Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Wang, Peipei; Deng, Chaolin; Wu, Bin

doi:10.3748/wjg.v27.i18.2122

Cited by 15 publications

(17 citation statements)

References 47 publications

(39 reference statements)

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“…We hypothesize that further studies, on a larger cohort of patients, could include several panels of clinical and paraclinical parameters in several machine learning algorithms or convolutional neural networks in order to better establish their predictive performance. These approaches allow better image segmentation or feature discrimination, and allow the analysis of a large dataset, even with high rates of missing data [ 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

Volovat,

Scripcariu,

Boboc

et al. 2024

Diagnostics

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(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023. Decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF) were used to analyze imagistic, surgical, and pathological data retrieved from the medical files, and their predictive performance was assessed; (3) Results: The best predictive performance was achieved by RF when used to predict disease recurrence (accuracy: 90.85%) or distant metastasis (accuracy: 89.63%). RF was closely followed by SVM (accuracy for recurrence 87.8%; accuracy for metastasis: 87.2%) in terms of predictive performance. NB and DT achieved moderate predictive power for the evaluated outcomes; (4) Conclusions: Complex algorithms such as RF and SVM could be useful for improving the prediction of adverse oncological outcomes in patients with low rectal adenocarcinoma.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

Volovat,

Scripcariu,

Boboc

et al. 2024

Diagnostics

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“…The application of radiomics in CRC has shown great promise in advancing cancer diagnosis, treatment, and prognosis [26]. By extracting and analyzing a multitude of quantitative imaging features from medical images, radiomics provides valuable insights into tumor characteristics and behavior [40]. In this discussion, we will explore the key findings and implications of the reviewed literature on the applications of radiomics in CRC.…”

Section: Discussionmentioning

confidence: 99%

Radiomics in colorectal cancer

Wu,

et al. 2023

iRADIOLOGY

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Colorectal cancer (CRC) is a global health challenge with high morbidity and mortality. Radiomics, an emerging field, utilizes quantitative imaging features extracted from medical images for CRC diagnosis, staging, treatment response assessment, and prognostication. This review highlights the potential of radiomics for personalized CRC management. Radiomics enables noninvasive tumor characterization, aiding in early detection and accurate diagnosis, and it can be used to predict tumor stage, lymph node involvement, and prognosis. Furthermore, radiomics guides personalized therapies by assessing the treatment response and identifying patients who could benefit. Challenges include standardizing imaging protocols and analysis techniques. Robust validation frameworks and user‐friendly software are needed for the integration of radiomics into clinical practice. Despite challenges, radiomics offers valuable insights into tumor biology, treatment response, and prognosis in CRC. Overcoming technical and clinical hurdles will unlock its full potential in CRC management.

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“…Wang, Deng, and Wu [23] CNN ML models can also use magnetic resonance imaging (MRI) results as inputs. This has been proved effective in predicting the responses of various patients towards chemotherapy with an accuracy rate of 95%.…”

Section: Luo Et Al [22] MLmentioning

confidence: 99%

“…Similarly, research spearheaded by Wang, Deng, and Wu [23] found that ML models that utilize magnetic resonance imaging (MRI) have proved effective in predicting the responses of various patients to chemotherapy, in addition to the evaluation of patient prognosis. The authors' focus was patients with rectal cancer, and the algorithm yielded an accuracy rate of 95% [23].…”

Section: Advantages and Limitations Of ML In Colorectal Surgerymentioning

confidence: 99%

Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review

Taha‐Mehlitz

Däster

Bach

et al. 2022

JCM

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Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on “ML in colorectal surgery”, with its viable applications. Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched. Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients’ perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients’ end. Confidence can only be developed within society if more research with precise results is carried out.

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Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Cited by 15 publications

References 47 publications

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

Radiomics in colorectal cancer

Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review

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