The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer

Li, Zhihui; Chen, Fangying; Zhang, Shaoting; Ma, Xiaolu; Xia, Yuwei; Fu, Shoukuan; Lu, Yong; Shao, Chengwei

doi:10.1007/s00261-021-03311-5

Cited by 16 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tumor budding, defined as a single or cluster of tumor cells at the front of the tumor, 23 is a vital histopathological factor that could help stratify cases into risk groups. Li et al 30 built a multicenter MRI‐based radiomics (T2WI, CE‐T1WI, and DWI) model to preoperatively evaluate tumor budding in LARC, with an accuracy of 81.2% in the validation set. Therefore, AI could help to predict risk factors in RC by mining high‐dimensional features of images and hopefully realize personalized treatment guidance.…”

Section: Clinical Applications Of Ai In Rc Based On Mrimentioning

confidence: 99%

MRI‐Based Artificial Intelligence in Rectal Cancer

Wong

et al. 2022

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future.

show abstract

Section: Clinical Applications Of Ai In Rc Based On Mrimentioning

confidence: 99%

MRI‐Based Artificial Intelligence in Rectal Cancer

Wong

et al. 2022

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…The magnitude of the coefficients in the LASSO algorithm were used to measure the relative weight of useful features as previously described (34,35). Furthermore, the useful features were grouped by category, such as shape feature group, firstorder feature group, and texture feature group, which were added sequentially to the final SVM model, and the AUC of each addition was calculated to assess whether all three groups of useful features contributed to the model.…”

Section: Relative Importance Of Useful Radiomics Featuresmentioning

confidence: 99%

Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer

et al. 2022

View full text Add to dashboard Cite

ObjectivesThis study aims to build radiomics model of Breast Imaging Reporting and Data System (BI-RADS) category 4 and 5 mammographic masses extracted from digital mammography (DM) for mammographic masses characterization by using a sensitivity threshold similar to that of biopsy.Materials and MethodsThis retrospective study included 288 female patients (age, 52.41 ± 10.31) who had BI-RADS category 4 or 5 mammographic masses with an indication for biopsy. The patients were divided into two temporal set (training set, 82 malignancies and 110 benign lesions; independent test set, 48 malignancies and 48 benign lesions). A total of 188 radiomics features were extracted from mammographic masses on the combination of craniocaudal (CC) position images and mediolateral oblique (MLO) position images. For the training set, Pearson’s correlation and the least absolute shrinkage and selection operator (LASSO) were used to select non-redundant radiomics features and useful radiomics features, respectively, and support vector machine (SVM) was applied to construct a radiomics model. The receiver operating characteristic curve (ROC) analysis was used to evaluate the classification performance of the radiomics model and to determine a threshold value with a sensitivity higher than 98% to predict the mammographic masses malignancy. For independent test set, identical threshold value was used to validate the classification performance of the radiomics model. The stability of the radiomics model was evaluated by using a fivefold cross-validation method, and two breast radiologists assessed the diagnostic agreement of the radiomics model.ResultsIn the training set, the radiomics model obtained an area under the receiver operating characteristic curve (AUC) of 0.934 [95% confidence intervals (95% CI), 0.898–0.971], a sensitivity of 98.8% (81/82), a threshold of 0.22, and a specificity of 60% (66/110). In the test set, the radiomics model obtained an AUC of 0.901 (95% CI, 0.835–0.961), a sensitivity of 95.8% (46/48), and a specificity of 66.7% (32/48). The radiomics model had relatively stable sensitivities in fivefold cross-validation (training set, 97.39% ± 3.9%; test set, 98.7% ± 4%).ConclusionThe radiomics method based on DM may help reduce the temporarily unnecessary invasive biopsies for benign mammographic masses over-classified in BI-RADS category 4 and 5 while providing similar diagnostic performance for malignant mammographic masses as biopsies.

show abstract

“…More recent studies have shown that radiomics can predict CRC histological grade before surgery[ 98 , 99 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Qi¹,

Zhou²,

Zhao³

et al. 2022

World J Gastroenterol

View full text Add to dashboard Cite

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

show abstract

The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer

Cited by 16 publications

References 42 publications

MRI‐Based Artificial Intelligence in Rectal Cancer

MRI‐Based Artificial Intelligence in Rectal Cancer

Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Contact Info

Product

Resources

About