An efficient strategy to select head and neck cancer patients for adaptive radiotherapy

BACKGROUND AND PURPOSE: Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy.MATERIALS AND METHODS: This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (D mean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of D mean (ΔD mean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔD mean of OARs in week 1 and 2 (maxΔD mean_1 and maxΔD mean_2). Four strategies to select candidates for ART, using cut-off maxΔD mean were compared. RESULTS: The Spearman's rank correlation test showed significant positive correlation between maxΔD mean and BIOΔNTCP (p-value &lt;0.001). For major BIOΔNTCP (&gt;5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔD mean_1 (3.01 and 5.14 Gy) and cut-off maxΔD mean_2 (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). CONCLUSIONS: We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with &gt;5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.</p

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Predictors of Patient-Reported Dysphagia Following IMRT Plus Chemotherapy in Oropharyngeal Cancer

The aim of this cross-sectional study is to evaluate the factors associated with patient-reported dysphagia in patients affected by locally advanced oropharyngeal cancer (OPC) treated with definitive intensity-modulated radiation therapy (IMRT) and concurrent chemotherapy (CHT), with or without induction CHT. We evaluated 148 OPC patients treated with IMRT and concurrent CHT, without evidence of disease and who had completed their treatment since at least 6 months. At their planned follow-up visit, patients underwent clinical evaluation and completed the M.D. Anderson dysphagia inventory (MDADI) questionnaire. The association between questionnaire composite score (MDADI-CS) and different patients\u2019 and tumor\u2019s characteristics and treatments (covariates) was investigated by univariable and multivariable analyses, the latter including only covariates significant at univariable analysis. With a median time from treatment end of 30 months [range 6\u201374 months, interquartile range (IQR) 16\u201350 months], the median (IQR) MDADI-CS was 72 (63\u201384). The majority of patients (82.4%) had a MDADI-CS 65 60. At multivariable analysis, female gender, human papilloma virus (HPV)-negative status, and moderate and severe clinician-rated xerostomia were significantly associated with lower MDADI-CS. Patient-perceived dysphagia was satisfactory or acceptable in the majority of patients. HPV status and xerostomia were confirmed as important predictive factors for swallowing dysfunction after radiochemotherapy. Data regarding female gender are new and deserve further investigation