Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer

Niu, Yue; Yu, Xiaoping; Wen, Lu; Bi, Feng; Jian, Lian; Liu, Siye; Yang, Yanhui; Zhang, Yi; Lu, Qiang

doi:10.3389/fonc.2023.1230698

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…In our study, we found signi cant associations between preoperative CA19-9 levels, mrN, and distant metastasis in tumors with lymph node metastasis (p < 0.05), which is consistent with previous research conclusions (31,32).To further verify the clinical application value of radiomics models and explore ways to improve their diagnostic e ciency and stability, we combined three clinical risk factors with SR- This study still has limitations. First, as a retrospective study, in order to make the pathological results more accurate and reliable, this study only collected patients who underwent surgery.…”

Section: Discussionsupporting

confidence: 89%

Prediction of Lymph Node Metastasis in Rectal Cancer Based on Super-Resolution MRI Radiomics and Clinical Baseline

Zhang,

Qu,

Duan

et al. 2024

Preprint

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Objective To explore the clinical practical value of the super-resolution(SR) MRI radiomics model based on clinical baseline for predicting lymph node metastasis in rectal cancer before surgery. Methods Retrospective inclusion of 302 eligible patients with rectal cancer (109 with lymph node metastasis). Patients from one hospital were included in the training set (n = 181), while patients from other hospitals were included in the external validation set (n = 121). Super-resolution algorithm was developed to axial T2-weighted imaging (T2WI) and subsequent SR-T2WI images were generated. The conventional radiomics models and SR radiomics model were built by 8 machine learning algorithms separately, and the best model was selected as the radiomics model. Using single-factor and multivariate logistic regression analysis to identify clinical risk factors for building a clinical model, and combining it with the radiomics model to construct a joint model. Comparing the diagnostic efficacy of the three models using area under the curve (AUC) in ROC curves. Finally, comparing the diagnostic efficacy of the best predicted model with different experienced radiologists. Results After feature screening and dimension reduction, 5 and 10 radiomics features were retained for conventional images and SR images, respectively. The diagnostic performance of the SR model on the external validation set was better than that of the conventional image model. Three clinical risk factors related to lymph node metastasis were screened to develop a clinical model. By combining SR radiomics features with clinical risk factors, a joint model was constructed, and compared with the three models, the joint model demonstrated the best diagnostic performance with an AUC, sensitivity, specificity and accuracy of 0.756 (95% confidence interval(CI): 0.658–0.854), 69.2%, 75.6%, and 73.6% on the external validation set, which was superior to that of a radiology expert with 36 years of experience (AUC, sensitivity, specificity, and accuracy of 0.679 (95% CI: 0.588–0.830), 84.6%, 51.2%, and 62.0%) on the external validation set (P = 0.02), indicating high clinical utility value. Conclusion The SR MRI radiomics model based on clinical baseline has high clinical practical value in predicting lymph node metastasis before surgery of rectal cancer.

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Section: Discussionsupporting

confidence: 89%

Prediction of Lymph Node Metastasis in Rectal Cancer Based on Super-Resolution MRI Radiomics and Clinical Baseline

Zhang,

Qu,

Duan

et al. 2024

Preprint

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CT and MRI bimodal radiomics for predicting EGFR status in NSCLC patients with brain metastases: A multicenter study

Ouyang,

Zhang,

et al. 2025

European Journal of Radiology

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Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Abbaspour,

Karimzadhagh,

Monsef

et al. 2024

International Journal of Surgery

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Background: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. Methods: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models’ diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. Results: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022–2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78–0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). Conclusion: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

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Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer

Cited by 4 publications

References 41 publications

Prediction of Lymph Node Metastasis in Rectal Cancer Based on Super-Resolution MRI Radiomics and Clinical Baseline

Prediction of Lymph Node Metastasis in Rectal Cancer Based on Super-Resolution MRI Radiomics and Clinical Baseline

CT and MRI bimodal radiomics for predicting EGFR status in NSCLC patients with brain metastases: A multicenter study

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

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