Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.

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“…These results are strongly related to the authors' research in the area of cancer diagnosis and prevention [31,32].…”

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confidence: 65%

Modelling in Synthesis and Optimization of Active Vaccinal Components

et al. 2021

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“…The next step in the research is to include in the diagnosis system the results obtained in [ 46 ] by the authors, the computer-aided diagnosis system using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumour position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localisation).…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Colorectal Polyps Using Transfer Learning

Dulf

Bledea

Mocan

et al. 2021

Sensors

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Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.

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“…However, only the P activations are useful features for differentiating images of different classes. For the < 5-year class, the relatively most P (brightest) patches on the 8 × 8 grids are (3,5) in Figure 3A, (7,3) in Figure 3B, (8,6) in Figure 3C, and (3, 5) and (6, 1) in Figure 3D. For the > 5-year class, the relatively brightest patches on the 8 × 8 grids are (7,3) in Figure 3E, (7,3) in Figure 3F, (7,3) in Figure 3G, (7,3) in Figure 3H, (7,3) in Figure 3I, (3,5) and (6,7) in Figure 3J, and (3, 5) in Figure 3K.…”

Section: Discussionmentioning

confidence: 99%

“…For the < 5-year class, the relatively most P (brightest) patches on the 8 × 8 grids are (3,5) in Figure 3A, (7,3) in Figure 3B, (8,6) in Figure 3C, and (3, 5) and (6, 1) in Figure 3D. For the > 5-year class, the relatively brightest patches on the 8 × 8 grids are (7,3) in Figure 3E, (7,3) in Figure 3F, (7,3) in Figure 3G, (7,3) in Figure 3H, (7,3) in Figure 3I, (3,5) and (6,7) in Figure 3J, and (3, 5) in Figure 3K. Although the spatial distribution of P activations in the IHC images of < 5 years are different from those of the ones of > 5 years, no consistency of locations of the relatively brightest patches can be found among the 4 IHC images of the < 5-year class.…”

Section: Discussionmentioning

confidence: 99%

“…Another AI-based diagnosis system, which applied deep learning for pattern classification, was developed for detecting patients with CRC using both quantitative and qualitative data, including pathology [7]. As in another study, 10 CNNs were trained for classifying images of heterogeneous hematoxylin and eosin (HE) stained tumor tissues that were obtained from four cohorts of CRC patients with good or poor disease outcomes [8].…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy

Pham

Ravi

Luo

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Aim: The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. Methods: This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. Results: The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. Conclusions: The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

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Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Cited by 18 publications

References 39 publications

Modelling in Synthesis and Optimization of Active Vaccinal Components

Modelling in Synthesis and Optimization of Active Vaccinal Components

Automatic Detection of Colorectal Polyps Using Transfer Learning

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy

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