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

Prediction on nature of cancer by fuzzy graphoidal covering number using artificial neural network

Predicting the chances of various types of cancers for different organs in the human body is a typical decision-making process in medicine and health. The signaling pathways have played a vital role in increasing or decreasing the possibility of the deadliest disease, cancer. To combine the pathways concept and ambiguity in the prediction techniques of such diseases, we have used the proposed research on fuzzy graphoidal covers of fuzzy graphs in this paper. Determining a path with uncertainty and shortest length is a challenging topic of graph theory, and a collection of such shortest paths maintaining specific conditions is defined as a fuzzy graphoidal cover for a fuzzy graph. Also, we have defined fuzzy graphoidal covering number as a new parameter, reflecting the measure of coverage by fuzzy graphoidal covering set in a system. Afterwards, some important characterizations of the fuzzy graphoidal covering number are established with justified proof. Also, specific limit values of this number are provided for particular cases. Then, we developed an efficient algorithm for finding the defined covering set with its space and time complexity. The findings of this proposed study have been composed with an artificial neural network to model a strong tool for resolving an essential issue of medical sciences, the prediction of cancer type in the human body. We have analyzed two types of neural networks such as one one-dimensional and two-dimensional specification, for clarity of the obtained results. Also, we have found out the most possible cancer type is breast cancer from the data of our considered case study as a concluding statement for any decision-maker in the field of health sciences. Finally, sensitivity analysis and comparative study have been done to show the stability of our proposed work.

A Scoring Method to Prioritize Fecal Occult Blood Testing as a First Step in Colorectal Cancer Screening in Resource-Limited Settings

This study aims to develop a scoring method that can be used by primary care physicians from remote areas or resource-limited settings to estimate the need for fecal occult blood testing (FOBT) as a first step in colorectal cancer screening. This method relies on several modifiable risk factors that can influence a positive FOBT, an indication of the presence of colorectal polyps, or even colorectal cancer. The scoring method considers, besides the age and gender of the patient, the body mass index (BMI), smoking status, and the diagnoses of diabetes mellitus (type 2 diabetes), dyslipidemia, and hypertension. It does not need any paraclinical exams, which is an advantage when access or material resources are limited. The retrospective study was spread over forty-three months, respectively, from October 2019 to April 2023, and included 112 patients. The score that we designed is a numerical value between 0 and 7. The values between 0 and 3 represent a smaller risk of a positive FOBT (9.68%), values 4 and 5 represent a medium risk (14.75%), while values 6 and 7 represent a greater risk (40%). Using this score, a physician can determine if a patient has a greater risk and recommend it to prioritize taking a FOB test.

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application

BACKGROUND

Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer.

METHODS

In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings.

RESULTS

The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.

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

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.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Modelling in Synthesis and Optimization of Active Vaccinal Components

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.

Automatic Detection of Colorectal Polyps Using Transfer Learning

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.