External validation of a prognostic model incorporating quantitative PET image features in esophageal cancer

Aim Enhanced prognostic models are required to improve risk stratification of patients with oesophageal cancer so treatment decisions can be optimised. The primary aim was to externally validate a published prognostic model incorporating PET image features. Transferability of the model was compared using only clinical variables. Methods This was a Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis (TRIPOD) type 3 study. The model was validated against patients treated with neoadjuvant chemoradiotherapy according to the Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS) trial regimen using pre- and post-harmonised image features. The Kaplan–Meier method with log-rank significance tests assessed risk strata discrimination. A Cox proportional hazards model assessed model calibration. Primary outcome was overall survival (OS). Results Between 2010 and 2015, 449 patients were included in the development (n = 302), internal validation (n = 101) and external validation (n = 46) cohorts. No statistically significant difference in OS between patient quartiles was demonstrated in prognostic models incorporating PET image features (X2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X2 = 1.19, df = 3, p = 0.75). The calibration slope β of both models was not significantly different from unity (p = 0.29 and 0.29, respectively). Risk groups defined using only clinical variables suggested differences in OS, although these were not statistically significant (X2 = 0.71, df = 2, p = 0.70). Conclusion The prognostic model did not enable significant discrimination between the validation risk groups, but a second model with exclusively clinical variables suggested some transferable prognostic ability. PET harmonisation did not significantly change the results of model validation

Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients

In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74–0.83) and 0.65 (95% ci: 0.57–0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83–0.90) and 0.79 (95% ci 0.72–0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings

The influence of gastric filling instructions on dose delivery in patients with oesophageal cancer: A prospective study

Purpose: To evaluate whether adaptive radiotherapy for unaccounted stomach changes in patients with adenocarcinoma of the gastroesophageal junction (GEJ) is necessary and whether dose differences could be prevented by giving patients food and fluid instructions before treatment simulation and radiotherapy. Material and methods: Twenty patients were randomly assigned into two groups: patients with and without instructions about restricting food and fluid intake prior to radiotherapy simulation and treatment. Redelineation and offline recalculation of dose distributions based on cone-beam computed tomography (n = 100) were performed. Dose-volume parameters were analysed for the clinical target volume extending into the stomach. Results: Four patients who did not receive instructions had a geometric miss (0.7-12cm3) in only one fraction. With instructions, 3 out of 10 patients had a geometric miss (0.1-1.9cm3) in one (n =2) or two (n =1) fractions. The V 95% was reduced by more than 5% for one patient, but this underdosage was in an in-air region without further clinical importance. Conclusions: Giving patients food and fluid instructions for the treatment of GEJ cancer offers no clinical benefit. Using a planning target volume margin of 1. cm implies that there is no need for adaptive radiotherapy for GEJ tumours

A qualitative synthesis of the evidence behind elective lymph node irradiation in oesophageal cancer

BACKGROUND AND PURPOSE: Oesophageal cancer is the sixth leading cause of cancer death worldwide and radiotherapy plays a prominent role in its treatment. The presence of lymph node (LN) metastasis has been demonstrated to be one of the most significant prognostic factors related to oesophageal cancer. The use of elective lymph node irradiation (ENI) is still a topic of persistent controversy. The conservative school is to irradiate positive lymph nodes only; the other school is to prophylactically irradiate the regional lymph node area according to different tumour sites. This review investigated the justification for including ENI in the treatment of patients with oesophageal cancer. MATERIAL AND METHODS: We performed a systematic literature search to find surgical data about lymph node distribution depending on different tumour subgroups: early, cervical, thoracic and gastroesophageal junction cancer. Furthermore, we performed a qualitative assessment of recurrence patterns in patients treated with or without ENI to derive estimates of the potential area at risk for lymph node harvest. RESULTS: We identified and reviewed 49 studies: 10 in early, 8 in cervical, 10 in thoracic and the remaining 21 in gastroesophageal junction cancer. In general, these studies were conclusive in incidence and location of pathologic lymph nodes for different subgroups. Data for lymph node recurrence patterns are scarce and contributed little to our review. CONCLUSIONS: This review resulted in five recommendations for radiation oncologists in daily practice. We used the available evidence about metastatic lymph node distribution to develop a careful reasonable radiation protocol for the corresponding tumour subgroups