Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer

Jin, Yumei; Li, Mou; Zhao, Yali; Huang, Chencui; Liu, Siyun; Liu, Shengmei; Wu, Min; Song, Bin

doi:10.3389/fonc.2021.710248

Cited by 16 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the current European Society for Medical Oncology (ESMO) preoperative risk assessment criteria, patients with TDs are classified into a high-risk group with a worse prognosis ( 9 ). A previous study confirmed that the presence of TDs is an independent risk factor for the prognosis of patients with RC ( 10 ). An analysis of two prognostic studies in N0 and N1c stages showed a significant difference in the five-year survival rates (N0, 91.5%; N1c, 37%) ( 11 ).…”

Section: Introductionsupporting

confidence: 58%

“…Meanwhile, artificial intelligence including deep neural networks has demonstrated high performance in the analysis of medical images ( 21 – 23 ), providing cancer risk assessment, recurrence, and survival predictions with higher accuracy than human experts. Recently, several radiomic models have been developed based on ultrasound (US), computed tomography (CT), and MRI to preoperatively predict TDs in patients with RC ( 10 , 24 , 25 ). However, the sample sizes in these studies were relatively small.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

Shao

Hou

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundThis study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). MethodsIn 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.ResultsA total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).ConclusionsA comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

show abstract

Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

Shao

Hou

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…For instance, the radiomics model allowed quantitative features to be extracted from CT images and subtle patterns that might be overlooked by human observers to be captured, while the clinical model provided additional demographic and clinical information. We also applied deep learning to predict the presence of OLNM in cT1-2N0 patients ( Jin et al, 2021 ). The deep learning model based on ResNet50 can directly analyze raw medical images and learn complex and high-level representations of the data ( Yu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

Han,

Wang,

et al. 2024

PeerJ

View full text Add to dashboard Cite

Background Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870–1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

show abstract

“…We found that T2WI-based radiomics from the intratumoral region could predict ENE with an AUC of 0.612. Jin et al and Chen et al reported that radiomics features obtained from intratumoral and peritumoral fat were used to construct a model for predicting tumor deposits [ 16 , 32 ]. These studies found that the combined model incorporating intratumoral and peritumoral fat and clinical factors provided good performance for predicting tumor deposits.…”

Section: Discussionmentioning

confidence: 99%

T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer

Li,

Chai,

et al. 2024

Insights Imaging

Self Cite

View full text Add to dashboard Cite

Objective To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. Methods One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). Results Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233–5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247–5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). Conclusion The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. Critical relevance statement A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. Key points • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS. Graphical Abstract

show abstract

Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer

Cited by 16 publications

References 24 publications

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer

Contact Info

Product

Resources

About