The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma

Yuan, Hang; Xu, Xiren; Tu, Shiliang; Chen, Bingchen; Wei, Yuguo; Ma, Yanqing

doi:10.1186/s12876-022-02525-1

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In the present study, radiomics signature of NCE-CT did not have a statistically significant difference from that of the CE-CT in the validation cohort (AUC, 0.676 vs 0.711, p=0.187). Yuan et al (21) recruited 788 patients with RC to construct a CT-based radiomics analysis to predict LNM in RC. It was found that there were no statistical significances of the intra-tumoral Bayes model among the non-enhanced, arterial or venous-phase CT in the training and validation cohorts, which was consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

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

Niu,

Yu,

Wen

et al. 2023

Front. Oncol.

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ObjectiveTo compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients.Methods234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong’s test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA).ResultsThe MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, p<0.05). The Clinical-IMG nomogram had the highest prediction efficiency than all other predictive models (p<0.05), of which the AUC was 0.947, the sensitivity was 0.870 and the specificity was 0.884.ConclusionMRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

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

Niu,

Yu,

Wen

et al. 2023

Front. Oncol.

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“…These three algorithms have advantages not only in small sample data processing and avoiding overfitting, but also in computing power requirements. Their performance has been verified by many studies [ 26 – 28 ]. In this research, the LR model achieved the highest AUC, specificity, and sensitivity, meeting the requirements for the detection of occult fractures in clinical diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 84%

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures

Li,

Zeng,

et al. 2023

BMC Musculoskelet Disord

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Purpose To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). Materials and methods 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. Results For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. Conclusion Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.

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“…This process ultimately results in a predictive or prognostic model based on extracted radiomic features and can be applied to any clinical endpoint. Although the methodology behind radiomics is rapidly evolving, several contemporary studies have highlighted the enormous potential of radiomics in a variety of diseases, including, but not limited to, cancers of the gastrointestinal tract [ 4 , 5 ], lung [ 6 ], brain [ 7 ], and genitourinary tract [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

Huynh

Hwang²,

Taylor

et al. 2023

Diagnostics

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The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.

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The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma

Cited by 8 publications

References 26 publications

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

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

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

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