Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET/CT

Analysing glycolysis-related genes reveals the prognostic and diagnostic relevance of IER3 and AGRN in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a significant global health issue, with early detection being critical to improving patient survival. Dysregulation of the glycolysis pathway plays a pivotal role in CRC progression, but specific gene-level mechanisms remain underexplored.

OBJECTIVE

This study aimed to investigate the role of glycolysis-related genes in CRC development and identify potential diagnostic and prognostic biomarkers.

METHODS

We utilized The Cancer Genome Atlas (TCGA) dataset to perform differential expression analysis of glycolysis-related genes in CRC. Protein-protein interaction (PPI) network analysis was conducted to identify central hub genes. The diagnostic potential of selected genes was evaluated using ROC curve analysis, while their expression levels were validated through RT-qPCR.

RESULTS

IER3 and AGRN were identified as significantly upregulated genes associated with reduced survival rates in CRC patients. PPI analysis revealed their roles as central hub genes within the glycolysis pathway. ROC curve analysis demonstrated their ability to distinguish CRC patients from healthy individuals. Validation through RT-qPCR confirmed their significant overexpression in CRC samples, highlighting their involvement in disease progression.

CONCLUSION

IER3 and AGRN are critical components of the glycolysis pathway, driving CRC development and progression while also showing potential as biomarkers for predicting outcomes, diagnosing CRC, and serving as treatment targets.

A Water-Soluble Small-Molecule Fluorescent Probe for Selective Imaging of Colorectal Cancer with High Biosafety

Early diagnosis of colorectal cancer (CRC), a malignant tumor with high incidence and mortality rates worldwide, can significantly reduce both its incidence and mortality. Among cancer diagnostic methods, tumor fluorescence imaging provides a non-invasive approach, eliminating the need for tissue biopsy and minimizing patient discomfort. In this study, we identified a water-soluble quinolinium molecular fluorescent probe (CYI), which exhibits a dose-dependent quantum yield in PBS solution, reaching 5.96% at a concentration of 20 µM. The results demonstrated that CYI selectively enters CRC cells and maintains stable fluorescence intensity within them by specifically targeting the mitochondria and lysosomes, leading to probe accumulation and enhanced intracellular fluorescence. Importantly, toxicity assays at both the cellular and animal levels confirmed that CYI is highly biocompatible at fluorescence imaging doses, with no toxic effects observed in normal colorectal cells or organisms. This study identifies CYI as a water-soluble molecular fluorescent probe with a high biosafety profile, excellent imaging stability, and preferential uptake by CRC cells, demonstrating strong potential for early CRC screening and in vivo monitoring.

DNA methylation biomarkers for predicting lymph node metastasis in colorectal cancer

Colorectal cancer is one of the most common cancers worldwide. Lymph node metastasis is an important marker of colorectal cancer progression and plays a key role in the evaluation of patient prognosis. Accurate preoperative assessment of lymph node metastasis is crucial for devising appropriate treatment plans. However, current clinical imaging methods have limitations in many aspects. Therefore, the discovery of a method for accurately predicting lymph node metastasis is crucial clinical decision-making. DNA methylation is a common epigenetic modification that can regulate gene expression, which also has an important impact on the development of colorectal cancer. It is considered to be a promising biomarker with good specificity and stability and has promising application in predicting lymph node metastasis in patients with colorectal cancer. This article reviews the characteristics and limitations of currently available methods for predicting lymph node metastasis in patients with colorectal cancer and discusses the role of DNA methylation as a biomarker.

Severe Venous Calcifications in Phlebosclerotic Colitis and Significantly Decreased Expression in Betaine

Phlebosclerotic colitis (PC) is a rare type of chronic ischemic colitis. Its etiology is still unknown, and PC is also known as idiopathic mesenteric phlebosclerosis colitis. Currently, many studies have reported that long-term use of Chinese herbal medicine and drinking history are related to its pathogenesis. In the early stage of the lesion, due to insufficient understanding of PC, it is difficult to distinguish it from inflammatory bowel disease and other nonneoplastic intestinal diseases. We reported a case of severe diffuse total colon calcification with multiple misdiagnosis, summarizing and analyzing the clinical pathological characteristics to increase clinical and pathological physicians' understanding of the disease and reduce misdiagnosis. Moreover, for the first time, we conducted metabolomics sequencing on fresh intestinal specimens of PC, in order to explore the possible mechanism of severe calcification in the patient. We found that betaine was significantly decreased in the intestinal specimens of the patient, which is an amino acid that has been shown to improve vascular risk factors, and may be one of the mechanisms underlying severe calcification in the patient.

Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

BACKGROUND

The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM).

AIM

To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.

METHODS

We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023. All participants were randomly assigned to the training or validation queue in a 7:3 ratio. We first apply generalized linear regression model (GLRM) and random forest model (RFM) algorithm to construct an MLM prediction model in the training queue, and evaluate the discriminative power of the MLM prediction model using area under curve (AUC) and decision curve analysis (DCA). Then, the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.

RESULTS

Among the 301 patients included in the study, 16.28% were ultimately diagnosed with MLM through pathological examination. Multivariate analysis showed that carcinoembryonic antigen, and magnetic resonance imaging radiomics were independent predictors of MLM. Then, the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation. The prediction performance of GLRM in the training and validation queue was 0.765 [95% confidence interval (CI): 0.710-0.820] and 0.767 (95%CI: 0.712-0.822), respectively. Compared with GLRM, RFM achieved superior performance with AUC of 0.919 (95%CI: 0.868-0.970) and 0.901 (95%CI: 0.850-0.952) in the training and validation queue, respectively. The DCA indicated that the predictive ability and net profit of clinical RFM were improved.

CONCLUSION

By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models, the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.

Preoperative CT lymph node size as a predictor of nodal metastasis in resectable Colon cancer: a retrospective study of 694 patients

PURPOSE

This study aimed to investigate the efficacy of measuring lymph node size on preoperative CT imaging to predict pathological lymph node metastasis in patients with colon cancer to enhance diagnostic accuracy and improve treatment planning by establishing more reliable assessment methods for lymph node metastasis.

METHODS

We retrospectively analyzed 1,056 patients who underwent colorectal resection at our institution between January 2004 and March 2020. From this cohort, 694 patients with resectable colon cancer were included in the study. We analyzed the relationship between lymph node size on preoperative CT imaging and lymph node metastasis identified on postoperative pathological examination.

RESULTS

The optimal cutoff values for the maximum long diameter and short diameter of regional lymph nodes on preoperative CT were identified as 6.5 mm and 5.5 mm, respectively, with an AUC of 0.7794 and 0.7755, respectively. Notably, the predictive accuracy varied by tumor location. Higher cutoff values were observed in the right-sided colon (maximum long diameter: 7.7 mm, maximum short diameter: 5.9 mm) compared to the left-sided colon (maximum long diameter: 5.8 mm, maximum short diameter: 5.2 mm).

CONCLUSION

Lymph node size on preoperative CT is a significant predictor of pathological lymph node metastasis in colon cancer. Notably, the optimal cutoff values for predicting lymph node metastasis vary depending on the specific region within the colon.

Nuclear medicine based multimodal molecular imaging facilitates precision medicine for gastrointestinal tumors

Gastric and colorectal cancers are the most common gastrointestinal malignancies, with high morbidity and mortality rates. Early diagnosis and accurate staging are of great significance for formulating reasonable clinical treatment plans, guiding surgical methods, effectively carrying out individualized comprehensive treatment, and estimating prognosis. As representatives of nuclear medicine based multimodal molecular imaging technologies, positron emission tomography/computed tomography and positron emission tomography/magnetic resonance imaging allow for obtaining the status of lesions throughout the body in one imaging procedure, and are less likely to miss distant and neighboring metastatic lesions. It is very important to truly achieve accurate disease classification and diagnosis, and develop individualized disease prevention and treatment plans. The emerging multimodal nuclide tracer molecular imaging technology has important clinical value in the diagnosis and treatment of gastric cancer and colorectal cancer. This article reviews the application and progress of the two examination methods in the diagnosis and staging of gastric cancer and colorectal cancer.

Specific small interfering RNAs (siRNAs) for targeting the metastasis, immune responses, and drug resistance of colorectal cancer cells (CRC)

Colorectal cancer (CRC) involves various genetic alterations, with liver metastasis posing a significant clinical challenge. Furthermore, CRC cells mostly show an increase in resistance to traditional treatments like chemotherapy. It is essential to investigate more advanced and effective therapies to prevent medication resistance and metastases and extend patient life. As a result, it is anticipated that small interfering RNAs (siRNAs) would be exceptional instruments that can control gene expression by RNA interference (RNAi). In eukaryotes, RNAi is a biological mechanism that destroys specific messenger RNA (mRNA) molecules, thereby inhibiting gene expression. In the management of CRC, this method of treatment represents a potential therapeutic agent. However, it is important to acknowledge that siRNA therapies have significant issues, such as low serum stability and nonspecific absorption into biological systems. Delivery mechanisms are thus being created to address these issues. In the current work, we address the potential benefits of siRNA therapy and outline the difficulties in treating CRCby focusing on the primary signaling pathways linked to metastasis as well as genes implicated in the multi-drug resistance (MDR) process.

Charge-Reversal Nano-Drug Delivery Systems in the Tumor Microenvironment: Mechanisms, Challenges, and Therapeutic Applications

The charge-reversal nano-drug delivery system (CRNDDS) is a promising system for delivering chemotherapy drugs and has gained widespread application in cancer treatment. In this review, we summarize the recent advancements in CRNDDSs in terms of cancer treatment. We also delve into the charge-reversal mechanism of the CRNDDSs, focusing on the acid-responsive, redox-responsive, and enzyme-responsive mechanisms. This study elucidates how these systems undergo charge transitions in response to specific microenvironmental stimuli commonly found in tumor tissues. Furthermore, this review explores the pivotal role of CRNDDSs in tumor diagnosis and treatment, and their potential limitations. By leveraging the unique physiological characteristics of tumors, such as the acidic pH, specific redox potential, and specific enzyme activity, these systems demonstrate enhanced accumulation and penetration at tumor sites, resulting in improved therapeutic efficacy and diagnostic accuracy. The implications of this review highlight the potential of charge-reversal drug delivery systems as a novel and targeted strategy for cancer therapy and diagnosis.

Assessment of the Diagnostic Accuracy of CT as Compared to MRI in Detecting Metastases in Patients With Colorectal Cancer

This study aimed to compare the diagnostic accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) in detecting metastases of colorectal cancer (CRC) in a hospital in Najran, Saudi Arabia. A total of 51 patients with CRC were included in the study. The radiological findings of metastatic lesions and the diagnostic accuracy measures of CT compared to MRI were analyzed. The results showed that CT had a false negative rate of 7.8%, a false positive rate of 7.8%, a true negative rate of 27.5%, and a true positive rate of 56.9% in detecting metastases. Diagnostic accuracy measures varied based on the number of metastatic lesions, with higher sensitivity observed for cases with fewer lesions. Gender, timing of imaging in relation to surgical intervention, and administration of nonsurgical therapy showed significant associations with diagnosis mismatch between CT and MRI. The site of metastases and the site of the primary tumor in the colon also demonstrated significant associations with diagnosis mismatch. The size of the largest metastasis detected by MRI was significantly associated with diagnosis mismatch. The overall diagnostic accuracy of CT in detecting any metastases, compared to MRI as the reference standard, was estimated to have a sensitivity of 87.8%, a specificity of 77.8%, a positive predictive value of 87.8%, and a negative predictive value of 77.8%. This study provides valuable insights into the comparative diagnostic performance of CT and MRI in detecting metastases of CRC, highlighting the importance of considering patient characteristics, disease outcome, and tumor characteristics in the interpretation of imaging results.

Comparative evaluation of CT and MRI in the preoperative staging of colon cancer

The aim of this study is to compare the diagnostic performance of magnetic resonance imaging (MRI) against computed tomography (CT) in various aspects of local staging in colon cancer patients. This study was a prospective single arm diagnostic accuracy study. All consecutive adult patients with confirmed colon cancer that met the current criteria for surgical resection were considered as eligible. Diagnostic performance assessment included T (T1/T2 vs T3/T4 and < T3ab vs > T3cd) and N (N positive) staging, serosa and retroperitoneal surgical margin (RSM) involvement and extramural vascular invasion (EMVI). Imaging was based on a 3 Tesla MRI system and the evaluation of all sequences (T1, T2 and diffusion-weighted imaging-DWI series) by two independent readers. CT scan was performed in a 128 row multidetector (MD) CT scanner (slice thickness: 1 mm) with intravenous contrast. Pathology report was considered as the gold standard for local staging. Sensitivity (SE), specificity (SP), and area under the curve (AUC) were calculated for both observers. MRI displayed a higher diagnostic performance over CT in terms of T1/T2 vs T3/T4 (SE: 100% vs 83.9%, SP: 96.6% vs 81%, AUC: 0.825 vs 0.983, p < 0.001), N positive (p < 0.001) and EMVI (p = 0.023) assessment. An excellent performance of MRI was noted in the T3ab vs T3cd (CT AUCReader1: 0.636, AUCReader2: 0.55 vs MRI AUCReader1: 0.829 AUCReader2 0.846, p = 0.01) and RSM invasion diagnosis. In contrast to these, MRI did not perform well in the identification of serosa invasion. MRI had a higher diagnostic yield than CT in several local staging parameters.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND

Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.

AIM

To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI).

METHODS

Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.

RESULTS

Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively.

CONCLUSION

A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

Construction of a nomogram based on clinicopathologic features to predict the likelihood of No. 253 lymph node metastasis in rectal cancer patients

PURPOSE

To explore the high-risk factors for rectal cancer No.253 lymph node metastasis (LNM) and to construct a risk nomogram for the individualized prediction of No.253 LNM.

METHODS

This was a retrospective analysis of 425 patients with rectal cancer who underwent laparoscopic-assisted radical surgery. Independent risk factors for rectal cancer No.253 LNM was identified using multivariate logistic regression analysis, and a risk prediction nomogram was constructed based on the independent risk factors. In addition, the performance of the model was evaluated by discrimination, calibration, and clinical benefit.

RESULTS

Multivariate logistic regression analysis showed that No.253 lymphadenectasis on CT (OR 10.697, P < 0.001), preoperative T4-stage (OR 4.431, P = 0.001), undifferentiation (OR 3.753, P = 0.004), and preoperative Ca199 level > 27 U/ml (OR 2.628, P = 0.037) were independent risk factors for No.253 LNM. A nomogram was constructed based on the above four factors. The calibration curve of the nomogram was closer to the ideal diagonal, indicating that the nomogram had a better fitting ability. The area under the ROC curve (AUC) was 0.865, which indicated that the nomogram had high discriminative ability. In addition, decision curve analysis (DCA) showed that the model could show better clinical benefit when the threshold probability was between 1% and 50%.

CONCLUSION

Preoperative No.253 lymphadenectasis on CT, preoperative T4-stage, undifferentiation, and elevated preoperative Ca199 level were found to be independent risk factors for the No.253 LNM. A predictive model based on these risk factors can help surgeons make rational clinical decisions.

Impact of grade on workup of rectal neuroendocrine tumors: a retrospective cohort study : Grade impact on workup of rectal NETs

BACKGROUND

Rectal neuroendocrine tumors (RNETs) are often discovered on screening colonoscopy. Indications for staging and definitive resection are inconsistent in current guidelines. We evaluated the role of grade in guiding staging and procedural decision-making.

METHODS

Patients with biopsy confirmed RNETs between 2004 and 2015 were reviewed. Baseline characteristics, staging investigations (biochemical and imaging), and endoscopic/surgical treatment were recorded. Associations between grade, preoperative staging, interventions, and survival were determined using Fisher-Freeman-Halton Exact, log-rank, and Kaplan-Meier analysis.

RESULTS

Amongst 139 patients with RNETs, 9% were aged ≥ 75 years and 44% female. Tumor grade was: 73% grade 1 (G1), 18%, grade 2 (G2) and 9% grade 3 (G3). Staging investigations were performed in 52% of patients. All serum chromogranin A and 97% of 24-hour urine 5-hydroxyindoleacetic acid tests were normal. The large majority of staging computed tomography (CT) scans were negative (76%) with subgroup analysis showing no G1 patients with CT identified distant disease compared with 38% of G2 and 50% of G3 patients (p < 0.001). G1 patients were more likely to achieve R0/R1 resections compared to G2 (95% vs. 50%, p < 0.001) and G1 patients had significantly better 5-year overall survival (G1: 98%, G2: 67%, G3: 10%, p < 0.001).

CONCLUSION

Tumor grade is important in preoperative workup and surgical decision-making. Biochemical staging may be omitted but staging CT should be considered for patients with grade ≥ 2 lesions. Anatomic resections should be considered for patients with grade 2 disease.

Current Advances of Nanomaterial-Based Oral Drug Delivery for Colorectal Cancer Treatment

Colorectal cancer (CRC) is a common malignant tumor, and traditional treatments include surgical resection and radiotherapy. However, local recurrence, distal metastasis, and intestinal obstruction are significant problems. Oral nano-formulation is a promising treatment strategy for CRC. This study introduces physiological and environmental factors, the main challenges of CRC treatment, and the need for a novel oral colon-targeted drug delivery system (OCDDS). This study reviews the research progress of controlled-release, responsive, magnetic, targeted, and other oral nano-formulations in the direction of CRC treatment, in addition to the advantages of oral colon-targeted nano-formulations and concerns about the oral delivery of related therapeutic agents to inspire related research.

Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer

Background

Colorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease.

Methods

The expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA.

Results

A total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate.

Conclusion

Our results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.

Value of glucose transport protein 1 expression in detecting lymph node metastasis in patients with colorectal cancer

BACKGROUND

There are limited data on the use of glucose transport protein 1 (GLUT-1) expression as a biomarker for predicting lymph node metastasis in patients with colorectal cancer. GLUT-1 and GLUT-3, hexokinase (HK)-II, and hypoxia-induced factor (HIF)-1 expressions may be useful biomarkers for detecting primary tumors and lymph node metastasis when combined with fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT).

AIM

To evaluate GLUT-1, GLUT-3, HK-II, and HIF-1 expressions as biomarkers for detecting primary tumors and lymph node metastasis with 18F-FDG-PET/CT.

METHODS

This retrospective study included 169 patients with colorectal cancer who underwent colectomy and preoperative 18F-FDG-PET/CT at Chungbuk National University Hospital between January 2009 and May 2012. Two tissue cores from the central and peripheral areas of the tumors were obtained and were examined by a dedicated pathologist, and the expressions of GLUT-1, GLUT-3, HK-II, and HIF-1 were determined using immunohistochemical staining. We analyzed the correlations among their expressions, various clinicopathological factors, and the maximum standardized uptake value (SUVmax) of PET/CT.

RESULTS

GLUT-1 was found at the center or periphery of the tumors in 109 (64.5%) of the 169 patients. GLUT-1 positivity was significantly correlated with the SUVmax of the primary tumor and lymph nodes, regardless of the biopsy site (tumor center, P < 0.001 and P = 0.012; tumor periphery, P = 0.030 and P = 0.010, respectively). GLUT-1 positivity and negativity were associated with higher and lower sensitivities of PET/CT, respectively, for the detection of lymph node metastasis, regardless of the biopsy site. GLUT3, HK-II, and HIF-1 expressions were not significantly correlated with the SUVmax of the primary tumor and lymph nodes.

CONCLUSION

GLUT-1 expression was significantly correlated with the SUVmax of 18F-FDG-PET/CT for primary tumors and lymph nodes. Clinicians should consider GLUT-1 expression in preoperative endoscopic biopsy in interpreting PET/CT findings.

Bayesian Landmark-based Shape Analysis of Tumor Pathology Images

Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this paper, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value < 0.001).

Evaluating the clinical performance of SDC2/NDRG4 methylation for colorectal cancer detection

Purpose: The performance and clinical accuracy of combined SDC2/NDRG4 methylation were evaluated in diagnosing colorectal cancer (CRC) and advanced adenoma. Methods: A total of 2333 participants were enrolled to assess the sensitivity and specificity of biomarkers in diagnosing CRC in a multicenter clinical trial through feces DNA methylation tests. Results: SDC2/NDRG4 methylation showed excellent performance for CRC detection in biomarker research and the real world. Its sensitivity for detecting CRC, early CRC and advanced adenoma were 92.06%, 91.45% and 62.61%, respectively. Its specificity was 94.29%, with a total coincidence rate of 88.28%. When interference samples were included, the specificity was still good (82.61%). Therefore, the SDC2/NDRG4 methylation test showed excellent performance in detecting CRC and advanced adenoma under clinical application.

Diagnostic performance of whole-body [18F]FDG PET/MR in cancer M staging: A systematic review and meta-analysis

OBJECTIVES

To calculate the pooled diagnostic performances of whole-body [18F]FDG PET/MR in M staging of [18F]FDG-avid cancer entities.

METHODS

A diagnostic meta-analysis was conducted on the [18F]FDG PET/MR in M staging, including studies: (1) evaluated [18F]FDG PET/MR in detecting distant metastasis; (2) compared[ 18F]FDG PET/MR with histopathology, follow-up, or asynchronous multimodality imaging as the reference standard; (3) provided data for the whole-body evaluation; (4) provided adequate data to calculate the meta-analytic performances. Pooled performances were calculated with their confidence interval. In addition, forest plots, SROC curves, and likelihood ratio scatterplots were drawn. All analyses were performed using STATA 16.

RESULTS

From 52 eligible studies, 2289 patients and 2072 metastases were entered in the meta-analysis. The whole-body pooled sensitivities were 0.95 (95%CI: 0.91-0.97) and 0.97 (95%CI: 0.91-0.99) at the patient and lesion levels, respectively. The pooled specificities were 0.99 (95%CI: 0.97-1.00) and 0.97 (95%CI: 0.90-0.99), respectively. Additionally, subgroup analyses were performed. The calculated pooled sensitivities for lung, gastrointestinal, breast, and gynecological cancers were 0.90, 0.93, 1.00, and 0.97, respectively. The pooled specificities were 1.00, 0.98, 0.97, and 1.00, respectively. Furthermore, the pooled sensitivities for non-small cell lung, colorectal, and cervical cancers were 0.92, 0.96, and 0.86, respectively. The pooled specificities were 1.00, 0.95, and 1.00, respectively.

CONCLUSION

[18F]FDG PET/MR was a highly accurate modality in M staging in the reported [18F]FDG-avid malignancies. The results showed high sensitivity and specificity in each reviewed malignancy type. Thus, our findings may help clinicians and patients to be confident about the performance of [18F]FDG PET/MR in the clinic.

CLINICAL RELEVANCE STATEMENT

Although [18F]FDG PET/MR is not a routine imaging technique in current guidelines, mostly due to its availability and logistic issues, our findings might add to the limited evidence regarding its performance, showing a sensitivity of 0.95 and specificity of 0.97.

KEY POINTS

• The whole-body [18F]FDG PET/MR showed high accuracy in detecting distant metastases at both patient and lesion levels. • The pooled sensitivities were 95% and 97% and pooled specificities were 99% and 97% at patient and lesion levels, respectively. • The results suggested that 18F-FDG PET/MR was a strong modality in the exclusion and confirmation of distant metastases.

Clinical value of SLC12A9 for diagnosis and prognosis in colorectal cancer

OBJECTIVE

The goal of the study is to assess the clinical value and the potential mechanism of SLC12A9 combing transcriptome and single cell sequencing data.

METHODS

In this study, the expression level and the receiver operating characteristic curve analysis of SLC12A9 in CRC and normal tissue were analyzed in multiple data cohort. The standardized mean difference (SMD) calculation and the summary receiver operating characteristic (SROC) analysis were performed further to detect its diagnostic ability and expression level. KM survival analysis was performed to assess the prognosis value of SLC12A9. The expression level of SLC12A9 in different clinical characteristics was analyzed to explore the clinical value. Single cell data was studied to reveal the potential mechanism of SLC12A9. The correlation analysis of immunoinfiltration was performed to detect the potential immune cell related to SLC12A9. The nomogram was drawn to assess the probable mortality rate of CRC patient.

RESULTS

We found that SLC12A9 was significantly up-regulated with the moderate diagnostic value in CRC. Patients with overexpressed SLC12A9 had a worse prognosis. SLC12A9 was related to Age, Pathologic N stage, Pathologic M stage, Lymphatic invasion and Pathologic stage (p < 0.05). The 1, 3 and 5-year survival rates of patient named TCGA-G4-6309 are 0.959, 0.897 and 0.827. PCR also showed that SLC12A9 was overexpressed in CRC comparing with normal tissue.

CONCLUSION

In conclusion, our study comprehensively analyzed the clinical value of SLC12A9 and its potential mechanism, as well as immune cell infiltration, which may accelerate the diagnosis and improve the prognosis of CRC.

Significance of carcinoembryonic antigen detection in the early diagnosis of colorectal cancer: A systematic review and meta-analysis

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignant tumor involving adenomas that develop into malignant lesions. Carcinoembryonic antigen (CEA) is a non-specific serum biomarker upregulated in CRC. The concentration of CEA is modulated by tumor stage and grade, tumor site in the colon, ploidy status, and patient smoking status. This study aimed to evaluate current evidence regarding the diagnostic power of CEA levels in the early detection of CRC recurrence in adults.

AIM

To evaluate current evidence regarding the diagnostic power of CEA levels in the early detection of CRC recurrence in adults.

METHODS

A systematic search was performed using four databases: MEDLINE, Cochrane Trials, EMBASE, and the Web of Science. The inclusion criteria were as follows: Adult patients aged ≥ 18 years who had completed CRC curative treatment and were followed up postoperatively; reporting the number of CRC recurrences as an outcome; and randomized, clinical, cohort, and case-control study designs. Studies that were not published in English and animal studies were excluded. The following data were extracted by three independent reviewers: Study design, index tests, follow-up, patient characteristics, and primary outcomes. All statistical analyses were performed using the RevMan 5.4.1.

RESULTS

A total of 3232 studies were identified, with 73 remaining following the elimination of duplicates. After screening on predetermined criteria, 12 studies were included in the final analysis. At a reference standard of 5 mg/L, CEA detected only approximately half of recurrent CRCs, with a pooled sensitivity of 59% (range, 33%-83%) and sensitivity of 89% (range, 58%-97%).

CONCLUSION

CEA is a significant marker for CRC diagnosis. However, it has insufficient sensitivity and specificity to be used as a single biomarker of early CRC recurrence, with an essential proportion of false negatives.

Progress of near-infrared-II fluorescence in precision diagnosis and treatment of colorectal cancer

Colorectal cancer is a malignant tumour with high incidence and mortality worldwide; therefore, improving the early diagnosis of colorectal cancer and implementing a targeted "individualized treatment" strategy is of great concern. NIR-II fluorescence imaging is a large-depth, high-resolution optical bioimaging tool. Around the NIR-II window, researchers have developed a variety of luminescent probes, imaging systems, and treatment methods with colorectal cancer targeting capabilities, which can be visualized and image-guided in clinical surgery. This article aims to overcome the difficulties in diagnosing and treating colorectal cancer. The present review summarizes the latest results on using NIR-II fluorescence for targeted colorectal cancer imaging, expounds on the application prospects of NIR-II optical imaging for colorectal cancer, and discusses the imaging-guided multifunctional diagnosis and treatment platforms.

Diagnostic Accuracy of Abdominal CT for Locally Advanced Colon Tumors: Can We Really Entrust Certain Decisions to the Reliability of CT?

Many different options of neoadjuvant treatments for advanced colon cancer are emerging. An accurate preoperative staging is crucial to select the most appropriate treatment option. A retrospective study was carried out on a national series of operated patients with T4 tumors. Considering the anatomo-pathological analysis of the surgical specimen as the gold standard, a diagnostic accuracy study was carried out on the variables T and N staging and the presence of peritoneal metastases (M1c). The parameters calculated were sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios, as well as the overall accuracy. A total of 50 centers participated in the study in which 1950 patients were analyzed. The sensitivity of CT for correct staging of T4 colon tumors was 57%. Regarding N staging, the overall accuracy was 63%, with a sensitivity of 64% and a specificity of 62%; however, the positive and negative likelihood ratios were 1.7 and 0.58, respectively. For the diagnosis of peritoneal metastases, the accuracy was 94.8%, with a sensitivity of 40% and specificity of 98%; in the case of peritoneal metastases, the positive and negative likelihood ratios were 24.4 and 0.61, respectively. The diagnostic accuracy of CT in the setting of advanced colon cancer still has some shortcomings for accurate diagnosis of stage T4, correct classification of lymph nodes, and preoperative detection of peritoneal metastases.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Guanylate cyclase-C Signaling Axis as a theragnostic target in colorectal cancer: a systematic review of literature

Introduction

Colorectal cancer (CRC) is a devastating disease that affects millions of people worldwide. Recent research has highlighted the crucial role of the guanylate cyclase-C (GC-C) signaling axis in CRC, from the early stages of tumorigenesis to disease progression. GC-C is activated by endogenous peptides guanylin (GU) and uroguanylin (UG), which are critical in maintaining intestinal fluid homeostasis. However, it has been found that these peptides may also contribute to the development of CRC. This systematic review focuses on the latest research on the GC-C signaling axis in CRC.

Methods

According to the aim of the study, a systematic literature search was conducted on Medline and PubMed databases. Ultimately, a total of 40 articles were gathered for the systematic review.

Results

Our systematic literature search revealed that alterations in GC-C signaling compartments in CRC tissue have demonstrated potential as diagnostic, prognostic, and therapeutic markers. This research highlights a potential treatment for CRC by targeting the GC-C signaling axis. Promising results from recent studies have explored the use of this signaling axis to develop new vaccines and chimeric antigen receptors that may be used in future clinical trials.

Conclusion

The findings presented in this review provide compelling evidence that targeting the GC-C signaling axis may be an advantageous approach for treating CRC.

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.

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.

Macroscopic Evaluation of Colon Cancer Resection Specimens

Colon cancer is a common disease internationally. Outcomes have not improved to the same degree as in rectal cancer, where the focus on total mesorectal excision and pathological feedback has significantly contributed to improved survival and reduced local recurrence. Colon cancer surgery shows significant variation around the world, with differences in mesocolic integrity, height of the vascular ligation and length of the bowel resected. This leads to variation in well-recognised quality measures like lymph node yield. Pathologists are able to assess all of these variables and are ideally placed to provide feedback to surgeons and the wider multidisciplinary team to improve surgical quality over time. With a move towards complete mesocolic excision with central vascular ligation to remove the primary tumour and all mechanisms of spread within an intact package, pathological feedback will be central to improving outcomes for patients with operable colon cancer. This review focusses on the key quality measures and the evidence that underpins them.

Preoperative prediction of the lymphovascular tumor thrombus of colorectal cancer with the iodine concentrations from dual-energy spectral CT

BACKGROUND

The aim of this study was to explore application value of iodine concentration from dual-energy spectral computed tomography (DESCT) in preoperative prediction of lymphovascular tumor thrombus in patients with colorectal cancer (CRC).

METHODS

We finally retrospectively analyzed 50 patients with CRC who underwent abdominal DESCT before receiving any preoperative treatment and underwent surgery to obtain pathological specimens which were stained with hematoxylin-eosin (HE) staining. According to the presence of cancer cell nests in blood vessels and lymphatic vessels, the subjects were divided into the positive group and negative group of lymphovascular tumor thrombus. Two radiologists independently measured the normalized iodine concentration (NIC) values, effective atomic number (Zeff) and CT values of virtual monochromatic images (VMIs) at 40-90 keV of the primary tumors in the arterial phase (AP) and venous phase (VP). Used SPSS 17.0 to calculate the receiver operating characteristic (ROC) curve to evaluate diagnostic value.

RESULTS

The patients were divided into lymphovascular tumor thrombus positive group(n = 16) and negative group(n = 34). The values of NIC-AP and NIC-VP in the positive group were 0.17 ± 0.09, 0.51 ± 0.13, respectively. And those in the negative group were 0.15 ± 0.06, 0.43 ± 0.12, respectively. There was significant difference in NIC-VP value between the two groups (p = 0.039), but there was no significant difference in NIC-AP value (p = 0.423). The optimal threshold value of NIC-VP value for diagnosis of lymphovascular tumor thrombus was 0.364. The sensitivity was 68.8% and the specificity was 67.6%.

CONCLUSIONS

The NIC-VP value of DESCT can be used to predict the presence or absence of the lymphovascular tumor thrombus in CRC patients before operation, which is helpful to select the best treatment scheme and evaluate its prognosis.

Lymph nodes primary staging of colorectal cancer in 18F-FDG PET/MRI: a systematic review and meta-analysis

OBJECTIVE

To assess the diagnostic efficacy of 18F-FDG PET/MRI for lymph node (LN) metastasis primary staging in patients with colorectal cancer (CRC).

METHODS

This study was conducted and reported in accordance with the PRISMA-DTA statement. Electronic databases (PubMed, Embase, Cochrane Library) were searched for studies on 18F-FDG PET/MRI for diagnosing LN metastasis. The pooled sensitivity (SEN), specificity (SPE), and area under the curve (AUC) were applied to assess the diagnostic performance. Heterogeneity was identified and processed using meta-regression and sensitivity analysis. All data analyses were performed via STATA 15 and Meta-Disc 1.4 software.

RESULTS

There were finally 7 studies included, involving a total of 184 patients. The Spearman rank correlation coefficient was 0.108 (P = 0.818), with no threshold-effect observed. The pooled SEN was 0.81 (95%CI 0.66-0.90) and the SPE was 0.89 (95% CI 0.73-0.96). In sub-groups, prospective groups demonstrated to have the highest SEN of 0.92 (95%CI 0.79-1.00). The studies conducted by Catalano et al. and Kang et al. were considered to be potential sources of heterogeneity.

CONCLUSION

18F-FDG PET/MRI has shown remarkable diagnostic performance in identification of LN metastases in newly diagnosed CRC patients. It would be of great application value for the primary staging of CRC lymph node metastases.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography

Identifying the precise topography of cancer for targeted biopsy in colonoscopic examination is a challenge in current diagnostic practice. For the first time we demonstrate the use of compression optical coherence elastography (C-OCE) technology as a new functional OCT modality for differentiating between cancerous and non-cancerous tissues in colon and detecting their morphological features on the basis of measurement of tissue elastic properties. The method uses pre-determined stiffness values (Young’s modulus) to distinguish between different morphological structures of normal (mucosa and submucosa), benign tumor (adenoma) and malignant tumor tissue (including cancer cells, gland-like structures, cribriform gland-like structures, stromal fibers, extracellular mucin). After analyzing in excess of fifty tissue samples, a threshold stiffness value of 520 kPa was suggested above which areas of colorectal cancer were detected invariably. A high Pearson correlation (r =0.98; p &lt;0.05), and a negligible bias (0.22) by good agreement of the segmentation results of C-OCE and histological (reference standard) images was demonstrated, indicating the efficiency of C-OCE to identify the precise localization of colorectal cancer and the possibility to perform targeted biopsy. Furthermore, we demonstrated the ability of C-OCE to differentiate morphological subtypes of colorectal cancer – low-grade and high-grade colorectal adenocarcinomas, mucinous adenocarcinoma, and cribriform patterns. The obtained ex vivo results highlight prospects of C-OCE for high-level colon malignancy detection. The future endoscopic use of C-OCE will allow targeted biopsy sampling and simultaneous rapid analysis of the heterogeneous morphology of colon tumors.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image

OBJECTIVES

Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC.

METHODS

We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS

In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features.

CONCLUSION

The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.

Curcumin Represses Colorectal Cancer Cell Proliferation by Triggering Ferroptosis via PI3K/Akt/mTOR Signaling

Curcumin is known to suppress the progression of colorectal cancer by inhibiting cancer cell proliferation. In this study, we explored the role of ferroptosis in the antiproliferative properties of curcumin. The effect of curcumin on ferroptosis In Vitro was evaluated in HCT-8 cells. Ferroptosis was first blocked by ferrostatin-1 (Fer-1) and the antiproliferative effect of curcumin was evaluated by determining the levels of ferroptotic markers, including glutathione (GSH), SLC7A11, GPX4, iron, malondialdehyde (MDA), and reactive oxygen species (ROS). An agonist and an inhibitor of PI3K were also used to verify the signaling pathway involved in the antiproliferative effects. Curcumin repressed HCT-8 cell proliferation in a dose-dependent manner. Treating HCT-8 cells with curcumin significantly downregulated GSH, SLC7A11, and GPX4, while significantly increasing levels of iron, MDA, and ROS. In addition, curcumin promoted ferroptosis and reduced proliferation of HCT-8 cells by suppressing the PI3K/Akt/mTOR pathway, and these effects were antagonized by Fer-1. The effects of curcumin were antagonized by a PI3K agonist and reinforced by a PI3K inhibitor. Curcumin triggers ferroptosis and suppresses proliferation of colorectal cancer cells by inhibiting the PI3K/Akt/mTOR signaling pathway. These results indicate its potential as a treatment against colorectal cancer.

Albumin-based nanoparticle for dual-modality imaging of the lymphatic system

The lymphatic system is a complex network of lymphatic vessels, lymph nodes, and lymphoid organs. The current understanding of the basic mechanism and framework of the lymphatic system is relatively limited and not ideal for exploring the function of the lymphatic system, diagnosing lymphatic system diseases, and controlling tumor metastasis. Imaging modalities for evaluating lymphatic system diseases mainly include lymphatic angiography, reactive dye lymphatic angiography, radionuclide lymphatic angiography, computed tomography, and ultrasonography. However, these are insufficient for clinical diagnosis. Some novel imaging methods, such as magnetic resonance imaging, positron emission computed tomography, single-photon emission computed tomography, contrast-enhanced ultrasonography, and near-infrared imaging with agents such as cyanine dyes, can reveal lymphatic system information more accurately and in detail. We fabricated an albumin-based fluorescent probe for dual-modality imaging of the lymphatic system. A near-infrared cyanine dye, IR-780, was absorbed into bovine serum albumin (BSA), which was covalently linked to a molecule of diethylenetriaminepentaacetic acid to chelate gadolinium Gd3+. The fabricated IR-780@BSA@Gd3+ nanocomposite demonstrates strong fluorescence and high near-infrared absorption and can be used as a T1 contrast agent for magnetic resonance imaging. In vivo dual-modality fluorescence and magnetic resonance imaging showed that IR-780@BSA@Gd3+ rapidly returned to the heart through the lymphatic circulation after it was injected into the toe webs of mice, facilitating good lymphatic imaging. The successful fabrication of the new IR-780@BSA@Gd3+ nanocomposite will facilitate the study of the mechanism and morphological structure of the lymphatic system.

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI

Objective

The mortality of colorectal cancer patients with pelvic bone metastasis is imminent, and timely diagnosis and intervention to improve the prognosis is particularly important. Therefore, this study aimed to build a bone metastasis prediction model based on Gray level Co-occurrence Matrix (GLCM) - based Score to guide clinical diagnosis and treatment.

Methods

We retrospectively included 614 patients with colorectal cancer who underwent pelvic multiparameter magnetic resonance image(MRI) from January 2015 to January 2022 in the gastrointestinal surgery department of Gezhouba Central Hospital of Sinopharm. GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. The effectiveness evaluation of each model mainly included decision curve analysis(DCA), area under the receiver operating characteristic (AUROC) curve and clinical influence curve(CIC).

Results

We captured fourteen categories of radiomics data based on GLCM for variable screening of bone metastasis prediction models. Among them, Haralick_90, IV_0, IG_90, Haralick_30, CSV, Entropy and Haralick_45 were significantly related to the risk of bone metastasis, and were listed as candidate variables of machine learning prediction models. Among them, the prediction efficiency of RFM in combination with Haralick_90, Haralick_all, IV_0, IG_90, IG_0, Haralick_30, CSV, Entropy and Haralick_45 in training set and internal verification set was [AUC: 0.926,95% CI: 0.873-0.979] and [AUC: 0.919,95% CI: 0.868-0.970] respectively. The prediction efficiency of the other four types of prediction models was between [AUC: 0.716,95% CI: 0.663-0.769] and [AUC: 0.912,95% CI: 0.859-0.965].

Conclusion

The automatic segmentation model based on diffusion-weighted imaging(DWI) using depth learning method can accurately segment the pelvic bone structure, and the subsequently established radiomics model can effectively detect bone metastases within the pelvic scope, especially the RFM algorithm, which can provide a new method for automatically evaluating the pelvic bone turnover of colorectal cancer patients.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

The effect of Mitomycin-C in neoadjuvant concurrent chemoradiotherapy for rectal cancer

BACKGROUND

Neoadjuvant concurrent chemoradiotherapy (nCCRT) followed by total mesorectal excision has become the standard of care for advanced rectal cancer, but the most effective regimen of chemotherapeutic agents has not yet been determined. The purpose of this study is to determine the effect of Mitomycin-C (MMC) in nCCRT for rectal cancer.

METHODS

From 2000 to 2017, patients with rectal adenocarcinoma who received nCCRT followed by radical surgery were enrolled in our study. The patients were retrospectively separated into two groups according to nCCRT regimens (with or without MMC). Other factors related to cancer down-staging after nCCRT, disease-free survival (DFS) and overall survival (OS) were analyzed.

RESULTS

One hundred ninety-five patients received radiotherapy (RT) + MMC + oral tegafur-uracil (UFUR), and 191 patients received RT + UFUR without MMC as neoadjuvant CCRT. Adding MMC might increase the down-staging rate (odds ratio [OR] = 1.520, p = 0.058), and down-staging had significant effect to improve OS (OR = 1.726, p = 0.002) and DFS (OR = 2.185, p < 0.001). The OS and DFS were improved in patients who received MMC, although this result did not reach a statistically significant difference. There was a higher incidence of low-grade toxicities in the MMC group, especially neutropenia, genitourinary side effects, and dermatological side effects ( p < 0.001).

CONCLUSION

Adding MMC to the regimen of nCCRT for rectal adenocarcinoma is shown to increase tumor down-staging rate and improve disease-free and OS, although these benefits come at the cost of increased low-grade toxicities. Prospective randomized studies are needed to explore the role of MMC in nCCRT for rectal cancer.

SIRT1 enhances oxaliplatin resistance in colorectal cancer through microRNA-20b-3p/DEPDC1 axis

Oxaliplatin (L-OHP) is a standard treatment drug for colorectal cancer (CRC), but acquired drug resistance limits the outcome of patients. We investigated the involvement of sirtuin 1 (SIRT1) in L-OHP resistance in the setting of CRC via microRNA-20b-3p/DEP domain containing 1 (miR-20b-3p/DEPDC1) axis. CRC tissues that were resistant or sensitive to L-OHP were harvested, in which SIRT1, miR-20b-3p, and DEPDC1 levels were tested. L-OHP-resistant-resistant CRC cells were transfected, subsequently, cellular proliferation, invasion, migration, and apoptosis were tested, and tumor resistance to L-OHP was observed. The binding of SIRT1 to miR-20b-3p promoter and the targeting relationship between miR-20b-3p and DEPDC1 were verified. An aberrant elevation in SIRT1 expression was seen in L-OHP-resistant CRC tissues and cells. Knockdown of SIRT1 sensitized CRC cells and xenografted CRC tumors to L-OHP. SIRT1 bound with miR-20b-3p promoter to regulate DEPDC1. Reducing miR-20b-3p or raising DEPDC1 levels weakened the effect of SIRT1 knockdown on L-OHP-resistant-CRC cells. SIRT1 enhances L-OHP resistance in CRC by mediating miR-20b-3p/DEPDC1 axis.

Diagnostic value of spiral CT energy spectrum imaging in lymph node metastasis of colorectal cancer

OBJECTIVES

To evaluate the value of preoperative CT energy spectrum imaging in detecting lymph node metastasis of colorectal cancer.

METHODS

From September 2019 to November 2021, a retrospective study was performed for the eighty-two patients with colorectal cancer through preoperative colonoscopy or surgical pathology confirmed in our hospital. Based on the lymph node metastasis status, these cases were divided into the metastasis and non-metastasis groups. GE Revolution CT scanner was used to scan the patients with energy spectrum imaging, it measured and recorded the single-energy CT values from 40 to 140 keV and various energy spectrum parameters of lymph nodes around the lesions in the arterial and venous phases, and statistically analyze the above indices.

RESULTS

In the arterial and venous phases: the single-energy CT values of 40-140 keV in the non-metastatic group were higher than those in the metastatic group (all P < 0.05); the parameter values of IC (iodine concentration), NIC (normalized iodine concentration), λ (the slope of the energy spectrum curve), and Eff-Z (effective-Z) in the non-metastatic group were higher than those in the metastatic group (all P < 0.05). Further evaluation of ROC curve showed that the higher AUC (area under curve) of the single-energy CT value of 50 keV in the arterial phase was 0.889, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the NIC had the better diagnostic efficiency and the AUC of the NIC was 0.873, the highest AUC of the combination of NIC and λ was 0.885 when the energy spectrum parameters were combined. The higher AUC of the single-energy CT value of 60 keV in the venous phase was 0.853, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the λ had the better diagnostic efficiency and the AUC of the λ was 0.822, the higher AUC of the combination of NIC, λ, and Eff-Z was 0.840 when the energy spectra were combined.

CONCLUSIONS

Parameters of energy spectrum CT imaging can effectively evaluate whether lymph nodes have metastases, and provide favorable imaging diagnosis basis for the range and the number of lymph nodes to be cleaned during clinical operation and can evaluate the prognosis of patients. It is worthy of clinical recommendation.

Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection

Current histopathological staging procedures in colorectal cancer (CRC) depend on midline division of the lymph nodes (LNs) with one section of hematoxylin and eosin staining. Cancer cells outside this transection line may be missed, which could lead to understaging of Union for International Cancer Control Stage II high-risk patients. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid molecular diagnostic tool for LN metastases detection. It is a molecular technique that can analyze the entire LN tissue using a reverse-transcriptase loop-mediated isothermal amplification reaction to detect tumor-specific cytokeratin 19 mRNA. Our findings suggest that the OSNA assay has a high diagnostic accuracy in detecting metastatic LNs in CRC and a high negative predictive value. OSNA is a standardized, observer-independent technique, which may lead to more accurate staging. It has been suggested that in stage II CRC, the upstaging can reach 25% and these patients can access postoperative adjuvant chemotherapy. Moreover, intraoperative OSNA sentinel node evaluation may allow early CRC to be treated with organ-preserving surgery, while in more advanced-stage disease, a tailored lymphadenectomy can be performed considering the presence of aberrant lymphatic drainage and skip metastases.

New scoring system to distinguish deep invasive submucosal and muscularis propria colorectal cancer during colonoscopy: a development and global multicenter external validation study (e-T2 Score)

BACKGROUND AND AIMS

Diagnostics to differentiate deep submucosal invasive (invasion depth ≥1000 μm [T1b]) colorectal cancer (CRC) from muscularis propria invasive (T2) CRC are limited. We aimed to establish and validate a scoring system that differentiates T1b from T2.

METHODS

A multicenter retrospective cross-validation study was performed. Four hundred sixty-one consecutive pathologically confirmed T1b or T2 CRCs were divided into the development (T1b, 222; T2, 189) and internal validation (T1b, 31; T2, 19) cohorts. Eight potential endoscopic findings were evaluated using the development cohort: loss of lobulation, deep depression, demarcated depressed area, protuberance within the depression, expanding appearance, fold convergency, erosion or white plaque, and Borrmann type 2 or 3 tumor. A scoring system that differentiates T1b from T2 was developed, and diagnostic performance was tested using the internal validation cohort by 8 endoscopists. External validation was conducted using 50 CRC images by 4 endoscopists from other institutions, including outside of Japan.

RESULTS

Multivariate analysis identified the following 5 independent predictive endoscopic findings of T2 CRC: deep depression (odds ratio [OR], 2.08; 95% confidence interval [CI], 1.07-4.04), demarcated depressed area (OR, 4.40; 95% CI, 1.39-13.9), 4-fold convergency or more (OR, 3.41; 95% CI, 1.90-6.11), erosion or white plaque (OR, 8.28; 95% CI, 2.77-24.7), and Borrmann type 2 or 3 tumor (OR, 8.76; 95% CI, 3.58-21.5). The area under the receiver-operating characteristic curve (AUROC) was .90 (95% CI, .87-.93) in the development cohort, .80 (95% CI, .76-.85) in the internal validation, and .76 (95% CI, .69-.83) in the external validation.

CONCLUSIONS

We established and validated a new scoring system to differentiate T1b from T2 CRC using 5 simple endoscopic findings.

Observation of Clinical Efficacy of Anisodamine and Chlorpromazine in the Treatment of Intractable Hiccup after Stroke

Objective

This study is aimed at investigating the clinical efficacy of anisodamine combined with chlorpromazine on intractable hiccups after stroke.

Methods

150 patients admitted to Affiliated Hospital of the Hebei University of Engineering from 2017 to 2021 were selected as the research objects, all of which received the computed tomography (CT) examination. During CT examination, intelligent algorithms were used to segment the images. An unsupervised multilayer image threshold segmentation algorithm was proposed by using Kullback-Leibler (K-L) divergence and the modified particle swarm optimization (MPSO) algorithm. All patients were divided into three groups, with each group of 50 patients. Patients in the control group (group A) took the calcium tablets, vitamin C tablets, and vitamin B1 tablets orally. Patients in the control group (group B) received the acupoint injection of anisodamine, and those in the observation group (group C) received the acupoint injection of anisodamine combined with chlorpromazine. The therapeutic effect and patient satisfaction of the three groups were compared.

Results

The two-dimensional (2D) K-L divergence was applied for the multilayer segmentation of images, which was helpful to obtain accurate images. The MPSO algorithm was adopted to reduce the computational complexity. The total efficiency of group C was 98%, that of group B was 56%, and that of group A was 22%. The total efficiency and satisfaction rate of group C were signally better than those of group A and group B (P < 0.05).

Conclusion

The combination of 2D K-L divergence and MPSO algorithm could improve the accuracy of multilayer image segmentation and CT imaging. Acupoint injection of anisodamine combined with chlorpromazine had better efficacy than the injection of anisodamine alone for the treatment of intractable hiccups after stroke, with high safety and clinical promotion value.

Role of Positron Emission Tomography/Computed Tomography in Gastrointestinal Malignancies: A Brief Review and Pictorial Essay

Positron emission tomography/computed tomography (PET/CT) is increasingly becoming a mainstay in diagnosis and management of many malignant disorders. However, its role in the assessment of gastro-intestinal lesions is still evolving. The aim of this review was to demonstrate the areas, where PET/CT is impactful and where it has limitations. This will allow for us to reduce unnecessary investigations and develop methods to overcome the limitations.

Assessment of the Use of Preoperative CT Scan Image for Predicting Lymph Nodes for Resection of Colorectal Cancer: A Retrospective Study

<b><i>Introduction:</i></b> Colorectal cancer (CRC) is the most common cancer that accounts for nearly 10% of the cancers, with 1.36 million people worldwide. Nodal status (N-stage) evaluation was inferior between observers, which considered CT scanning a good N-stage. We hypothesized that CRC patients’ preoperative CT scan imaging predicts the nodal and metastatic stage. <b><i>Methods:</i></b> This noninterventional retrospective study was carried out using patients’ medical records, including medical history and results of diagnostic tests, and preoperative clinical and pathological stages. All direct identifiers have been removed from all patient data. This study included 96 patients who underwent resection curative surgery for CRC at the Tertiary Hospital, Sudan, between March 2009 and December 2020. <b><i>Results:</i></b> The median age was 69 years (47–74 years), and 49 (51.04%) were female. The tumor stage of the patients was 4, 11, 74, and 7 as T1, T2, T3, and T4, respectively. A total of 38 patients with a malignant spread in lymph nodes were observed, and the median lymph node count was 11 (range 4–52). Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value (PPV), negative predictive value (NPV), and accuracy calculated for the malignant lymph nodes were 75.56% (95% CI: 60.46–87.12%), 45.10% (95% CI: 31.13–59.66%), 1.38 (95% CI: 1.02–1.86), 0.54 (95% CI: 0.30–0.98), 54.84% (95% CI: 47.38–62.09%), 67.65% (95% CI: 53.53–79.15%), and 59.38% (95% CI: 48.87–69.29%), respectively. <b><i>Conclusion:</i></b> The preoperative CT scan images were used to predict lymph nodes with a diagnostic accuracy of 59.38% for N-stage in patients with CRC.

Lymph Node Mapping for Tumor Micrometastasis

Lymph node mapping for tumor micrometastasis is of great significance for the prevention, prognosis, and treatment of cancer. Currently, the traditional clinical detection methods (computed tomography, magnetic resonance imaging, or positron emission tomography/computed tomography) in clinical lymph node mapping still have some inherent disadvantages, which have prompted the development of various fluorescent probes for lymph node mapping. However, the conventional fluorescent probes such as indocyanine green or methylene blue in lymph node mapping are still accompanied by several problems such as impaired surgical field vision due to dye staining or less accumulation and shorter retention time in the lymph node. In a recent achievement, newly designed nanoparticles are prepared with novel properties that could be attractive for lymph node mapping. In this review, we will provide details on the progress of various nanoparticles for lymph node mapping and emphasize other multivariant properties in different nanoparticles, including strong tumor-targeting affinity and specificity, self-luminescence, and even with the function to kill metastatic cancer cells.

Computed Tomography Image Features under Denoising Algorithm for Benign and Malignant Diagnosis of Renal Parenchymal Tumor

To improve the quality of computed tomography (CT) images and provide help for benign and malignant diagnosis of renal parenchymal tumors, the independent component analysis (ICA) denoising algorithm was used. An improved ICA X-ray CT (X-CT) medical image denoising algorithm was proposed. ICA provided a higher signal-to-noise ratio for CT image denoising. Forty patients with renal tumor were selected as the observation group. The CT image performance of patients was evaluated by the denoising algorithm and compared with the wavelet transform algorithm, and the peak signal-to-noise ratio of the proposed algorithm was analyzed and compared. The results showed that among the 40 patients with renal tumors, 12 were renal clear cell carcinoma cases and 28 were cystic renal carcinoma cases. The accuracy of the enhanced CT image was 93.8%, and that of the CT image using the denoising algorithm was 96.3%; the difference between the two was significant (P < 0.05). The peak signal-to-noise ratio (PSNR) of the algorithm proposed was higher than the PSNR values of CT and noisy images. The PSNR of the proposed algorithm was significantly higher than that of mean filtering. The root mean square error (RMSE) algorithm of the proposed algorithm was significantly lower than that of the mean algorithm in image data processing (P < 0.05), which showed the superiority of the proposed algorithm. Enhanced CT can be staged significantly. In conclusion, the algorithm had a significant effect on the edge contour of detailed features, and the accuracy of CT images based on intelligent calculation was significantly higher than that of conventional CT images for benign and malignant renal parenchyma tumors, which was worth promoting in clinical diagnosis.

Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer

BACKGROUND

Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images.

METHODS

A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities.

RESULTS

Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients.

CONCLUSION

The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.