Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer

Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue

OBJECTIVE

This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue.

STUDY DESIGN

In total, MR images of 196 patients with lingual squamous cell carcinoma were divided into training (n = 156) and test (n = 40) cohorts. Radiomics and DL features were extracted from MR images and selected to construct machine learning models. A DL radiomics nomogram was established via multivariate logistic regression by incorporating the radiomics signature, the DL signature, and MRI-reported LN status.

RESULTS

Nine radiomics and 3 DL features were selected. In the radiomics test cohort, the multilayer perceptron model performed best with an area under the receiver operating characteristic curve (AUC) of 0.747, but in the DL cohort, the best model (logistic regression) performed less well (AUC = 0.655). The DL radiomics nomogram showed good calibration and performance with an AUC of 0.934 (outstanding discrimination ability) in the training cohort and 0.757 (acceptable discrimination ability) in the test cohort. The decision curve analysis demonstrated that the nomogram could offer more net benefit than a single radiomics or DL signature.

CONCLUSION

The DL radiomics nomogram exhibited promising performance in predicting LNM, which facilitates personalized treatment of tongue cancer.

Diagnostic role of F-18 FDG PET/CT in determining preoperative Lymph node status of patients with rectal cancer: a meta-analysis

PURPOSE

To obtain performance values of PET/CT for determining the nodal status of rectal cancer.

MATERIALS

A comprehensive literature search was performed on PubMed and Embase for original diagnostic accuracy studies on the diagnostic performance of PET-CT for detection of LN metastasis in rectal cancer. The QUADAS-2 was used to evaluate the methodological quality of each study. Pooled sensitivity, specificity, and AUC were calculated to estimate the diagnostic role of PET/CT using a random-effects model. A subgroup analysis was performed to investigate the influence of different parameters on diagnostic performance.

RESULTS

A total of 15 studies and 1209 patients were included. A publication bias was observed. The pooled sensitivity, specificity, and AUC for PET/CT was 0.62 (95% CI 0.49, 0.74), 0.94 (95% CI 0.87, 0.97), and 0.87 (95% CI 0.83-0.89), respectively. Per-node basis yields higher accuracy than per-patient basis, with pooled sensitivities of 0.65 (95% CI 0.50-0.79) vs. 0.56 (95% CI 0.36-0.77) and specificities of 0.96 (95% CI 0.92-1.00) vs. 0.88 (95% CI 0.76-1.00), but there were no significant differences in diagnostic accuracy.

CONCLUSION

PET/CT has high specificity but moderate sensitivity for the detection of LN metastasis in rectal cancer. The current data suggests that the diagnostic capabilities of this method is limited due to its moderate sensitivity.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer

BACKGROUND

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge.

AIM

To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.

METHODS

In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis.

RESULTS

The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively.

CONCLUSION

The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings

OBJECTIVE

We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC).

METHODS

Our retrospective study included 77 CRC patients who had preoperative 18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance.

RESULTS

A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB.

CONCLUSIONS

Intratumoral and peritumoral radiomics derived from 18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC.

A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study

The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objective

For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.

Methods

In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).

Results

For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).

Conclusions

This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.