A scoring system based on artificial neural network for predicting 10-year survival in stage II A colon cancer patients after radical surgery

Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis-A Pilot Comparative Study

We aimed to comparatively assess the prognostic preoperative value of the main peripheral blood components and their ratios-the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)-to the use of artificial-neural-network analysis in determining undesired postoperative outcomes in colorectal cancer patients. Our retrospective study included 281 patients undergoing elective radical surgery for colorectal cancer in the last seven years. The preoperative values of SII, NLR, LMR, and PLR were analyzed in relation to postoperative complications, with a special emphasis on their ability to accurately predict the occurrence of anastomotic leak. A feed-forward fully connected multilayer perceptron network (MLP) was trained and tested alongside conventional statistical tools to assess the predictive value of the abovementioned blood markers in terms of sensitivity and specificity. Statistically significant differences and moderate correlation levels were observed for SII and NLR in predicting the anastomotic leak rate and degree of postoperative complications. No correlations were found between the LMR and PLR or the abovementioned outcomes. The MLP network analysis showed superior prediction value in terms of both sensitivity (0.78 ± 0.07; 0.74 ± 0.04; 0.71 ± 0.13) and specificity (0.81 ± 0.11; 0.69 ± 0.03; 0.9 ± 0.04) for all the given tasks. Preoperative SII and NLR appear to be modest prognostic factors for anastomotic leakage and overall morbidity. Using an artificial neural network offers superior prognostic results in the preoperative risk assessment for overall morbidity and anastomotic leak rate.

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.

Stage IV colon cancer patients without DENND2D expression benefit more from neoadjuvant chemotherapy

According to the EPOC study, chemotherapy could improve 5-year disease-free survival of stage IV colon cancer patients by 8.1%. However, more molecular biomarkers are required to identify patients who need neoadjuvant chemotherapy. DENND2D expression was evaluated by immunohistochemistry in 181 stage IV colon cancer patients. The prognosis was better for patients with DENND2D expression than patients without DENND2D expression (5-year overall survival [OS]: 42% vs. 12%, p = 0.038; 5-year disease-free survival: 20% vs. 10%, p = 0.001). Subgroup analysis of the DENND2D-negative group showed that patients treated with neoadjuvant chemotherapy achieved longer OS than patients without neoadjuvant chemotherapy (RR = 0.179; 95% CI = 0.054-0.598; p = 0.003). DENND2D suppressed CRC proliferation in vitro and in vivo. Downregulation of DENND2D also promoted metastasis to distant organs in vivo. Mechanistically, DENND2D suppressed the MAPK pathway in CRC. Colon cancer patients who were DENND2D negative always showed a worse prognosis and were more likely to benefit from neoadjuvant chemotherapy. DENND2D may be a new prognostic factor and a predictor of the need for neoadjuvant chemotherapy in stage IV colon cancer.

Machine learning in gastrointestinal surgery

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

The emerging role of super enhancer-derived noncoding RNAs in human cancer

Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.

Application of artificial intelligence to the diagnosis and therapy of colorectal cancer

Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.

External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival

Purpose

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality.

Materials and Methods

The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS).

Results

Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation.

Conclusion

The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Emerging artificial intelligence applications in gastroenterology: A review of the literature

Artificial intelligence (AI) allows machines to provide disruptive value in several industries and applications. Applications of AI techniques, specifically machine learning and more recently deep learning, are arising in gastroenterology. Computer-aided diagnosis for upper gastrointestinal endoscopy has growing attention for automated and accurate identification of dysplasia in Barrett’s esophagus, as well as for the detection of early gastric cancers (GCs), therefore preventing esophageal and gastric malignancies. Besides, convoluted neural network technology can accurately assess Helicobacter pylori (H. pylori) infection during standard endoscopy without the need for biopsies, thus, reducing gastric cancer risk. AI can potentially be applied during colonoscopy to automatically discover colorectal polyps and differentiate between neoplastic and non-neoplastic ones, with the possible ability to improve adenoma detection rate, which changes broadly among endoscopists performing screening colonoscopies. In addition, AI permits to establish the feasibility of curative endoscopic resection of large colonic lesions based on the pit pattern characteristics. The aim of this review is to analyze current evidence from the literature, supporting recent technologies of AI both in upper and lower gastrointestinal diseases, including Barrett's esophagus, GC, H. pylori infection, colonic polyps and colon cancer.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

BACKGROUND

Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN-based survival prediction model for patients with gastric cancer.

METHODS

Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time-dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.

RESULTS

An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5-year survival prediction, respectively. In the calibration curve analysis, the ANN-predicted survival had a high consistency with the actual survival. Comparison of the DCA and time-dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.

CONCLUSIONS

The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Application of Artificial Intelligence to Gastroenterology and Hepatology

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Pretreatment TACC3 expression in locally advanced rectal cancer decreases the response to neoadjuvant chemoradiotherapy

Chemoradiotherapy combined with surgical resection is the standard treatment for locally advanced rectal cancer, but not all the patients respond to neoadjuvant treatment. Transforming acidic coiled-coil protein-3 (TACC3) is frequently aberrantly expressed in rectal cancer tissue. In this study, we investigated whether TACC3 could serve as a biomarker predictive of the efficacy of chemoradiotherapy. In all, 152 rectal cancer patients with tumor tissue collected at biopsy and set aside before treatment were enrolled in this study. All patients received chemoradiotherapy and surgical resection. Immunohistochemically detected tumoral TACC3 expression significantly decreased sensitivity to chemoradiotherapy [risk ratio (RR) = 2.236, 95% confidence interval (CI): 1.447-3.456; P = 0.001] and thus the pathological complete response rate (P = 0.001). TACC3 knockdown using specific siRNA enhanced radiotherapy-induced decreases in proliferation and colony formation by HCT116 and SW480 cells and increased the incidence of radiotherapy-induced apoptosis. Cox multivariate analysis showed that TACC3 was a significant prognostic factor for overall survival (P = 0.017) and disease-free survival (P = 0.020). These findings suggest TACC3 expression may be predictive of chemoradiotherapy sensitivity and prognosis in locally advanced rectal cancer.

Artificial Neural Network Individualised Prediction of Time to Colorectal Cancer Surgery

Aim

Colorectal cancer pathway targets mandate prompt treatment although practicalities may mean patients wait for surgery. This variable period could be utilised for patient optimisation; however, there is currently no reliable predictive system for time to surgery. If individualised surgical waits were prospectively known, tailored prehabilitation could be introduced.

Methods

A dedicated, prospectively populated elective laparoscopic surgery for colorectal cancer with a curative intent database was utilised. Primary endpoint was the prediction of the individualised waiting time for surgery. A multilayered perceptron artificial neural network (ANN) model was trained and tested alongside uni- and multivariate analyses.

Results

668 consecutive patients were included. 8.5% underwent neoadjuvant chemoradiotherapy. The mean time from diagnosis to surgery was 53 days (95% CI 48.3-57.8). ANN correctly identified those having surgery in <8 (97.7% and 98.8%) and <12 weeks (97.1% and 98.8%) of the training and testing cohorts with area under the receiver operating curves of 0.793 and 0.865, respectively. After neoadjuvant treatment, an ASA physical status score was the most important potentially modifiable risk factor for prolonged waits (normalised importance 64%, OR 4.9, 95% CI 1.5-16). The ANN findings were accurately cross-validated with a logistic regression model.

Conclusion

Artificial neural networks using demographic and diagnostic data successfully predict individual time to colorectal cancer surgery. This could assist the personalisation of preoperative care including the incorporation of prehabilitation interventions.

Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks

The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA) <5/≥5 and Clinical stage I-II/III-IV. The performance of each model was evaluated by predictive accuracy (ACC), the area under the receiver operating characteristic curve (AUC) and a 10-fold cross-validation test. The ACC and AUC of the Cancer/Normal, M0/M1, CEA and Clinical stage models were 100%, 1.000; 87.14%, 0.670; 100%, 1.000; and 100%, 1.000, respectively. The 10-fold cross-validation test of the ACC values and sensitivity for each test were 93.75-99.39%, 1.0000; 80.58-88.24%, 0.9286-1.0000; 67.21-92.31%, 0.7091-1.0000; and 59.13-68.85%, 0.6017-0.6585, respectively. The diagnosis models developed in the current study combined gene expression profiling data and artificial intelligence algorithms to create tools for improved diagnosis of CRC.

Cytoplasmic Pin1 expression is correlated with poor prognosis in colorectal cancer

OBJECTIVE

The aim of this study was to determine the clinicopathological significance and prognostic role of Pin1 expression and subcellular localization in colorectal cancer (CRC).

METHODS

The Pin1 expression, as well as cytoplasmic and nuclear localization, was investigated using immunohistochemistry in 265 human CRC tissues. The impact of subcellular localization of Pin1 on clinicopathological significance and prognosis in CRC was evaluated.

RESULTS

Pin1 was expressed in 164 of 265 CRCs (61.9%). Pin1 expression was not significantly correlated with any clinicopathological parameters. However, Pin1 expression was significantly correlated with worse overall and recurrence-free survivals (P =  0.002 and P =  0.001, respectively). CRCs with only nuclear Pin1 expression showed no difference in survival compared to CRCs with no Pin1 expression. Over half (51.7%, 137/265) of the CRCs had any cytoplasmic Pin1 expression, and 26.8% (71/265) had both cytoplasmic and nuclear expression. Cytoplasmic Pin1 expression was more frequent than only nuclear or no Pin1 expression in cases with vascular invasion and distant metastasis. Cytoplasmic Pin1 expression was significantly correlated with worse overall and recurrence-free survivals (P <  0.001 and P <  0.001, respectively).

CONCLUSION

Taken together, our results indicated different prognostic roles of subcellular Pin1expression in CRC. Cytoplasmic expression of Pin1, with or without nuclear expression, is an important factor in predicting aggressive tumor behavior and worse prognosis.

RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer

Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.

Expression of a novel CNPY2 isoform in colorectal cancer and its association with oncologic prognosis

Colorectal cancer (CRC) is a leading cause of cancer-related mortality. Recently, we identified a novel biomarker, canopy fibroblast growth factor signaling regulator 2 (CNPY2) isoform2, and subsequently investigated its expression and prognostic value in CRC patients. We initially generated CNPY2 isoform2 monoclonal antibodies and examined CNPY2 isoform2 expression in CRC cell lines and tissues using quantitative real-time polymerase chain reaction, western blot and immunohistochemistry analyses. We found that CNPY2 isoform2 expression significantly increased in tumor cell lines and tissues compared with that in normal colon epithelial cells and tumor-adjacent normal tissues. Survival analysis indicated that patients with low CNPY2 isoform2 expression had poorer 5-year overall survival (OS) in both the training cohort (41.7% vs. 77.7%, P = 0.007) and validation cohort (47.1% vs. 78.8%, P = 0.002). In multivariable analysis, CNPY2 isoform2 was identified as a predictor of 5-year OS in both the training cohort [hazard ratio (HR) = 5.001; 95% confidence interval (CI) 2.156–11.598, P < 0.001) and validation cohort (HR= 2.443; 95% CI 1.197- 4.983, P = 0.014). In conclusion, CNPY2 isoform2 represents as a novel and valuable prognostic indicator for CRC patients, while the oncologic function of CNPY2 requires further study.

Pim-3 as a potential predictor of chemoradiotherapy resistance in locally advanced rectal cancer patients

Approximately 30% of locally advanced rectal cancer patients might not benefit from chemoradiotherapy; however, the response to neoadjuvant chemoradiotherapy in these cases is difficult to predict. Pim-3 is a member of the provirus integration site for a moloney murine leukemia virus family of proteins that contributes to cell proliferation, survival, and chemotherapy resistance. Therefore, the relationship between Pim-3 expression and response to neoadjuvant chemoradiotherapy in rectal cancer patients is important to evaluate. 175 rectal cancer patients who underwent neoadjuvant treatment enrolled in this study. The relationship between Pim-3 expression on immunohistochemical analysis of rectal cancer tissue, which was obtained before treatment, the response to chemoradiotherapy and survival was investigated. The patients with no Pim-3 expression were more likely to achieve a pathologic complete response to chemoradiotherapy than patients with Pim-3 expression (P = 0.001). Cox multivariate analysis showed that the significant prognostic factors were Pim-3 expression (P = 0.003) and the number of neoadjuvant chemotherapy cycles (P = 0.005) for overall survival. Neoadjuvant chemotherapy cycles (P = 0.007), adjuvant chemotherapy cycles (P = 0.004) and pathology types (P = 0.049) were significant prognostic factors for disease-free survival. Pim-3 is a potential predictive biomarker for the response of rectal cancer to chemoradiotherapy.

Role of mathematical medicine in gastrointestinal carcinoma: Current status and perspectives

Mathematical medicine has already played an important role in clinical and basic research as a major interdisciplinary branch of medicine. Mathematical medicine has an important role not only in imaging diagnosis, image storage and transmission in gastrointestinal (GI) cancer, but also in tumor precision therapy. Specifically, in the field of minimally invasive treatment such as precise ablation, 3-dimension modeling, navigation, and surgical simulation significantly improve the therapeutic safety and efficiency in GI cancer. In addition, in the era of big data, data analysis and individualized therapy using mathematical medicine will become a trend in the future, offering an effective method for diagnosing and treating GI cancer and promoting clinical and scientific research.