Multimodal Anti-tumor Approaches Combined with Immunotherapy to Overcome Tumor Resistance in Esophageal and Gastric Cancer

Bolm, Louisa; Käsmann, Lukas; Paysen, Annika; Karapetis, Christos S.; Rades, Dirk; Wellner, Ulrich; Keck, Tobias; Watson, David I.; Hummel, Richard; Hussey, Damian J.

doi:10.21873/anticanres.12588

Cited by 18 publications

(19 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The developed model can provide powerful prognostic information to clinicians before radiation therapy and help tailor treatment strategy for patients. For patients with high risk as evaluated by our prediction model the outcome is dismal with chemoradiotherapy and chemoradiotherapy in combination with target drug or immunotherapy are suggested as shown in many clinical trials [[26], [27], [28]].…”

Section: Discussionmentioning

confidence: 99%

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

et al. 2019

View full text Add to dashboard Cite

Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods Independent patient cohorts from two hospitals were included for training ( n = 87) and validation ( n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

show abstract

Section: Discussionmentioning

confidence: 99%

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the present study, we indicated that ESCC patients with higher IBM scores had a greater likelihood of worse survival rate and failed to response to CCRT. High-risk ESCC patients identi ed in the present studies might lead to an appropriate group for more effective systemic approaches to improve survival outcomes [36,37]. Thus, the IBM score was a prognostic tool for ESCC patients after de nitive CCRT.…”

Section: Discussionmentioning

confidence: 73%

Imaging Biomarkers of Contrast-enhanced Computed Tomography Predict Survival in Oesophageal Cancer After Definitive Concurrent Chemoradiotherapy

Zeng

Zhai

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT).Methods: A total of 151 ESCC patients who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=97) and the validation cohort (n=54). 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 logistic regression analysis in training cohort and tested in the validation cohort. IBMsnomograms were built based on IBM score. The concordance index (C-index) was used to assess the performance of the nomograms. Finally, decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: A total of 96 IBMs were extracted from each contrast-enhanced CT scan. The IBM score were consisted of 13 CT-based IBMs and were significantly correlated with 3-year overall survival (OS) and 3-year progression-free survival (PFS). Multivariate analysis revealed that IBM score was the independent prognostic factor. In the training cohort, the IBM score yielded an area under the curves (AUCs) of 0.802 (95% CI: 0.713–0.891, p<0.001) and 0.742 (95% CI: 0.620–0.889, p<0.001) in terms of 3-year OS and 3-year PFS, respectively. In validation cohort, the AUCs were 0.761(95% CI: 0. 639–0.900, p<0.001) and 0.761(95% CI: 0.629–0.893, p=0.001) for 3-year OS and 3-year PFS,respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The nomograms were built based on the IBM score showed good discrimination. In the training cohort, with the C-indices of IBMsnomograms were 0.732 (95%CI, 0.661–0.803) and 0.670(95%CI, 0.595–0.745) for OS and PFS, respectively. In the validation cohort C-indices were 0.677(95%CI, 0.583–0.771) and 0.678(95%CI, 0.591–0.765) for OS and PFS, respectively. The decision curve showed the clinical usefulness of nomograms.Conclusions: TheIBM score based on pre-treatment contrast-enhanced CT could predict the 3-year OS and 3-year PFS for OSCC patients after definitive CCRT. Further multicenter studies with larger sample sizes are warranted.

show abstract

“…In the present study, our ndings indicated that patients with OSCC with higher IBM scores had a greater likelihood of worse survival rates and failure to respond to CCRT. High-risk patients with OSCC identi ed in the present studies may bene t from more effective approaches to improve survival outcomes [40,41]. Thus, the IBM score may serve as a prognostic tool for OSCC patients after de nitive CCRT, whereby patients with higher IBM scores may have higher probability of poor survival outcomes.…”

Section: Discussionmentioning

confidence: 85%

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

Zeng

Zhai

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT). Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=99) and 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 in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualized 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. The IBM score constructed by 11 CT-based IBMs, using LASSO-Cox regression method in training cohort. The multivariate analysis revealed that IBM score was the independent prognostic factor correlated with overall survival (OS) and progression-free survival (PFS). In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.678 (95%CI, 0.607–0.745) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.662 (95%CI, 0.573–0.751) for OS and PFS, respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The decision curve showed the clinical usefulness of 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.

show abstract

Multimodal Anti-tumor Approaches Combined with Immunotherapy to Overcome Tumor Resistance in Esophageal and Gastric Cancer

Cited by 18 publications

References 99 publications

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

Imaging Biomarkers of Contrast-enhanced Computed Tomography Predict Survival in Oesophageal Cancer After Definitive Concurrent Chemoradiotherapy

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

Contact Info

Product

Resources

About