Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

Wei, Qiurong; Yuan, Weiqi; Jia, Ziqi; Chen, Jialiang; Li, Ling; Yan, Zhaoxian; Liao, Yuting; Mao, Liting; Hu, Shaowei; Liu, Xian; Chen, Weicui

doi:10.1007/s00261-022-03731-x

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…In this study, SR technology was combined with radiomics to establish multiple machine learning models using various machine learning algorithms. It was found that logistic regression has the highest diagnostic e ciency and robustness, possibly due to its being based on a probability model that effectively handles classi cation problems and has strong robustness against abnormal values and noise that may negatively affect model performance (30). Furthermore, this study indicates that SR technology outperforms conventional radiomics, as there is a certain degree of over tting in using conventional radiomics, which is consistent with previous research (9,30).…”

Section: Discussionsupporting

confidence: 88%

“…It was found that logistic regression has the highest diagnostic e ciency and robustness, possibly due to its being based on a probability model that effectively handles classi cation problems and has strong robustness against abnormal values and noise that may negatively affect model performance (30). Furthermore, this study indicates that SR technology outperforms conventional radiomics, as there is a certain degree of over tting in using conventional radiomics, which is consistent with previous research (9,30). This suggests that the radiomics model reconstructed based on SR technology is more robust because the reconstructed image data contains more information, allowing the model to more robustly learn effective features.…”

Section: Discussionmentioning

confidence: 99%

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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: 88%

Section: Discussionmentioning

confidence: 99%

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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“…Additionally, two studies with 328 patients employed both MRI and CT scans for radiomics development. Moreover, only five studies utilized external validation cohorts 24 , 26 , 28 , 48 , 53 .…”

Section: Resultsmentioning

confidence: 99%

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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“…This makes the algorithm advantageous in modeling moderate nonlinearities, given the complexity and nonlinearity between radiomics and tumor response 46,47 . In other rectal cancer‐related studies, the SVM model also achieved the best predictive efficacy, such as in the prediction of KRAS mutations 48 and pathological features 49 ; however, there are also reports related to the superiority of the RF 30,50–52 and Bayes 53 classifiers over SVM models in predicting different endpoints. Therefore, the best classifier may vary in different clinical applications, and no classifier is better than any other for all problems.…”

Section: Discussionmentioning

confidence: 99%

Magnetic resonance imaging‐based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study

Hu,

Gong,

Sun

et al. 2024

MedComm

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Our study investigated whether magnetic resonance imaging (MRI)‐based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR‐detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760–0.838), 0.797 (95% CI, 0.733–0.860), 0.754 (95% CI, 0.678–0.829), and 0.727 (95% CI, 0.641–0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan–Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease‐free survival than those with a low probability. This radiomics model was developed based on large‐sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

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Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

Cited by 10 publications

References 35 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

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

Magnetic resonance imaging‐based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study

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