Outcomes in Advanced Head and Neck Cancer Treated with Up-front Neck Dissection prior to (Chemo)Radiotherapy.

OBJECTIVE Our aim was to compare outcomes with and without up-front neck dissection prior to (chemo)radiotherapy in head and neck squamous cell carcinoma. STUDY DESIGN Case series with chart review. SETTING Tertiary referral center. SUBJECTS AND METHODS Outcomes of oropharyngeal, laryngeal, and hypopharyngeal squamous cell carcinoma cases with neck lymph node metastases treated from January 2001 to March 2012 were analyzed. Due to imbalances in baseline characteristics between groups treated with (n = 129) and without (n = 95) up-front neck dissection, propensity score matching was performed. RESULTS Median follow-up was 48 months (range, 12-148). With up-front neck dissection, the hazard ratio for the primary end point, disease-free survival, was 0.63 (95% confidence interval: 0.37-1.06, P = .08). Up-front neck dissection reduced acute grade ≥3 toxicity significantly when xerostomia was excluded (odds ratio: 0.40, 95% confidence interval: 0.20-0.82, P = .012). CONCLUSION Our results indicate less acute treatment toxicity without any significant difference in terms of oncologic outcome with up-front neck dissection prior to (chemo)radiotherapy as compared with (chemo)radiotherapy alone. Well-designed randomized trials are required to verify this result and further investigate the impact of this strategy on late toxicity and oncologic outcome.

When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer.

BACKGROUND The aim of this study was to identify clinical variables that may predict the need for adjuvant radiotherapy after neoadjuvant chemotherapy (NACT) and radical surgery in locally advanced cervical cancer patients. METHODS A retrospective series of cervical cancer patients with International Federation of Gynecology and Obstetrics (FIGO) stages IB2-IIB treated with NACT followed by radical surgery was analyzed. Clinical predictors of persistence of intermediate- and/or high-risk factors at final pathological analysis were investigated. Statistical analysis was performed using univariate and multivariate analysis and using a model based on artificial intelligence known as artificial neuronal network (ANN) analysis. RESULTS Overall, 101 patients were available for the analyses. Fifty-two (51 %) patients were considered at high risk secondary to parametrial, resection margin and/or lymph node involvement. When disease was confined to the cervix, four (4 %) patients were considered at intermediate risk. At univariate analysis, FIGO grade 3, stage IIB disease at diagnosis and the presence of enlarged nodes before NACT predicted the presence of intermediate- and/or high-risk factors at final pathological analysis. At multivariate analysis, only FIGO grade 3 and tumor diameter maintained statistical significance. The specificity of ANN models in evaluating predictive variables was slightly superior to conventional multivariable models. CONCLUSIONS FIGO grade, stage, tumor diameter, and histology are associated with persistence of pathological intermediate- and/or high-risk factors after NACT and radical surgery. This information is useful in counseling patients at the time of treatment planning with regard to the probability of being subjected to pelvic radiotherapy after completion of the initially planned treatment.

Treatment planning aspects and Monte Carlo methods in proton therapy

Over the last years, the interest in proton radiotherapy is rapidly increasing. Protons provide superior physical properties compared with conventional radiotherapy using photons. These properties result in depth dose curves with a large dose peak at the end of the proton track and the finite proton range allows sparing the distally located healthy tissue. These properties offer an increased flexibility in proton radiotherapy, but also increase the demand in accurate dose estimations. To carry out accurate dose calculations, first an accurate and detailed characterization of the physical proton beam exiting the treatment head is necessary for both currently available delivery techniques: scattered and scanned proton beams. Since Monte Carlo (MC) methods follow the particle track simulating the interactions from first principles, this technique is perfectly suited to accurately model the treatment head. Nevertheless, careful validation of these MC models is necessary. While for the dose estimation pencil beam algorithms provide the advantage of fast computations, they are limited in accuracy. In contrast, MC dose calculation algorithms overcome these limitations and due to recent improvements in efficiency, these algorithms are expected to improve the accuracy of the calculated dose distributions and to be introduced in clinical routine in the near future.