Lymph Node Metastasis In Rectal Cancer Research Articles

Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer.

To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparisonwith the ESGAR category and size of lymph node (LN). Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinicalfeatures was also established and evaluated. Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037). The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study

PurposeTo develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer. MethodsThis retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram. ResultsThe Radscore and clinical models exhibited similar diagnostic efficacy (DeLong’s test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram. ConclusionThe radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.

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

BackgroundTo 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.MethodsPreoperative 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.ResultsNode-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).ConclusionsThe 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.

T1 Mapping and Amide Proton Transfer Weighted Imaging for Predicting Lymph Node Metastasis in Patients with Rectal Cancer.

Accurate preoperative judgment of lymph node (LN) metastasis is a critical step in creating a treatment strategy and evaluating prognosis in rectal cancer (RC) patients. This study aimed to explore the value of T1 mapping and amide proton transfer weighted (APTw) imaging in predicting LN metastasis in patients with rectal cancer. In a retrospective study, twenty-three patients with pathologically confirmed rectal adenocarcinoma who underwent MRI and surgery from August 2019 to August 2021 were selected. Then, 3.0T/MR sequences included conventional sequences (T1WI, T2WI, and DWI), APTw imaging, and T1 mapping. Patients were divided into LN metastasis (group A) and non-LN metastasis groups (group B). The intra-group correlation coefficient (ICC) was used to test the inter-observer consistency. Mann-Whitney U test was used to compare the differences between the two groups. Spearman correlation analysis was performed to evaluate the correlation between T1 and APT values. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the differential performance of each parameter and their combination. The difference between AUCs was compared using the DeLong test. The APT value in patients with LN metastasis was significantly higher than in those without LN metastasis group (P=0.020). Also, similar results were observed for the T1 values (P=0.001). The area under the ROC curve of the APT value in the prediction of LN metastasis was 0.794; when the cutoff value was 1.73%, the sensitivity and specificity were 71.4% and 88.9%, respectively. The area under the ROC curve of the T1 value was 0.913; when the cutoff value was 1367.36 ms, the sensitivity and specificity were 78.6% and 100.0%, respectively. The area under the ROC curve of T1+APT was 0.929, with a sensitivity of 78.6% and specificity of 100.0%. APT and T1 values show great diagnostic efficiency in predicting LN metastasis in rectal cancer.

Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.

Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

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

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. 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-resolutionT2WI 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. 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. The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.

Prognostic and staging value of tumor deposits in patients with rectal cancer after neoadjuvant chemoradiotherapy.

BackgroundThe presence of tumor deposits (TDs) is only considered in the absence of lymph node metastases (LNMs) in the current TNM staging system. However, the prognostic value of TDs when concomitant with LNM for rectal cancer after neoadjuvant chemoradiotherapy (NCRT) remains unclear. This study aimed to evaluate the prognostic value of TDs and when concomitant with LNMs in rectal cancer after NCRT.MethodsPatients with rectal cancer who had received NCRT between 2010 and 2016 were obtained from the Surveillance, Epidemiology and End Results (SEER) 18 (year range, 1975–2016) database. Data were extracted on the following: age, sex, race, TNM stage, total LNs harvested, positive LNs, histologic type, perineural invasion, grade, carcinoma embryonic antigen status, TD number, and cancer-specific survival (CSS) rates. The primary objective was to determine the prognostic impact of TDs on CSS. The effect of the addition of TD to the LNM count for a novel N stage was also evaluated. Univariate and multivariate analyses were performed using the Kaplan-Meier method and Cox models.ResultsOf 9,620 patients, 865 (9.0%) had TDs. TD-positive patients showed a worse prognosis than TD-negative patients (HR =2.39, 95% CI: 2.04–2.80, P<0.001), and multivariate analysis showed that the presence of TDs was an independent poor prognostic factor (HR =1.41, 95% CI: 1.19–1.67, P<0.001). Regarding the LN status, TDs were associated with a higher risk of cancer-specific death in the LNM- group (HR =2.43, 95% CI: 1.86–3.18, P<0.001), M1 group (HR =1.51, 95% CI: 1.08–2.10, P<0.001), and ypN1 group (HR =2.08, 95% CI: 1.61–2.70, P<0.001), but not in the ypN2 group (HR =0.97, 95% CI: 0.69–1.36, P=0.84). Patients with concomitant TDs and LNM showed significantly worse survival than those with TDs or LNM alone (5-year CSS: 48.2%, 72.2%, and 67.8%, respectively). The 5-year CSS rates were 86.2%, 77.4%, 65.1%, 53.8%, and 46.5% for the novel N0, N1a, N1b, N2a, and N2b groups, respectively (P<0.05 across all groups). Time dependent receiver operating characteristic curve analysis and decision curve analysis showed that the novel N stage was superior to the current ypN stage.ConclusionsThe presence of TDs is an independent poor prognostic factor for LARC patients after NCRT. The concomitant presence of TDs and LNM indicates a significantly worse survival, and the addition of TD to LNM may help to better prompt appropriate risk stratification.

Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer.

An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE). A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status. Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485). The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

Metastatic lymph node calcification in rectal cancer: comparison of CT and high-resolution MRI.

Calcification causes mixed signal intensity in the lymph node (LN) on high-resolution magnetic resonance imaging (MRI), which is a strong indicator of regional LN metastasis in rectal cancer. Calcified metastatic LNs in rectal cancer commonly display scattered fine punctate calcifications to varying degrees on computed tomography (CT). On high-resolution MRI, the calcifications manifest a patchy area of signal loss in corresponding calcified area that is larger than on CT. It is necessary to recognize the appearance of metastatic LN calcifications on high-resolution MRI in rectal cancer because it is the primary imaging method for local staging in rectal cancer. This pictorial essay aims to introduce an important imaging finding that can contribute to the diagnosis of LN metastasis by illustrating features and differences between CT and high-resolution MRI of metastatic LN calcifications in rectal cancer.

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

ObjectiveTo develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Methods186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong’s test.ResultsThe clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711–0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717–0.915), P = 0.015).ConclusionMultiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.

Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?

To explore if there is a correlation between T2WI histogram features of the primary tumor and the existence of regional lymph node (LN) metastasis in rectal cancer. Eighty-eight patients with pathologically proven rectal adenocarcinoma, who received direct surgical resection and underwent preoperative rectal MRIs, were enrolled retrospectively. Based on pathological analysis of surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. The degree of differentiation and pathological T stage were recorded. Clinical T stage, tumor location, and maximum diameter of tumor were evaluated of each patient. Whole-tumor texture analysis was independently performed by two radiologists on axial T2WI, including skewness, kurtosis, energy, and entropy. The interobserver agreement was overall good for texture analysis between two radiologists, with intraclass correlation coefficients (ICCs) ranging from 0.626 to 0.826. The LN- group had a significantly higher skewness (p < 0.001), kurtosis (p < 0.001), and energy (p = 0.004) than the LN+ group, and a lower entropy (p = 0.028). These four parameters showed moderate to good diagnostic power in predicting LN metastasis with respective AUC of 0.750, 0.733, 0.669, and 0.648. In addition, they were both correlated with LN metastasis (rs = - 0.413, - 0.385, - 0.28, and 0.245, respectively). The multivariate analysis showed that lower skewness was an independent risk factor of LN metastasis (odds ratio, OR = 9.832; 95%CI, 1.171-56.295; p = 0.01). Signal intensity histogram parameters of primary tumor on T2WI were associated with regional LN status in rectal cancer, which may help improve the prediction of nodal stage. • Histogram parameters of tumor on T2WI may help to reduce uncertainty when assessing LN status in rectal cancer. • Histogram parameters of tumor on T2WI showed a significant difference between different regional LN status groups in rectal cancer. • Skewness was an independent risk factor of regional LN metastasis in rectal cancer.