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

Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review

We systematically reviewed the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in T1 colorectal cancer (CRC). Thirteen studies with 8417 patients were included. AI demonstrated high potential in predicting LNM with sensitivity, specificity, and AUC ranging from 0.561 to 1.0, 0.45 to 1.0, and 0.717 to 1.0, respectively, reducing unnecessary surgeries by approximately 70%.

Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis

BACKGROUND

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.

AIM

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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

Predicting rectal cancer tumor budding grading based on MRI and CT with multimodal deep transfer learning: A dual-center study

Objective

To investigate the effectiveness of a multimodal deep learning model in predicting tumor budding (TB) grading in rectal cancer (RC) patients.

Materials and methods

A retrospective analysis was conducted on 355 patients with rectal adenocarcinoma from two different hospitals. Among them, 289 patients from our institution were randomly divided into an internal training cohort (n = 202) and an internal validation cohort (n = 87) in a 7:3 ratio, while an additional 66 patients from another hospital constituted an external validation cohort. Various deep learning models were constructed and compared for their performance using T1CE and CT-enhanced images, and the optimal models were selected for the creation of a multimodal fusion model. Based on single and multiple factor logistic regression, clinical N staging and fecal occult blood were identified as independent risk factors and used to construct the clinical model. A decision-level fusion was employed to integrate these two models to create an ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), DeLong's test, calibration curve, and decision curve analysis (DCA). Model visualization Gradient-weighted Class Activation Mapping (Grad-CAM) was performed for model interpretation.

Results

The multimodal fusion model demonstrated superior performance compared to single-modal models, with AUC values of 0.869 (95% CI: 0.761-0.976) for the internal validation cohort and 0.848 (95% CI: 0.721-0.975) for the external validation cohort. N-stage and fecal occult blood were identified as clinically independent risk factors through single and multivariable logistic regression analysis. The final ensemble model exhibited the best performance, with AUC values of 0.898 (95% CI: 0.820-0.975) for the internal validation cohort and 0.868 (95% CI: 0.768-0.968) for the external validation cohort.

Conclusion

Multimodal deep learning models can effectively and non-invasively provide individualized predictions for TB grading in RC patients, offering valuable guidance for treatment selection and prognosis assessment.

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

Background

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.

Materials and methods

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.

Results

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).

Conclusion

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 registration unique identifying number UIN

Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

Imaging of colon and rectal cancer

Colon and rectal cancer imaging has traditionally been performed to assess for distant disease (typically lung and liver metastases) and to assess the resectability of the primary tumor. With technological and scientific advances in imaging and the evolution of treatment options, the role of imaging has expanded. Radiologists are now expected to provide a precise description of primary tumor invasion extent, including adjacent organ invasion, involvement of the surgical resection plane, extramural vascular invasion, lymphadenopathy, and response to neoadjuvant treatment, and to monitor for recurrence after clinical complete response.