilized. Prospective studies evaluating more limited margins based on high-quality preoperative magnetic resonance imaging and pathological assessment are required.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

Background

This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC).

Methods

In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.

Results

A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).

Conclusions

A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Watch and Wait, Worth It?

Background The surgery with total mesorectal excision recommended by R. J. Heald in 1982 is the gold standard. Rectal cancer (RC) surgery has a morbidity rate ranging from 6 to 35%, and it can cause functional issues such as sexual, urinary, and bowel dysfunction in the long term. Neoadjuvant chemoradiotherapy (CRT) has been gaining ground in patients with lesions in the middle and lower rectum. The aim of the present study is to present the experience of a reference service in the treatment of RC.

Patients and Methods A retrospective study involving 53 patients diagnosed with RC between January 2017 and December 2019 with follow-up until December 2020. We examined tumor location, disease stage, digital rectal exam findings, carcinoembryonic antigen (CEA), therapeutic modality offered, and follow-up time.

Results A total of 32% of the patients were men and 68% were women, with a mean age of 60 years old. Location: upper rectum in 6 cases, middle rectum in 21 cases, and lower rectum in 26 cases with evolution from 9.8 to 13.5 months. The most frequent complaints were hematochezia and constipation. A total of 36 patients underwent neoadjuvant therapy: 11 complete clinical response (CCR) (30.5%), 20 (55.5%) partial clinical response (PCR), and no response in 5 patients (14%). The follow-up ranged from 12 to 48 months, with a mean of 30.5 months. A total of 25% of the patients had RC that went beyond the mesorectal fascia, and 22.64% had metastases in other parts of the body when they were diagnosed.

Conclusion Neoadjuvant radio and chemotherapy present themselves as an alternative in the treatment of rectal cancer. In 36 patients, 30.5% had a complete clinical response, 55.5% had a partial clinical response, and 14% had no response. It was worth doing the “Watch and Wait” (W&W) to sample. A definitive colostomy was avoided. However, it is necessary to expand the study to a larger follow-up and more patients. Additionally, it is necessary to implement a multicenter study.

Technique to match mesorectal lymph nodes imaging findings to histopathology: node-by-node comparison

BACKGROUND

Lymph node status is critical for staging rectal cancer and determining neoadjuvant therapy regimens. Establishing a matching between imaging and histopathological lymph nodes is fundamental for predicting lymph node status. This study reports a technique to achieve node-by-node pairing of mesorectal lymph nodes between imaging findings and histopathology.

METHODS

Fifty-two patients with histopathologically verified rectal cancer underwent magnetic resonance imaging before surgery. The status of each lymph node in the surgical specimens was analyzed histopathologically and matched with preoperative imaging after the operation.

RESULTS

A total of 346 mesorectal lymph nodes were located on imaging evaluation, of which 313 were confirmed histopathologically, and 33 were unmatched. The total success rate of the technique was 90.5%. Node-by-node analysis revealed 280 benign and 33 malignant nodal structures.

CONCLUSION

The technique to match mesorectal lymph node imaging findings to histopathology was feasible and effective. It simplified the technical method and had a reasonable success matching rate, which could provide a standardized approach for obtaining a prospective correlation between imaging and histological findings, supporting all subsequent related studies at the level of mesorectal lymph nodes.

Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer

Background

The status of pelvic lymph node (PLN) metastasis affects treatment and prognosis plans in patients with cervical cancer. However, it is hard to be diagnosed in clinical practice.

Purpose

The present study aimed to evaluate the diagnostic value of multimodal magnetic resonance imaging (MRI) in discriminating between metastatic and non-metastatic pelvic lymph nodes (PLNs) in cervical cancer.

Methods

This retrospective study analyzed MRIs of 209 PLNs in 25 women with pathologically proven cervical cancer. All PLNs had been assessed by pre-treatment multimodal MRIs, and their status was finally confirmed by histopathology. In conventional MRI, lymph node characteristics were compared between metastatic and non-metastatic PLNs. Signal intensity, time–intensity curve (TIC) patterns minimal and mean apparent diffusion coefficients (ADC) were compared between them in DWI. In DCE-MRI, quantitative (K_trans, K_ep and V_e) analyses were performed on DCE-MRI sequences, and their predictive values were analyzed by ROC curves.

Results

Of 209 PLNs, 22 (10.53%) were metastases and 187 (89.47%) were non-metastases at histopathologic examination. Considering a comparison of lymph node characteristics, the short axis size, the long axis size, and the boundary differed significantly between the two groups (P<0.05).The differences in ADC_min, TIC types, K_trans and V_e between metastatic and non-metastatic PLNs were significant as well (P<0.05). The good diagnostic performance of multimodal MRI was shown in discriminating between metastatic and non-metastatic PLNs, with the sensitivity of 85.0% (17/20), specificity of 97.3% (184/189), and accuracy of 96.2% (201/209). ROC analyses showed that the diagnostic accuracy of ADC_min, K_trans and V_e for discriminating between metastatic and non-metastatic PLNs in cervical cancer was 83.7%, 91.4%, and 92.4% with the cut-off values of 0.72 × 10⁻³mm²/s, 0.52 min⁻¹, and 0.53 min⁻¹, respectively.

Conclusion

Multimodal MRI showed good diagnostic performance in determining PLN status in cervical cancer.

A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer

Objective

To explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer.

Materials and Methods

One hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).

Results

Eight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P&lt;0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income.

Conclusion

Predictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.

Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.

A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

BACKGROUND

Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists.

PURPOSE

To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively.

STUDY TYPE

Retrospective.

POPULATION

A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma.

FIELD STRENGTH/SEQUENCE

A 3.0 Tesla (T); T₂ -weighted imaging (T₂ WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence.

ASSESSMENT

Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T₂ WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed.

STATISTICAL TESTS

General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA).

RESULTS

The LN+ group had a significantly lower D* value ([13.20 ± 13.66 vs. 23.25 ± 18.71] × 10^-3  mm² /sec) and a higher f value (0.43 ± 0.12 vs. 0.34 ± 0.10) than the LN- group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort.

DATE CONCLUSION

The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.

Intra- and Interobserver Variability in Magnetic Resonance Imaging Measurements in Rectal Cancer Patients

Colorectal cancer is the second most common cancer in Europe, and accurate lymph node staging in rectal cancer patients is essential for the selection of their treatment. MRI lymph node staging is complex, and few studies have been published regarding its reproducibility. This study assesses the inter- and intraobserver variability in lymph node size, apparent diffusion coefficient (ADC) measurements, and morphological characterization among inexperienced and experienced radiologists. Four radiologists with different levels of experience in MRI rectal cancer staging analyzed 36 MRI scans of 36 patients with rectal adenocarcinoma. Inter- and intraobserver variation was calculated using interclass correlation coefficients and Cohens-kappa statistics, respectively. Inter- and intraobserver agreement for the length and width measurements was good to excellent, and for that of ADC it was fair to good. Interobserver agreement for the assessment of irregular border was moderate, heterogeneous signal was fair, round shape was fair to moderate, and extramesorectal lymph node location was moderate to almost perfect. Intraobserver agreement for the assessment of irregular border was fair to substantial, heterogeneous signal was fair to moderate, round shape was fair to moderate, and extramesorectal lymph node location was substantial to almost perfect. Our data indicate that subjective variables such as morphological characteristics are less reproducible than numerical variables, regardless of the level of experience of the observers.

Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging (MRI)-based lymph node staging remains a significant challenge in the treatment of rectal cancer. Pretreatment evaluation of lymph node metastasis guides the formulation of treatment plans. This systematic review aimed to evaluate the diagnostic performance of MRI in lymph node staging using various morphological criteria.

Methods

A systematic search of the EMBASE, Medline, and Cochrane databases was performed. Original articles published between 2000 and January 2021 that used MRI for lymph node staging in rectal cancer were eligible. The included studies were assessed using the QUADAS-2 tool. A bivariate random-effects model was used to conduct a meta-analysis of diagnostic test accuracy.

Results

Thirty-seven studies were eligible for this meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio of preoperative MRI for the lymph node stage were 0.73 (95% confidence interval [CI], 0.68–0.77), 0.74 (95% CI, 0.68–0.80), and 7.85 (95% CI, 5.78–10.66), respectively. Criteria for positive mesorectal lymph node metastasis included (A) a short-axis diameter of 5 mm, (B) morphological standard, including an irregular border and mixed-signal intensity within the lymph node, (C) a short-axis diameter of 5 mm with the morphological standard, (D) a short-axis diameter of 8 mm with the morphological standard, and (E) a short-axis diameter of 10 mm with the morphological standard. The pooled sensitivity/specificity for these criteria were 75%/64%, 81%/67%, 74%/79%, 72%/66%, and 62%/91%, respectively. There was no significant difference among the criteria in sensitivity/specificity. The area under the receiver operating characteristic (ROC) curve values of the fitted summary ROC indicated a diagnostic accuracy rate of 0.75–0.81.

Conclusion

MRI scans have minimal accuracy as a reference index for pretreatment staging of various lymph node staging criteria in rectal cancer. Multiple types of evidence should be used in clinical decision-making.

T stage-dependent lymph node and distant metastasis and the accuracy of lymph node assessment in rectal cancer

Objective

To analyze data obtained in a representative number of patients with primary rectal cancer with respect to lymph node diagnostics and related tumor stages.

Methods

In pT2-, pT3-, and pT4 rectal cancer lesions, the impact of investigated lymph nodes on the frequency of pN+ status, the cumulative risk of metachronous distant metastases, and overall survival was studied by means of a prospective multicenter observational study over a defined period of time.

Results

From 2000 to 2011, the proportion of surgical specimens with ≥ 12 investigated lymph nodes increased significantly, from 73.6% to 93.2% (p < 0.001; the number of investigated lymph nodes from 16.2 to 20.8; p < 0.001). Despite this, the percentage of pN+ rectal cancer lesions varied only non-significantly (39.9% to 45.9%; p = 0.130; median, 44.1%). For pT2-, pT3-, and pT4 rectal cancer lesions, there was an increasing proportion of pN+ findings correlating significantly with the number of investigated lymph nodes up to n = 12 investigated lymph nodes. Only in pT3 rectal cancer was there a significant increase in pN+ findings in case of > 12 lymph nodes (p = 0.001), but not in pT2 (p = 0.655) and pT4 cancer lesions (p = 0.256). For pT3pN0cM0 rectal cancer, the risk of metachronous distant metastases and overall survival did not depend on the number of investigated lymph nodes.

Conclusion

In rectal cancer, at least n = 12 lymph nodes are to be minimally investigated. The investigation of fewer lymph nodes is associated with a higher risk of false-negative pN0 findings. In particular, in pT3 rectal cancer, the investigation of more than 12 lymph nodes lowers the risk of false-negative pN0 findings. An upstaging effect by the investigation of a possibly maximal number of lymph nodes could not be detected.

USPIO-enhanced MRI of lymph nodes in rectal cancer: A node-to-node comparison with histopathology

PURPOSE

To evaluate the initial results of predicting lymph node metastasis in rectal cancer patients detected in-vivo with USPIO-enhanced MRI at 3 T compared on a node-to-node basis with histopathology.

METHODS

Ten rectal cancer patients of all clinical stages were prospectively included for an in-vivo 0.85 mm³ isotropic 3D MRI after infusion of Ferumoxtran-10. The surgical specimens were examined ex-vivo with an 0.29 mm³ isotropic MRI examination. Two radiologists evaluated in-vivo MR images with a classification scheme to predict lymph node status. Ex-vivo MRI was used for MR-guided pathology and served as a key link between in-vivo MRI and final histopathology for the node-to-node analysis.

RESULTS

138 lymph nodes were detected by reader 1 and 255 by reader 2 (p = 0.005) on in-vivo MRI with a median size of 2.6 and 2.4 mm, respectively. Lymph nodes were classified with substantial inter-reader agreement (κ = 0.73). Node-to-node comparison was possible for 55 lymph nodes (median size 3.2 mm; range 1.2-12.3), of which 6 were metastatic on pathology. Low true-positive rates (3/26, 11 % for both readers) and high true negative rates were achieved (14/17, 82 %; 19/22, 86 %). Pathological re-evaluations of 20 lymph nodes with high signal intensity on USPIO-enhanced MRI without lymph node metastases (false positives) did not reveal tumor metastasis but showed benign lymph node tissue with reactive follicles.

CONCLUSIONS

High resolution MRI visualizes a large number of mesorectal lymph nodes. USPIO-enhanced MRI was not accurate for characterizing small benign versus small tumoral lymph nodes in rectal cancer patients. Suspicious nodes on in-vivo MRI occur as inflammatory as well as metastatic nodes.

High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer

PURPOSE

To establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients.

METHODS

A total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature.

RESULTS

The predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort.

CONCLUSION

HRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.

The Role of Magnetic Resonance Imaging in (Future) Cancer Staging: Note the Nodes

The presence or absence of lymph node metastases is a very important prognostic factor in patients with solid tumors. Current invasive and noninvasive diagnostic methods for N-staging like lymph node dissection, morphologic computed tomography/magnetic resonance imaging (MRI), or positron emission tomography-computed tomography have significant limitations because of technical, biological, or anatomical reasons. Therefore, there is a great clinical need for more precise, reliable, and noninvasive N-staging in patients with solid tumors. Using ultrasmall superparamagnetic particles of ironoxide (USPIO)-enhanced MRI offers noninvasive diagnostic possibilities for N-staging of different types of cancer, including the 4 examples given in this work (head and neck cancer, esophageal cancer, rectal cancer, and prostate cancer). The excellent soft tissue contrast of MRI and an USPIO-based differentiation of metastatic versus nonmetastatic lymph nodes can enable more precise therapy and, therefore, fewer side effects, essentially in cancer patients in oligometastatic disease stage. By discussing 3 important questions in this article, we explain why lymph node staging is so important, why the timing for more accurate N-staging is right, and how it can be done with MRI. We illustrate this with the newest developments in magnetic resonance methodology enabling the use of USPIO-enhanced MRI at ultrahigh magnetic field strength and in moving parts of the body like upper abdomen or mediastinum. For prostate cancer, a comparison with radionuclide tracers connected to prostate specific membrane antigen is made. Under consideration also is the use of MRI for improvement of ex vivo cancer diagnostics. Further scientific and clinical development is needed to assess the accuracy of USPIO-enhanced MRI of detecting small metastatic deposits for different cancer types in different anatomical locations and to broaden the indications for the use of (USPIO-enhanced) MRI in lymph node imaging in clinical practice.

Oncological Outcomes of Transanal Endoscopic Microsurgery Plus Adjuvant Chemoradiotherapy for Patients with High-Risk T1 and T2 Rectal Cancer

Background: Radical surgery is recommended for high-risk pathological stage T1 (pT1) or pT2 rectal cancer after transanal endoscopic microsurgery (TEM). However, in clinical practice, many patients may unfit or decline radical surgery. In recent years, adjuvant chemoradiotherapy (CRT) after TEM was considered as an alternative to radical surgery for these patients. This study aimed to assess oncological outcomes of adjuvant CRT after TEM for high-risk early rectal cancer. Materials and Methods: We collected retrospectively data of 97 patients who underwent TEM with pT1 and pT2 between January 2008 and December 2018. Of these, 35 patients were excluded. Of the remaining 62 patients, 42 were managed by TEM alone and 20 by TEM plus adjuvant CRT. Demographics, recurrence, and survival were analyzed between the two groups. Results: At a median follow-up of 52.5 months, the 3-year local recurrence-free survival and disease-free survival (DFS) in TEM alone group were significantly lower than those in TEM+CRT group (66.6% versus 93.3%, P = .035; 63.7% versus 93.3%, P = .022). Although the 3-year overall survival in TEM+CRT group was higher than TEM alone group (100% versus 83.3%), the difference was not statistically significant (P = .13). The local recurrence rate in TEM alone was 31%, compared with 5% in TEM+CRT group (P = .025). Multivariate analysis showed that adjuvant CRT was an independent prognostic factor for DFS (hazard ratio: 0.094; 95% confidence interval: 0.001-0.764; P = .027). Conclusions: Our study suggests that adjuvant CRT after TEM may be an alternative for pT1 high-risk and T2 rectal cancer who are not suitable or unwilling to undergo salvage radical surgery.

Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer

Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer.

Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis.

Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772.

Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.

Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study

BACKGROUND

Accurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI.

METHODS

In total, 5789 annotated LNs (diameter ≥ 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC).

FINDINGS

For LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81-0.82.

INTERPRETATION

This deep learning-based auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.

[Preoperative intensity-modulated radiotherapy of rectal cancers: Relevance and modalities]

Preoperative radiotherapy boosted by chemotherapy is a recommended treatment in locally advanced rectal cancers. This treatment is delivered by three dimensional conformal irradiation, which is usually well tolerated but can induce potential toxicity such as rectitis, cystitis and hematologic adverse effects. Intensity-modulated radiotherapy, widely available nowadays, allows optimization of volume covering and sparing of organs at risk such as bladder and bone marrow. This review presents relevant clinical situations and requirements for a beneficial and safe preoperative irradiation of rectal cancers by intensity-modulated technique. This technique is compared to three-dimensional conformal radiotherapy.

Risk factors and therapeutic significance of inguinal lymph node metastasis in advanced lower rectal cancer

PURPOSE

This study aimed to clarify predictors and therapeutic significance of inguinal lymph node metastasis (ILNM) in patients with rectal cancer.

METHODS

Patients with rectal adenocarcinoma invading the anal canal who underwent curative surgery between 2003 and 2019 were retrospectively reviewed. Synchronous and metachronous lymph node (LN) metastasis were collectively defined as final nodal metastasis (f-LNM). Factors associated with f-LNM were analyzed. Moreover, the "modified therapeutic value index," defined by multiplication of the frequency of f-LNM by the 5-year overall survival rate for patients who received treatment for f-LNM, was calculated for each LN area.

RESULTS

A total of 145 patients were enrolled (16 patients with f-ILNM). To predict f-ILNM, the cutoff of the inguinal lymph node (ILN) diameter of 8.5 mm gave an area under the curve of 0.889. Dentate line involvement (odds ratio 33.4) and ILN larger than the cutoff of 8 mm (odds ratio 11.9) were independently associated with f-ILNM. The modified therapeutic value indices of the inguinal, lateral pelvic, and mesorectal LNs in the entire population were 6.1, 8.2, and 20.3 points, respectively. In patients with dentate line invasion by cancer, the index of the ILN increased to 11.7 points. In patients with an ILN > 8 mm, the index further increased to 21.1 points.

CONCLUSION

Dentate line involvement and ILN > 8 mm predicted the development of ILNM in patients with rectal cancer invading the anal canal. Treatment of the ILN should be considered for patients with the above predictors given the significant therapeutic outcomes.

Can Ex Vivo Magnetic Resonance Imaging of Rectal Cancer Specimens Improve the Mesorectal Lymph Node Yield for Pathological Examination?

Supplemental digital content is available in the text.

The 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.

Additional value of MRI-detected EMVI scoring system in rectal cancer: applicability in predicting synchronous metastasis

INTRODUCTION

Extramural vascular invasion (EMVI) has been recommended as an independent prognostic factor for poor overall survival rate in rectal cancer and can be used as a potential biomarker. Early prediction of prevalence of synchronous metastasis can elevate the disease-free survival rate. We aimed to evaluate the magnetic resonance imaging (MRI)-detected EMVI (mrEMVI) scoring system in predicting distant metastasis in T3 rectal cancer.

METHODS

Patients with postoperative histopathologically confirmed T3 rectal cancer without previous treatment from July 2014 to December 2015 were enrolled in this study. Two blinded radiologists evaluated mrEMVI status. mrEMVI was categorized as EMVI-positive or EMVI-negative in T2-weighted images using an mrEMVI scoring system. The results, along with other clinical characteristics (age, sex, tumor location, MRI-detected distance of mesorectal extension, lymphatic invasion, perineural invasion, mrEMVI score, and carcinoembryonic antigen [CEA]), were then correlated with synchronous metastases to determine the risk factors using univariate and multivariate analysis.

RESULTS

Of 180 patients, 38 were confirmed to be mrEMVI-positive, 142 mrEMVI-negative. There were 34 patients with synchronous metastasis, of whom 25 were mrEMVI-positive and 9 were mrEMVI-negative. Three factors were significantly associated with synchronous metastasis: mrEMVI (p = 0.001; odds ratio = 8.665), histopathologic lymphatic invasion (p = 0.001; odds ratio = 12.940), and preoperative CEA (p = 0.026; odds ratio = 4.124). mrEMVI score 4 was more likely for synchronous metastasis (p = 0.044; odds ratio = 9.429) than mrEMVI score 3 in rectal cancer.

CONCLUSIONS

mrEMVI positivity is an independent risk factor for synchronous distant metastasis in rectal cancer. mrEMVI score 4 is a stronger risk factor for synchronous metastasis than mrEMVI score 3 in rectal cancer.

Reference intervals of mesenteric lymph node size according to lymphocyte counts in asymptomatic children

There is no acknowledged reference interval of mesenteric lymph node size in healthy children, and the size criterion for mesenteric lymph node enlargement (MLNE) has long been controversial. This study aimed to explore the reference intervals of mesenteric lymph node size according to lymphocyte counts in asymptomatic children and to develop a more appropriate definition of MLNE. The asymptomatic children included were divided into five age strata: 2 to 3 yr; 3 to 4 yr; 4 to 5 yr; 5 to 6 yr; and 6 to 7 yr. Correlation analyses between lymphocyte counts and the long-axis diameter, short-axis diameter, and average diameter of the largest mesenteric lymph node (LMLN) were performed. A reference interval of the short-axis diameter of LMLN was established according to this correlation analysis in each age group. We also report a reference interval of lymphocyte count in each age group. This study revealed significant correlations between the short-axis diameter of LMLN and lymphocyte count in all age groups, as well as in subdivided boy groups and girl groups. The overall reference interval of the short-axis diameter of LMLN in children was 0.54 cm—1.03 cm, with mean value of 0.75 cm. This study supports the use of the short-axis diameter greater than 8–10 mm as the diagnostic criterion for primary mesenteric lymphadenitis based on the presence of a cluster of three or more mesenteric lymph nodes and in the absence of other abnormalities.

Non-invasive MR assessment of the microstructure and microcirculation in regional lymph nodes for rectal cancer: a study of intravoxel incoherent motion imaging

Background

The aim of this study is to evaluate the microstructure and microcirculation of regional lymph nodes (LNs) in rectal cancer by using non-invasive intravoxel incoherent motion MRI (IVIM-MRI), and to distinguish metastatic from non-metastatic LNs by quantitative parameters.

Methods

All recruited patients underwent IVIM-MRI (b = 0, 5, 10, 20, 30, 40, 60, 80, 100, 150, 200, 400, 600, 1000, 1500 and 2000 s/mm²) on a 3.0 T MRI system. One hundred sixty-eight regional LNs with a short-axis diameter equal to or greater than 5 mm from 116 patients were evaluated by two radiologists independently, including 78 malignant LNs and 90 benign LNs. The following parameters were assessed: the short-axis diameter (S), long-axis diameter (L), short- to long-axis diameter ratio (S/L), pure diffusion coefficient (D), pseudo-diffusion coefficient (D^*), and perfusion factor (f). Intraclass correlation coefficients (ICCs) were calculated to assess the interobserver agreement between two readers. Receiver operating characteristic curves were applied for analyzing statistically significant parameters.

Results

Interobserver agreement of IVIM-MRI parameters between two readers was excellent (ICCs> 0.75). The metastatic group exhibited higher S, L and D (P < 0.001), but lower f (P < 0.001) than the non-metastatic group. The area under the curve (95% CI, sensitivity, specificity) of the multi-parameter combined equation for D, f and S was 0.811 (0.744~0.868, 62.82%, 87.78%). The diagnostic performance of the multi-parameter model was better than that of an individual parameter (P < 0.05).

Conclusion

IVIM-MRI parameters provided information about the microstructure and microcirculation of regional LNs in rectal cancer, also improved diagnostic performance in identifying metastatic LNs.

The use of PET/MRI for imaging rectal cancer

Combined PET/MRI is a proposed imaging modality for rectal cancer, leveraging the advantages of MRI and 18F-fluorodeoxyglucose PET. Rectal cancer PET/MRI protocols typically include dedicated pelvis bed positions utilizing small field-of-view T2-weighted imaging. For staging of the primary tumor, PET/MRI can help delineate the extent of tumor better as well as the extent of tumor beyond the muscularis propria. PET uptake may help characterize small lymph nodes, and the use of hepatobiliary phase imaging can improve the detection of small hepatic metastases. The most beneficial aspect of PET/MRI may be in treatment response, although current data are limited on how to combine PET and MRI data in this setting. Limitations of PET/MRI include the inability to detect small pulmonary nodules and issues related to attenuation correction, although the development of new attenuation correction techniques may address this issue. Overall PET/MRI can improve the staging of rectal cancer, although this potential has yet to be fulfilled.

Lymph node metastasis in rectal cancer: comparison of MDCT and MR imaging for diagnostic accuracy

PURPOSE

To compare the diagnostic accuracies of MDCT and high-resolution MRI (HR-MRI) for regional nodal metastases with different short-axis diameter ranges in rectal cancer patients.

METHODS

Rectal adenocarcinoma patients who underwent both MDCT and HR-MRI before surgery were included. The maximum short-axis diameters of the nodes were measured, and were classified as benign or malignant on imaging findings. All of the nodes were subdivided as follows: ≤ 5 mm (Group A), > 5 mm and ≤ 10 mm (Group B) , and > 10 mm (Group C). The postoperative pathological reports were used as the standard, and the sensitivity, specificity, accuracy, ROC curve, and AUC value were calculated for each subgroup.

RESULTS

A total of 592 nodes were included in the node-to-node evaluation. In Group A, the specificity and accuracy of HR-MRI were significantly higher than those of MDCT (99.28% vs. 93.99%, P < 0.001; 95.78% vs. 89.56%, P = 0.010; respectively). In Group B, the specificity and accuracy of HR-MRI were also higher than those of MDCT (98.36% vs. 55.74%, P < 0.001; 80.45% vs. 66.17%, P < 0.001; respectively). For Groups A and B, the AUCs of MDCT were both 0.65, whereas those of HR-MRI were 0.76 and 0.82, respectively. In Group C, all nine malignant nodes were correctly diagnosed metastases on MDCT, whereas one was misjudged as benign on HR-MRI.

CONCLUSIONS

The diagnostic value of HR-MRI is superior to that of MDCT, with higher specificity, accuracy, and AUC values for HR-MRI than for MDCT.

Effect of activated carbon nanoparticles on lymph node harvest in patients with colorectal cancer

AIM

The aim was to examine the effect of activated carbon nanoparticles (ACNs) on lymph node retrieval in colorectal cancer (CRC) patients.

METHODS

This prospective randomized study of 80 subjects was performed between March 2016 and December 2016. Eighty patients with CRC were randomly divided into two groups, the ACN group and a control group. The patients in the ACN group were subjected to 1 ml of ACN injection in the subserosa around the tumour before colectomy and D3 lymphadenectomy. The patients in the control group received the same procedure without the injection of ACNs. After surgery, lymph nodes were isolated, and the greatest dimensions were measured by the same pathologist.

RESULTS

The average number of lymph nodes harvested from each patient was markedly more in the ACN group (31.3 ± 8.1) than in the control group (21.9 ± 5.3; P < 0.001), and the average number of lymph nodes less than 5 mm in greatest dimension was significantly more in the ACN group (11.9 ± 4.9) than in the control group (4.1 ± 2.4; P < 0.001). The ACN group (15/40) had a higher rate of Stage III patients compared to the control group (6/39; P = 0.026). Besides, the greatest dimension of 32.8% metastatic lymph nodes was less than 5 mm.

CONCLUSION

There is significant upstaging following the use of ACNs, which could find more involved nodes. Therefore, ACNs can be used as a tracer to harvest more lymph nodes in CRC patients, with improvement in the accuracy of pathological staging.

Difficulty of predicting lymph node metastasis on CT in patients with rectal neuroendocrine tumors

Background

Surgical indications for rectal neuroendocrine tumors with potential lymph node metastasis remain controversial. Although accurate preoperative diagnosis of nodal status may be helpful for treatment strategy, scant data about clinical values of lymph node size have been reported. The aim of this retrospective study was to investigate the relationship between lymph node size and lymph node metastasis.

Methods

Participants comprised 102 patients who underwent rectal resection with total mesenteric excision or tumor-specific mesenteric excision and in some cases additional lateral pelvic lymph node dissection for rectal neuroendocrine tumor between June 2005 and September 2016. All lymph nodes from specimens were checked and measured.

Results

Pathological lymph node metastasis was confirmed in 37 patients (36%), including 6 patients (5.8%) with lateral pelvic lymph node metastasis. A total of 1169 lymph nodes in the mesorectum were retrieved from all specimens, with 78 lymph nodes (6.7%) showing metastasis. Mean length (long-axis diameter) of metastatic lymph nodes in the mesorectum was 4.31 mm, significantly larger than that of non-metastatic lymph nodes (2.39 mm, P<0.01). The optimal cut-off of major axis length for predicting mesorectal lymph node metastasis was 3 mm. We could predict lymph node metastasis in only 7 patients (21%) from preoperative multidetector-row computed tomography.

Conclusions

Metastatic lymph nodes were small, so predicting lymph node metastasis from preoperative computed tomography is difficult. Alternative modalities with a scan width less than 3 mm may be needed to predict lymph node metastasis of rectal NET with low cost and labour requirements.

A systematic review and meta-analysis of pT2 rectal cancer spread and recurrence pattern: Implications for target design in radiation therapy for organ preservation

BACKGROUND

There are no guidelines on clinical target volume (CTV) delineation for cT2 rectal cancer treated with organ preservation.

MATERIALS AND METHODS

A systematic review and meta-analysis were performed to determine the extent of distal mesorectal (DMS) and distal intramural spread (DIS), the risk of lateral lymph node (LLN) metastases in pT2 tumours, and regional recurrence pattern after organ preservation.

RESULTS

The rate of DMS > 1 cm was 1.9% (95% CI: 0.4-5.4%), maximum extent: 1.3 cm. The rate of DIS > 0.5 cm was 4.7% (95% CI: 1.3-11.5%), maximum extent: 0.8 cm. The rate of LLN metastases was 8.2% (95% CI: 6.7-9.9%) for tumours below or at peritoneal reflexion and 0% for higher tumours. Regional nodal recurrences alone were recorded in 1.0% (95% CI: 0.5-1.7%) of patients after watch-and-wait and in 2.1% (95% CI: 1.2-3.4%) after preoperative radiotherapy and local excision. Thus, the following rules for CTV delineation are proposed: caudal border 1.5 cm from the tumour to account for DMS or 1 cm to account for DIS, whichever is more caudal; cranial border at S2/S3 interspace; inclusion of LLN for tumours at or below peritoneal reflexion. A planning study was performed in eight patients to compare dose-volume parameters obtained using these rules to that obtained using current guidelines for advanced cancers. The proposed rules led to a mean 18% relative reduction of planning target volume, which resulted in better sparing of organs-at-risk.

CONCLUSION

This meta-analysis suggests a smaller CTV for cT2 tumours than the current guidelines designed for advanced cancers.

Role of Quantitative Dynamic Contrast-Enhanced MRI in Evaluating Regional Lymph Nodes With a Short-Axis Diameter of Less Than 5 mm in Rectal Cancer

OBJECTIVE

The aim of this study was to discriminate metastatic from nonmetastatic regional lymph nodes (LNs) with short-axis diameters of less than 5 mm in rectal cancer using quantitative parameters derived from dynamic contrast-enhanced (DCE) MRI.

SUBJECTS AND METHODS

Sixty-five LNs from 122 patients were evaluated, including malignant LNs (n = 27) and benign LNs (n = 38). The following parameters were assessed: the forward volume transfer constant (K^trans), reverse volume transfer constant (k_ep), fractional extravascular extracellular space volume (Ve), short-axis diameter, long-axis diameter, and short- to long-axis diameter ratio. ROC curves were used to analyze statistically significant parameters.

RESULTS

Metastatic LNs exhibited a lower K^trans than did nonmetastatic LNs (p < 0.001), but the other parameters were not significantly different between the two groups. The AUC of the K^trans was 0.732, with a 95% CI of 0.610-0.854, and the diagnostic cutoff value was 0.088 min^-1 (sensitivity, 60.5%; specificity, 81.5%).

CONCLUSION

K^trans had moderate diagnostic performance in assessing small regional LNs in rectal cancer and appears to be a useful predictor when distinguishing malignant LNs from benign LNs only by morphology is difficult.

Lymph node ratio as a valuable prognostic factor for patients with colorectal liver-only metastasis undergoing curative resection

Background

Recent studies have suggested that the lymph node ratio (LNR) is a prognostic indicator for various malignancies. However, LNR has not been evaluated in colorectal liver-only metastasis (CRLM). This study aimed to investigate the prognostic value of LNR in patients with CRLM after curative resection.

Patients and methods

We retrospectively investigated the clinicopathologic features of 154 CRLM patients who underwent curative resection between 2005 and 2015. We classified patients into low and high groups based on their LNR by using the X-tile software. Survival curves were plotted through Kaplan–Meier method and compared by log-rank test. Cox proportional hazards analysis was performed to identify the factors associated with recurrence-free survival (RFS) and overall survival (OS).

Results

The patients were divided into two groups in which 124 patients were identified as LNR ≤0.33 and 30 patients as LNR >0.33. Compared to low LNR, high LNR was significantly associated with poor 3-year RFS (47.2% vs 16.7%, P=0.001) and OS (72.8% vs 45.3%, P=0.003) rates. Multivariate analysis indicated that the LNR was an independent predictor for 3-year RFS (hazard ratio, 2.124; 95% CI, 1.339–3.368; P=0.001) and OS (HR, 2.287; 95% CI, 1.282–4.079; P=0.005). However, the node (N) stage and lymph node distribution were not significantly associated with the 3-year RFS (P=0.071, P=0.226) or OS (P=0.452, P=0.791) in patients with CRLM.

Conclusion

This study demonstrated that LNR was an independent predictor for 3-year RFS and OS in patients with CRLM who underwent curative resection and that its prognostic value was superior to that of N stage and lymph node distribution.

Improved detection of a tumorous involvement of the mesorectal fascia and locoregional lymph nodes in locally advanced rectal cancer using DCE-MRI

PURPOSE

The prediction of an infiltration of the mesorectal fascia (MRF) and malignant lymph nodes is essential for treatment planning and prognosis of patients with rectal cancer. The aim of this study was to assess the additional diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the detection of a malignant involvement of the MRF and of mesorectal lymph nodes in patients with locally advanced rectal cancer.

METHODS

In this prospective study, 22 patients with locally advanced rectal cancer were examined with 1.5-T MRI between September 2012 and April 2015. Histopathological assessment of tumor size, tumor infiltration to the MRF, and malignant involvement of locoregional lymph nodes served as standard of reference. Sensitivity and specificity of detecting MRF infiltration and malignant nodes (nodal cut-off size [NCO] ≥ 5 and ≥ 10 mm, respectively) was determined by conventional MRI (cMRI; precontrast and postcontrast T1-weighted, T2-weighted, and diffusion-weighted images) and by additional semi-quantitative DCE-MRI maps (cMRI+DCE-MRI).

RESULTS

Compared to cMRI, additional semi-quantitative DCE-MRI maps significantly increased sensitivity (86 vs. 71% [NCO ≥ 5 mm]/29% [NCO ≥ 10 mm]) and specificity (90 vs. 70% [NCO ≥ 5 mm]) of detecting malignant lymph nodes (p < 0.05). Moreover, DCE-MRI significantly augmented specificity (91 vs. 82%) of discovering a MRF infiltration (p < 0.05), while there was no change in sensitivity (83%; p > 0.05).

CONCLUSION

DCE-MRI considerably increases both sensitivity and specificity for the detection of small mesorectal lymph node metastases (≥ 5 mm but < 10 mm) and sufficiently improves specificity of a suspected MRF infiltration in patients with locally advanced rectal cancer.