Advanced rectal cancer with submesenteric lymph node metastasis is a common complication of advanced rectal cancer, which has an important impact on the treatment and prognosis of patients.

To investigate the clinical and pathological characteristics of inferior mesenteric artery (IMA) root lymph node metastases in patients with rectal cancer. The findings of this study provided us with fresh medical information that assisted us in determining the appropriate treatment for these patients.

Our study searched PubMed, Google Scholar, and other databases and searched the relevant studies and reports on the risk factors of IMA root lymph node metastasis of rectal cancer published in the self-built database until December 31, 2023. After data extraction, the Newcastle-Ottawa scale was used to evaluate the quality of the included literature, and RevMan5.3 software was used for meta-analysis and heterogeneity testing. The fixed effect modules without heterogeneity were selected to combine the effect size, and the random effect modules with heterogeneity were selected to combine the effect size. The cause of heterogeneity was found through sensitivity analysis, and the data of various risk factors were combined to obtain the final effect size, odds ratio (OR) value, and 95% confidence interval (CI). Publication bias was tested by drawing funnel plots.

A total of seven literature were included in this study. By combining the OR value of logistic multivariate regression and the 95%CI of various risk factors, we concluded that the risk factors for lymph node metastasis in the IMA region of rectal cancer were as follows: Preoperative carcinoembryonic antigen (CEA) > 5 ng/mL (OR = 0.32, 95%CI: 0.18-0.55, P < 0.05), tumor located above peritoneal reflexive (OR = 3.10, 95%CI: 1.78-5.42, P < 0.05), tumor size ≥ 5 cm (OR = 0.36, 95%CI: 0.22-0.57, P < 0.05), pathological type (mucinous adenocarcinoma/sig-ring cell carcinoma) (OR = 0.23, 95%CI: 0.13-0.41, P < 0.05), degree of tumor differentiation (low differentiation) (OR = 0.17, 95%CI: 0.10-0.31, P < 0.05), tumor stage (T3-4 stage) (OR = 0.11, 95%CI: 0.04-0.26, P < 0.05), gender and age were not risk factors for IMA root lymph node metastasis in rectal cancer (P > 0.05).

Preoperative CEA level, tumor location, tumor size, tumor pathologic type, tumor differentiation, and T stage were correlated with IMA root lymph node metastasis.

The aim of the study is to explore the clinical value of the apparent diffusion coefficient (ADC) derived from the readout segmentation of long variable echo trains (RESOLVE) technique for identifying clinicopathologic features of distal rectal cancer and correlations between ADC and Ki-67 expression.

The data of 112 patients with a proven pathology of distal rectal cancer who underwent preoperative magnetic resonance imaging were retrospectively analyzed. The mean ADC value was measured using the "full-layer and center" method. Differences in ADC values and Ki-67 expression in different clinical stages, pathological types, and tumor differentiation were compared using analysis of variance. Correlations between ADC value and clinicopathologic features were assessed using Spearman correlation analysis.

Interobserver agreement of confidence levels from 2 radiologists was excellent for ADC measurement ( k =  0.85). Patients with a lower clinical stage, well-differentiated adenocarcinomas, and a higher possibility of mucinous adenocarcinoma exhibited a positive correlation with higher ADC values, but these factors were negatively correlated with Ki-67 expression (all P < 0.05). We found that ADC value was negatively correlated with Ki-67 expression ( r = -0.62, P < 0.001).

The ADC value generated by RESOLVE sequences was significantly associated with clinicopathologic features and Ki-67 expression in patients with distal rectal cancer in this study. Thus, the ADC value could be considered a new noninvasive imaging biomarker that could be helpful in predicting the biological properties of distal rectal cancer.

The tumour-draining lymph node (TDLN) plays a pivotal role in the suppression of malignant tumour, however, the immunological profile and prognostic differences between large TDLN (L-TDLN) and small TDLN (S-TDLN) in colorectal cancer (CRC) remain unclear.

We conducted a study using data from the Chinese National Cancer Center (CNCC) database, identifying 837 CRC patients with non-metastatic TDLN, and categorised them into L-TDLN and S-TDLN groups. The long-term survival outcomes and adjuvant therapy efficacy were compared between the two groups. Furthermore, we evaluated the differences in immune activation status and immune cell subsets between patients in L-TDLN and S-TDLN groups by RNA sequencing and immunohistochemical (IHC) staining.

Patients with L-TDLN demonstrated better long-term outcomes compared to those with S-TDLN. Among patients with L-TDLN, there was no significant difference in long-term outcomes between those who received adjuvant chemotherapy and those who did not. The RNA sequencing data revealed a wealth of immune-activating pathways explored in L-TDLN. Furthermore, IHC analysis demonstrated higher numbers of CD3+ and CD8 + T cells in L-TDLN and the corresponding CRC lesions, as compared to patients with S-TDLN.

Enlarged TDLN exhibited an activated anti-tumour immune profile and may serve as an indicator for favourable survival in non-metastatic CRC.

This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients.

We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included 'artificial intelligence', 'CT,' 'MRI', 'breast cancer' and 'lymph nodes'. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC).

A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79-0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66-0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0-4.8), a negative likelihood ratio of 0.18 (95% CI 0.13-0.26) and a diagnostic odds ratio of 25 (95% CI 17-38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79-0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69-0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7-7.0), a negative likelihood ratio of 0.15 (95% CI 0.08-0.27) and a diagnostic odds ratio of 30 (95% CI 12-72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82-0.88) and 0.91 (95% CI 0.88-0.93), respectively.

Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.

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).

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).

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.

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.

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.