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Abstract With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot. [ABSTRACT FROM AUTHOR]

Despite improvements in treatment options for advanced colorectal cancer (CRC), survival outcomes are still best for patients with non-metastasised disease. Diagnostic tools to identify blood-based biomarkers and assist in CRC subtype classification could afford a means to track CRC progression and treatment response. Cancer cell-derived small extracellular vesicles (EVs) circulating in blood carry an elevated cargo of lipids and proteins that could be used as a signature of tumour suppressor/promoting events or stages leading up to and including metastasis. Here, we used pre-characterised biobanked plasma samples from surgical units, typically with a low volume (~100 µL), to generate and discover signatures of CRC-derived EVs. We employed nanostructured porous silicon (pSi) surface assisted-laser desorption/ionisation (SALDI) coupled with high-resolution mass spectrometry (HR-MS), to allow sensitive detection of low abundant analytes in plasma EVs. When applied to CRC samples, SALDI-HR-MS enabled the detection of the peptide mass fingerprint of cancer suppressor proteins, including serine/threonine phosphatases and activating-transcription factor 3. SALDI-HR-MS also allowed the detection of a spectrum of glycerophospholipids and sphingolipid signatures in metastatic CRC. We observed that lithium chloride enhanced detection sensitivity to elucidate the structure of low abundant lipids in plasma EVs. pSi SALDI can be used as an effective system for label-free and high throughput analysis of low-volume patient samples, allowing rapid and sensitive analysis for CRC classification.

In 2013, recognizing that Colorectal Cancer (CRC) is the second leading cause of death by cancer worldwide and that it was a neglected disease increasing rapidly in Mexico, the community of researchers at the Biomedicine Research Unit of the Facultad de Estudios Superiores Iztacala from the Universidad Nacional Autónoma de México (UNAM) established an intramural consortium that involves a multidisciplinary group of researchers, technicians, and postgraduate students to contribute to the understanding of this pathology in Mexico. This article is about the work developed by the Mexican Colorectal Cancer Research Consortium (MEX-CCRC): how the Consortium was created, its members, and its short- and long-term goals. Moreover, it is a narrative of the accomplishments of this project. Finally, we reflect on possible strategies against CRC in Mexico and contrast all the data presented with another international strategy to prevent and treat CRC. We believe that the Consortium's characteristics must be maintained to initiate a national strategy, and the reported data could be useful to establish future collaborations with other countries in Latin America and the world. [ABSTRACT FROM AUTHOR]

Despite improvements in treatment options for advanced colorectal cancer (CRC), survival outcomes are still best for patients with non-metastasised disease. Diagnostic tools to identify blood-based biomarkers and assist in CRC subtype classification could afford a means to track CRC progression and treatment response. Cancer cell-derived small extracellular vesicles (EVs) circulating in blood carry an elevated cargo of lipids and proteins that could be used as a signature of tumour suppressor/promoting events or stages leading up to and including metastasis. Here, we used pre-characterised biobanked plasma samples from surgical units, typically with a low volume (~100 µL), to generate and discover signatures of CRC-derived EVs. We employed nanostructured porous silicon (pSi) surface assisted-laser desorption/ionisation (SALDI) coupled with high-resolution mass spectrometry (HR-MS), to allow sensitive detection of low abundant analytes in plasma EVs. When applied to CRC samples, SALDI-HR-MS enabled the detection of the peptide mass fingerprint of cancer suppressor proteins, including serine/threonine phosphatases and activating-transcription factor 3. SALDI-HR-MS also allowed the detection of a spectrum of glycerophospholipids and sphingolipid signatures in metastatic CRC. We observed that lithium chloride enhanced detection sensitivity to elucidate the structure of low abundant lipids in plasma EVs. pSi SALDI can be used as an effective system for label-free and high throughput analysis of low-volume patient samples, allowing rapid and sensitive analysis for CRC classification. [ABSTRACT FROM AUTHOR]

Worldwide, colon cancer is the third most frequent malignancy and the second most common cause of death. Although it can strike anybody at any age, colon cancer mostly affects the elderly. Small, non-cancerous cell clusters inside the colon, commonly known as polyps, are typically where colon cancer growth starts. But over time, if left untreated, these benign polyps may develop into malignant tissues and develop into colon cancer. For the diagnosis of colon cancer, with routine inspection of the colon region for polyps, several techniques, including colonoscopy and cancer scanning, are used. In the case identifying the polyps in the colon area, efforts are being taken to surgically remove the polyps as quickly as possible before they become malignant. If the polyps become malignant, then colon cancer treatment strategies, such as surgery, chemotherapy, targeted therapy, and immunotherapy, are applied to the patients. Despite the recent improvements in diagnosis and prognosis, the treatment of colorectal cancer (CRC) remains a challenging task. The objective of this review was to discuss how CRC is initiated, and its various developmental stages, pathophysiology, and risk factors, and also to explore the current state of colorectal cancer diagnosis and treatment, as well as recent advancements in the field, such as new screening methods and targeted therapies. We examined the limitations of current methods and discussed the ongoing need for research and development in this area. While this topic may be serious and complex, we hope to engage and inform our audience on this important issue.

In 2013, recognizing that Colorectal Cancer (CRC) is the second leading cause of death by cancer worldwide and that it was a neglected disease increasing rapidly in Mexico, the community of researchers at the Biomedicine Research Unit of the Facultad de Estudios Superiores Iztacala from the Universidad Nacional Autónoma de México (UNAM) established an intramural consortium that involves a multidisciplinary group of researchers, technicians, and postgraduate students to contribute to the understanding of this pathology in Mexico. This article is about the work developed by the Mexican Colorectal Cancer Research Consortium (MEX-CCRC): how the Consortium was created, its members, and its short- and long-term goals. Moreover, it is a narrative of the accomplishments of this project. Finally, we reflect on possible strategies against CRC in Mexico and contrast all the data presented with another international strategy to prevent and treat CRC. We believe that the Consortium’s characteristics must be maintained to initiate a national strategy, and the reported data could be useful to establish future collaborations with other countries in Latin America and the world.

Abstract Background In addition to conventional approaches, detecting and characterizing CTCs in patient blood allows for early diagnosis of cancer metastasis. Methods We blended poly(ethylene oxide) (PEO) into nylon-6 through electrospinning to generate a fibrous matbased circulating tumour cells (CTCs) assay. The contents of nylon-6 and PEO in the electrospun blend fibrous mats (EBFMs) were optimized to facilitate high cell-substrate affinity and low leukocyte adsorption. Results Compared with the IsoFlux System, a commercial instrument for CTC detection, the CTC assay of EBFMs exhibited lower false positive readings and high sensitivity and selectivity with preclinical specimens. Furthermore, we examined the clinical diagnosis accuracy of colorectal cancer, using the CTC assay and compared the results with those identified through pathological analyses of biopsies from colonoscopies. Our positive expressions of colorectal cancer through CTC detection completely matched those recognized through the pathological analyses for the individuals having stage II, III, and IV colorectal cancer. Nevertheless, two in four individuals having stage I colorectal cancer, recognized through pathological analysis of biopsies from colonoscopies, exhibited positive expression of CTCs. Ten individuals were identified through pathological analysis as having no colorectal tumours. Nevertheless, two of these ten individuals exhibited positive expression of CTCs. Conclusions Thus, in this population, the low cost EBFMs exhibited considerable capture efficiency for the non-invasive diagnosis of colorectal cancer.

Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results. [ABSTRACT FROM AUTHOR]

Although luminescence metal organic framework (MOFs) has displayed the significant advantages, the limitations in the electrochemical performance (e.g. rapid charge recombination rates and inadequate charge transport) limited the sensing application of MOFs. Herein, a novel Cu-MOFs/BiVO 4 nanorod-based electrogenerated chemiluminescence (ECL) sensor has been developed. Firstly, Cu-MOFs with strong luminescence were synthesized via the three-layer approach as ECL emitter. Furthermore, BiVO 4 nanorods was modified on the electrode as the actuator to improve the electrochemical activity of Cu-MOFs in the ECL process. As an n-type semiconductor, BiVO 4 formed a complementary structure with p-type semiconductor Cu-MOF. Therefore, electrons in the conduction band of BiVO 4 transferred to that of Cu-MOF. As a result, more electrons reacted with coreactant on the surface of Cu-MOF, which effectively enhanced the ECL performance of 2D Cu-MOFs nanosheets. As a result, the quantitation of KRAS gene was realized in the linear range of 0.1 pM–1 nM with a detection limit of 0.02 fM. Moreover, the detection of KRAS gene in actual colorectal cancer samples was also carried out with good recovery, which offered a broad application possibility for ECL research and clinical analysis. [Display omitted] • Cu-MOFs with strong luminescence were synthesized via the three-layer approach as ECL emitter. • BiVO 4 nanorods as the actuator to improve the electrochemical activity of Cu-MOFs. • The detection of KRAS gene in colorectal cancer samples was carried out with good recovery. [ABSTRACT FROM AUTHOR]

The aim of the study is to model a new mathematical tool effective in the diagnosis of colorectal cancer and useful as a mass screening test. This work enrolled 345 subjects from January 2011 to January 2013, in 4 Italian Surgical Departments: 97 (27,4%) out of the total were healthy controls (class 0) whereas 248 (72,6%) patients were affected by colorectal cancer. The cancer patients were divided into four classes, according to TNM staging classification: 74 (20,9%) patients stage I (class 1); 61 (17,2) patients stage II (class 2); 76 (21,5%) patients vstage III (class 3); 37 (10,5%) stage IV (class 4). Blood samples were collected after 24h from hospital admission and 17 biochemical parameters (CEA, ceruloplasmin, haptoglobin, transferrin, TPA, CA 19.9, CA 72.4, PCR, Ca 50, C4 Complement, CA 125, Alfa-1-antitrypsin, alpha-2-Macroglobulin, Ferritin, RBP, Alpha-1-Acid Glycoprotein, Complement C3) were statistically analysed together with the clinical and pathological disease staging (pre and postoperative evaluation, respectively). Evaluation and comparison were made between two groups: healthy controls and the total of affected patients. Using the collected data, it was developed a mathematical model (artificial neural network ANN) allowing the distribution of colorectal cancer and controls (apparently in good health) patients in the two groups. This led to the definition of an index (B-index) that, simply analysing the combined values of the 17 biochemical parameters, decides on the either healthy or disease status of the patient. The use of the B-index, with a neural network based on real values, allows colorectal cancer diagnosis with 80.078% accuracy, probability of false positive (FP) = 0.333, probability of false negatives (FN) = 0.102, a 0.898 sensitivity and 0.667 specificity. However, by using B-index with a neural network based on the implementation of extended reality values our analysis reaches an accuracy of 91.11% in colorectal cancer diagnosis, with probability of false positive (FP) = 0.472, probability of false negatives (FN) = 0.03%, a 0.9997 sensitivity and 0.7642 specificity. The results suggest a promising role for B-index in colorectal cancer mass screening with an easily available, low cost and non-invasive test. [ABSTRACT FROM AUTHOR]

Colonoscopy is considered the gold standard for colorectal cancer diagnosis and prognosis. However, existing methods are less accurate and prone to overlooking lesions during gastrointestinal endoscopic examinations. Computer-assisted diagnosis combined with robot-assisted minimally invasive surgery (RMIS) can significantly help medical practitioners detect and treat lesions. Therefore, two novel architectures are developed for polyp and surgical instrument segmentation to aid colorectal cancer diagnosis, assessment, and treatment. Colorectal cancer segmentation network (CCS-Net) is the base network used in this study. It uses the maximum convolutional layers near the input image for effective feature extraction from low-level information. In addition, CCS-Net uses an efficient feature upsampling unit to efficiently increase the input spatial features' map size. Hence, CCS-Net is capable of providing a fair performance with satisfactory computational efficiency The multi-scale feature retention and aggregation network (MFRA-Net) is the final network in this study. MFRA-Net is developed to improve the segmentation accuracy of the CCS-Net further as it uses multi-scale feature retention to retain low-level spatial features and transfers them to deep stages of the network. MFRA-Net also combines multi-scale high-strided low-level information with high-level information to boost network segmentation performance. Finally, all the transferred multi-scale features from the early stages of the network are aggregated with high-level features in the deep levels of the network. This multi-scale feature retention and aggregation mechanism enables the network to maintain a better segmentation performance compared with other methods even with challenging blur, specular reflection, low contrast, and high variation cases. We evaluated both architectures on four challenging datasets: Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument, and the UW-Sinus-Surgery-Live dataset. The proposed method achieves dice similarity coefficients of 95.98%, 94.19%, 92.81%, and 88.57% for the CVC-ClinicDB, Kvasir-SEG, Kvasir-Instrument, and UW-Sinus-Surgery-Live datasets. The proposed method achieves superior segmentation performance compared with state-of-the-art methods and requires only 4.9 million trainable parameters for complete training. Therefore, the proposed networks can effectively assist health professionals in surgical procedures and colorectal cancer diagnosis through surgical instruments and polyp segmentation, respectively. • We proposed two models for diagnostic and surgical assistance of colorectal cancer. • CCS-Net is a base model using large number of convolution layers near input image. • MFRA-Net is a final network using multi-scale feature retention and aggregation. • MFRA-Net outperforms the state-of-the-art methods using only 4.9 million parameters. [ABSTRACT FROM AUTHOR]

Worldwide, colon cancer is the third most frequent malignancy and the second most common cause of death. Although it can strike anybody at any age, colon cancer mostly affects the elderly. Small, non-cancerous cell clusters inside the colon, commonly known as polyps, are typically where colon cancer growth starts. But over time, if left untreated, these benign polyps may develop into malignant tissues and develop into colon cancer. For the diagnosis of colon cancer, with routine inspection of the colon region for polyps, several techniques, including colonoscopy and cancer scanning, are used. In the case identifying the polyps in the colon area, efforts are being taken to surgically remove the polyps as quickly as possible before they become malignant. If the polyps become malignant, then colon cancer treatment strategies, such as surgery, chemotherapy, targeted therapy, and immunotherapy, are applied to the patients. Despite the recent improvements in diagnosis and prognosis, the treatment of colorectal cancer (CRC) remains a challenging task. The objective of this review was to discuss how CRC is initiated, and its various developmental stages, pathophysiology, and risk factors, and also to explore the current state of colorectal cancer diagnosis and treatment, as well as recent advancements in the field, such as new screening methods and targeted therapies. We examined the limitations of current methods and discussed the ongoing need for research and development in this area. While this topic may be serious and complex, we hope to engage and inform our audience on this important issue.

Background: In addition to conventional approaches, detecting and characterizing CTCs in patient blood allows for early diagnosis of cancer metastasis. Methods: We blended poly(ethylene oxide) (PEO) into nylon-6 through electrospinning to generate a fibrous matbased circulating tumour cells (CTCs) assay. The contents of nylon-6 and PEO in the electrospun blend fibrous mats (EBFMs) were optimized to facilitate high cell-substrate affinity and low leukocyte adsorption. Results: Compared with the IsoFlux System, a commercial instrument for CTC detection, the CTC assay of EBFMs exhibited lower false positive readings and high sensitivity and selectivity with preclinical specimens. Furthermore, we examined the clinical diagnosis accuracy of colorectal cancer, using the CTC assay and compared the results with those identified through pathological analyses of biopsies from colonoscopies. Our positive expressions of colorectal cancer through CTC detection completely matched those recognized through the pathological analyses for the individuals having stage II, III, and IV colorectal cancer. Nevertheless, two in four individuals having stage I colorectal cancer, recognized through pathological analysis of biopsies from colonoscopies, exhibited positive expression of CTCs. Ten individuals were identified through pathological analysis as having no colorectal tumours. Nevertheless, two of these ten individuals exhibited positive expression of CTCs. Conclusions: Thus, in this population, the low cost EBFMs exhibited considerable capture efficiency for the noninvasive diagnosis of colorectal cancer. [ABSTRACT FROM AUTHOR]

Purpose Colonoscopy is not widely and easily available in all countries, even for symptomatic patients. This is one of the causes of tumors not being diagnosed until an advanced stage. The aim of this study was to estimate the efficacy of the fecal occult blood test (FOBT) in the diagnostic work-up of outpatients referred to a colorectal unit due to indistinct abdominal symptoms. Patients/methods Among 10 418 consecutive symptomatic individuals referred to the outpatient clinic, an immunochemical FOBT (Hem-Check 1 ® ) was recommended for 9432 patients with indistinct symptoms as a tool for qualifying them for colonoscopy. All the subjects were treated according to their diagnosis and followed-up for the next 10 years. Results Colorectal cancer (CRC) was diagnosed in 535 individuals: 393/986 (39.9%) among patients with red-flag symptoms, and 142/951 (14.9%) of individuals with indistinct symptoms and a positive FOBT. In the latter group, less-advanced tumors, classed as such using Dukes’ classification, were twice as common and more advanced CRC occurred twice as seldom than in the former. Cancer recurrence-free and overall survival periods after surgical treatment for CRC were significantly longer in patients with indistinct symptoms who qualified for diagnostic procedures on the basis of a positive FOBT. Conclusions Patients with symptoms suggesting organic colon disease had a worse prognosis compared to individuals with non-specific symptoms. If bowel endoscopy is not widely and easily available, qualification for colonoscopy on the basis of alarm symptoms and a positive FOBT seems to be an effective strategy in early CRC diagnosis. [ABSTRACT FROM AUTHOR]

• This study proposes a predictive system based on serum biomarkers and ensemble learning for early colorectal cancer diagnosis in mass screening based on 17 serum biomarkers; the system consists of a binary predictor, which predicts the presence/absence of colorectal cancer, and a staging predictor, which predicts the related TNM stage. • Plasmatic proteins revealed to be significant in predicting the absence/presence and the related TNM stage of colorectal cancer in patients. • Ceruloplasmin and α -2-Macroglobulin are significant in predicting the early colorectal cancer presence/absence outcome through XGBoost and Random Forest models, while CA 50 and α -2-Antitrypsin can be neglected. • Extended reality allows interpreting the significance of serum biomarkers in performing early colorectal cancer diagnosis through the predictors' bias-variance ratio. • Ensemble learning through majority voting permits to reduce the noise in the prediction of early colorectal cancer presence/absence outcome. Background and objective : Colorectal cancer is one of the most common malignancies among the general population. Artificial Intelligence methodologies based on serum parameters are in continuous development to obtain less expensive tools for highly sensitive diagnoses. This study proposes a predictive system based on serum biomarkers and ensemble learning to predict colorectal cancer presence and the related TNM stage in patients. Methods : We have selected 17 significant plasmatic proteins, i.e., Carcinoembryonic Antigen, CA 19-9, CA 125, CA 50, CA 72-4, Tissue Polypeptide Antigen, C-Reactive Protein, Ceruloplasmin, Haptoglobin, Transferrin, Ferritin, α -1-Antitrypsin, α -2-Macroglobulin, α -1 Acid Glycoprotein, Complement C4, Complement C3, and Retinol Binding Protein, regarding 345 patients (248 affected by the neoplastic disease). The proposed system consists of two predictors, i.e., binary and staging; the former predicts the presence/absence of cancer, while the latter identifies the related TNM stage (I, II, III, or IV). The experiments were conducted by deploying and comparing Random Forest, XGBoost, Support Vector Machine, and Multilayer Perceptron with feature selection based on Gini Importance and with dimensionality reduction via PCA. Results : The results show that the system composed of XGBoost as binary and staging predictor reaches 91.30% accuracy, 90% sensitivity, and 93.33% specificity for the absence/presence outcome, while 66.66% accuracy for the staging response. With the expansion of the training set in favor of positive patients and majority voting, the system composed of the combination of Support Vector Machine, XGBoost, and Multilayer Perceptron as the binary predictor reaches 98.03% accuracy, 100% sensitivity, and 92.30% specificity, while the combination of Random Forest, XGBoost, and Multilayer Perceptron as staging predictor achieves 60% accuracy. The final system reaches, in terms of accuracy, 98.03%, and 66.66% for the binary and staging predictors, respectively. It was also found that the biomarkers which contribute most to the binary decision are Ceruloplasmin and α -2-Macroglobulin, while the least significant dimensions are CA 50 and α -1-Antitrypsin; instead, Carcinoembryonic Antigen and α -1 Acid Glycoprotein are the most significant to the staging decision. Conclusions : The present study proves the effectiveness of deploying serum biomarkers as feature dimensions for early colorectal cancer diagnosis and of using majority voting for noise reduction in the prediction. [ABSTRACT FROM AUTHOR]

Introduction: We evaluate the impact of COVID-epidemic in colorectal cancer (CRC) diagnosis during Spain's state of emergency., Methods: We compared newly diagnosed patients with patients diagnosed in the same period of 2019., Results: A new diagnosis of CRC decreased 48% with a higher rate of patients diagnosed in the emergency setting (12.1% vs. 3.6%; p = .048) and a lower rate diagnosed in the screening program (5.2% vs. 33.3%; p = .000)., Conclusions: Fewer patients have been diagnosed with CRC, with a higher rate of patients diagnosed in an emergency setting., (© 2020 Wiley Periodicals LLC.)