Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Predicting Surgical Difficulty in Rectal Cancer Surgery: A Systematic Review of Artificial Intelligence Models Applied to Pre-Operative MRI

Introduction: Following the rapid advances in minimally invasive surgery, there are a multitude of surgical modalities available for resecting rectal cancers. Robotic resections represent the current pinnacle of surgical approaches. Currently, decisions on the surgical modality depend on local resources and the expertise of the surgical team. Given limited access to robotic surgery, developing tools based on pre-operative data that can predict the difficulty of surgery would streamline the efficient utilisation of resources. This systematic review aims to appraise the existing literature on artificial intelligence (AI)-driven preoperative MRI analysis for surgical difficulty prediction to identify knowledge gaps and promising models warranting further clinical evaluation. Methods: A systematic review and narrative synthesis were undertaken in accordance with PRISMA and SWiM guidelines. Systematic searches were performed on Medline, Embase, and the CENTRAL Trials register. Studies published between 2012 and 2024 were included where AI was applied to preoperative MRI imaging of adult rectal cancer patients undergoing surgeries, of any approach, for the purpose of stratifying surgical difficulty. Data were extracted according to a pre-specified protocol to capture study characteristics and AI design; the objectives and performance outcome metrics were summarised. Results: Systematic database searches returned 568 articles, 40 ultimately included in this review. AI to support preoperative difficulty assessments were identified across eight domains (direct surgical difficulty grading, extramural vascular invasion (EMVI), lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), T staging, and the requirement for multiple linear stapler firings. For each, at least one model was identified with very good performance (AUC scores of >0.80), with several showing excellent performance considerably above this threshold. Conclusions: AI tools applied to preoperative rectal MRI to support preoperative difficulty assessment for rectal cancer surgeries are emerging, with the progressing development and strong performance of many promising models. These warrant further clinical evaluation, which can aid personalised surgical approaches and ensure the adequate utilisation of limited resources.

Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection: A retrospective study

BACKGROUND

Paradoxically, patients with T4N0M0 (stage II, no lymph node metastasis) colon cancer have a worse prognosis than those with T2N1-2M0 (stage III). However, no previous report has addressed this issue.

AIM

To screen prognostic risk factors for T4N0M0 colon cancer and construct a prognostic nomogram model for these patients.

METHODS

Two hundred patients with T4N0M0 colon cancer were treated at Tianjin Medical University General Hospital between January 2017 and December 2021, of which 112 patients were assigned to the training cohort, and the remaining 88 patients were assigned to the validation cohort. Differences between the training and validation groups were analyzed. The training cohort was subjected to multivariate analysis to select prognostic risk factors for T4N0M0 colon cancer, followed by the construction of a nomogram model.

RESULTS

The 3-year overall survival (OS) rates were 86.2% and 74.4% for the training and validation cohorts, respectively. Enterostomy (P = 0.000), T stage (P = 0.001), right hemicolon (P = 0.025), irregular review (P = 0.040), and carbohydrate antigen 199 (CA199) (P = 0.011) were independent risk factors of OS in patients with T4N0M0 colon cancer. A nomogram model with good concordance and accuracy was constructed.

CONCLUSION

Enterostomy, T stage, right hemicolon, irregular review, and CA199 were independent risk factors for OS in patients with T4N0M0 colon cancer. The nomogram model exhibited good agreement and accuracy.

Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography

Objectives

To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.

Methods

This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.

Results

To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.

Conclusion

A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

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

BACKGROUND

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

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

Research: Construction and validation of elbow function prediction model after supracondylar humerus fracture in children

This article's objectives are to develop a model to predict children's recovery of elbow function following supracondylar fracture, analyze the risk factors affecting those children's elbow function after surgery, and propose a individualized treatment strategy for elbow function in various children. We retrospectively analyzed clinical data from 410 children with supracondylar humerus fracture. A modeling set and a validation set of kids in the included studies were arbitrarily split into 2 groups on a 7:3 basis. To identify statistically significant risk factors, univariate logistic regression analysis was used. Then, multivariate logistic regression was used with the risk factors, and the best logistic regression model was chosen based on sensitivity and accuracy to create a nomogram; A total of 410 children were included in the study according to the inclusion criteria. Among them, there were 248 males and 162 females, and the fracture type: 147 cases of type IIb and 263 cases of type III. There were no significant changes in the afflicted limb's lateral difference, surgical method, onset season, and number of K-wires, according to univariate logistic regression analysis. Age (P < .001), weight (P < .001), height (P < .001), preoperative elbow soft tissue injury (OR = 1.724, 95% CI: 1.040-2.859, P = .035), sex (OR = 2.220, 95% CI: 1.299-3.794, P = .004), fracture classification (Gartland IIb) (OR = 0.252, 95% CI: 0.149-0.426, P < .001), no nerve injury before surgery (OR = 0.304, 95% CI: 0.155-0.596, P = .001), prying technique (OR = 0.464, 95% CI: 0.234-0.920, P = .028), postoperative daily light time > 2 hours (OR = 0.488, 95% CI: 0.249-0.955, P = .036) has a significant difference in univariate analysis; Multivariate regression analysis yielded independent risk factors: fracture classification; No nerve injury before surgery; The daily light duration after surgery was > 2 hours; soft tissue injury; Age, postoperative cast fixation time. The establishment of predictive model is of significance for pediatric orthopedic clinicians in the daily diagnosis and treatment of supracondylar humerus fracture.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer

OBJECTIVE

To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients.

METHODS

234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong's test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA).

RESULTS

The MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884.

CONCLUSION

MRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.

Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography

BACKGROUND

Risk evaluation of lymph node metastasis (LNM) in superficial colorectal cancer resected by endoscopic surgery is critical for determining subsequent therapeutic strategies, but the role of existing clinical methods, including computed tomography, remains limited.

METHODS

Features of the nomogram were determined by logistic regression analysis, and the performance was validated by calibration plots, ROC curves and DCA curves in both the training set and the validation set.

RESULTS

A total of 608 consecutive superficial CRC cases were randomly divided into 426 training and 182 validation cases. Univariate and multivariate logistic regression analyses revealed that age < 50, tumour budding, lymphatic invasion and lower HDL levels were risk factors for LNM. Stepwise regression and the Hosmer‒Lemeshow goodness of fit test showed that the nomogram had good performance and discrimination, which was validated by ROC curves and calibration plots. Internal and external validation demonstrated that the nomogram had a higher C-index (training group, 0.749, validation group, 0.693). DCA and clinical impact curves graphically show that the use of the nomogram to predict LNM had remarkable predictive power. Finally, in comparison with CT diagnosis, the nomogram also visually showed higher superiority, as demonstrated by ROC, DCA and clinical impact curves.

CONCLUSION

Using common clinicopathologic factors, a noninvasive nomogram for individualized prediction of LNM after endoscopic surgery was conveniently established. Nomograms have great superiority in the risk stratification of LNM compared with traditional CT imaging.

Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors

BACKGROUND

Dose adjuvant chemotherapy (AC) should be offered in nasopharyngeal carcinoma (NPC) patients? Different guidelines provided the different recommendations.

METHODS

In this retrospective study, a total of 140 patients were enrolled and followed for 3 years, with 24 clinical features being collected. The imaging features on the enhanced-MRI sequence were extracted by using PyRadiomics platform. The pearson correlation coefficient and the random forest was used to filter the features associated with recurrence or metastasis. A clinical-radiomics model (CRM) was constructed by the Cox multivariable analysis in training cohort, and was validated in validation cohort. All patients were divided into high- and low-risk groups through the median Rad-score of the model. The Kaplan-Meier survival curves were used to compare the 3-year recurrence or metastasis free rate (RMFR) of patients with or without AC in high- and low-groups.

RESULTS

In total, 960 imaging features were extracted. A CRM was constructed from nine features (seven imaging features and two clinical factors). In the training cohort, the area under curve (AUC) of CRM for 3-year RMFR was 0.872 (P <0.001), and the sensitivity and specificity were 0.935 and 0.672, respectively; In the validation cohort, the AUC was 0.864 (P <0.001), and the sensitivity and specificity were 1.00 and 0.75, respectively. Kaplan-Meier curve showed that the 3-year RMFR and 3-year cancer specific survival (CSS) rate in the high-risk group were significantly lower than those in the low-risk group (P <0.001). In the high-risk group, patients who received AC had greater 3-year RMFR than those who did not receive AC (78.6% vs. 48.1%) (p = 0.03).

CONCLUSION

Considering increasing RMFR, a prediction model for NPC based on two clinical factors and seven imaging features suggested the AC needs to be added to patients in the high-risk group and not in the low-risk group.

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study

Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte

BACKGROUND

Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa.

METHODS

We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm.

RESULTS

197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved.

CONCLUSION

Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

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

Purpose

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

Materials and methods

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

Result

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

Data conclusion

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

Uncovering the potential functions of lymph node metastasis-associated aberrant methylation differentially expressed genes and their association with the immune infiltration and prognosis in bladder urothelial carcinoma

Background

Bladder urothelial carcinoma (BLCA) is a malignant tumor of the urinary system. This study aimed to explore the potential role of lymph node metastasis-associated aberrant methylation differentially expressed genes (DEGs) in BLCA.

Methods

CHAMP and limma packages were used to identify lymph node metastasis-associated aberrant methylation DEGs. Univariate Cox analysis and Lasso analysis were performed to identify the signature genes, and multivariate Cox analysis was used to construct the risk score. Subsequently, the molecular characteristics of the signature genes and the relationship between risk score and prognosis, clinical characteristics and immune cell infiltration were analyzed. The signature gene AKAP7 was selected for functional verification.

Results

A novel risk score model was constructed based on 12 signature genes. The risk score had a good ability to predict overall survival (OS). The nomogram constructed based on age, N stage and risk score had a higher value in predicting the prognosis of patients. It was also found that stromal activation in TIME may inhibit the antitumor effects of immune cells. Functional enrichment analysis revealed that ECM receptor interaction and focal adhesion were two important pathways involved in the regulation of BLCA. Immunohistochemistry showed that AKAP7 may be associated with the occurrence, clinical stages and grades, and lymph node metastasis of BLCA. In vitro cell experiments showed that the migration and invasion ability of EJ cells was significantly inhibited after AKAP7 overexpression, while the migration and invasion ability of T24 cells was significantly promoted after AKAP7 knockdown.

Conclusion

The risk score model based on lymph node metastasis-associated aberrant methylation DEGs has a good ability to predict OS and is an independent prognostic factor for BLCA. It was also found that stromal activation in TIME may inhibit the antitumor effects of immune cells. This implicates aberrant methylation modifications as an important factor contributing to the heterogeneity and complexity of individual tumor microenvironments. Functional enrichment analysis revealed that ECM receptor interaction and focal adhesion were two important pathways involved in the regulation of BLCA, which contributed to the exploration of the pathological mechanism of BLCA. In addition, immunohistochemistry showed that AKAP7 may be associated with the occurrence, progression and lymph node metastasis of BLCA. In vitro cell experiments showed that AKAP7 could also inhibit the migration and invasion of cancer cells.

A Nomogram Model for Prediction of Tracheostomy in Patients With Traumatic Cervical Spinal Cord Injury

OBJECTIVE

To develop a nomogram for the prediction of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI).

METHODS

A total of 689 TCSCI patients were included in our study. First, the variable selection was performed using between-group comparisons and LASSO regression analysis. Second, a multivariate logistic regression analysis (MLRA) with a step-by-step method was performed. A nomogram model was developed based on the MLRA. Finally, the model was validated on the training set and validation set.

RESULTS

The nomogram prediction model incorporated 5 predictors, including smoking history, dislocation, thoracic injury, American Spinal Injury Association (ASIA) grade, and neurological level of injury (NLI). The area under curve in the training group and in the validation group were 0.883 and 0.909, respectively. The Hosmer-Lemeshow test result was p = 0.153. From the decision curve analysis curve, the model performed well and was feasible to make beneficial clinical decisions.

CONCLUSION

The nomogram combining dislocation, thoracic injury, ASIA grade A, NLI, and smoking history was validated as a reliable model for the prediction of tracheostomy.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

A nomogram model for predicting prognosis of obstructive colorectal cancer

Background

The prognosis of obstructive colorectal cancer (oCRC) is worse than that of nonobstructive colorectal cancer. However, no previous study has established an individualized prediction model for the prognosis of patients with oCRC. We aimed to screen the factors that affect the prognosis of oCRC and to use these findings to establish a nomogram model that predicts the individual prognosis of patients with oCRC.

Methods

This retrospective study collected data of 181 patients with oCRC from three medical hospitals between February 2012 and December 2017. Among them, 129 patients from one hospital were used as the training cohort. Univariate and multivariate analyses were used in this training cohort to select independent risk factors that affect the prognosis of oCRC, and a nomogram model was established. The other 52 patients from two additional hospitals were used as the validation cohort to verify the model.

Results

Multivariate analysis showed that carcinoembryonic antigen level (p = 0.037, hazard ratio [HR] = 2.872 [1.065–7.740]), N stage (N1 vs. N0, p = 0.028, HR = 3.187 [1.137–8.938]; N2 vs. N0, p = 0.010, HR = 4.098 [1.393–12.051]), and surgical procedures (p = 0.002, HR = 0.299 [0.139–0.643]) were independent prognostic factors of overall survival in patients with oCRC. These factors were used to construct the nomogram model, which showed good concordance and accuracy.

Conclusion

Carcinoembryonic antigen, N stage, and surgical method are independent prognostic factors for overall survival in patients with oCRC, and the nomogram model can visually display these results.