Purpose: To ask whether the expression of immune markers and IFN signaling in tumor biopsies changes during concurrent chemoradiotherapy (CCRT). Experimental Design: Tumor biopsies and peripheral mononuclear blood cells (PMBC) before and immediately after 20 Gy/10 fractions (F) of radiation treatment (RT) from 30 patients with cervical cancer receiving CCRT were evaluated by IHC and qRT-PCR for immune markers and correlated with the short-term response. Results: Tumor immune response to radiation before and after 10F RT as reflected by CD8+ T-cell infiltration had substantial heterogeneity with increases, decreases, and no change all evident. Increases in CD8+ T cells during CCRT correlated with the presence of nuclear IRF1 in tumor cells (r = 0.68, P < 0.0001) and the patient short-term response (P < 0.01). Similarly, in a subset of patients (∼40%) PD-L1 positivity in tumor cells increased, which also correlated with nuclear IRF1 staining (r = 0.48, P < 0.01). Patients with augmented PMBC IFN signature expression after 10F had a significantly higher probability of PD-L1 induction (83% vs. 7%, P < 0.0001). Most patients exhibited abundant expression of SERPINB9 and CD47 in tumor cells, and tumor infiltration by CD68+ cells. SERPINB9 expression correlated with STAT1 signaling in tumor cells. Conclusions: CCRT leads to differential tumor immunogenicity and IFN signaling in patients with cervical cancer, suggesting radiation induction of immunity is limited to a subset of patients and may reflect the heterogeneity of intratumoral induction of IFNs. See related commentary by Mondini and Deutsch, p. 3815

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.

PurposeStereotactic 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.

PurposeTo 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 materialsTwo 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.ResultsSixteen 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).ConclusionRadiomics 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.

PurposeWe aimed to evaluate the long-term outcomes of concurrent chemoradiotherapy (CCRT) with a simultaneous integrated boost (SIB) of radiotherapy for esophageal squamous cell carcinoma (ESCC).Methods and MaterialsEighty-seven patients with primary ESCC enrolled in this phase II trial. The majority (92.0%) had locoregionally advanced disease. They underwent definitive chemoradiotherapy. The radiotherapy doses were 66 Gy for the gross tumor and 54 Gy for the subclinical disease. Doses were simultaneously administered in 30 fractions over 6 weeks. The patients also underwent concurrent and adjuvant chemotherapy, which comprised cisplatin and fluorouracil. The study end points were acute and late toxicities, first site of failure, locoregional tumor control, and overall survival rates.ResultsThe median follow-up time was 65.7 (range, 2.2-97.5) months for all patients and 81.5 (range, 19.4-97.5) months for those alive. There were 17 cases (19.5%) of severe late toxicities, including four cases (4.6%) of grade 5 and seven (8.0%) of grade 3 esophageal ulceration, four (4.6%) of grade 3 esophageal stricture, and two (2.3%) of grade 3 radiation-induced pneumonia. Twenty-three (26.4%) patients had locoregional disease progression. Most (86.7%) locally progressive lesions were within the dose-escalation region in the initial radiation plan, while majority of the recurrent lymph nodes were found out-of-field (83.3%) and in the supraclavicular region (75.0%). The 1-, 2-, 3-, and 5-year locoregional tumor control and overall survival rates were 79.2%, 72.4%, 72.4%, 70.8%, and 82.8%, 66.6%, 61.9%, 58.4%, respectively. Incomplete tumor response, which was assessed immediately after CCRT was an independent risk predictor of disease progression and death in ESCC patients.ConclusionsCCRT with SIB was well tolerated in ESCC patients during treatment and long-term follow-up. Moreover, patients who underwent CCRT with SIB exhibited improved local tumor control and had better survival outcomes compared to historical data of those who had standard-dose radiotherapy.

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.

<p>Table S1-S7</p>

No abstract