BACKGROUND
Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.


PURPOSE
To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy.


STUDY TYPE
Retrospective.


POPULATION
After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52).


FIELD STRENGTH/SEQUENCE
3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI).


ASSESSMENT
The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used.


STATISTICAL TESTS
The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant.


RESULTS
The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789).


DATA CONCLUSION
In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis.


EVIDENCE LEVEL
3 TECHNICAL EFFICACY: Stage 2.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

The heterogeneous response and prognosis of patients with colorectal cancer (CRC) to standard treatment regimens remains a challenge for clinical management. Individually weak prognostic markers, defined by gene mutations and protein expression, are difficult to apply in routine clinical practice because of their high acquisition cost and mediocre prediction accuracy. Visual evaluation of medical images, including radiology and digital pathology images, is an important part of CRC management. With the rapid development of artificial intelligence (AI), high-dimensional imaging features other than visual information are increasingly being used to develop imaging markers. At different stages of treatment, accurate predictions of treatment response and prognosis may help in selecting patients and tailoring their treatment. Here, we review the current state of AI applied to the medical imaging of CRC and describe its recent progress in short-term response and long-term survival prediction. In addition, we illustrate how these AI-based approaches may affect clinical decision-making. Although few approaches have been applied in routine clinical practice, their results are promising. Finally, we discuss the challenges in applying AI in clinical practice and possible future solutions from three perspectives: model interpretability, model generalizability, and patient privacy protection. This comprehensive assessment underscores the transformative potential of AI in CRC management and emphasizes the need for further exploration and integration into routine clinical workflows.


Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis

Rectal cancer is a heterogeneous disease, presenting with distinct biological behaviors and treatment responses which suggest the complexity and diversity of its genetic and molecular characteristics. Over the past decades, several studies have focused on the clinical application of tumor genetics, which has gained even more importance with the increasing development of targeted therapies. In this issue of JMRI, the authors of the study, “A deep learning model based on MRI and clinical factors facilitates noninvasive prediction of KRAS mutation in rectal cancer,” propose two deep learning models to predict Kirsten rat sarcoma oncogene homolog (KRAS) mutation in patients with rectal cancer using T2-weighted images alone and T2-weighted images combined with clinical factors. KRAS mutations in colorectal cancer (CRC) are present in approximately 30%–50% of cases. They are associated with poorer prognosis as well as resistance to drugs targeting the epidermal growth factor receptor (EGFR), such as cetuximab or panitumumab, which are commonly used in metastatic CRC. In the era of precision medicine, where individual variability affects patient management, all efforts are made to tailor treatment strategies to maximize treatment efficacy while avoiding unnecessary toxicity. Thus, new therapies targeting the KRAS mutation have been investigated to optimize treatment for this subgroup of rectal cancer. Moreover, recently, the National Comprehensive Cancer Network (NCCN) guidelines recommend RAS (KRAS/ NRAS) genotyping of tumor tissue (either primary tumor or metastasis) in all patients with metastatic CRC, and that patients with KRAS or NRAS mutations should not be treated with either cetuximab or panitumumab, either alone or in combination with other anticancer agents. Determining KRAS mutation status can enable more accurate risk stratification and improved identification of appropriate therapeutic strategies. Currently, in clinical practice, KRAS mutation status can only be collected preoperatively from tissue sampling via biopsy (of the primary tumor or metastasis). However, biopsy is an invasive procedure and may not represent the entire tumor due to intratumoral heterogeneity. Therefore, several studies have investigated non-invasive methods to determine KRAS mutation status, including liquid biopsy and imaging. Magnetic resonance imaging (MRI) is the modality of choice for local staging and re-staging of rectal cancer and has been widely used in the preoperative setting. Recently, several studies have investigated the potential of MRI as a non-invasive method to assess tumor biology and genetic mutations. For instance, promising associations between quantitative imaging features and genetic mutations, but not between qualitative imaging features and genetic mutations, were shown in a preliminary study. When it comes to published literature specifically focusing on predicting KRAS mutation status in CRC based on MRI, the diagnostic performance varies but is nevertheless promising. Xu et al reported that two diffusion-weighted imaging-derived parameters had a moderate significance (area under the curve [AUC] of 0.710 and 0.756) for predicting KRAS mutation status. In a more recent study, Xu et al reported that MRI texture features were also promising to predict KRAS mutation status (AUC of 0.703–0.813). In another study, Shin et al found that KRAS mutations were associated with N stage, a polypoid pattern, axial tumor length, and the ratio of the axial to the longitudinal dimensions of the tumor. Deep learning is an emerging area in the field radiology where deep neural networks are employed to recognize patterns in a predetermined dataset which are then used to develop models to perform diagnostic tasks. In addition to creating predictive models based solely on imaging, deep learning can also be employed to create predictive models based on multimodal data, such as those combining radiological and clinical data, as was done in the present study. In the present study, the authors used a cohort of 376 patients with rectal adenocarcinoma to develop deep learning models to predict KRAS mutation status using T2-weighted images of

Editorial for “A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Prediction of KRAS Mutation in Rectal Cancer”

Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.

Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review

AIM: to study modern approaches to the application of machine learning and deep learning technologies for the management of patients with colorectal cancer.MATERIALS AND METHODS: after screening 398 publications, 112 articles were selected and the full text of the works was studied. After studying the full texts of the articles, the works were selected, machine learning models in which showed an accuracy of more than 80%. The results of 41 original publications were used to write this review.RESULTS: several areas have been identified that are the most promising for the use of artificial intelligence technologies in the management of patients with colorectal cancer. They are predicting the response to neoadjuvant treatment, predicting the risks of metastasis and recurrence of the disease, predicting the toxicity of chemotherapy, assessing the risks of leakage of colorectal anastomoses. As the most promising factors that can be used to train models, researchers consider clinical parameters, the immune environment of the tumor, tumor RNA signatures, as well as visual pathomorphological characteristics. The models for predicting the risk of liver metastases in patients with stage T1 (AUC = 0.9631), as well as models aimed at assessing the risk of 30-day mortality during chemotherapy (AUC = 0.924), were characterized with the greatest accuracy. Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.CONCLUSION: the current level of digital technologies in oncology and in the treatment of colorectal cancer does not allow us to talk about a strong AI capable of making decisions about the treatment of patients without medical supervision. Personalized treatment based on the microbiotic and mutation spectrum and, for example, personal pharmacokinetics, so far look fantastic, but certainly promising for future developments.

Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)

Recent studies showed the potential of MRI‐based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI‐based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear.

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Minda Lu

Author Tools

Chat about Author

Papers

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer