Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.

BACKGROUND: Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes. METHODS: Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC). RESULTS: The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004). CONCLUSION: The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089).

Purpose: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). Methods and materials: Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models. Results: Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05). Conclusion: Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

A combined predicting model for benign esophageal stenosis after simultaneous integrated boost in esophageal squamous cell carcinoma patients (GASTO1072).

Purpose: We aimed to develop a combined predicting model for benign esophageal stenosis (BES) after simultaneous integrated boost (SIB) with concurrent chemotherapy in patients with esophageal squamous cell carcinoma (ESCC). Methods: This study included 65 patients with EC who underwent SIB with chemotherapy. Esophageal stenosis was evaluated using esophagograms and the severity of eating disorders. Risk factors were investigated using univariate and multivariate analyses. Radiomics features were extracted based on contrast-enhanced CT (CE-CT) before treatment. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature selection and radiomics signature construction. The model's performance was evaluated using Harrell's concordance index and receiver operating characteristic curves. Results: The patients were stratified into low- and high-risk groups according to BES after SIB. The area under the curves of the clinical model, Rad-score, and the combined model were 0.751, 0.820 and 0.864, respectively. In the validation cohort, the AUCs of these three models were 0.854, 0.883 and 0.917, respectively. The Hosmer-Lemeshow test showed that there was no deviation from model fitting for the training cohort (p=0.451) and validation cohort (p=0.481). The C-indexes of the nomogram were 0.864 and 0.958 for the training and validation cohort, respectively. The model combined with Rad-score and clinical factors achieved favorable prediction ability. Conclusion: Definitive chemoradiotherapy could alleviate tumor-inducing esophageal stenosis but result in benign stenosis. We constructed and tested a combined predicting model for benign esophageal stenosis after SIB. The nomogram incorporating both radiomics signature and clinical prognostic factors showed favorable predictive accuracy for BES in ESCC patients who received SIB with chemotherapy. Trial registration number and date of registration: Registered in www.Clinicaltrial.gov, ID: NCT01670409, August 12, 2012.

Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy.

BACKGROUND: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT). METHODS: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n = 99) or the validation cohort (n = 55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. RESULTS: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95% CI 0.664-0.804) and 0.658 (95% CI 0.587-0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95% CI 0.578-0.766) and 0.666 (95% CI 0.574-0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score. CONCLUSIONS: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters.

PURPOSE: Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters. METHODS: The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve. RESULTS: The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility. CONCLUSIONS: The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.